Distribution of Cable Royalty Funds, 3552-3611 [2019-01544]
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Federal Register / Vol. 84, No. 29 / Tuesday, February 12, 2019 / Notices
by phone at (202) 707–7658 or by email
at crb@loc.gov.
SUPPLEMENTARY INFORMATION:
LIBRARY OF CONGRESS
Copyright Royalty Board
[Docket No. CONSOLIDATED 14–CRB–
0010–CD (2010–2013)]
Distribution of Cable Royalty Funds
Copyright Royalty Board (CRB),
Library of Congress.
ACTION: Final allocation determination.
AGENCY:
The Copyright Royalty Judges
announce the allocation of shares of
cable and satellite royalty funds for the
years 2010, 2011, 2012, and 2013 among
six claimant groups.
ADDRESSES: The final distribution order
is also published in eCRB at https://
app.crb.gov/.
Docket: For access to the docket to
read background documents, go to
eCRB, the Copyright Royalty Board’s
electronic filing and case management
system, at https://app.crb.gov/and
search for CONSOLIDATED docket
number 14–CRB–0010–CD (2010–2013).
For older documents not yet uploaded
to eCRB, go to the agency website at
https://www.crb.gov/or contact the CRB
Program Specialist.
FOR FURTHER INFORMATION CONTACT:
Anita Blaine, CRB Program Specialist,
SUMMARY:
Final Determination of Royalty
Allocation
The purpose of this proceeding is to
determine the allocation of shares of the
2010–2013 cable royalty funds among
six claimant groups: The Joint Sports
Claimants, Commercial Television
Claimants, Public Television Claimants,
Canadian Claimants Group, Settling
Devotional Claimants, and Program
Suppliers.1 The parties have agreed to
settlements regarding the shares to be
allocated to the Music Claimants and
National Public Radio (NPR). Public
Television Claimants Proposed Findings
of Fact and Conclusions of Law (PFFCL)
¶ 1.
Between 2012 and 2015, the Judges
ordered partial distributions of the
2010–2013 cable funds to the ‘‘Phase I’’
participants (including Music Claimants
and NPR) according to allocation
percentages agreed upon by the
participants. Order Granting Phase I
Claimants’ Motion for Partial
Distribution of 2010 Cable Royalty
Funds, Docket No. 2012–4 CRB CD 2010
(Sept. 14, 2012), Order Granting Phase
I Claimants’ Motion for Partial
Distribution of 2011 Cable Royalty
Funds, Docket No. 2012–9 CRB CD 2011
(Mar. 13, 2013), Order Granting Motion
of Phase I Claimants for Partial
Distribution, Docket No. 14–CRB–0007
CD (2010–12) (Dec. 23, 2014); Order
Granting Motion of Phase I Claimants
for Partial Distribution, Docket No. 14–
CRB–0010 CD (2013) (May 28, 2015).
In December 2016, the Judges ordered
the final distribution of the settled
shares from the remaining funds to
Music Claimants and National Public
Radio. Amended Order Granting Motion
for Final Distribution of 2010–2013
Cable Royalty Funds to Music Claimants
(Aug. 23, 2017); Order Granting Motion
for Final Distribution of 2010–2013
Cable Royalty Funds to National Public
Radio (Aug. 23, 2017). When the Judges
ultimately order the final distribution of
the remaining 2010–13 cable royalty
funds, they will direct the Licensing
Division of the Copyright Office to
adjust distributions to each participant
to account for partial distributions and
to apply the allocation percentages
determined herein.
Based on the record in this
proceeding, the Judges make the
following allocation of deposited
royalties.2
TABLE 1—ROYALTY ALLOCATIONS
2010
(%)
Basic Fund:
Canadian Claimants .................................................................................
Commercial TV .........................................................................................
Devotional Programs ................................................................................
Program Suppliers ....................................................................................
Public TV ..................................................................................................
Sports .......................................................................................................
3.75% Fund:
Canadian Claimants .................................................................................
Commercial TV .........................................................................................
Devotional Programs ................................................................................
Program Suppliers ....................................................................................
Public TV ..................................................................................................
Sports .......................................................................................................
Syndex Fund:
Program Suppliers ....................................................................................
1 The program categories at issue are as follows:
Canadian Claimants Group: All programs broadcast
on Canadian television stations, except (1) live
telecasts of Major League Baseball, National Hockey
League, and U.S. college team sports and (2)
programs owned by U.S. Copyright owners; Joint
Sports Claimants: Live telecasts of professional and
college team sports broadcast by U.S. and Canadian
television stations, except programming in the
Canadian Claimants category; Commercial
Television Claimants: Programs produced by or for
a U.S. commercial television station and broadcast
only by that station during the calendar year in
question, except those listed in subpart (3) of the
Program Suppliers category; Public Television
Claimants: All programs broadcast on U.S.
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2011
(%)
Frm 00002
Fmt 4701
Sfmt 4703
2013
(%)
5.0
16.8
4.0
26.5
14.8
32.9
5.0
16.8
5.5
23.9
18.6
30.2
5.0
16.2
5.5
21.5
17.9
33.9
5.5
15.3
4.3
19.3
19.5
36.1
5.9
19.7
4.7
31.1
0.0
38.6
6.1
20.6
6.8
29.4
0.0
37.1
6.1
19.7
6.7
26.2
0.0
41.3
6.8
19.0
5.3
24.0
0.0
44.9
100
100
100
100
noncommercial educational television stations;
Settling Devotional Claimants: Syndicated programs
of a primarily religious theme, but not limited to
programs produced by or for religious institutions;
and Program Suppliers: Syndicated series, specials,
and movies, except those included in the
Devotional Claimants category. Syndicated series
and specials are defined as including (1) programs
licensed to and broadcast by at least one U.S.
commercial television station during the calendar
year in question, (2) programs produced by or for
a broadcast station that are broadcast by two or
more U.S. television stations during the calendar
year in question, and (3) that are comprised
predominantly of syndicated elements, such as
music videos, cartoons, ‘‘PM Magazine,’’ and
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2012
(%)
locally hosted movies. Public TV PFFCL at ¶ 4;
Notice of Participant Groups, Commencement of
Voluntary Negotiation Period (Allocation), and
Scheduling Order, Docket No. 14–CRB–0010–CD, at
Ex. A (Nov. 25, 2015). The categories are mutually
exclusive and, in aggregate, comprehensive.
2 In reviewing responses to Program Suppliers’
request for rehearing, the Judges became aware of
an error in the Initial Determination. The Judges
used an incorrect base figure in calculating the
royalty shares for 2012 and 2013. The Judges
detailed that correction in the Order on Rehearing.
The corrected values appear in this Final
Determination.
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Program Suppliers filed a timely
request for rehearing on November 2,
2018 (Rehearing Request). The Judges
issued their ruling on the Rehearing
Request on December 13, 2018 (Order
on Rehearing), denying rehearing on any
basis asserted by Program Suppliers in
the Rehearing Request. The Initial
Determination is, therefore, the Judges’
Final Determination in this proceeding.
I. Background
A. Legal Context
In 1976, Congress granted cable
television operators a statutory license
to enable them to clear the copyrights to
over-the-air television and radio
broadcast programming which they
retransmit to their subscribers. The
license requires cable operators to
submit semi-annual royalty payments,
along with accompanying statements of
account, to the Copyright Office for
subsequent distribution to copyright
owners of the broadcast programming
that those cable operators retransmit.
See 17 U.S.C. 111(d)(1). To determine
how the collected royalties are to be
distributed among the copyright owners
filing claims for them, the Copyright
Royalty Judges (Judges) conduct a
proceeding in accordance with chapter
8 of the Copyright Act. This
determination is the culmination of one
of those proceedings.3 Proceedings for
determining the distribution of the cable
license royalties historically have been
conducted in two phases. In Phase I, the
royalties were divided among
programming categories. The claimants
to the royalties have previously
organized themselves into eight
categories of programming retransmitted
by cable systems: Movies and
syndicated television programming;
sports programming; commercial
broadcast programming; religious
broadcast programming; noncommercial
television broadcast programming;
Canadian broadcast programming;
noncommercial radio broadcast
3 Prior to enactment of the Copyright Royalty and
Distribution Reform Act of 2004, which established
the Judges program, royalty allocation
determinations under the Section 111 license were
made by two other bodies. The first was the
Copyright Royalty Tribunal, which made
distributions beginning with the 1978 royalty year,
the first year in which cable royalties were collected
under the 1976 Copyright Act. Congress abolished
the Tribunal in 1993 and replaced it with the
Copyright Arbitration Royalty Panel (‘‘CARP’’)
system. Under this regime, the Librarian of
Congress appointed a CARP, consisting of three
arbitrators, which recommended to the Librarian
how the royalties should be allocated. Final
distribution authority, however, rested with the
Librarian. The CARP system ended in 2004. See
Copyright Royalty Distribution and Reform Act of
2004, Public Law 108–419, 118 Stat. 2341 (Nov. 30,
2004).
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programming; and music contained on
all broadcast programming. In Phase II,
the royalties allotted to each category at
Phase I were subdivided among the
various copyright holders within that
category.4 In the current proceeding, the
Judges broke with past practice by
combining Phase I and Phase II into a
single proceeding in which the
functions of allocating funds between
program categories and distributing
funds among claimants within those
categories would proceed in parallel.5
This determination addresses the
Allocation Phase for royalties collected
from cable operators for the years 2010,
2011, 2012 and 2013.
The statutory cable license places
cable systems into three classes based
upon the fees they receive from their
subscribers for the retransmission of
over-the-air broadcast signals. Smalland medium-sized systems pay a flat
fee. See 17 U.S.C. 111(d)(1). Large cable
systems (‘‘Form 3’’ systems) 6—whose
royalty payments comprise the lion’s
share of the royalties distributed in this
proceeding—pay a percentage of the
gross receipts they receive from their
subscribers for each distant over-the-air
broadcast station signal they
retransmit.7 The amount of royalties
4 The Judges last adjudicated an allocation (Phase
I) determination for royalty years 2004–05. See
Distribution of the 2004 and 2005 Cable Royalty
Funds, Distribution Order, 75 FR 57063 (Sept. 17,
2010) (2004–05 Distribution Order). In the Phase I
cable proceeding relating to royalties deposited
between 2000 and 2003, the parties stipulated that
the only unresolved issue would be the Phase I
share awarded to the Canadian Claimants Group.
The remaining balance would be awarded to the
Settling Parties. See Distribution of the 2000–2003
Cable Royalty Funds, Distribution Order, 75 FR
26798–99 (May 12, 2010) (2000–03 Distribution
Order). The Judges adopted the stipulation.
5 Second Reissued Order Granting In Part
Allocation Phase Parties’ Motion to Dismiss
Multigroup Claimants and Denying Multigroup
Claimants’ Motion For Sanctions Against Allocation
Phase Parties, Docket No. 14–CRB–0010–CD (2010–
13) (Apr. 25, 2018). The Judges discontinued use of
the terms Phase I and Phase II and use the terms
Allocation Phase and Distribution Phase instead. Id.
at n.4. This determination addresses the Allocation
Phase of the proceeding.
6 ‘‘Form 3’’ cable systems, so named because they
account to the Copyright Office for retransmissions
and royalties on ‘‘Form 3.’’ The Form 3 filing is
required because they have semiannual gross
receipts in excess of $527,600. These systems must
submit an SA3 Long Form to the U.S. Copyright
Office. They are the only systems required to
identify which of the stations they carry are distant
signals. Royalty payments from Form 3 systems
accounted for over 90% of the total royalties that
cable systems paid during 2010–2013. Corrected
Testimony of Christopher J. Bennett ¶ 10 n.2
(Bennett CWDT).
7 The cable license is premised on the
Congressional judgment that large cable systems
should only pay royalties for the distant broadcast
station signals that they retransmit to their
subscribers and not for the local broadcast station
signals they provide. However, cable systems that
carry only local stations are still required to submit
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that a cable system must pay for each
broadcast station signal it retransmits
depends upon how the carriage of that
signal would have been regulated by the
Federal Communications Commission
(‘‘FCC’’) in 1976, the year in which the
current Copyright Act was enacted.
The royalty scheme for large cable
systems employs a statutory device
known as the distant signal equivalent
(DSE), which is defined at 17 U.S.C.
111(f)(5). The cable systems, other than
those paying the minimum fee, pay
royalties based upon the number of
DSEs they retransmit. The greater the
number of DSEs a cable system
retransmits the larger its total royalty
payment. The cable system pays these
royalties to the Copyright Office. These
fees comprise the ‘‘Basic Fund.’’ See 17
U.S.C. 111(d)(1)(B). In addition to the
Basic Fund, large cable systems also
may be required to pay royalties into
one of two other funds that the
Copyright Office maintains: The Syndex
Fund and the 3.75% Fund.
As noted above, the utilization of the
cable license is linked with how the
FCC regulated the cable industry in
1976.8 FCC rules at the time restricted
the number of distant broadcast signals
a cable system was permitted to carry
(‘‘the distant signal carriage rules’’).
National Cable Television Assoc., Inc. v.
Copyright Royalty Tribunal, 724 F.2d
176, 180 (D.C. Cir. 1983). FCC rules also
allowed local broadcasters and
copyright holders to require cable
systems to delete (or blackout)
syndicated programming from imported
signals if the local station had
purchased exclusive rights to the
programming (‘‘syndicated exclusivity’’
or ‘‘syndex’’ rules). Id. at 187. In 1980,
the FCC repealed both sets of rules. Id.
at 181.
The Copyright Royalty Tribunal (CRT)
initiated a cable rate adjustment
proceeding to compensate copyright
owners for royalties lost as a result of
the FCC’s repeal of the rules.
Adjustment of the Royalty Rate for
Cable Systems; Federal
Communications Commission’s
Deregulation of the Cable Industry,
Docket No. CRT 81–2, 47 FR 52146
(Nov. 19, 1982). The CRT adopted two
new rates applicable to large cable
systems making section 111 royalty
a statement of account and pay a basic minimum
fee. See 2000–03 Distribution Order, 75 FR at 26,798
n.2.
8 FCC regulation of the cable industry was
impacted by passage of the 1976 Copyright Act that
created the compulsory license for cable
retransmissions codified in section 111. See Report
and Order, Docket Nos. 20988 & 21284, 79 F.C.C.
663 (1980), aff’d sub nom. Malrite T.V., v. FCC, 652
F.2d 1140, 1146 (2d Cir. 1981).
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payments. The first, to compensate for
repeal of the distant signal carriage
rules, was a 3.75% surcharge of a large
cable system’s gross receipts for each
distant signal the carriage of which
would not have been permitted under
the FCC’s distant signal carriage rules.
Royalties paid at the 3.75% rate—
sometimes referred to by the cable
industry as the ‘‘penalty fee’’—are
accounted for by the Copyright Office in
the ‘‘3.75% Fund,’’ which is separate
from royalties kept in the Basic Fund.
See id.; see also 17 U.S.C. 111(d); 37
CFR, part 387.The second rate the CRT
adopted, to compensate for the FCC’s
repeal of its syndicated exclusivity
rules, is known as the ‘‘syndex
surcharge.’’ Large cable operators were
required to pay this additional fee for
carrying signals that were or would have
been subject to the FCC’s syndex rules.
Syndex Fund fees are accounted for
separately from royalties paid into the
Basic Fund.9
Royalties in the three funds—Basic,
3.75%, and Syndex—are the royalties to
be distributed to copyright owners of
non-network broadcast programming in
a Section 111 cable license distribution
proceeding. See 37 CFR, part 387.10
Cable system operators are required to
file Statements of Account with the
Copyright Office detailing subscription
revenues and specific television signals
they retransmit distantly, and to deposit
section 111 royalties calculated
according to the reported figures. Ex.
2004, Testimony of Gregory S. Crawford
9 In 1989, in response to changes in the cable
television industry and passage of the Satellite
Home Viewer Act of 1988, the FCC reinstated
syndicated exclusivity rules. The reinstated rules
differed from the original syndex rules, giving rise
to a petition to the CRT for adjustment or
elimination of the syndex surcharge. See Final Rule,
Adjustment of the Syndicated Exclusivity
Surcharge, Docket No. 89–5–CRA, 55 FR 33604
(Aug. 16, 1990).
The CRT held that the syndicated exclusivity
surcharge paid by Form 3 cable systems in the top
100 television markets is eliminated, except for
those instances when a cable system is importing
a distant commercial VHF station which places a
predicted Grade B contour, as defined by FCC rules,
over the cable system, and the station is not
‘‘significantly viewed’’ or otherwise exempt from
the syndicated exclusivity rules in effect as of June
24, 1981. In such cases, the syndicated exclusivity
surcharge shall continue to be paid at the same
level as before. Id.
See Final Rule, 54 FR 12,913 (Mar. 29, 1989),
aff’d sub nom. United Video, Inc. v. FCC, 890 F.2d
1173 (D.C. Cir. 1989); 47 CFR 73.658(m)(2) (1989);
47 CFR 76.156 (1989). The present proceeding deals
only with allocation of those royalties among
copyright owners in the various program categories.
10 The CRB last adjusted cable Basic, 3.75%, and
Syndex rates in 2016, for the period January 1,
2015, through December 31, 2019. See Final Rule,
Adjustment of Royalty Fees for Cable Compulsory
License, Docket No. 15–CRB–0010–CA, 81 FR
62,812 (Sept. 13, 2016). This adjustment was
pursuant to a negotiated agreement.
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¶ 74 & n.37. As cable system operators
merged they created contiguous cable
systems that were required to file
consolidated Statements of Account.
The consolidated systems were required
to pay royalties calculated on the
aggregate subscription income of the
corporate operator, even though not all
the systems under the corporate
umbrella, not even the contiguous
systems, carried or retransmitted
compensable distant signals.
Between the time of the last
adjudicated cable royalty allocation
proceeding and the present proceeding,
Congress passed the Satellite Television
and Localism Act of 2010 (STELA).11
Before STELA, cable operators were
required to pay for the carriage of
distant signals on a system-wide basis,
even though each signal was not made
available to every subscriber in the cable
system. U.S. Copyright Office,
Frequently Asked Questions on the
Satellite Television Extension and
Localism Act of 2010. Distant broadcast
signals that subscribers could not
receive were called ‘‘phantom signals.’’
Id. STELA addressed the phantomsignal issue by amending section
111(d)(1) of the Copyright Act, which
details the method by which cable
operators can calculate royalties on a
community-by-community or
subscriber-group basis. Id. From the
2010/1 accounting period and all
periods thereafter, cable operators have
been required to pay royalties based
upon where a distant broadcast signal is
offered rather than on a system-wide
basis.12 Id. As discussed below, this
statutory change permitted the
participants to analyze relative value at
the subscriber-group level. See, e.g.,
Corrected Written Direct Testimony of
Gregory Crawford, Ex. 2004 (Crawford
CWDT) ¶ 66.
B. Posture of the Current Proceeding
In December 2014, the Copyright
Royalty Board (CRB) published notice in
the Federal Register announcing
commencement of proceedings and
seeking Petitions to Participate to
determine distribution of 2010, 2011,
and 2012 royalties under the cable and
satellite licenses.13 On June 5, 2015, the
11 Public Law 111–175, 124 Stat. 1218 (May 27,
2010), reauthorized by Public Law 113–200, 128
Stat. 2059 (Dec. 4, 2014),
12 CSOs continue to be liable to pay a ‘‘minimum
fee’’ for systems that do not retransmit distant
signals. See 17 U.S.C. 111(d)(1)(B)(i). Calculation of
royalties at subscriber group levels segregates
minimum fee systems from systems that pay
royalties based on retransmission of distant signals
in excess of one DSE.
13 Docket Nos. 14–CRB–0007–CD (2010–12) and
14–CRB–0008–SD (2010–12), 79 FR 76396 (Dec. 22,
2014). The CRB received Petitions to Participate
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CRB published a notice in the Federal
Register announcing commencement of
a proceeding to determine distribution
of 2013 royalties deposited with the
Copyright Office under the cable license
and the satellite license.14 The Judges
determined that controversies existed
with respect to distribution of the cable
(and satellite) retransmission royalties
deposited for 2013, and directed
interested parties to file Petitions to
Participate.15 On September 9, 2015, the
Judges consolidated the proceedings
regarding the cable license for the years
2010, 2011, 2012, and 2013. See Notice
of Participants, Notice of Consolidation,
and Order for Preliminary Action to
Address Categories of Claims.
On November 25, 2015, the Judges
issued a Notice of Participant Groups,
Commencement of Voluntary
Negotiation Period (Allocation), and
Scheduling Order, in which the Judges
identified eight categories of claimants
for the proceeding: (1) Canadian
Claimants, (2) Commercial Television
Claimants; (3) Devotional Claimants, (4)
Joint Sports Claimants, (5) Music
Claimants, (6) National Public Radio, (7)
Program Suppliers, and (8) Public
Television Claimants. National Public
Radio and Music Claimants reached
settlements with the other claimants
groups and received respective final
distributions. Order Granting Motion for
from: ASCAP/BMI (joint), Canadian Claimants,
Major League Soccer, PBS for Public Television
Claimants, Certain Devotional Claimants aka certain
Devotional Claimants or Settling Devotional
Claimants (SDC), Joint Sports Claimants, MPAA for
Program Suppliers, Multigroup Claimants, NAB for
Commercial Television Claimants, NPR, SESAC,
and Spanish Language Producers. Major League
Soccer subsequently withdrew its petition to
participate.
14 Docket Nos. 14–CRB–0010–CD (2013) and 14–
CRB–0011–SD (2013), 80 FR 32182 (June 5, 2015).
15 The Judges received petitions from: ASCAP/
BMI (joint), Canadian Claimants, SDC, Joint Sports
Claimants, Major League Soccer, MPAA for Program
Suppliers, Multigroup Claimants, NAB for
Commercial Television Claimants, NPR,
Professional Bull Riders, PBS for Public Television
Claimants, SESAC, and Spanish Language
Producers. Professional Bull Riders and Major
League Soccer subsequently withdrew their
Petitions to Participate. Major League Soccer
withdrew its Petition to Participate in the Joint
Sports Category for 2010–2013 but maintained its
2013 satellite and cable claims in the Program
Suppliers category and indicated it would be
represented by MPAA. Major League Soccer LLC
Withdrawal of Certain Claims Relating to the
Distribution of the 2010–2013 Cable and Satellite
Royalty Funds (Sept. 21, 2016). Multigroup
Claimants, which had sought to participate in the
Allocation and Distribution phases of the
proceeding failed to file a written direct statement
in the Allocation Phase and was dismissed from
participating in that phase of the proceeding.
[Second Reissued] Order Granting in Part
Allocation Phase Parties’ Motion to Dismiss
Multigroup Claimants and Denying Multigroup
Claimants’ Motion for Sanctions Against Allocation
Phase Parties (April 25, 2018).
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Final Distribution of 2010–2013 Cable
Royalty Funds to Music Claimants (Aug.
11, 2017) and Order Granting Motion for
Final Distribution of 2010–2013 Cable
Royalty Funds to National Public Radio
(Aug. 23, 2017).
With the settlement of the Music
Claimants’ share, only the Program
Suppliers claimant group has an interest
in the royalties in the Syndex Fund.
Program Suppliers Proposed
Conclusions of Law ¶ 2 & n.3 and
references cited therein. Public TV
Claimants claim a share only of the
Basic Fund. Public TV PFFCL ¶ 43.
The hearing in the present proceeding
commenced on February 14, 2018, and
concluded on March 19, 2018.16 During
that period, the Judges heard live
testimony from 23 witnesses and
admitted written and designated
testimony from a number of additional
witnesses. The Judges admitted into the
record more than 200 exhibits.
Participants made closing arguments on
April 24, 2018, after which time the
Judges closed the record.
After reviewing the record, the Judges
identified a controversy among the
parties relating to the allocation of
royalties held in the 3.75% Fund and
requested additional briefing from the
parties. Order Soliciting Further Briefing
(June 29, 2018) (3.75% Order).
Responding to the Judges’ order, the
parties submitted additional briefs and
responses to address the issue framed by
the Judges:
Whether the interrelationship between and
among the Basic Fund, the 3.75% Fund, and
the Syndex Fund affects the allocations
within the Basic Fund, if at all, and, if so,
how that affect should be calculated and
quantified.
Id. The Judges’ disposition of the 3.75%
Fund and Syndex Fund issues is set
forth at section VII, infra. The allocation
described in Table 1 of this
Determination incorporates the Judges’
resolution of this issue.
C. Allocation Standard
Congress did not establish a statutory
standard in section 111 for the Judges
(or their predecessors) to apply when
allocating royalties among copyright
owners or categories of copyright
owners. However, through
determinations by the Judges and their
predecessors (the Copyright Royalty
Tribunal, the CARPs, and the Librarian
of Congress), the allocation standard has
evolved, and the present standard is one
16 The Judges also held a hearing on June 15,
2016, to address concerns the parties raised about
changes to the historical bifurcation of proceedings
into a first and a second phase.
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of ‘‘relative marketplace value.’’ 17 See
Distribution Order, 75 FR 57063, 57065
(Sept. 17, 2010) (2004–05 Distribution
Order).
‘‘Relative marketplace values’’ in
these proceedings have been defined as
valuations that ‘‘simulate [relative]
market valuations as if no compulsory
license existed.’’ 1998–99 Librarian
Order, 69 FR at 3608. Because such a
market does not exist (having been
supplanted by the regulatory structure),
the Judges are required to construct a
‘‘hypothetical market’’ that generates the
relative values that approximate those
that would arise in an unregulated
market. 2004–05 Distribution Order, 75
FR at 57065; see also Program Suppliers
v. Librarian of Congress, 409 F.3d 395,
401–02 (D.C. Cir. 2001) (‘‘[I]t makes
perfect sense to compensate copyright
owners by awarding them what they
would have gotten relative to other
owners . . . .’’).
In the present proceeding, the parties
disagree as to the appropriate
specification of the sellers in the
hypothetical market. Program Suppliers
assert that the hypothetical sellers are
the owners of the copyrights in the
retransmitted programs. See Corrected
Written Rebuttal Testimony of Jeffrey S.
Gray, Trial Ex. 6037, ¶ 11 (Gray CWRT).
Other parties assert that the sellers are
the local stations offering for licensing
the entire bundle of programs on the
retransmitted signal. See Corrected
Written Direct Testimony of Gregory S.
Crawford, Trial Ex. 2004, ¶ 45 (Crawford
CWDT) and Corrected Written Direct
Testimony of Lisa George, Trial Ex.
4005, at 8 (George CWDT). After
considering the record and arguments in
this proceeding, the Judges find that,
from an economic perspective, this is a
disagreement without a difference, and
therefore, consistent with prior rulings,
identify the local stations as the
hypothetical sellers. If the hypothetical
sellers (licensors) were assumed to be
the owners of the individual programs
(instead of the local stations), then (as
a matter of elementary economics) they,
like any sellers, would attempt to
maximize the royalties they receive
from licensing the retransmission rights
to CSOs.18 Because the CSOs are
17 In this proceeding, the Judges distinguish
between ‘‘relative values’’ (to describe the
allocation shares), and absolute ‘‘fair market
values.’’ Because the royalties at issue in this
proceeding are regulated and not derived from any
actual market transactions, they do not correspond
with absolute dollar royalties that would be
generated in a market and thus would not reflect
absolute ‘‘fair market value.’’
18 Because the programs already exist, production
costs have been ‘‘sunk,’’ and the copyright owners
incur no marginal physical cost in the
retransmission of their programs. Thus, the
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3555
assumed to be the buyers (licensees),
they would each negotiate one-to-one
with owners of the program copyrights.
The corollary to the assumption that the
hypothetical sellers are the individual
program copyright owners is the
assumption that the CSOs, as buyers,
would need to create one or more new
channels to bundle these programs for
retransmission. That raises the
economically important question of
whether the transaction costs 19 that a
CSO would incur to negotiate separate
contracts with individual copyright
owners would be so prohibitive as to
preclude one-to-one negotiations from
going forward. Transaction costs are
relatively ubiquitous in the licensing of
copyrighted products to licensees,
resulting in the creation of a collective
to represent the licensees, and in
blanket or standardized licenses to
reduce transaction costs further. See
Watt, supra note 19, at 17, 164–67.
But in the present case, a ‘‘collective’’
of sorts already exists—the broadcaster
who bundles programs for transmission
within a single signal. Therefore, it
remains reasonable to consider the local
stations that have bundled the programs
into their respective signals to be the
hypothetical sellers.
As noted supra, the values of the
programs in the several categories that
are determined in this proceeding are
‘‘relative values,’’ i.e., values relative to
each other, from the perspective of the
CSOs, when the programs from these
different categories are offered for
copyright owners would seek only to maximize
marginal revenue (but would still consider marginal
‘‘opportunity cost’’ if applicable, e.g., if
retransmission would cannibalize their profits from
local broadcasting of the identical program or
another program owned by the copyright owner). In
a more dynamic long-run model, copyright owners
might consider even the costs of production to be
variable and would then also seek to recover an
appropriate portion of production costs from
retransmission royalties, thereby maximizing longterm profits (rather than only shorter-term revenue),
with respect to retransmission royalties. However,
because retransmissions of local broadcasts are
‘‘only a very small fraction of a typical CSO’s
programming budget,’’ it is unlikely that, in the
hypothetical market, owners of copyrights to the
retransmitted programs would have the market
power to compel CSOs to contribute to the long-run
program production costs. See Rebuttal Testimony
of Sue Ann R. Hamilton, Trial Ex. 6009, at 14
(Hamilton WRT). Thus, the Judges agree with the
pronouncement in prior determinations that the
royalties that would be paid in the hypothetical
market would essentially be a function only of the
CSOs’ demand and the copyright owners’ costs, and
their supply curves (if any) would not be important
determinants of the market-based royalty. See, e.g.,
Distribution of 1998 and 1999 Cable Royalty Funds,
Final Order, 69 FR 3606, 3608 (Jan. 26, 2004) (1998–
99 Librarian Order).
19 Transaction costs are ‘‘pure reductions in the
total amount of resources to be distributed that are
necessary to achieve and maintain any given
allocation.’’ Richard Watt, Copyright and Economic
Theory at 15 (2000).
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distant retransmission in the form of
bundles from local stations. Relative
value is based on the preferences of the
CSOs (derived from those of their
subscribers). Because relative
preferences are components of market
demand, the CSOs’ choices represent
important elements of a market
transaction. See generally P. Krugman &
R. Wells, Microeconomics, 284–85 (2d
ed. 2009) (relative ‘‘preferences’’ lead to
buyers’ ‘‘choices’’ and an ‘‘optimal
consumption bundle’’); A. Schotter,
Microeconomics: A Modern Approach
(2009) (revealed ‘‘preferences’’ allow for
an analysis of how buyers ‘‘behave in
markets,’’ and those preferences are
building blocks for ‘‘individual and
market demand’’). Thus, any
methodology based on the identification
of the relative preferences and values of
CSOs is indeed a market-based
approach to the allocation of royalties in
this proceeding.
Because the pricing of the licenses is
regulated, however, it is not possible to
identify the actual royalties that would
be established by these ranked
preferences. To identify such royalties
would require an application of game
theoretic/bargaining power
considerations and the extent and
allocation of costs attributable to the
licensed programs—facts that are not in
the record and likely are not reasonably
or accurately ascertainable.20
Nonetheless, the raison d’eˆtre of this
section 111 proceeding is to allocate
royalties that have already been paid in
a manner that reflects relevant market
factors. To do so, it is sufficient to relate
CSOs’ revealed preferences among
program categories, whether through a
CSO survey or a regression analysis, to
the sum of all royalties paid. Prior
determinations may have described the
allocations that resulted as the ‘‘relative
market value,’’ 21 but there is no doubt
20 For example, in a hypothetical market, a
copyright owner could refuse to grant distant
retransmission rights to a local station unless the
local station (and the retransmitting CSO) agreed to
pay an additional royalty (to cover a share of sunk
costs and/or additional profit). The ability of the
copyright owner to obtain such value would be a
function of his or her market and bargaining power.
(Because the costs are sunk, the copyright owner
would not rationally walk away from a
retransmission agreement as long as some positive
royalty would be paid.) Even at the level of the
‘‘collective,’’ a local station in the hypothetical
market could use its market/bargaining power to
maximize royalty payments, assuming it had the
economic incentive to do so.
21 Actually, in the 2004–05 Determination, the
Judges recognized that neither a survey approach
nor a regression approach (both of which they
nonetheless relied upon) identified all aspects of
actual market values as opposed to relative values
based on market forces. See 2004–05 Distribution
Order, 75 FR at 57066, 57068 (noting that a CSO
survey ‘‘is certainly not a fully equilibrating model
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that royalties determined in these ways
reveal ‘‘relative values’’ that are based
on the critical market factor of identified
preferences.
In the present proceeding, the parties
presented five discrete analytical
methodologies for the Judges to consider
in determining relative market value of
the programming types at issue:
Regression analyses, CSO survey results,
viewership measurements, a changed
circumstances analysis, and a cable
content analysis.
II. Regression Analyses
Regression analysis, when properly
constructed and applied, ‘‘is an accurate
and reliable method of determining the
relationship between two or more
variables, and it can be a valuable tool
for resolving factual disputes.’’ 22 A
particular approach, multiple regression
analysis, ‘‘is the technique used in most
econometric studies, because it is well
suited to the analysis of diverse data
necessary to evaluate competing
theories about the relationships that
may exist among a number of
explanatory facts.’’ ABA Econometrics,
supra note 22, at 4.
A regression can take one of several
forms. The linear form is the most
common form, though not the most
appropriate for all analyses. As one
court has explained:
[A] linear regression is an equation for the
straight line that provides the best fit for the
data being analyzed. The ‘‘best fit’’ is the
[regression] line that minimizes the sum of
the squares of the vertical distance between
each data point and the line . . . . The
regression equation that generates that line
can be written as
Y = a + bX + u
Where Y is the dependent variable, a is the
intercept [with the vertical axis], X the
independent variable, b the coefficient of the
independent variable (that is, the number
that indicates how changes in the
independent variable produces changes in
the dependent variables), and u the
regression residual—the part of the
dependent variable that is not explained or
predicted by the independent variable . . .
or, in other words, what is ‘‘left over.’’
ATA Airlines, Inc. v. Fed. Express Corp.,
665 F.3d 882, 890 (7th Cir. 2011)
(Posner, J.), cert. denied, 568 U.S. 820
(2012).23 See Crawford CWDT ¶¶ 94–95.
of supply and demand in the relevant hypothetical
market,’’ and a regression does not ‘‘necessarily
identif[y]’’ all of ‘‘the determinants of distant signal
prices in a hypothetical free market . . . .’’).
22 American Bar Association, Econometrics 1–2
(2005) (ABA Econometrics).
23 In a multiple linear regression, the equation
would be expanded, for example as Y = a + bX +
cZ + u¥ with Z an additional independent variable
and c its coefficient.
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An economist testifying in the present
proceeding, Professor Lisa George,
explained how the regression approach
may be useful to test economic theories,
describing regression analysis as ‘‘a tool
for understanding how variations in an
outcome of interest . . . depends on
various factors affecting that outcome
. . . when the factors of interest are not
separately priced or traded.’’ George
CWDT at 2. Professor George noted a
basic difference between regression
analysis and survey methodology.
Regression analysis, unlike survey
methodology, ‘‘infers value for decisions
actually made in a market.’’ Id.
Although regression analysis is a
powerful tool, it is important to
appreciate the subtle distinction
between econometric correlation
identified by a regression, on one hand,
and economic causation explained by
economic theory, on the other:
Econometrics provides a means for
determining whether a correlation, which
may reflect a . . . causal relationship, may
exist between various events that involve
complex sets of facts. The principle value of
econometrics . . . lies in its use for
developing an empirical foundation in order
to prove or disprove assertions that are based
on a particular economic theory . . . .
[E]conometric evidence coupled with
economic theory [may] show the likelihood
of a causally-driven correlation between two
events or facts. . . . [Thus] [c]orrelation is
distinct from causation. . . . [T]he
correlation is simply circumstantial
confirmation of a hypothesized relationship.
If the hypothesized relationship does not
make theoretical sense, the existence of a
correlation between the two variables is
irrelevant.
ABA Econometrics, supra note 22, at 1,
3, 5 (emphasis added).
In the present proceeding, the
economic theory that the experts put to
the test via regression analysis is
whether or not royalties paid are a
function of (caused by) the types of
program categories bundled in distantly
retransmitted local stations.
A. Waldfogel-Type Regressions
Professors Crawford, Israel, and
George each used a regression approach
based on the regression approach
undertaken by Dr. Joel Waldfogel, an
economist who appeared in the 2004–05
proceeding on behalf of the joint
‘‘Settling Parties,’’ including three of the
present parties: The JSC, Commercial
Television Claimants (CTV), and PTV.
2004–05 Distribution Order, 75 FR at
57064. The Judges’ findings concerning
his regression (Waldfogel regression) are
instructive with regard to the Judges’
analysis in the present proceeding of the
‘‘Waldfogel-type’’ regressions proffered
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by Professor Crawford, Professor George,
and Professor Israel.
Several features characterize a
Waldfogel-type regression. Most
importantly, such an approach attempts
to correlate ‘‘variation in the [program
category] composition of distant signal
bundles along with royalties paid to
estimate the relative marketplace value
of programming.’’ George CWDT at 6.
Specifically, Dr. Waldfogel ‘‘regress[ed]
observed royalty payments for the
bundle on the numbers of minutes in
each programming category. . . . ’’
Israel WDT ¶ 22. He also employed
‘‘ ‘control variables’ . . . to hold other
drivers of CSO payments constant.’’ Id.
Dr. Waldfogel’s control variables
included the number of subscribers,
local median income, and the number of
local channels. Id.
In the 2004–05 allocation proceeding,
the Judges found the Waldfogel
regression ‘‘helpful to some degree’’ in
assisting the Judges ‘‘to more fully
delineate all of the boundaries of
reasonableness with respect to the
relative value of distant signal
programming. 2004–05 Distribution
Order, 75 FR at 57068. The Judges
described the Waldfogel regression as an
‘‘attempt [ ] to analyze the relationship
between the total royalties payed by
cable operators for carriage of distant
signals . . . and the quantity of
programming minutes by programming
category . . . .’’ Id. Conceptually, the
Judges found that, ‘‘Dr. Waldfogel’s
regression coefficients do provide some
additional useful, independent
information about how cable operators
may view the value of adding distant
signals based on the programming mix
on such signals.’’ Id. The Judges also
found Dr. Waldfogel’s methodology
‘‘generally reasonable.’’ Id. They
cautioned, however, that the wide
confidence intervals around Dr.
Waldfogel’s coefficients limited the
usefulness of his analysis in
corroborating survey-based evidence in
that proceeding. Id.24
The SDC challenge the use of
Waldfogel-type regressions in this
proceeding, thus raising as a
preliminary question whether or not the
Judges’ past acceptance of this
regression approach is binding on the
Judges in the present proceeding as a
24 The Judges noted that ‘‘Dr. Waldfogel’s
specification was similar in its choice of
independent variables to a regression model
utilized by Dr. Gregory Rosston to corroborate the
Bortz survey results in the 1998–99 CARP
proceeding. Id. See Report of the Copyright
Arbitration Royalty Panel to the Librarian of
Congress, Docket No. 2001–8 CARP CD 98–99
(1998–99 CARP Report) at 46 (Oct. 21, 2003).
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matter of what has been loosely
described as ‘‘precedent.’’
The Librarian and the Register
considered the extent to which a CARP
should be bound by prior
determinations of acceptable royalty
allocation methodologies in the 1998–99
Phase I cable distribution proceeding.25
The Register acknowledged that ‘‘[t]he
concept of ‘precedent’ . . . plays an
important role in [these] proceedings,’’
but observed that ‘‘prior decisions are
not cast in stone and can be varied from
when there are (1) changed
circumstances from a prior proceeding
or; (2) evidence on the record before it
that requires prior conclusions to be
modified regardless of whether there are
changed circumstances.’’ 1998–99
Librarian Order, 69 FR at 3613–14
(citations omitted). The Register also
referred to a prior Librarian’s decision
in which the Register had stated that a
CARP ‘‘may deviate from [a prior
decision] if the Panel provides a
reasoned explanation of its decision to
vary from precedent . . . .’’ Id.
The Judges understand that they have
the authority and, indeed, the duty, to
consider all appropriate factual
presentations regarding the
establishment of value in this
proceeding in order to allocate royalties
among the several program categories.
The Judges consider the loose use of the
term ‘‘precedent’’ in this context to be
unhelpful. The concept of ‘‘precedent’’
typically relates to judicial deference to
prior legal determinations, not factual
ones.26
However, the 1998–99 Librarian Order
clearly indicates that factual challenges
to previously-accepted methodologies
shall be subject to a particular
evidentiary standard. Specifically, the
Judges have been directed that they may
disregard or modify prior methodologies
only in the event of ‘‘changed
circumstances’’ or because of evidence
in the record that ‘‘requires’’ such a
change. See Program Suppliers v.
Librarian of Congress, 409 F.3d 395, 402
(D.C. Cir. 2005). The Judges understand
this instruction to be in the nature of a
‘‘precedent’’ setting forth the legal
standard for the evaluation of fact
evidence.
25 The CARPs were governed by a statutory
provision regarding precedent that was nearly
identical to the current section 803(a)(1). See 17
U.S.C. 802(c) (2003) (repealed). Consequently, the
1998–99 Librarian Order remains relevant in spite
of the intervening statutory amendments abolishing
the CARP system and creating the Judges.
26 Legal precedents provide stare decisis effect to
‘‘legal issues . . . prescribing the norms that apply
and consequences that attach to’’ facts presented at
trial. See A. Larsen, Factual Precedents, 162 U. Pa.
L. Rev. 59, 68 (2013).
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Accordingly, the Judges consider the
challenges in this proceeding to the
application of Waldfogel-type
regressions by considering whether
there have been either ‘‘changed
circumstances’’ or the presentation of
other record evidence that ‘‘requires’’ a
departure from considering the
Waldfogel-type regressions introduced
into the record in this proceeding.
Absent evidence of relevant ‘‘changed
circumstances’’ or other new evidence
in the record specifically identified as
such by any critics of the Waldfogeltype regression approach, the Judges
will evaluate the proffered Waldfogeltype regressions consistent with their
treatment of Dr. Waldfogel’s analysis in
the 2004–2005 allocation proceeding.
In the current proceeding, the SDC’s
economic expert, Dr. Erkan Erdem,
leveled broad criticisms at the use of
Waldfogel-type regressions by Professor
Crawford, Professor George, and Dr.
Israel, notwithstanding the Judges’ prior
contrary conclusions in the 2004–05
Determination. See Written Rebuttal
Testimony of Erkan Erdem, Trial Ex.
5007, at 5–6 (Erdem WRT).27 Dr. Erdem
opined that, conceptually, ‘‘Waldfogeltype regressions do not measure relative
market value’’ for two reasons. First,
according to Dr. Erdem, CSO royalty
payments are uninformative because
they are determined by a statutory
formula, not through free-market
negotiations between CSOs and content
owners; 28 and, second, in Dr. Erdem’s
view, the volume of programming does
not necessarily equate to value. Written
Direct Testimony of Erkan Erdem, Trial
Ex. 5002, at 14 (Erdem WDT). Dr. Erdem
thus concluded that ‘‘[o]verall, the
Waldfogel-type regressions say little
about relative market value’’ and at most
are ‘‘marginally informative’’ as
corroborative evidence. . . . .’’ Id. at 18.
The Judges have found previously
that Waldfogel-type regressions are
relevant in cable distribution
proceedings and find nothing in Dr.
Erdem’s testimony in the current
proceeding to support changing that
position. Therefore, the Judges reject Dr.
Erdem’s broad argument that Waldfogel27 Dr. Erdem referred to the Crawford, Israel, and
George analyses as ‘‘Waldfogel-type’’ regressions
because they ‘‘attempted to estimate the marginal
effect of each minute of programming for claimant
categories using regression analysis in which the
dependent variable is the royalty fees paid by a
system and independent variables include minutes
of programming for each claimant category and
other control variables.’’ Id.
28 Another SDC witness, Mr. John Sanders (a
valuation expert rather than an economic expert),
echoed this criticism, as discussed infra. A Program
Supplier economic expert witness, Dr. Jeffrey Gray,
criticized the regression approach to the extent it
included minimum fee-paying CSOs in the analysis,
as also discussed infra.
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type regressions are not useful in
establishing relative value in this
proceeding.29 Of course, this point does
not mean that the Judges therefore
necessarily accept all aspects of the
application of the Waldfogel-type
regressions by Professor Crawford,
Professor George, and Dr. Israel in this
proceeding. Rather, the Judges analyze
infra the more granular critiques of
those regressions leveled by various
witnesses, to determine the weight to be
accorded to each such regression.
B. Crawford Regression Analysis
1. General Principles
CTV called Professor Gregory
Crawford as an economic expert
witness. Professor Crawford undertook a
Waldfogel-type regression, which he
opined was an appropriate approach for
estimating relative market value among
the six allocation-phase categories.
Crawford CWDT ¶ 5. Professor Crawford
envisaged a hypothetical market
consistent with the actual market for
cable channel carriage in general.
Crawford CWDT ¶¶ 8, 36. In Professor
Crawford’s hypothetical market, the
owners of the distantly retransmitted
stations (i.e., broadcasters) are the
sellers of bundles of programming (their
respective program lineups), and the
CSOs are the buyers. Crawford CWDT
¶ 6.30 Professor Crawford opined that
CSOs are more likely to retransmit
‘‘distant signals that carry more highlyvalued programming.’’ Id. ¶ 7. Although
this reasoning appears self-evident
(ceteris paribus, re-sellers prefer to sell
products that are more valuable),
according to Professor Crawford, this
point also has a subtler meaning in
connection with CSO decision-making.
Id. ¶ 46. Specifically, he opined that,
because such stations bundle various
types of programming, there can exist
across subscribers a ‘‘negative
correlation’’ in their ‘‘Willingness to
Pay’’ (WTP) (in other words, making the
bundle relatively less preferable when a
program from one category is added to
the bundle, as opposed to one from
another category). Id. ¶ 6 (emphasis
added).
29 In this determination, when the use of a
particular Waldfogel-type regression is challenged
on one of these broad bases, the Judges address
those specific challenges.
30 Professor Crawford does not hypothesize that
in this ersatz market the CSO could replace
advertising that was included in the local broadcast
with advertising targeted to the distant market in
which it has been retransmitted. Crawford CWDT
¶ 37. The Judges find this approach reasonable
because they did not identify any evidence that
would sufficiently support the hypothesis that
CSOs would insert replacement advertising into
distantly retransmitted stations.
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Accordingly, Professor Crawford
concluded that when deciding whether
to enlarge its channel lineup by
distantly retransmitting a television
station, a rational CSO would consider
the variety, or mix, of programming on
that channel in light of the existing
programming mix offered by the CSO to
subscribers across the channel lineup.
According to Professor Crawford, to
achieve an optimal programming mix a
CSO would recognize that ‘‘niche taste[ ]
channels are more likely to increase
CSO profitability due to the likelihood
that household tastes for such
programming are ‘negatively correlated’
with tastes for other components of
cable bundles.’’ Id. ¶ 7. For example, if
a channel lineup were saturated with
programming from five of the six
program categories, but had little or no
programming in the sixth category, e.g.,
PTV, then a CSO might enhance its
profitability through fees from new
subscribers, by adding PTV
programming, which may have a
following among subscribers who have
little or no taste for marginal increases
in programming in other categories.
Professor Crawford’s regression
adopted the general concept from the
Waldfogel-type regressions. Specifically,
Professor Crawford concluded that the
‘‘most suitable’’ econometric regression
would ‘‘relat[e] existing distant signal
royalty payments to the minutes of
programming of different types carried
on distant signals under the compulsory
license . . . .’’ Id. ¶ 46. He favored a
regression model because it is a
standard econometric approach utilized
to establish the discrete prices of
different elements in a bundle of goods,
or the value of a bundle of attributes in
a single good. Id. ¶ 47.31
Thus, Professor Crawford inferred the
‘‘average marginal value’’ of content
type (by program category), based on the
decisions CSOs made. 2/28/18 Tr. 1400–
02 (Crawford). More precisely, as in any
Waldfogel-type regression, he related
the relative variation in royalties across
categories to the relative variation in
minutes of different categories of
programming. Crawford CWDT ¶¶ 53–
54.
In econometric terms, Professor
Crawford related the natural log 32 of
31 Despite his advocacy for a regression approach,
and for his particular regression, Professor Crawford
acknowledged the possibility ‘‘for economists to
apply alternative approaches to this problem.’’ Id.
32 The ‘‘natural log’’ (shorthand for logarithm) is
‘‘[a] mathematical function defined for a positive
argument; its slope is always positive but with a
diminishing slope tending to zero,’’ and it ‘‘is the
inverse of the exponential function X = ln(ex).’’ J.
Stock & M. Watson, Introduction to Econometrics
821 (3d ed. 2015). For purposes of applied
econometrics, using the logarithmic functional
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royalties: (1) To the minutes of claimed
programming by category; and (2) to
other ‘‘control’’ variables.33 Id. ¶ 91.
Professor Crawford’s regression looked
for a correlation in a subscriber group
between changes in the number of
minutes of programming the subscribers
watched by categories and changes in
the percentage of royalties the
subscriber group paid while holding
constant other potential explanatory
variables (called control variables).34
The variables Professor Crawford
controlled for included the numbers of
local and distant stations, the number of
activated cable channels, and the size of
the CSO. Id. ¶ 118 & App. A.
Professor Crawford first estimated the
average marginal value per minute of
each type of programming by subscriber
group. Id. ¶ 128.35 Econometrically,
these values are referred to as the
coefficients for each program-category
parameter.36 Professor Crawford then
summed the marginal value of the
compensable minutes each subscriber
group retransmitted. Id. ¶ 131. Finally,
Professor Crawford divided the total
form, showing percentage changes in the variables,
may be more practical.
33 A ‘‘control variable’’ is an independent
(explanatory) variable that ‘‘is not the object of
interest in the study; rather it is a regressor
included to hold constant factors that, if neglected,
could lead the estimated . . . effect of interest to
suffer from omitted variable bias.’’ Stock & Watson,
supra note 32, at 280.
34 By investigating the change (effect) in
percentage terms on royalties (the dependent
variable) from a change in the number of minutes
per program category (the independent variable),
Professor Crawford adopted what is known as a
‘‘log-level’’ (a/k/a ‘‘log-linear’’) functional form. See,
e.g., J. Wooldridge, Introductory Economics 865 (3d
ed. 2006). This approach allowed Professor
Crawford to compare the effect of a change in the
number of program category minutes to the percent
increase in subscriber group royalties of different
sizes. For example, a 100-minute increase in
Program Supplier minutes for a subscriber group in
which 10,000 such minutes are retransmitted
represents a 1% increase in such minutes, whereas
the same 100-minute increase for a subscriber group
in which only 1,000 such minutes are retransmitted
would represent a 10% increase. See Crawford
CWDT ¶¶ 113–114.
35 The royalty data on which Dr. Crawford relied
came from the Licensing Division of the Copyright
Office via the Cable Data Corporation (CDC), and
were provided to Dr. Christopher Bennett, another
CTV economic witness, who directed the
preparation of the data for Professor Crawford’s
regression analysis. Crawford CWDT ¶ 73. Dr.
Bennett also obtained and compiled the data
relating to the minutes of different programming
types, using raw data obtained from FYI Television.
Crawford CWDT ¶¶ 78–79.
36 A ‘‘parameter’’ is ‘‘[a] numerical characteristic
of a population or a model,’’ whereas a
‘‘coefficient’’ is ‘‘an estimated regression
parameter.’’ D. Rubinfeld, Reference Guide on
Multiple Regression, reprinted in Reference Manual
on Scientific Evidence 463, 466 (2011). The ‘‘true’’
value of the parameter is ‘‘unknown,’’ but can be
estimated, and the coefficient is that estimate. See
Peter Kennedy, A Guide to Econometrics 4 (5th ed.
2003).
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value of each given programming
category by the total value of all
compensated minutes, which produced
a percentage reflecting the relative value
of each program category as produced
by his regression.
The percentage totals estimated by
Professor Crawford, and the standard
errors 37 associated with those estimates,
by year and averaged across all four
years, were as follows (with standard
errors in parentheses):
TABLE 2—IMPLIED SHARES OF DISTANT MINUTES BY CLAIMANT CATEGORIES
Program
suppliers
(%)
Year
2010 .........................................................
2011 .........................................................
2012 .........................................................
2013 .........................................................
2010–13 ...................................................
27.66
25.44
22.84
20.31
23.95
Id. ¶ 141 and Fig. 17.
Professor Crawford did not use these
values, however, as his only estimates of
relative market value across the six
programming categories. Rather, he
identified an issue with regard to
network (and to a lesser extent, nonnetwork) programming that he believed
to require a further adjustment.
Specifically, Professor Crawford noted
that on some distantly retransmitted
stations there existed programming that
duplicated programming on the local
channels in that market. Id. at ¶ 87.
According to Professor Crawford,
‘‘[n]etwork duplication is a non-trivial
issue, accounting for 4.6% of minutes
carried on distant broadcast signals
. . . .’’ Id. This issue, he noted, is
particularly applicable to Big 3 (ABC,
(1.89)
(1.67)
(1.64)
(1.52)
(1.68)
Commercial
TV
(%)
Sports
(%)
34.29
32.12
36.09
38.00
35.19
(3.78)
(3.65)
(3.86)
(3.94)
(3.82)
17.48
17.93
17.29
16.08
17.18
(1.50)
(1.49)
(1.52)
(1.45)
(1.49)
CBS, and NBC) network programming,
because a number of local markets to
which Big 3 affiliate stations were
distantly retransmitted by a CSO already
had a local Big 3 network affiliate,
rendering the retransmitted network
programming duplicative. Professor
Crawford understood the relative
percentages attributable to the six
categories of programming—because
they were averaged across all minutes of
programming—to be distorted by these
duplicative minutes. Id. ¶¶ 81, 85–87,
143. Accordingly, even though network
programming is not compensable in this
proceeding, Professor Crawford made
this adjustment as a ‘‘deaveraging’’
device, stating: ‘‘I am attributing the full
value of the positive non-duplicate
Public TV
(%)
15.44
19.77
19.03
20.51
18.75
(1.01)
(1.22)
(1.29)
(1.44)
(1.25)
Devotional
(%)
1.02
0.71
0.55
0.51
0.69
(0.27)
(0.19)
(0.15)
(0.14)
(0.18)
Canadian
(%)
4.10
4.02
4.19
4.59
4.23
(0.33)
(0.32)
(0.35)
(0.39)
(0.35)
programming just to the non-duplicate
programming (and the zero value of the
duplicate programming to the duplicate
programming).’’ Id. ¶ 147.
Assuming a zero value for the
duplicative network programming,
Professor Crawford instructed his data
analysts to remove the duplicate
network programming.38 With those
duplications removed, Professor
Crawford re-ran his regression and
averaged the relative values of the six
program categories at issue in this
proceeding.
After making this adjustment,
Professor Crawford estimated the
following percentage allocations (with
the associated standard errors set forth
below each allocation):
TABLE 3—IMPLIED SHARES OF DISTANT MINUTES BY CLAIMANT CATEGORIES: NON-DUPLICATE MINUTES ANALYSIS
Program
suppliers
(%)
Year
2010 .........................................................
2011 .........................................................
2012 .........................................................
2013 .........................................................
2010–13 ...................................................
27.06
24.67
22.50
19.74
23.40
Id. ¶ 153 & Fig. 20.
(1.97)
(1.73)
(1.72)
(1.60)
(1.76)
Commercial
TV
(%)
Sports
(%)
34.02
31.78
35.93
38.56
35.13
(3.96)
(3.82)
(4.06)
(4.17)
(4.02)
19.76
20.18
19.64
18.44
19.49
(1.48)
(1.45)
(1.51)
(1.48)
(1.48)
Public TV
(%)
14.01
18.64
17.17
18.09
17.02
(1.00)
(1.25)
(1.27)
(1.41)
(1.23)
Devotional
(%)
1.05
0.73
0.56
0.53
0.71
(0.25)
(0.18)
(0.14)
(0.13)
(0.17)
Canadian
(%)
4.10
4.00
4.20
4.65
4.24
(0.36)
(0.35)
(0.38)
(0.44)
(0.38)
Dr. Erkan Erdem, the SDC’s
economist, claimed to have identified a
flaw in the algorithm Professor
Crawford used to allocate royalties to
minutes of programming across
categories. Dr. Erdem testified that,
because of this alleged flaw, Professor
Crawford’s model was highly sensitive
to the sequencing in which data was
inputted and sorted into his regression
model. Erdem WRT at 2, 14.
However, Dr. Erdem acknowledged
receiving additional data from CTV that
pertained to this issue. When Dr. Erdem
re-ran the updated data using Professor
Crawford’s regression model, Dr. Erdem
found only ‘‘slightly different’’ results
with regard to ‘‘implied shares of distant
minute royalties by claimant categories
for both the initial and nonduplicated
analyses . . . presented by Professor
Crawford.’’ Erdem WRT at 15 n.13.
Dr. Erdem further testified that he did
not review and test Professor Crawford’s
37 The ‘‘standard error is ‘‘[a]n estimate of the
standard deviation of the regression error . . .
calculated as an average of the squares of the
residuals associated with a particular multiple
regression analysis.’’ Rubinfeld, supra note 36, at
467. The standard error measures the probability
distribution for the estimates of each parameter in
the regression if ‘‘the expert continued to collect
more and more samples and generated additional
estimates . . . .’’ ABA Econometrics, supra note 22,
at 404.
38 Professor Crawford assumed that duplicated
programming, whether or not it was blacked out
upon retransmission, had zero value because the
programming was already available on a local
station. Id. ¶¶ 86, 144–145. The Judges find this
assumption reasonable because identical network
programs that are broadcast locally and
retransmitted distantly into the same local market
are essentially perfect substitutes. Why are they
essentially perfect and not just perfect substitutes?
Because they are on different channels, the search
cost might be different for viewers. For example a
viewer might find a show on local channel 4, but
the same show on a distantly retransmitted station
might appear on channel 157, which is not
included in the viewer’s usual ‘‘channel surfing.’’
2. The SDC Criticisms of Dr. Crawford’s
Analysis
a. Alleged Flaw in the Algorithm
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algorithm fully because it would have
taken him a week to do so. Id. at 14.
Additionally, neither Dr. Erdem nor the
SDC pursued this point further, either in
Dr. Erdem’s further testimony or in posthearing filings and arguments.
Based on the foregoing, the Judges
find this criticism to be insufficient to
invalidate or call into question the
evidentiary value of Professor
Crawford’s regression.
b. Economic Principles Allegedly Not
Embodied in Crawford Regression
Analysis
Dr. Erdem noted approvingly certain
general economic points that Professor
Crawford made. First, he agreed with
Professor Crawford that it is reasonable
to posit that a rational CSO would likely
tend to select stations for distant
retransmission that maximize the
difference between anticipated revenue
and the cost of acquiring the
retransmission rights. Second, Dr.
Erdem agreed with Professor Crawford
that a ‘‘negative correlation’’ rationally
should exist among subscribers between
different categories of programs, leading
CSOs to engage in strategic bundling of
program categories. Id. at 12.
However, Dr. Erdem faulted Professor
Crawford for failing to incorporate these
economic observations into the latter’s
regression model. With regard to the
first point—maximizing the spread
between revenues and costs—Dr. Erdem
noted that the royalty fees are set by
statute, so this concept is not applicable
in the regulated market. Id. at 12.
With regard to the second point—the
negative correlation of different
programming types between and among
subscribers—Dr. Erdem noted that
Professor Crawford did not incorporate
this principle into his regression
analysis. Id. Dr. Erdem acknowledged
that the program bundling that results
from the negative correlation between
program types has ‘‘important
implications,’’ but not implications that
support Professor Crawford’s regression
model. Dr. Erdem asserts that the
negative correlation between program
types implies ‘‘that subscribers likely do
not think of distant broadcasts in terms
of total minutes . . . . A more natural
unit would be the availability of
particular programs, regardless of their
duration or frequency.’’ Id. at 13
(emphasis added). Thus, Dr. Erdem
suggested that Professor Crawford’s
reliance (as is the case in all Waldfogeltype regressions) on programming
minutes as the independent
(explanatory) variable with respect to
program type valuation misses the real
economic correlation pertinent to a
value estimate, which is the correlation
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between royalties and the number of
subscribers. Id.
In response to the first point,
Professor Crawford noted that his
regression analysis implicitly
incorporated this revenue maximization
principle because it identified, ranked,
and estimated the relative value of
program categories that maximize
economic value for subscribers given
the existence of retransmission costs.
Written Rebuttal Testimony of Gregory
Crawford, Trial Ex. 2005, ¶¶ 70–71
(Crawford WRT). With regard to the
second point, Professor Crawford did
not expressly state that the negative
correlation between programming types
applied to his results. Rather, he noted
that the negative coefficients he had
estimated for duplicated network
programming39 in part represented the
fact that, on average, a station bundle
containing duplicated network minutes
would be less valuable to subscribers
than one that did not. 2/28/18 Tr. 1404,
1607–08 (Crawford) (duplicate
programming adds no value and might
be blacked-out).40
The Judges agree with Dr. Erdem that
Professor Crawford’s regression analysis
does not literally demonstrate that CSOs
seek to maximize the difference between
revenues and costs as they would in an
unregulated market. Because royalty
costs are determined independently
from retransmission decisions
(especially with regard to the first DSE,
which is retransmitted in exchange for
a mandatory minimum fee, as discussed
infra), CSOs do not and cannot engage
in the sort of marginal profit
maximization decisions buyers/
licensees would undertake in an
unregulated market. However, that does
not mean that CSOs do not engage in
maximizing behavior through marginal
analyses that weigh the relative values
of adding additional programming from
different program categories,
39 He estimated no negative coefficients for the six
program categories at issue in this proceeding.
40 Professor Crawford also estimated a negative
coefficient for nonduplicated network minutes, but
he testified that this was solely an artifact of the
regulated rate structure, in which distantly
retransmitted networks ‘‘only pay royalties of .25
DSE.’’ 2/28/18 Tr. 1605 (Crawford). The Canadian
Claimant Group’s expert, Professor George,
understood the negative coefficients for a program
category to reflect that programs in such a category
would reduce the value of a station bundle
compared with programs from other program
categories. 3/5/18 Tr. 2117–18 (George); see id. at
2031 (‘‘the negative coefficient here is telling us that
this is effectively dragging down the value of the
Canadian signals. . . . [I]if we could replace the
Program Supplier content on Canadian signals in a
sort of hypothetical world . . . with Joint Sports or
Canadian Claimant programming, the value of the
signal would be higher. And so this coefficient, the
negative coefficient, isn’t really surprising to me in
this context . . . .’’).
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–notwithstanding the presence of the
regulated royalty rate.
The Judges give no weight, however,
to Dr. Erdem’s speculation as to how
subscribers value programs of varying
lengths. Dr. Erdem did not undertake
any affirmative analysis and presented
no original methodology. Thus, even
assuming arguendo there might be value
in such a subscriber-based value
analysis, Dr. Erdem did not present one
here.
c. The ‘‘Distant Minutes’’ Criticism
Dr. Erdem noted that Professor
Crawford’s regression, because it is a
Waldfogel-type regression, ‘‘assigned a
predominant role’’ to the number of
distant minutes retransmitted by each
program category. Dr. Erdem thus
characterized Dr. Crawford’s regression
as a ‘‘volume focused’’ approach. Erdem
WDT at 14. Dr. Erdem questioned
whether Professor Crawford’s key
variable—‘‘distant minutes’’ by
category—really explained a
‘‘significant share of the variation in
royalty fees.’’ Erdem WRT at 15. To
answer that question, Dr. Erdem
‘‘estimate[ed] a regression model with
only total distant minutes for each
claimant group as the independent
(explanatory) variable.’’ Id. Dr. Erdem
found that the number of distant
minutes by claimant group explained
‘‘very little’’ of the variation in royalties
as measured by adjusted R2. Id. at 15–
16.41
In response, Professor Crawford noted
that his regression, like all Waldfogeltype regressions, ‘‘does not measure the
relative value of a programming type
using only the number of minutes of
. . . programming type.’’ Crawford WRT
¶ 74. Rather, such regressions also
‘‘measure the average value per minute
to CSOs of each programming type[,]
[and then] multiply[ ] the average value
per minute by the number of minutes of
programming, giv[ing] the total value of
each program type.’’ Id. ¶ 75. Then, the
total value of each program type is
converted to ‘‘average values per minute
of each claimant’s programming via
Professor Crawford’s regression (and,
41 R2 in a multiple regression model is ‘‘the
proportion of the total sample variation in the
dependent variable [royalties-by-category here] that
is explained by the independent variable here, [the
number of distant minutes by claimant group].’’
Wooldridge, supra note 34, at 868. In more practical
terms, ‘‘R2 provides a measure of the overall
goodness-of-fit of the multiple regression equation
[with] value ranges from 0 to 1. An R2 of 0 means
the explanatory variables explain none of the
variation of the dependent variable; an R2 of 1
means that the explanatory variables explain all of
the variation.’’ ABA Econometrics, supra note 22, at
409. ‘‘There is no clear-cut answer [as] to [w]hat
level of R2, if any, should lead to a conclusion that
the model is satisfactory.’’ Id.
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indeed, any Waldfogel-type regression).
As Professor Crawford opined, it is the
‘‘variation in the royalties paid by
CSOs’’ across each programming
category that allows the regression ‘‘to
infer the average value per minute’’ of
each programming category, and
‘‘[t]hese estimated average values per
minute are the estimated coefficients’’
in the regression. Id. ¶ 76.
The Judges find that Dr. Erdem’s
analysis, although apparently accurate,
is off-point and does not diminish the
value of Professor Crawford’s regression
(or any similarly-constructed Waldfogeltype regression). The Judges recognize
that the two elements multiplied in
such a regression—the volume of total
minutes per program category and the
value-per-minute are both functions of
volume. The former, volume of minutes
per program category, is facially a
volume metric. Professor Crawford
recognized that if a regression measured
only volume, then it would be properly
subject to criticism. Crawford WRT ¶ 74.
But the latter factor in the product, the
value-per-minute, is not subject to the
same criticism. The value-per-minute
factor is a metric for relative value,
estimating the CSOs’ relative demand
for different categories of programming.
To criticize the product as related to
volume, therefore, misses the mark,
because it is relative value that the
Judges must determine in this
proceeding.
With regard to Dr. Erdem’s rebuttal
critique, in which he found the R2
calculation to demonstrate little
correlation between categorical
programming minutes and royalties,
Professor Crawford had a persuasive
rejoinder. Professor Crawford explained
that it would be as uninformative as it
would be unsurprising that the number
of distant minutes alone—as Dr. Erdem
found—would better estimate the
royalties paid (via a higher R2).
Professor Crawford explained that the
purpose of his regression is to
demonstrate the ‘‘effect’’ of different
programming (by category) on the
relative royalties, not simply to find the
regressor (independent variable) that
best ‘‘predicts’’ the level of royalties.
Crawford WRT ¶¶ 91–95. Thus,
Professor Crawford opined, his
regression is relevant to the economic
issue at hand: The relative value of
program categories.42
The Judges do not agree that Dr.
Erdem’s calculation of a higher R2 alone
for his alternative approach
42 Professor Crawford calculated an R2 of .247 for
his duplicate analysis and an R2 of.246 for his nonduplicate analysis. Crawford CWDT Appx. B at B–
2.
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demonstrated a deficiency in Professor
Crawford’s regression. As one
econometric expert has explained:
[A] low R2 does not necessarily imply a poor
model (or vice versa) . . . What level of R2,
if any, should lead to a conclusion that the
model is satisfactory? Unfortunately, there is
no clear cut answer to this question, since the
magnitude of R2 depends on the
characteristics of the data series being
studied . . . . [A] high R2 does not by itself
mean that the variables included in the
model are the appropriate ones. . . . As a
general rule, courts should be reluctant to
rely on a statistic such as R2 to choose one
model over another.
Rubinfeld, supra note 36, at 425, 457.
Dr. Rubinfeld’s emphasis on
identifying the ‘‘appropriate’’ variables
leads to Professor Crawford’s next
response to Dr. Erdem’s critique.
According to Professor Crawford, from
the perspective of economic analysis (as
opposed to purely econometric
analysis), Dr. Erdem’s critique failed to
address the institutional and economic
concerns in this proceeding, viz., how to
determine the relative value of the
different program categories in an
allocation proceeding. Crawford WRT
¶ 95. Professor Crawford maintained
that his regression properly identifies
the relative relationships at issue in this
proceeding.
d. Alleged Failure To Focus on Impact
of the ‘‘Number of Distant Subscribers’’
Dr. Erdem asserted that a control
variable in Professor Crawford’s
regression—the ‘‘number of distant
subscribers’’—was statistically
significant and accounted for a large
share of the variability in the royalties.
Erdem WRT at 17. Accordingly, Dr.
Erdem concluded that Professor
Crawford’s regression inaccurately and
wrongly emphasized a correlation
between program minutes (across
categories) and royalty variability, when
the more significant correlation was
between the number of distant
subscribers and the variability of
royalties. Id.
In response, Professor Crawford
explained that Dr. Erdem had failed to
use the proper measure of ‘‘distant
subscribers,’’ which led Dr. Erdem in
essence to double-count the number of
distant subscribers, thus invalidating his
argument. Crawford WRT ¶ 104.43 Dr.
Erdem was compelled to concede at the
hearing that his manipulations in his
Models numbered 1 through 6 should
43 In fact, as discussed infra, Dr. Erdem
subsequently agreed with Professor Crawford’s
criticism in this regard, and the SDC moved for
leave to correct Dr. Erdem’s testimony, but the
Judges entered an order denying that motion as out
of time.
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all be ignored. 3/8/12 Tr. 2779–80
(Erdem).
Accordingly, the Judges do not give
any weight to this criticism.44
e. The Zero Minutes Issue
Dr. Erdem pointed out that Professor
Crawford’s two models contained
numerous zeros (i.e., instances when
there was no distant content being
retransmitted for a particular claimant
category). More particularly, Dr. Erdem
noted that for the duplicated analysis,
the Canadian distant programming
minutes had about 94 percent zeros,
followed by PTV with approximately 59
percent, the JSC with approximately 10
percent, and between 5–8 percent for
the remaining categories. (These
percentages remain essentially
unchanged for the nonduplicated
analysis.) Erdem WRT at 17–18.
Dr. Erdem asserted that because zero
represented a floor on the number of
minutes any programming category
could have offered, Professor Crawford’s
failure to control for the presence of a
non-trivial number of zeros has the
‘‘potential’’ to skew the coefficients
Professor Crawford estimated in his
models. In an attempt to address this
issue, Dr. Erdem reworked Professor
Crawford’s regression approach by
including ‘‘indicator variables’’ for
instances in which the distant minute
variables were zero. He then reestimated Professor Crawford’s two
models, creating what he called ‘‘Model
3.’’ Dr. Erdem’s Model 3 cumulatively
reworked Professor Crawford’s
duplicated and nonduplicated
regressions to incorporate, inter alia, the
distant subscriber instances and the
zero-minutes indicator issue. Erdem
WRT at 38, 40.
Dr. Erdem found that, relative to
Professor Crawford’s regression model,
adding the indicators for instances with
zero distant minutes increased the PS
and PTV shares by approximately 6
percentage points and 1–2 percentage
points, respectively. The Devotional
share increased by approximately 1
percentage point while the CTV share
decreased by approximately 10
percentage points. The JSC share
increased by approximately 1
44 Dr. Erdem modeled several of his additional
critiques, discussed infra, by combining the impact
of those critiques with the impact of his admittedly
erroneous measure of the number of ‘‘distant
subscriber minutes.’’ The Judges separately
consider those further critiques on their own merits,
not only in the interest of completeness, but also
to consider whether or not these other criticisms
have qualitative value, notwithstanding that their
impact cannot be quantified by resort to Dr. Erdem’s
modeling that bundled those critiques with the
admittedly tainted measure of ‘‘distant subscriber
minutes.’’
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percentage point, and the Canadian
share decreased by approximately 0.4–
0.5 percentage points. Id.
Because these revised percentages
also incorporate Dr. Erdem’s erroneous
adjustment for his ‘‘distant subscriber
instances’’ variable, his ‘‘Model 3,’’
must be ignored. 3/8/18 Tr. 2779–80
(Erdem). Further, as a separate problem
with Dr. Erdem’s critique, he did not
opine that Professor Crawford’s
treatment of the number of zeros was
improper or that it had caused a
skewing of the coefficients; rather Dr.
Erdem testified only that such skewing
was a ‘‘potential’’ problem—one that Dr.
Erdem would have elected to address
with the use of an indicator variable.45
The Judges understand this point to
indicate that although Dr. Erdem would
have undertaken a different approach,
he did not opine that Professor
Crawford’s approach was unreasonable.
Accordingly, the Judges are
unpersuaded that this criticism served
to undermine the usefulness of
Professor Crawford’s regression
analysis.46
f. Sensitivity of Nonduplicated Minutes
Model
In his nonduplicated model, Professor
Crawford included as an additional
variable the total number of
nonduplicated minutes. Dr. Erdem
noted that Professor Crawford explained
that ‘‘[t]his new covariate plays the
same role in the final econometric
model that the number of distant signals
plays in the initial econometric model.’’
Erdem WRT at 19 (quoting Crawford
CWDT ¶ 165 n.57). However, Dr. Erdem
discovered that in this nonduplicated
model the number of distant signals was
still present, together with the new
variable, (i.e., the total number of
nonduplicated minutes). Dr. Erdem
determined that these two variables
were almost perfectly correlated (a 0.998
correlation), rendering ‘‘the rationale for
including that additional variable . . .
less clear.’’ Erdem WRT at 19.47
45 An ‘‘indicator variable,’’ also known as a
‘‘dummy variable’’ is a ‘‘[a]variable that takes on
only two values, usually 0 and 1, with one value
indicating the presence of a characteristic, attribute
or effect and the other value indicating absence.’’
Rubinfeld, supra note 36, at 464.
46 The Judges are also unconvinced that the
number of zeros is as striking as Dr. Erdem
suggested. For example, the high percent of zeros
for Canadian claimants would be consistent with
the inevitable absence of any retransmissions of
Canadian stations outside the Canadian zone.
47 When two covariates are highly or perfectly
correlated with each other, the regression can suffer
from a ‘‘multicollinearity’’ problem, whereby the
model does not reveal the separate effects of each
of the two variables. See Rubinfeld, supra note 36,
at 465 (‘‘Multicollinearity [a]rises in multiple
regression analysis when two or more variables are
highly correlated.’’).
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To analyze this issue, Dr. Erdem
performed a sensitivity analysis, or
test 48, rerunning the nonduplicated
model without the total nonduplicated
minutes variable. Dr. Erdem’s ‘‘Model
5’’ presented regression results and
estimated royalty shares from this
analysis. See Erdem WRT Ex. R3.
Compared to his Model 4, excluding the
added variable decreased the Program
Supplier share by approximately 0.2
percentage points, the JSC share by
about 2 percentage points, the CTV
share by about 2 percentage points the
PTV share by about 0.3 percentage
points. The Devotional and Canadian
shares remained approximately the
same. See Erdem WRT at 19, Ex. R3.
The Judges find that these modest
percentage point differences would not
diminish the value of Professor
Crawford’s nonduplicate minute
regression, in part because the
regression approach is by design an
estimate rather than a precise
measure.49 Moreover, Dr. Erdem’s
modest changes are derived from his
alternative models that also incorporate
his erroneous distant subscriber minutes
approach, which Dr. Erdem
acknowledged to invalidate his
adjustments to a number of his models,
including Models 4 and 5. See 3/8/18
Tr. 2779–80 (Erdem).
g. The WGNA Indicator Variable
Dr. Erdem altered Professor
Crawford’s approach by including a
dummy variable to indicate the
presence (or absence) of WGNA. This
48 A ‘‘sensitivity analysis’’ is ‘‘[t]he process of
checking whether the estimated effects and
statistical significance of key explanatory variables
are sensitive to inclusion of other explanatory
variables, functional form, dropping of potential
out-lying observations, or different modes of
estimating.’’ Wooldridge, supra note 34, at 869. The
issue of robustness is related to the issue of
sensitivity: ‘‘The issue of robustness [addresses]
whether regression results are sensitive to slight
modifications in assumptions.’’ Rubinfeld, supra
note 36, at 43; see also Peter Kennedy, A Guide to
Econometrics at 11 (5th ed. 2003) (defining the
‘‘robustness’’ of an estimator as ‘‘insensitivity to
violations of the assumptions under which the
estimator has desirable properties . . . .’’).
Importantly, because ‘‘[e]valuating the robustness of
multiple regression results is a complex endeavor
. . . there is no agreed-on set of tests for robustness
which analysts should apply. In general, it is
important to explore the reasons for unusual data
points.’’ ABA Econometrics, supra note 22, at 24;
accord Rubinfeld, supra note 36, at 437.
49 The Judges also do not find this to be a
potential problem with regard to the use of
Professor Crawford’s regression to identify relative
values, because these two covariates (the number of
nonduplicated minutes and the number of distant
signals) are control variables used to hold all other
potential effects fixed while analyzing program
category minutes as the independent variables—and
the Judges do not identify in Dr. Erdem’s testimony
any impact of his claimed multicollinearity on the
purported explanatory effect of program categories
on royalties.
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alteration increased the Program
Supplier share by approximately 2
percentage points, increased the CTV
and PTV shares by approximately 1
percentage point, respectively, and
decreased the JSC shares by about 4
percentage points. The shares of the
Devotional and Canadian categories
increased by 0.1 and 0.3 percentage
points, respectively. Erdem WRT at 18–
19.
However, Dr. Erdem did not expressly
conclude that the absence of this WGNA
indicator variable in Professor
Crawford’s regression analysis
demonstrated that the latter’s approach
was inappropriate or less relevant.
Indeed, Dr. Erdem ended this particular
analysis by suggesting only that the use
of an indicator variable regarding the
presence (or absence) of WGNA among
the distantly retransmitted stations
could be suggestive of an outlier effect
arising from the presence of WGNA, yet
Dr. Erdem conceded that ‘‘Professor
Crawford’s model does not exhibit
sensitivity to outliers.’’ Erdem WRT at
19 n.17.50 Accordingly, Dr. Erdem’s
criticism in this regard does not
diminish the value of Professor
Crawford’s regression analysis. And,
once more, Dr. Erdem’s estimate of the
impact of this criticism was bundled
together with, inter alia, his admittedly
erroneous adjustment for distant
subscriber minutes, thereby tainting the
measure of this adjustment.
h. Geographical Effects
The SDC noted that a CTV economic
expert witness, Dr. Christopher Bennett,
found that ‘‘over 90% of the distant
signals imported were within 150 miles
of the community served, and over 95%
were within 200 miles.’’ Corrected
Written Direct Testimony of Christopher
Bennett, Trial Ex. 2006, ¶ 31 & Fig. 6
(Bennett CWDT).51 Accordingly, Dr.
Erdem asserted that the positive
coefficients in Professor Crawford’s
regression ‘‘could’’ have been driven by
factors ‘‘like’’ geography, emphasizing
50 More particularly, Dr. Erdem acknowledged
that because Professor Crawford had utilized a
‘‘larger sample,’’ Erdem WRT at 20, n.17, Professor
Crawford’s regression analysis was not subject to an
outlier problem. In fact, Professor Crawford’s data
included programming minutes using the
population of programs carried on all imported
distant broadcast signals, rather than using
estimates of programming minutes based on
sampling the programs carried on distant broadcast
signals. Crawford CWDT ¶ 72.
51 Dr. Bennett, who compiled data for Professor
Crawford’s regression analyses, excluded
superstations such as ‘‘WGN, WPIX, WSBK, and
WWOR, which historically were distributed
nationwide by satellite [and] were excluded in
distance analyses presented in previous copyright
royalty distribution proceedings.’’ Bennett CWDT
¶ 30, n.15.
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the values and preferences of large
urban areas and de-emphasizing the
values and preferences of smaller rural
areas. 3/8/18 Tr. 2688–91 (Erdem).
In response, CTV pointed out that
Professor Crawford’s regression
contained variables that controlled for
geographic effects. In particular, CTV
noted that the SDC had in fact
acknowledged that Professor Crawford’s
regression included ‘‘system-level fixed
effects [that] introduce a form of
geographic control . . . .’’ 52 SDC PFF
¶ 101 (citing 3/8/18 Tr. 2709–10
(Erdem)).53 Moreover, CTV pointed out
that Professor Crawford’s regression also
included as a control variable the
number of local signals at the subgroup
level, which also helped account for
geographical market differences
(including market and Designated
Market Area (DMA) size) across
subgroups within the systems. See
Crawford CWDT App. B Fig. 22; see also
Written Rebuttal Testimony of Ceril
Shagrin, Trial Ex. 2009, ¶ 20 & Exs. A,
B (Shagrin WRT) (number of local
stations is prime indicator of market
size).
The Judges find that Professor
Crawford’s regression controlled for
geographic effects. Dr. Erdem’s criticism
to the contrary appears to be based on
a difference of opinion as to how to
account for the geographic issue rather
than any error in Professor Crawford’s
regression analysis. Additionally, the
Judges do not find that a regression that
weighs more heavily the value of
programs retransmitted to more people
is inherently suspect. Indeed, the
opposite is the case. To use Dr. Erdem’s
example, population density is greater
in areas adjacent to urban areas where
professional sports teams are based and
will demand more professional sports.
See 3/8/18 Tr. 2689 (Erdem). This
subscriber demand causes a CSO
serving their subscriber group to have a
52 ‘‘Fixed effects’’ variables are potential effects
on the dependent variable (here, categorical
royalties) by other factors that are unobserved by
the regression. Wooldridge, supra note 34, at 461.
(To put the ‘‘fixed effects’’ variables in context, they
differ from the ‘‘error term,’’ which reflects
‘‘idiosyncratic error,’’ id., and differ from a control
variable in that, as noted supra, a control variable
is one that is known and expected to impact the
dependent variable (categorical royalties here), but
‘‘is not the object of interest in the study’’ and thus
held constant by the econometrician. Stock &
Watson, supra note 32, at 280.
53 The SDC argue that this control caused a new
geographic effect that Professor Crawford’s
regression ignored: ‘‘some’’ stations ‘‘could’’ be
local as well as distant within some subscriber
groups. SDC PFF ¶ 101 (and record citations
therein). However, speculation as to the existence
of this possibility and its possible extent are
insufficient to invalidate or diminish the
evidentiary value of the geographic controls used by
Professor Crawford in his regression.
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derived demand for the retransmission
of stations with more JSC programming.
More JSC programming leads to higher
JSC royalties relative to whatever other
programming is more popular in areas
where, as Dr. Erdem testified, there exist
‘‘smaller systems with smaller number
of subscribers and smaller fees . . . .’’
3/8/18 Tr. 2690 (Erdem). In short, the
Judges see this phenomenon as an
attribute of Waldfogel-type regressions,
including Professor Crawford’s
regression analysis.54
i. Ignoring Signals That CSOs Chose Not
To Carry
The SDC also criticized Professor
Crawford for not taking into account in
his regression the impact on value of the
stations that were ‘‘not retransmitted.’’
SDC PFF ¶ 81 (citing 2/28/18 Tr. 1494–
5 (Crawford)) (emphasis added). The
SDC noted that Professor Crawford had
written a published article that
indicated that an approach accounting
for stations that were not retransmitted
could have been applied to determine
program category value in the present
proceeding. SDC PFF ¶ 82 (citing 2/28/
18 Tr. 1497–98 (Crawford)). However,
nothing in the record suggested that the
potential usefulness of such an
alternative regression approach called
into question the validity,
reasonableness, or persuasiveness of the
regression approach undertaken by
Professor Crawford in the present
54 This point regarding geographic effects also
relates to what Dr. Erdem asserted is an anomaly
in a Waldfogel-type regression such as undertaken
by Professor Crawford. Dr. Erdem claims that if a
certain type of programming (Devotional, for
example) were more popular on lower fee paying
cable systems, the lower fee status of that system
would cause Devotional programming to have a
lower coefficient and a lower royalty share under
the regression. However, if that cable system
decided ‘‘this category of programming isn’t doing
it for us’’ and thus eliminated Devotional
programming, that programming category
elimination would anomalously cause the
Devotional coefficient to increase, because it would
no longer be associated with that lower fee paying
cable system. 3/8/18 Tr. 2685–86 (Erdem). The flaw
in that argument is two-fold. First, although the
Devotional coefficient might increase, there would
be fewer minutes of programming to multiply by
that coefficient, which would reduce the relative
share allocated to Devotional programming under a
Waldfogel-type regression. Second, a cable system
would distantly retransmit Devotional
programming, even if it generated lower royalties
relative to other CSOs in other regions, because the
CSO is incentivized by increasing or retaining
subscribers, not by maximizing royalties compared
with other CSOs. Again, the Judges emphasize that
the hypothetical buyer is the CSO, not the copyright
owner, and the relative value of a program category
is based on its economic contribution as part of a
bundle to the CSO, not the royalty it might generate
in any other context. The royalties flow from such
carriage decisions and those decisions are made by
each CSO with varying receipts (constrained by the
WTP of its subscriber base), averaged through a
Waldfogel-type regression.
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proceeding, which approached the
relative value analysis from a
perspective that analyzed the programs
and stations that were transmitted.
Indeed, the SDC do not cite any expert
witness in the present proceeding to
support their conclusory assertions in
proposed findings of fact that Professor
Crawford’s decision not to analyze nontransmitted stations and programs
compromised his analysis in this
proceeding. See SDC RPFF ¶¶ 81–82.
Accordingly, the Judges find that this
criticism does not diminish the value of
Professor Crawford’s regression analysis
in this proceeding.
j. Number of Subscribers as Control
Variable
The SDC noted that Professor
Crawford used the log of fees paid as his
dependent variable (expressing changes
in fees paid in percentage terms), but he
expressed changes in ‘‘the number of
subscribers—one of his control
variables—in level form (i.e., linear, or
non-log). SDC PFF ¶ 102 (citing 2/28/18
Tr. 1541, 1550 (Crawford)). The SDC’s
expert, Dr. Erdem, testified that
Professor Crawford’s use of the linear
form for this control variable was
improper, because it failed to
correspond with the actual relationship
between royalty fees and subscribers,
i.e., a percentage change in the number
of subscribers corresponds with an
equal change in the percentage of
royalty fees). 3/8/18 Tr. 2770–71
(Erdem). As a consequence, Dr. Erdem
maintained, Professor Crawford had
introduced statistical ‘‘bias’’ 55 into his
regression. Id. at 2716–17 (Erdem).
To address this criticism, Dr. Erdem,
undertook a sensitivity test and
transformed the control variable for the
number of subscribers into log form. 3/
8/18 Tr. 2767 (Erdem). He found that
this linear-to-log transformation
improved the fit of the regression,
increasing the R2 metric from
approximately .24 to .97. (A higher R2
indicates a tighter fit of within the data
points, see supra note 41).
In response, CTV and Professor
Crawford argued that Dr. Erdem
misapplied a principle that might be
valid in a ‘‘prediction’’ regression.
Professor Crawford maintained though
that his own regression on behalf of
CTV was an ‘‘effects’’ regression,
55 ‘‘Bias’’ is ‘‘[a]ny effect . . . tending to produce
results that depart systematically (either too high or
too low) from the true values. A biased estimator
of a parameter [e.g., a regression parameter] differs
on average from the true parameter.’’ Rubinfeld,
supra note 36, at 463–64. Somewhat more formally,
‘‘bias’’ reflects ‘‘[t]he difference between the
expected value of an estimator and the population
value that the estimator is supposed to be
estimating.’’ Wooldridge, supra note 34, at 859.
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the dependent variable can be explained
by the control or explanatory variables.’’
Crawford WRT ¶ 93.
Applying this distinction more
particularly to the present dispute,
Professor Crawford defended his use of
a linear control variable for the number
of subscribers as sufficient for its
intended purpose—to avoid statistical
bias and distortion. He contrasted his
approach with Dr. Erdem’s claim that a
log control variable would be preferable,
with Professor Crawford asserting that
Dr. Erdem’s proposed log transformation
did not merely control for the royalty
formula, but rather essentially
replicated the formula for calculating
royalties, thereby distorting the
regression results. 2/28/18 Tr. 1429–30,
Dr. Erdem misunderstands the purpose of
1552 (Crawford). That is, Dr. Erdem’s
an econometric analysis in this
log approach might well have been
proceeding. . . . For the goal of prediction,
appropriate to predict a meaningful
the focus is on finding the explanatory
variables that best predict the outcome of
correlation between the percentage
interest . . . . [I]f the goal is to predict stock
change in royalties and the percentage
prices and the price of tea in China helps,
change in the number of subscribers, but
then . . . include it in the model (and don’t
that is not informative (and thus not
worry about the economic interpretation of
relevant) as to the effect, if any, of the
its coefficient).
impact of the different program
That is not the purpose in this proceeding,
categories within the distantly
however. In this proceeding, experts are
retransmitted stations on the dollar
using econometric analyses to help the
amount of royalties that were paid.
Judges determine . . . relative marketplace
The Judges find that Professor
value . . . . The dependent variable in these
regressions, the royalties cable operators pay
Crawford’s regression is not
for the carriage of the distant signals, are
compromised by his use of the linear
informative of this relationship . . . . The
form to express the number of
key explanatory variables in this
subscribers in this control variable. If
relationship, the minutes of programming of
the Judges’ statutory task were to
the various types carried on distant signals,
identify and rank all the causes of a
are informative as the impact they have on
change in total royalties, the change in
royalties reveals the relative market value of
the number of subscribers apparently
each programming type. Other explanatory
variables are included in the model to control might be the chief causal element
for other possible determinants of cable
because the statutory royalty fee is a
operator royalties. This helps improve the
percent of receipts. Changes in the
statistical fit of the regression (to ‘‘reduce its
dollar value of receipts, naturally, are
noise’’), providing more precise estimates of
directly related, on a percentage basis,
the impact of programming minutes that are
to percentage changes in the number of
the focus of the analysis.
subscribers. But the Judges’ legal,
. . .
regulatory, and economic task in this
The goal here is to find the econometric
proceeding is to determine the relative
model that can best reveal relative
market value of different categories of
marketplace value. Doing so means crafting
the econometric model to reflect the
programming; thus, any correlation
institutional and economic features of the
between the number of subscribers and
environment that is generating the data being royalties is not in furtherance of that
used. . . . The econometrician determines
objective. Rather, Professor Crawford’s
which explanatory variables to include not
use of a linear form for the number of
based exclusively on statistical criteria
regarding the overall fit of the model, but also subscribers served to control for the size
of the system without overriding the
on whether there are good economic and/or
purpose of the regression, which was to
institutional justifications for including that
measure the effects (if any) of different
variable.
program categories on royalties paid.
Crawford WRT ¶¶ 91–94 (footnotes
The Judges not only find Professor
omitted) (emphasis added).
Crawford’s assertions in this regard
Accordingly, Professor Crawford
persuasive, they note that his opinion
testified that the R2 measure on which
has some support in the academic
Dr. Erdem relied is not relevant to the
literature.56 See G. Shmueli, To Explain
task at hand, because that measure does
not explain the relative values of the
56 Professor Crawford did not support his lengthy
several program categories, but rather
exposition (quoted in some detail in the text,
supra), with any references to learned treatises or
shows ‘‘how much of the variation in
seeking to explain the issue at hand, i.e.,
how different program categories
correlate with the royalties paid.
According to Professor Crawford, his
regression analysis was not a
‘‘prediction’’ regression designed to
identify the best predictors of royalties
paid. Thus, he argued, it was important
to use control variables that keep
constant the effects on the dollar
amount of royalties paid in order to
determine the relative values among
program categories, which was the
purpose of the regression. 2/28/18 Tr.
1393–94, 1430, 1549–50 (Crawford).
Professor Crawford explained what he
understood to be a fundamental mistake
made by Dr. Erdem:
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or to Predict?, 25 Statistical Science 289,
290–91, 297 (2010) (‘‘The criteria for
choosing variables differ markedly in
explanatory versus predictive
contexts.’’); see also F.M. Fisher,
Multiple Regression in Legal
Proceedings, 80 Colum. L. Rev. 702, 720
(1980) (The R2 measure ‘‘must be
approached with a fair amount of
caution, since R2 can be affected by
otherwise trivial changes in the way in
which the problem is set up.’’).
The Waldfogel-type regression is an
example of modeling utilized to explain
the effects of different program
categories on the relative payment of
royalties—rather than an attempt to
predict the level of royalties. Thus, as
Professor Shmueli wrote, the choice of
variables can reasonably be based on the
‘‘underlying theoretical model.’’ Id.; see
also F.M. Fisher, Econometricians and
Adversary Proceedings, 81 J. Am. Stat.
Ass’n 277, 279 (1986) (‘‘There is a
natural view that models are supposed
to do nothing other than predict . . .’’
resulting in the ‘‘danger’’ of ignoring
‘‘better models that do not fit or predict
quite so well but are in fact informative
about the phenomena being
investigated.’’) (emphasis added).57
Because the Judges find in this
proceeding, as in past proceedings, that
the theoretical model of a Waldfogeltype regression is reasonable and useful
in this context, Dr. Erdem’s criticism
regarding Professor Crawford’s use of a
linear control variable for the number of
subscribers does not diminish the value
of his regression analysis in this
proceeding.
k. Purportedly Incorrect Consideration
of Network Programming
The SDC asserted that Professor
Crawford failed to analyze correctly the
impact of the number of distant signals
and the total number of minutes in his
nonduplicated minutes analysis, which
caused his coefficients to be
uninterpretable and certain coefficients
to turn negative, falsely implying a
negative value for such retransmitted
distant programming. However, a
substantial portion of this assertion
grew out of Dr. Erdem’s tardy and thus
other authorities, nor did Dr. Erdem support his
critique in such a manner. The experts for all
parties were guilty of this omission throughout their
respective testimonies, a problem the Judges find
disturbing particularly in the present context,
causing dueling esoteric econometric positions
sometimes to devolve into ipse dixit disputes.
57 This econometric point regarding the
appropriate use of different models is of a piece
with the Judges’ statement in Web IV that no one
economic model is appropriate to explain all
market activity. Determination of Royalty Rates and
Terms for Ephemeral Recording and Webcasting
Digital Performance of Sound Recordings (Web IV),
81 FR 26 316, 26 334–35 (May 2, 2016).
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rejected proposed rebuttal testimony.
See 3/8/18 Tr. 2704–05 (Erdem). Thus,
Dr. Erdem’s written testimony and the
SDC’s affirmative case at the hearing do
not support the SDC’s criticisms in this
regard.
However, the SDC had some success
in raising this issue on crossexamination of Professor Crawford, who
appeared to acknowledge that
nonduplicated network programming
had positive value that he had not
added back into his analysis. 2/28/18
Tr. 1572 (Crawford). Professor Crawford
attempted to discount the import of this
factor, asserting that adding in such
values would have caused a ‘‘common
level shift’’ in all the coefficients. 2/28/
18 Tr. 1573 (Crawford). However, when
confronted on cross-examination with
the logarithmic (percentage) impact on
the coefficients (and thus the relative
values), Professor Crawford became
uncertain as to whether he should have
considered the logarithmic (percentage)
impact of nonduplicated network
programming. More particularly, having
considered the issue on the witness
stand, Professor Crawford was then
asked by cross-examining counsel
whether he was ready to agree that he
‘‘should have taken into account the
value of the . . . coefficient that would
be implied for the nonduplicated
network programming’’—to which he
replied: ‘‘So I am not sure that I do
[agree] [a]nd I am not sure that I don’t.’’
2/28/18 Tr. 1581 (Crawford).
Professor Crawford and CTV further
responded to this nonduplicated
network minutes argument by noting
that the impact of the issue, if any, was
indeterminate, because Professor
Crawford had lumped nonduplicated
network minutes with off-air
programming as a single control
variable, not as an input to determine
the values of the coefficients of interest.
2/28/18 Tr. 1625–29 (Crawford).
Additionally, Professor Crawford
explained that, in any event, the
purpose of the ‘‘total non-duplicate
minutes’’ variable was to serve the same
volume control function as the ‘‘number
of distant signals’’ variable in the initial
regression.
The Judges find that Professor
Crawford’s admitted uncertainty as to
the impact of nonduplicated network
programming minutes on the relative
values of his coefficients somewhat
diminishes the probative value of his
non-duplicated model. Further, the fact
that Professor Crawford’s purpose in
adding these minutes was to insert a
control variable did not address whether
this variable did not also affect the
calculation of coefficients for the
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program categories at issue.58 However,
the absence of any hard evidence of the
extent of this problem on the
measurement of the coefficients makes
this deficiency difficult to quantify.
Accordingly, this criticism leads the
Judges to consider the accuracy of the
estimates in Professor Crawford’s
nonduplicated analysis to be less
certain, and the Judges thus will look to
Professor Crawford’s duplicatedminutes regression results when
incorporating his analysis and
conclusions into their determination of
the appropriate allocation of shares.
l. Overfitting
The SDC contended that Professor
Crawford’s regression methodology
suffered from a problem known as
‘‘overfitting.’’ In econometrics, and in
statistics more broadly, overfitting
occurs when the regression attempts to
‘‘estimat[e] too large a model with too
many parameters.’’ C. Brooks,
Introductory Econometrics for Finance
690 (3d ed. 2014). See also T. Powell &
P. Lewecki, Statistics: Methods and
Applications 681 (2006) (‘‘overfitting’’ is
‘‘[w]hen [a regression] produc[es] a
curve . . . that fits the data points well,
but does not model the underlying
function well [because] its shape is
being distorted by the noise inherent in
the data.’’).
On the other hand, when an
econometrician attempts to avoid
overfitting, he or she must be mindful
not to eliminate potentially important
data from the regression. Otherwise a
different problem—underfitting—can
arise. To wit:
There is actually a dual problem to
overfitting, which is called underfitting. In
[an] attempt to reduce overfitting, the
[modeler] might actually begin to head to the
other extreme and . . . start to ignore
important features of [the] data set. This
happens when [the modeler] choose[s] a
model that is not complex enough to capture
these important features . . . . [T]his
incredibly important problem is known as
the bias-variance dilemma[ 59] [and] is just as
much an art as it is a science.
58 The
Judges note that although the shares are
not drastically different in the two models, the
shares for CTV, who engaged Dr. Crawford,
increased more substantially under his
nonduplicated analysis, i.e., the approach as to
which he expressed uncertainty under crossexamination than any other program category.
Further, a number of categories saw either a decline
or essentially no change in their shares in the
nonduplicated model compared to the duplicated
model. Compare Crawford CWDT Fig. 17 with
Crawford CWDT Fig. 20 (both reproduced supra).
59 The ‘‘bias-variance dilemma’’ refers to the
problem that arises when a model that tends to
overfitting (too few observations per variable) will
have a low bias in the regression coefficient (i.e.,
a regression line based on the data will tightly fit
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D. Geng and S. Shih, Machine Learning
Crash Course: Part 4—The BiasVariance Dilemma, ML@B, The Official
Blog of Machine Learning @Berkeley
(July 13, 2017), available at https://
ml.berkeley.edu/blog/2017/07/13/
tutorial-4/(last visited May 1, 2018)
(emphasis added).
In the present case, the SDC argued
that Professor Crawford’s regressions
suffered from overfitting for several
reasons.
First, because he used ‘‘systemaccounting period fixed effects [as
distinguished from the subscriber group
level], Professor Crawford’s regression
employs more than 7,300 variables [and]
approximately 26,000 observations . . .
only about 3.55 observations per
variable.’’ SDC PFF ¶ 109 (citing
Crawford CWDT at C–3; 2/28/18 Tr.
1646 (Crawford)). According to the SDC,
Professor Crawford acknowledged that
‘‘[a]s a rule of thumb, fewer than ten
observations per variable can yield a
likelihood of overfitting.’’ SDC PFF
¶ 111 (citing 2/28/18 Tr. 1461
(Crawford)). Because Professor Crawford
had less than ten observations per
variable (3.55), the SDC argued that
Professor Crawford’s regression suffered
from overfitting, calling into question
the usefulness of the estimates Professor
Crawford produced.
However, Professor Crawford denied
that he endorsed this test, and the
Judges agree with Professor Crawford,
based on the following crossexamination colloquy:
SDC COUNSEL: [H]ave you ever heard of
the One-in-Ten Rule? One-in-Ten?
PROFESSOR CRAWFORD: Not—if you
could describe it, perhaps I have.
SDC COUNSEL: A rule of thumb—not
saying it is precise—a rule of thumb that you
should have at least ten observations per . . .
per coefficient.
PROFESSOR CRAWFORD: I have not
heard that specific rule, but I understand the
idea behind it. And generally the idea behind
that is if you don’t have ten observations per
one tends to get imprecise parameter
estimates. . . . I don’t subscribe to the Onein-Ten Rule.
2/28/18 Tr. 1461, 1463 (Crawford)
(emphasis added). Nowhere in this
testimony did Professor Crawford
indicate a familiarity with the supposed
‘‘one-in-ten’’ rule in counsel’s question,
and Professor Crawford instead
the data points) but will suffer from a relatively
higher variance, (i.e., a relatively higher expected
distance from the variable from its true value. See
ABA Econometrics, supra note 22, at 275–76 nn.13
& 14 (‘‘The higher the variance, the less precise is
the estimate [i.e.,] the less the data say about the
true value of the coefficient. . . . A biased estimate
differs systemically from the true value, rather than
departing from the true value only because of
sampling error.’’).
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attempted merely to explain his
understanding of this heuristic as the
SDC’s counsel had presented it.60
Without a more developed record
regarding the existence and
applicability of this one-in-ten heuristic,
the Judges cannot find that Professor
Crawford’s use of ‘‘only’’ 3.55
observations per variable would have a
negative impact on his regression
methodology. Moreover, because the
SDC presented this principle as a
heuristic rather than a rule, the
underdeveloped nature of the record is
of even greater importance. Finally,
because the problem of overfitting
versus underfitting (the bias/variance
dilemma discussed supra) appears to be
a judgment call for the econometric
modeler, the Judges are loath to impose
this heuristic as an invalidating
principle in connection with Professor
Crawford’s regression.
Relatedly, Professor Crawford only
acknowledged that overfitting would be
a problem if there were a one-to-one
ratio of variables to observations that
would perfectly predict the variables,
but with very wide confidence intervals.
Professor Crawford testified that, in his
opinion, his confidence intervals were
not so wide as to diminish the value of
his regression results. See 2/28/18 Tr.
1460–62 (Crawford). The Judges agree
that Professor Crawford did not go
further than acknowledging that an
absolute identity in the number of
variables and observations would create
an overfitting problem.
As a more theoretical rejoinder,
Professor Crawford asserted that
concerns with regard to overfitting
apply to ‘‘prediction’’ regressions—not
‘‘effects’’ regressions such as Professor
Crawford’s regressions and all the
Waldfogel-type regressions introduced
60 Moreover, Professor Crawford’s testimony was
at odds with what the SDC’s counsel actually meant
by the ‘‘one in ten’’ rule as it relates to overfitting.
In the immediately subsequent testimony, the SDC’s
counsel challenged Professor Crawford’s opinion
that ‘‘the idea behind that is if you don’t have ten
observations per coefficient, one tends to get
imprecise parameter estimates.’’ Id. The SDC’s
counsel then disagreed with the expert witness,
Professor Crawford, and asserted that ‘‘[a]n
overfitted model will be able to estimate the
parameters [a]nd you might not be able to project
it to other data, but will be able to estimate the
parameters with great precision.’’ Id. As the
introductory discussion of overfitting (set forth
supra) makes clear, the SDC’s counsel was correct
in his presentation of the overfitting problem, but
that is unrelated to the fact that Professor
Crawford’s testimony demonstrated his
unfamiliarity with both the ‘‘one-in- ten’’ heuristic
and its alleged econometric importance. (The
Judges are not suggesting that a ‘‘one-in-ten’’
heuristic is not utilized by econometricians, but
rather note that the record does not establish its
existence and its applicability in this proceeding.).
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in this proceeding. Id. at 1460, 1463.61
However, Professor Crawford did not
provide a sufficient explanation as to
the disparate impacts of overfitting in a
‘‘prediction’’ regression and an ‘‘effects’’
regression to allow the Judges to find
that the relatively low number of
observations per variable is less
important in his ‘‘effects’’ regression.
Second, according to SDC, Professor
Crawford’s total observations were
diminished, and his regressions
compromised, because he ‘‘effectively
discarded’’ approximately 15% of his
observations by disregarding
observations from systems with a single
subscriber group, which totaled
‘‘approximately half of all systems in his
data set’’, by virtue of his reliance on
‘‘system-accounting period fixed effect.’’
SDC PFF ¶ 110 (citing 2/28/18 Tr. 1458
(Crawford); Crawford CWDT at 21, Fig.
10; 3/8/18 Tr. 2710–11 (Erdem)).
The Judges are troubled by CTV’s
failure to respond expressly to this
criticism.62 Similarly, the Judges are
troubled that CTV neither cited nor
addressed the SDC’s criticism that
Professor Crawford did not test his
model for overfitting.
The final reason the SDC criticized
Professor Crawford’s analysis for
overfitting was their claim that he
essentially selected his regression model
out of ‘‘more than one’’ model he had
previously run. SDC PFF ¶ 118 (citing 3/
1/18 Tr. 1888 (Bennett)). More
particularly, the SDC contended that
Professor Crawford and his team
disregarded at least two regressions.
First, Professor Crawford allegedly
discarded a regression without the topsix multiple-system operator (MSO)
interaction variables that were in his
final model. 2/28/18 Tr. 1642–44
(Crawford). Second, the SDC asserted
that Professor Crawford disregarded ‘‘a
model run at the system level instead of
the subscriber group level,’’ i.e., a model
that would not have treated systemaccounting period data as a fixed effect.
3/1/18 Tr. 1888 (Bennett). See SDC PFF
¶ 113 (relying on Crawford and Bennett
testimony).
According to the SDC, Professor
Crawford’s rejection of several models
before deciding on the one he presented
in evidence in this proceeding indicated
a potential likelihood of overfitting in
the regression model in evidence
through his consumption of ‘‘phantom
degrees of freedom,’’ i.e., ‘‘variables that
61 The Judges discussed the distinction between
an ‘‘effects’’ regression and a ‘‘prediction’’
regression at length, supra, section 0.
62 In its Response to the SDC’s PFF, CTV
helpfully cited (and reproduced) each numbered
paragraph of the SDCPFF, and conspicuously
absent from that response is any reference to ¶ 110.
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were tried and rejected’’—rather than
included in the regression model in
evidence.63 SDC PFF ¶ 113 (citing 3/8/
18 Tr. 2711 (Erdem)).
The SDC claimed this issue is
important in the context of its
overfitting criticism because, as
Professor Crawford’s testimony
indicated, it is not generally good
econometric practice to ‘‘to try a
regression, to reject some variable or to
reject a form, and then try another
specification and find you get a
statistically improved result.’’ SDC PFF
¶ 115 (citing 2/28/18 Tr. 2109
(Crawford)). According to Dr. Erdem
when such an approach is taken, ‘‘the
reliability of the coefficients at the end
of that model selection process is
questionable.’’ 3/8/18 Tr. 2711 (Erdem).
In response, CTV noted that it had
addressed the issue of the first supposed
‘‘discarded’’ regression without the topsix MSO interaction variables, in its
opposition to a Motion to Strike filed by
SDC. In that Opposition, CTV made
particular note of Professor Crawford’s
written direct testimony in which he
explained why his regression analysis
did not originally treat the interaction of
these top-six MSOs as a fixed effect. See
Crawford CWDT ¶ 166 (‘‘Dummy
variables for each of the six largest
MSOs—Comcast, Time Warner, AT&T,
Verizon, Cox, and Charter—are included
as covariates to capture potential
differences in factors not included in
the econometric model that could shift
demand for bundles that include
imported distant broadcast signals.’’).
CTV further referred to the Judges’
Order Denying SDC Motion to Strike
63 ‘‘Degrees of freedom’’ are defined ‘‘[i]n
multiple regression analysis, [as] the number of
observations minus the number of estimated
parameters.’’ Wooldridge, supra note 34, at 837.
Accordingly, statisticians understand ‘‘degrees of
freedom’’ to be measures of how much can be
learned from a regression, with the quality of
knowledge improved by increasing the number of
observations, reducing the number of estimated
parameters, or by some combination of both that
serves to widen the difference between the number
of observations and parameters. See What are
degrees of freedom?, https://support.minitab.com/
en-us/minitab/18/help-and-how-to/statistics/basicstatistics/supporting-topics/tests-of-means/whatare-degrees-of-freedom/(last visited June 14, 2018).
Dr. Erdem does not define a ‘‘phantom degree of
freedom’’ except to describe it as an ‘‘economic
concept . . . not a statistic.’’ 3/8/18 Tr. 2711
(Erdem). More particularly, a ‘‘phantom degree of
freedom’’ can be generated when the modeler
reduces the number of parameters by his or her
rejection of other models that would have added a
greater number of parameters—nothing more has
really been learned but the explicit number of
degrees of freedom appears larger, as an artifact (a
‘‘phantom’’) arising from the econometrician’s
rejection of models containing additional
parameters. See Minitab Blog Editor, Beware of
Phantom Degrees of Freedom that Haunt Your
Regression Models!, The Minitab Blog (Oct. 29,
2015), http://blog.minitab.com/blog.
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Testimony of Gregory S. Crawford
(Crawford Order), which credited CTV’s
position that Professor Crawford had not
run such an alternative course of action
by generating a regression and then
discarding it, but rather had decided to
add the top-six MSO effects as ‘‘fixed
effects’’ in the course of developing his
regression approach, in order better to
isolate the correlation, if any, between
the explanatory (independent) variables
at issue in this proceeding—the
different programming categories—and
the dependent variable, i.e., total
royalties. As the Judges explained in the
Crawford Order:
Dr. Crawford’s WDT . . . explained how
he first described differences that were
observed in the data among the six largest
MSOs in terms of their average receipts per
subscriber. CTV Opp’n at 10–11 and Ex.
2004, Figure 6. Dr. Crawford’s WDT also
explained that these differences may suggest
other important differences among these
large MSOs regarding their signal carriage
strategies, pricing, and other relevant
dimensions. CTV Opp’n at 11; Ex. 2004 ¶ 61.
Dr. Crawford also described a regression
without the six MSO Interaction variables.
Ex. 2004 ¶ 61 (unobserved differences in
average revenue per subscriber could bias
estimates of relative value of different
programming).
Crawford Order at 5.
The Judges find that the SDC’s
criticism of Professor Crawford’s models
for consuming ‘‘phantom degrees of
freedom’’ is essentially a restatement of
Dr. Erdem’s general claim of overfitting.
Accordingly, this argument does not
add a new basis for reducing the weight
the Judges place on Professor Crawford’s
regression analysis.64
On balance, the Judges find that there
may be some degree of overfitting in
Professor Crawford’s regression analyses
that he did not adequately explain. It
further appears that this problem was
the result of a tradeoff, arising from
Professor Crawford’s use of a subscriber
group analysis and thus a reliance on
system-accounting period fixed effects
that, as the SDC noted, reduced the
number of observations in Professor
Crawford’s data set. Although such
potential overfitting may exist, there is
nothing in the record to demonstrate
sufficiently that this problem would
support a decision to diminish the
64 Although the Judges denied the SDC’s Motion
to Strike, they indicated in the Crawford Order that
they would consider whether the absence of that
prior work diminished the weight they might
otherwise give to the regression methodology that
Professor Crawford presented at the hearing. After
considering the entire record, the Judges do not
reduce the weight they accord to Dr. Crawford’s
regression analysis based on this argument.
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judges’ reliance on Professor Crawford’s
regression analysis.65
3. Program Suppliers’ Criticisms of Dr.
Crawford’s Analysis
a. Assumption Regarding CSO Behavior
Sue Ann Hamilton, an industry
expert, testified that Professor Crawford
made a significant error (one that would
apply to any Waldfogel-type regression)
when he posited that CSOs make
decisions regarding distant
retransmission based on their intention
to maximize profits by selecting those
stations with an optimal bundle of
programming. Corrected Written
Rebuttal Testimony of Sue Ann
Hamilton, Trial Ex. 6009, at 13–14
(Hamilton CWRT). Rather, Ms. Hamilton
testified, a CSOs’ selection of stations
for distant retransmission is marked by
inertia, not by an affirmative analysis
and weighing of alternative stations. Id.
She identified two reasons for CSO
inertia. First, distant retransmission
costs represent a non-material
expenditure for CSOs compared with
their other more expensive
programming and carriage decisions. Id.
at 9. Second, she testified that CSOs are
more concerned with losing existing
subscribers if they drop certain stations
and the associated programs than they
are with whether or not any new
retransmitted station and its associated
programs might entice new
subscribers.66 Id. In industry jargon,
CSOs are more concerned with ‘‘legacy
distant signal carriage’’ than with
adjusting the roster of distantly
retransmitted stations. Id. at 15. Thus,
Ms. Hamilton implied, any correlation
between program categories and
royalties is spurious, because it is
‘‘inconsistent with [her] understanding
of how CSOs actually make distant
signal carriage decisions.’’ Id.67
65 Also, Professor Crawford’s use of data from the
entire population of Form 3 CSOs provided him
with a wealth of data that mitigated a potential
problem with regard to potential overfitting arising
from sampling that provided too little data relative
to the number of parameters. Crawford CWDT
¶ 123.
66 Ms. Hamilton’s assertion that CSOs are more
interested in satisfying niche signal viewers than
with attracting and retaining new subscribers is
contrary to assumptions underlying much of the
survey analysis of CSO attitudes and valuations.
Survey analyses are described in Section III, infra.
67 Ms. Hamilton also criticized Professor
Crawford for assuming duplicated network minutes
had zero value, because: (1) Some people prefer to
watch a program at times other than when aired by
a local network affiliate and (2) all programming
has a value greater than zero to a CSO. Id. at 13–
14. However, Professor Crawford explained in his
oral testimony that: (1) He only dropped duplicated
network programming that was aired at the same
time as the local network programming and (2) Ms.
Hamilton’s conclusory assertion that all
programming has value to a CSO flies in the face
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The Judges find that Ms. Hamilton
was a knowledgeable and credible
witness, particularly with regard to the
de minimis impact of distantly
retransmitted stations on CSOs and the
importance of ‘‘legacy carriage.’’
Moreover, the Judges take note that CSO
time and effort are themselves finite
resources (opportunity costs), and, as
Ms. Hamilton implied, it would
behoove a rational CSO to expend more
of those resources making carriage and
programming decisions with a greater
financial impact.68
However, the Judges do not find that
the relative unimportance of distantly
retransmitted stations to a CSO deprived
the regression by Professor Crawford, or
any of the regressions in evidence, of
value in this proceeding. If the reasons
articulated by Ms. Hamilton caused
CSOs to emphasize legacy carriage over
potential increases in value from adding
or substituting different local stations
for distant retransmission, then
otherwise well-constructed regressions
should capture the relative values of
those legacy-based decisions. The
Judges are mindful that regression
analysis is of benefit because it looks for
a correlation between economic actors’
choices (the independent explanatory
variables) and the dependent variables
as potential circumstantial evidence of a
causal relationship, but it does not
purport to explain what lies behind
such a potential causal relation. Thus,
Ms. Hamilton has not so much criticized
regression analyses as she has provided
an answer to a different question.
Indeed, if legacy-based decisionmaking is prevalent, the Judges would
expect to see relatively stable shares
over the royalty years encompassed
within and across the Allocation/Phase
I proceedings. In fact, the record does
reflect relative stability. See, e.g.,
Crawford CWDT ¶¶ 12, 15 (in his two
regressions in this proceeding, ‘‘the
estimated parameters underlying these
marginal values are stable across years
. . . .’’), ¶ 39, Table V–3. It thus appears
that past decision-making has to an
extent generally locked in (through an
emphasis on legacy carriage) decisions
as to the carriage of distantly
of the economic principle that consumers value
only one version of perfectly substitutable goods. 2/
28/18 Tr. 1426 (Crawford).
68 Given the low value of retransmitted stations,
a CSO might rationally emphasize the value of
‘‘legacy carriage’’ as a heuristic (without further
analytical effort), assuming as Ms. Hamilton
implies, that eliminating a distantly retransmitted
legacy station and its programs is more likely to
cause a loss in subscribers than a change in station
lineup is likely (without further and costly
analytical effort) to increase the number of
subscribers.
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retransmitted stations for the 2010–2013
period.
In sum, therefore, Ms. Hamilton’s
testimony, while informative and
credible, does not diminish the value of
Professor Crawford’s regression or, for
that matter, any other Waldfogel-type
regression.
b. Minimum Fee Issue
Dr. Jeffrey Gray criticized Professor
Crawford’s regression because the
analysis included in the dependent
variable royalties that are paid as part of
the statutorily mandated minimum fees.
Gray CWRT ¶¶ 17–18. Any Form 3 cable
system must pay a system-wide
minimum fee equal to 1.064% of its
gross receipts into the royalty pool for
distantly retransmitted stations, even if
it does not retransmit any stations to
distant markets, up to the
retransmission of one full DSE. 17
U.S.C. 111(d)(1)(B)(i) and (ii). Dr. Gray
asserted that, consequently, the data
used by Professor Crawford is not
informative, because the minimum fee
cost is decoupled from the marginal
economic decision regarding the
retransmission of the first DSE. Gray
CWRT ¶¶ 20–22.
Dr. Gray noted that approximately
50% of CSOs did retransmit more than
one DSE, and thus voluntarily paid a
royalty greater than the minimum fee.
Dr. Gray acknowledged that the data
regarding this subgroup of CSOs was
informative because these CSOs had
made a discretionary choice to incur
additional royalty charges in exchange
for carriage of additional distantly
retransmitted stations and their
constituent programs. Accordingly, he
ran what he described as Professor
Crawford’s regression using only the
CSOs that paid more than the minimum
fee, and his results were different from
Professor Crawford’s results. However,
although Dr. Gray had characterized his
work as a rerun of Professor Crawford’s
regression, at the hearing Dr. Gray
confirmed that he had been ‘‘unable to
replicate’’ Dr. Crawford’s regression. 3/
14/18 Tr. 3739 (Crawford).69
In any event, Dr. Gray’s analysis
resulted in the allocations among
program categories—presented in the
table below alongside Professor
Crawford’s allocations (and Dr. Gray’s
viewership-based allocations discussed
elsewhere in this Determination):
TABLE 4—IMPACT OF ACCOUNTING FOR MINIMUM FEES REQUIREMENT ON CRAWFORD ROYALTY SHARES, 2010–2013
Claimant category
Crawford
royalty
Shares
Crawfordmodified
royalty
shares
Distant
viewing
royalty
shares
(%)
(1)
(2)
(3)
CCG .............................................................................................................................................
CTV ..............................................................................................................................................
Devotionals ..................................................................................................................................
Program Suppliers .......................................................................................................................
PTV ..............................................................................................................................................
JSC ..............................................................................................................................................
3.51
16.50
0.60
23.44
17.72
38.23
5.46
13.54
0.75
61.19
19.06
0.00
3.70
13.50
1.44
45.43
33.04
2.89
Gray CWRT ¶ 24, Table 3.
In response, Professor Crawford
pointed out that, contrary to Dr. Gray’s
assertions, Dr. Crawford’s regression did
not ignore the impact of the minimum
fee, because he included an indicator
variable as a control, subsumed within
his fixed effects variables, to reflect
whether the minimum fee was paid at
the system level. 2/28/18 Tr. 1422
(Crawford). Thus, Professor Crawford
maintained that he had already
accounted for the minimum fee effect.
Accordingly, Professor Crawford argued
that Dr. Gray’s analysis merely
attempted to account for minimum fee
systems in a different way—by omitting
those systems instead of replicating
Professor Crawford’s regression that
used control variables and fixed effects
to account for the minimum fee paying
systems.70
Dr. Gray is correct with regard to his
general principle that a CSO’s decision
to distantly retransmit any particular
station, when that CSO is otherwise
obligated to pay the minimum royalty
fee, does not indicate a direct
correlation between the decision to
retransmit and the decision to incur a
royalty obligation. By contrast, when a
CSO decides to incur an increase in its
marginal royalty costs by retransmitting
more than one DSE, that decision
reveals the CSO’s preference to incur
the royalty cost in exchange for the
perceived value of the distantly
retransmitted station and the programs
in that station’s lineup.
As Dr. Gray noted, the minimum
royalty fee is somewhat akin to a ‘‘tax’’
that is paid regardless of whether the
CSO decided to distantly retransmit a
local station. 3/14/18 Tr. 3704 (Gray).
Nonetheless, the CSO still has several
choices to make, because it will receive
something of potential value, i.e.,
distantly retransmitted stations, in
exchange for the ‘‘tax.’’ The first choice
is binary; should it retransmit any
station or no station? As Dr. Gray noted,
during the 2010–2013 period, on
average 527 out of the 1,004 Form 3
CSOs analyzed (52.5%) chose to
retransmit the exact or fewer number of
signals than the regulated fees
permitted; 83 paid the minimum fee yet
elected not to retransmit any local
stations. Gray CWRT ¶ 17. Those
decisions reveal that the CSO has
concluded (whether by analysis or
resort to a heuristic) that any of the
marginal costs (physical or opportunity)
associated with retransmission likely
exceed the value to the CSO of such
retransmission, even accounting for
minimum royalties, which the CSO
must pay in any event.
69 Not only was Dr. Gray unable to replicate
Professor Crawford’s work, Professor Crawford also
challenged Dr. Gray’s assertion that he otherwise
faithfully reran Professor Crawford’s regression. 2/
28/18 Tr. 1422 (Crawford) (asserting that Dr. Gray
changed a ‘‘key element of my regression analysis
. . . the subscriber group variation [by]
aggregate[ing] that subscriber group level
information up to the level of the systems, which
means . . . he cannot do fixed effects anymore . . .
and he then adds additional variables.’’).
70 Professor Crawford testified that after
reviewing the rebuttal testimony, he did a ‘‘test’’ in
which he claimed to have ‘‘dropped the minimum
fee systems from the regression analysis and re-ran
the regression,’’ which showed that the implied
royalty shares were ‘‘very, very close: to his own
original results. . ..’’ 2/28/18 Tr. 1424 (Crawford).
However, Professor Crawford and CTV did not
produce this regression because, as CTV’s counsel
acknowledged in response to a rebuttal, ‘‘this is not
a new analysis [and] [w]e are not presenting any
numbers here.’’ 2/28/18 Tr. 18 (John Stewart, CTV
counsel).
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These statistics also reveal that many
CSOs decided to retransmit stations
when they were obligated to pay only
the minimum royalty. Although there is
no marginal royalty cost associated with
this decision, the CSO’s decision as to
which stations to retransmit remains a
function of choice, preference, and
ranking.71 Thus, the CSO in this context
would still have the incentive to select
distant local stations for retransmission
that are more likely to maximize CSO
profits, through either an increase in
subscribership or, as Ms. Hamilton
emphasized, by avoiding the loss of
subscribers through the preservation of
‘‘legacy carriage’’ through the nonanalytical heuristic of maintaining the
status quo.72
There are substantial economic bases
for this finding. Because the ‘‘tax’’ of the
minimum fee is paid regardless of
whether distant retransmission occurs,
that ‘‘tax’’ is also in the nature of a sunk
cost. Fundamental economic analysis
provides that a seller should ignore
sunk costs when making marginal
decisions (although they should try to
recoup these costs if the buyers’
willingness-to-pay allows it).
Nonetheless, a CSO that decides to
distantly retransmit a station when the
marginal royalty cost is zero has
revealed that the particular station
contains programming that would
71 In constructing a hypothetical market, the
Judges assume CSO rationality or bounded
rationality, at the least. ‘‘Bounded rationality’’
means that economic actors behave rationally (e.g.,
preferring potential profits to possible losses), but
that rationality is inevitably limited by their lack of
full information or the resources and ability to
obtain full information necessary to make a
completely (‘‘unbounded’’) rational decision. See C.
Sunstein, Behavioral Law & Economics 14–15
(2000).
72 A more homespun analogy is perhaps
instructive. Consider a child who has misbehaved
and is thus punished by her parents who prohibited
her from playing outside, as is her preference.
Instead, she is sent by her parents to her room for
the evening, where she is permitted to watch
television (either the offense is not so great in this
example as to warrant a suspension of TV privileges
or the child has relatively permissive parents). The
child has been compelled to pay a cost
(confinement to her room) and precluded from her
first choice (no confinement). If watching television
is her only (or next best) option given confinement,
she will rationally select the programs that provide
her with the most utility. The fact that she was
compelled to remain in her room would not provide
her any incentive to abandon her order of
preference as to the programs she would watch,
even though she would not watch any of them but
for the ‘‘tax’’ imposed by her parents (this analogy
assumes that she would not refuse to watch
television, as ‘‘cutting off her nose to spite her face’’
is assumed to be an irrational response). The CSO
that is ‘‘confined’’ to a market in which the
minimum royalty fee is imposed likewise rationally
would make the best of a bad situation and
retransmit stations based on the capacity of the
station to increase CSO utility/profits, that is,
assuming marginal non-royalty costs were not
prohibitive.
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increase marginal value to that CSO,
over and above the next best alternative
‘‘retransmittable’’ local station and
above any other marginal costs (e.g.,
physical retransmission costs or the
opportunity cost of foregoing a different
type of cable channel in the CSO’s
channel lineup).
Finally, Dr. Gray’s emphasis on the
CSOs that retransmit more than one DSE
is misleading. Those other CSOs that
pay only the minimum royalty fee and
elect to distantly retransmit one station
might have elected to pay a positive fee
in the absence of the minimum fee. For
example, assuming Program Suppliers’
programs were more valuable to a CSO
than the minimum fee and
disproportionately more valuable than
any other program category, that CSO
would have retransmitted a station that
disproportionately included Program
Supplier content and willingly paid the
minimum fee (or more). Dr. Gray’s
criticism fails to address this issue.
With regard to Dr. Gray’s own
regression, run for the first time in
rebuttal, the Judges are not surprised
that his different regression approach
would yield different results. However,
the Judges do not rely on
methodological approaches proffered for
the first time in rebuttal, except to the
extent they appropriately demonstrate
defects in another party’s approach.
Because Dr. Gray acknowledged that he
could not replicate Professor Crawford’s
regression and because Dr. Gray
therefore utilized a different approach,
the Judges do not find that Dr. Gray’s
critique as it related to the minimum fee
issue was sufficient to discredit
Professor Crawford’s approach.73
4. Conclusion Regarding Professor
Crawford’s Regression Analysis
Not only did Professor Crawford
sufficiently respond to the criticisms of
his regression analysis, that analysis is
based on a number of other factors as to
which no criticisms were leveled. First,
he used the universe of all programming
on all distant signals, rather than a
sampling, thus avoiding any problems
73 An expert economic witness, Professor George,
who otherwise approved of Professor Crawford’s
analysis, notes that the treatment of minimum fee
only systems by Professor Crawford generally
resulted in a tradeoff between accuracy and bias.
Specifically, Professor George testified that lumping
together CSOs paying only the minimum fee with
other CSOs (as Professor Crawford did) ‘‘introduces
some uncertainty [and] wider confidence intervals,’’
but, on the other hand, Dr. Gray introduces ‘‘bias’’
because he has ‘‘pull[ed] out systems . . . where
their choices are very valid.’’ 3/5/18 Tr. 2045
(George). Because the Judges have found Professor
Crawford’s confidence intervals to be relatively
narrow, Professor George’s testimony in this regard
does not affect the Judges’ reliance on Professor
Crawford’s analysis.
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3569
that may be associated by improper
sampling or inadequately sized samples.
2/28/18 Tr. 1186 (Crawford). Second, by
using data and royalties at the
subscriber group level, his regression
analysis related more specifically to
programs and signals actually available
to subscribers and provided more
variation and observations than past
regressions. 2/28/18 Tr. 1512, 1517–19,
1661 (Crawford). Third, his use of a
fixed effects approach avoided the
criticism that he had omitted key
variables. Crawford CWDT ¶ 107; 2/28/
18 Tr. 1398 (Crawford). Fourth, the
confidence intervals for his proposed
shares were relatively narrow at the
95% confidence level (i.e., at a .05
significance level). Crawford CWDT
¶¶ 117 and 176, Tables 23 & 24. Fifth,
Professor Crawford acknowledged the
potential problem that his fixed effects
could lead to the ‘‘costs’’ of higher
standard errors and wider confidence
intervals (and, as Professor George
noted, with specific reference to the
minimum fee issue), but he was able to
mitigate that effect with his rich data
set, so that his parameters remained
relatively precise. Crawford CWDT
¶ 123. Finally, unlike the other
regressions, Professor Crawford does not
estimate any negative coefficients for
the coefficients of interest in this
proceeding, which makes his regression
analysis (especially his duplicated
analysis that also had no negative
coefficients for network programming)
more of a stand-alone estimate of
relative value and less in need of
reconciliation with the survey analysis.
Thus, on balance, the Judges find
Professor Crawford’s regression
analysis, especially his duplicateminutes approach, to be highly useful in
estimating relative values in this
proceeding.
C. Dr. Israel’s Regression Analysis
1. Introduction
On behalf of the Joint Sports
Claimants, its economic expert, Dr.
Mark Israel, conducted a regression also
in the general form of a Waldfogel-type
regression, but with minor
modifications intended to improve the
reliability of the methodology. Written
Direct Testimony of Mark Israel, Trial
Ex. 1003, ¶¶ 23, 25 (Israel WDT). Dr.
Israel’s primary purpose was to
determine whether such a regression
would corroborate the results of the
2004–05 and the 2010–13 Bortz
Surveys. He concluded that the
‘‘observable marketplace behavior’’ he
had analyzed did indeed corroborate the
results of both Bortz Surveys. Id. ¶ 8. Dr.
Israel further testified that, if the Judges
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were to find that the 2010–13 Bortz
Survey did not support a finding of
relative market value, his and Professor
Crawford’s respective regressions
constituted the best alternative evidence
of such value. 3/12/18 Tr. 3079
(Israel).74
2. Dr. Israel’s Regression
Dr. Israel analyzed royalties CSOs
paid over a three-year period, 2010–
2012, rather than the full four-year
period at issue in this proceeding, 2010–
2013. Id. ¶ 7. Dr. Israel testified that he
did not analyze the full 2010–2013 fouryear period because he had begun his
analysis when the proceeding was
limited to the three-year 2010–2012
period. However, he testified that he
was able to confirm the accuracy of his
regression estimates against the results
from the Bortz Survey that covered all
four years. He also noted that his results
corresponded closely to the results that
Professor Crawford obtained in his
regression, which spanned the full fouryear period. 3/12/18 Tr. 2838–40
(Israel).
Dr. Israel, like Professor Crawford,
utilized the royalty data from the ‘‘Form
3’’ CSOs, i.e., the larger CSOs, which
paid the largest dollar amount of
royalties for distantly retransmitted
stations by virtue of the large amount of
‘‘gross receipts’’ they earned from their
cable operations. Israel WDT ¶ 9.
Referring to the regulated nature of
the cable market, Dr. Israel noted:
‘‘There is no market price for distant
signal programming to use in assessing
relative marketplace value.’’ Id. ¶ 16. Dr.
Israel further noted that, applying the
principles laid out in prior proceedings,
‘‘relative marketplace value’’ must be
estimated by consideration of evidence
as to what royalties would be paid for
different categories of programming in a
‘‘hypothetical free market.’’ Id. To
ascertain that value, and consistent with
his understanding of prior
determinations, Dr. Israel focused on the
relative value of program categories to
the buyers, i.e., CSOs. Id.75
To assemble the specifications of his
regression model, Dr. Israel applied the
essentials of a Waldfogel-type
regression. That is, he tested to find a
correlation between: (1) Royalties paid
by CSOs (the dependent variable) and
(2) minutes of programing in each
category of programming as established
in this proceeding (the independent/
explanatory variable). He utilized
control variables to hold constant other
potential drivers of CSO royalty
payments, itemized infra. Id. ¶ 22.
However, he altered his approach
from the Waldfogel regression approach
in the following important ways:
• To reflect the fact that not all
subscriber groups among a CSO’s total
subscriber base received any given
distant signal, Dr. Israel prorated each
signal ‘‘based on the fraction of the
number of subscribers who received it
. . . by using the variable in the CDC
data called ‘Prorated DSE’ as a measure
of the prorated distant signal
equivalents that each distant signal
represents for each CSO—Accounting
Period.’’ Id. ¶ 26.76
• To account for the retransmission of
non-compensable ‘‘Network
Programming’’ minutes in the estimates,
Dr. Israel included those minutes to
‘‘effectively act’’ as a control variable,
thus excluding them from the
calculation of shares of the royalty fund.
That is, he included these minutes in
his regression because they are in fact
retransmitted and ‘‘therefore are part of
the cost-benefit analysis that a [CSO]
undertakes when deciding whether or
not to carry [a] distant signal . . .
[h]ence explaining total royalty
payments [even though] they are not
compensable minutes in this
proceeding.’’ Id. ¶ 27.
• To improve the quality of his
estimates, Dr. Israel utilized a larger
sample than employed in the Waldfogel
regression. Specifically, Dr. Israel used
data from a random sample of 28 days
in each six-month accounting period in
his 2010–2012 analysis, a 33% increase
in the number of sample days (21)
utilized in the Waldfogel regression. Id.
¶ 30.77
Dr. Israel controlled for other
independent variables in essentially the
same manner as in the Waldfogel
regression, by including the following
control variables in his regression
model:
• Number of CSO subscribers from the
previous accounting period
• Number of activated channels for the CSO
in the previous accounting period
• Count of broadcast channels for the CSO
• Indicator for whether a CSO pays the
special 3.75 percent rate royalty fee
• Indicator for whether or not the CSO pays
the minimum statutory payment
• Average household income for the CSO’s
Designated Market Area (DMA)
• Indicators for the accounting period of
each observation
Id. ¶ 33.
Through these specifications, Dr.
Israel stated that he was able to answer
what he characterized as the
fundamental question: ‘‘How much do
CSO royalty payments increase with
each additional minute of each category
of programming content?’’ Id. ¶ 34.
Applying his regression model, Dr.
Israel made the following estimations:
TABLE 5—ISRAEL REGRESSION MODEL RESULTS
Regression model
all categories
Variables
Minutes of Sports Programming ....................................................................................................................................................
Minutes of Program Suppliers Programming ................................................................................................................................
Minutes of Commercial TV Programming .....................................................................................................................................
Minutes of Public Broadcasting Programming ..............................................................................................................................
74 In addition to performing a regression analysis,
Dr. Israel also reviewed data relating to the
economics of a different market—that in which
large cable networks generally, and TNT and TBS
specifically, bought sports and other programming.
The Judges discuss that analysis infra.
75 Dr. Israel did not consider the relative value of
program categories from the perspective of the
hypothetical sellers, which he identified as the
stations retransmitting the programs in a bundled
signal. 3/12/18 Tr. 3064 (Israel).
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76 Thus, Dr. Israel’s regression differs from
Professor Crawford’s regression in that Professor
Crawford analyzed the relationship between
royalties and program categories at the subscriber
group level, whereas Dr. Israel ran the regression at
the CSO level, using CDC data that prorated the DSE
to reflect the proportion of CSO subscribers who
received the distant signal. Israel WDT ¶ 27.
77 Dr. Israel made note of two other adjustments
he made to his regression that caused it to differ
from the Waldfogel regression. First, he eliminated
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** 4.836
(2.466)
*** 0.469
(0.104)
*** 1.010
(0.355)
** 0.660
(0.306)
a ‘‘Mexican Stations’’ category because no such
category was identified in this proceeding. Israel
WDT ¶ 29. Second, Dr. Israel grouped the programs
from ‘‘low power’’ stations according to their
appropriate program categories, rather than carving
out a miscellaneous category for ‘‘low power’’
stations, as had been done in the Waldfogel
regression. Israel WDT ¶ 31.
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3571
TABLE 5—ISRAEL REGRESSION MODEL RESULTS—Continued
Regression model
all categories
Variables
Minutes of Canadian Programming ...............................................................................................................................................
Minutes of Devotional Programming .............................................................................................................................................
Minutes of Network Programming .................................................................................................................................................
Minutes of Other Programming .....................................................................................................................................................
Number of Subscribers (Previous Accounting Period) ..................................................................................................................
Number of Activated Channels (Previous Accounting Period) .....................................................................................................
Median Household Income in Designated Marketing Area ...........................................................................................................
Count of Broadcast Channels .......................................................................................................................................................
Indicator for Special 3.75% Royalty Rate .....................................................................................................................................
Minimum Payment Indicator ..........................................................................................................................................................
Observations ..................................................................................................................................................................................
R-squared ......................................................................................................................................................................................
*** ¥0.973
(0.212)
*** ¥0.701
(0.246)
*** ¥0.985
(0.290)
** 0.916
(0.462)
*** 1.351
(0.0601)
*** 141.8
(18.73)
*** 1.339
(0.286)
¥493.5
(326.5)
*** 41,918
(4,711)
*** ¥16,501
(3,689)
5,465
0.692
Source: TMS/Gracenote; Cable Data Corporation; Kantar media/SRDS.
Note: Robust standard errors in parentheses.
*** p<0.01, ** p<0.05, * p<0.1.78
Israel WDT ¶ 36 Table V–I (citations
omitted).
Although Dr. Israel reported the
standard errors generated by his
regression (in the parentheticals in the
table above, pursuant to conventional
78 The ‘‘p-value’’ provides a measure of statistical
significance. It represents ‘‘[t]he smallest
significance level at which the null hypothesis can
be rejected.’’ Wooldridge, supra note 34, at 867. A
statistical significance level of .01, .05 and .1, as
used in the table in the accompanying text is ‘‘often
referred to inversely as the . . . confidence level,’’
equivalent to 99%, 95% and 90%, respectively.
ABA Econometrics, supra note 22, at 18. Although
‘‘[s]ignificance levels of five percent and one
percent are generally used by statisticians in testing
hypotheses . . . this does not mean that only
results significant at the five percent level should
be presented or considered [because] [ l]ess
significant results may be suggestive, even if not
probative, and suggestive evidence is certainly
worth something.’’ Fisher, 80 Col. L. Rev., supra at
717–718. Thus, ‘‘[in] multiple regressions, one
should never eliminate a variable that there is a
firm foundation for including, just because its
estimated coefficient happens not to be significant
in a particular sample.’’ Id. However, care must be
taken not to confuse the ‘‘significance level’’ with
the ‘‘preponderance of the evidence’’ standard,
because ‘‘the significance level tells us only the
probability of obtaining the measured coefficient if
the true value is zero,’’ so one cannot ‘‘subtract[] the
significance level from one hundred percent’’ to
determine whether a hypothesis is more or less
likely to be correct. Id. See also D. Rubinfeld,
Econometrics in the Courtroom, 85 Col. L. Rev.
1048, 1050 (1985) (‘‘[I]f significance levels are to be
used, it is inappropriate to set a fixed statistical
standard irrespective of the substantive nature of
the litigation.’’); D. McCloskey & S. Ziliak, The
Standard Error of Regressions, 34 J. Econ. Lit. 97,
98, 101 (1996) (‘‘statistically significant’’ means
neither ‘‘economically significant’’ nor ‘‘significant
[in] everyday usage [where] ‘significant’ means ‘of
practical importance’ . . ..’’).
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regression notation), he did not set forth
the confidence intervals that result from
these standard errors, either for his
coefficients or for the resulting shares.
He acknowledged that it would be
difficult to calculate meaningful
confidence intervals in this exercise
because shares of any one category are
dependent on the shares in other
categories and the econometrician must
‘‘do something more than just a simple
linear calculation.’’ 3/12/18 Tr. 2975
(Israel).
Nonetheless, Dr. Israel acknowledged
that confidence intervals could be
calculated from the standard errors in
his regression. In cross-examination,
and by way of example, he
acknowledged that the confidence
interval applicable to the JSC
programming coefficient in his
regression ranged from 0.003 to 9.669.
3/12/18 Tr. 2976 (Israel). Given this
range, he agreed that the math would
create a range for the value of JSC
programming, with a 95% degree of
confidence, between ‘‘a fraction of a
penny and $9.67 per minute.’’ 3/12/18
Tr. 2977 (Israel). Similarly, Dr. Israel
acknowledged that, given his standard
error for CTV, he could state with 99%
confidence that the value for a minute
of CTV programming ranged between 31
cents and $1.71. 3/12/18 Tr. 2978
(Israel). In similar fashion, Dr. Israel
acknowledged that his regression, and
the standard errors he reported,
generated the following confidence
intervals for each minute of
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programming: For PTV, between $.06
and $1.26, for Canadian Programming,
between ¥$1.39 and ¥$0.56, and, for
SDC programming, between ¥$1.18 and
¥$0.22.
Dr. Israel further acknowledged that
the coefficients he estimated in his
regression all fell within the confidence
intervals of each other, which suggested
an overlapping that could undermine
the usefulness of his results. However,
he denied that such a consequence had
statistical meaning detrimental to his
opinion because ‘‘confidence intervals
tell you something about the precision
of those coefficients, but you can’t step
from a statement about statistical
significance to a statement about
magnitude of value.’’ 3/12/18 Tr. 3014
(Israel).
Nonetheless, Dr. Israel conceded that
‘‘the confidence intervals are . . .
important if I have no other information
to compare it to, so I am testing a
hypothesis based on just the
regression.’’ 3/12/18 Tr. 2981 (Israel).
However, Dr. Israel further testified that
he reached the opinion that the
regression he ran generated meaningful
coefficients because they corroborated
the Bortz Survey, which was both the
primary purpose of his regression
analysis and a corroborative result that
mitigated any uncertainty generated by
the wide confidence intervals arising
out of his regression. 3/12/18 Tr. 2981–
82 (Israel).
Dr. Israel described the coefficients
derived by his regression analysis as
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representing the ‘‘average value across
all cable systems of an additional
minute of that category of
programming.’’ Israel WDT ¶ 37; 3/12/
18 Tr. 2831 (Israel). Thus, it became a
simple algebraic matter ‘‘to determine
Israel WDT ¶ 38. Applying this ratio to
each of the six categories Dr. Israel
calculated the following estimated
percentage shares per category averaged
over the 2010–2012 period for which he
had data:
TABLE 6—ISRAEL REGRESSION:
ESTIMATED PERCENTAGE SHARES
JSC .......................................
Program Suppliers ................
CTV .......................................
PTV .......................................
SDC ......................................
37.54
26.82
22.16
13.48
0.00
minutes applicable to that category, and
divided that product by the total value
of all such products summed across all
categories. He expressed the ratio for
any program category X as:
TABLE 6—ISRAEL REGRESSION: ESTI- consistent with the results of the
MATED PERCENTAGE SHARES—Con- regression undertaken by Dr. Rosston,
referenced supra, in an earlier
tinued
2010–2012
average share
(%)
Category
CCG ......................................
2010–2012
average share
(%)
Category
the relative value of each type of
programming.’’ That is, as with any
Waldfogel-type regression, Dr. Israel
simply took the coefficient estimated by
his regression for each program category
and multiplied it by the number of
0.00
Id. Table V–2. However, Dr. Israel did
not calculate share allocations for
specific years, which is how the Judges
are required by statute to make the
allocations.79
Dr. Israel further noted that these
results were not only consistent with
the results of the Waldfogel regression
for the 2004–05 years, they were
proceeding covering 1998 and 1999.
Specifically, Dr. Israel’s regression
implied the same rank order for the top
four programming categories and a
generally similar magnitude of royalty
allocations for the top three categories
as in Dr. Waldfogel’s regression. Id. ¶ 39.
Further, with regard to his assigned
task, Dr. Israel noted that his rank order
for the top four program categories was
consistent with—and thus corroborative
of—the top four rank order determined
by the Bortz Survey. Dr. Israel set forth
and also depicted the consistency of his
regression and the Bortz Survey as
follows:
TABLE 7—COMPARISON OF BORTZ SURVEY RESULTS TO ISRAEL REGRESSION
2010
(%)
Programming category
Sports .......................................................
Program Suppliers ...................................
CTV ..........................................................
PTV ..........................................................
Devotional ................................................
Canadian ..................................................
2011
(%)
40.9
31.9
18.7
4.4
4.0
0.1
2012
(%)
36.4
36.0
18.3
4.7
4.5
0.2
Bortz Survey
average
2010–2013
(%)
2013
(%)
37.9
28.8
22.8
5.1
4.8
0.6
37.7
27.3
22.7
6.2
5.0
1.2
38.2
31.0
20.6
5.1
4.6
0.5
Israel
regression
2010–2012
(%)
37.5
26.8
22.2
13.5
0.0
0.0
79 Dr. Israel testified that he did run a test to
determine whether his regression results changed
depending upon the time period evaluated and that
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he found that his results were stable over time.
Israel WDT App. C–1. However, he did not link that
result with any sufficient assertion explaining how
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or why the Judges might apply his findings for each
year.
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Id. ¶ 40 Table V–4.
Dr. Israel acknowledged that although
his ranking of the top four categories
(JSC, Program Suppliers, CTV and PTV)
was consistent with the Bortz Survey
ranking, that consistency did not extend
to the bottom tier (PTV, SDC and
Canadian programming). Id. ¶ 41.
Rather, he acknowledged that his
regression estimated no value for the
SDC and Canadian programming.
However, he noted that, when the three
low-tier categories are viewed
collectively, his regression estimated a
total share of value (13.5%) to all three
categories (actually just PTV) and the
Bortz Survey provided what he
understood to be a roughly equivalent
relative value range between roughly
9% and 13% in total for Public TV,
Devotional, and Canadian programming.
3/12/18 Tr. 2880–81 (Israel).
To test the robustness of his findings,
Dr. Israel conducted several sensitivity
analyses. He concluded that each of his
sensitivity analyses ‘‘confirm[ed] the
relative ranking of the various
categories, particularly of the top three
categories relative to the bottom three.’’
Israel WRT ¶ 43. See also Id. App. C.
More particularly, Dr. Israel ran three
sensitivity analyses to determine
whether the following changes in his
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model would alter his results in any
meaningful way. These analyses
examined changes that would result
from: (1) Isolating JSC minutes and
comparing these minutes ‘‘to all other
programming minutes combined . . . to
test whether the value for [JSC] minutes
is sensitive to splitting out the
individual programming categories’’ (as
in his regression), (2) controlling for any
additional ‘‘market-specific traits of the
CSO’’ (through application of a DMA
‘‘fixed effect’’), and (3) controlling for
any royalties ‘‘that [resulted from] the
3.75% fee [rather than] the base rate fee
royalties.’’ In each sensitivity analysis,
Dr. Israel found that the changes had
‘‘no effect on any of [his] conclusions.’’
Id.
3. Program Suppliers’ Criticisms
Dr. Gray expressed a number of
specific criticisms of Dr. Israel’s
regression, in addition to Dr. Gray’s
criticisms of Waldfogel-type regressions
generally.
a. Alleged Sensitivity of Regression
First, Dr. Gray asserted that Dr.
Israel’s regression exhibits ‘‘remarkable
sensitivity’’ because of the wide range of
proposed relative shares. For example,
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3573
when Dr. Israel’s standard errors are
converted into confidence intervals, Dr.
Israel’s regression indicates a range for
the JSC share ‘‘from 0% to 63.29%’’,
when assumptions are changed
‘‘regarding the choice of explanatory
variables or the assumed functional
relationship those variables have on
royalty fees paid.’’ Gray CWRT ¶ 28.
Dr. Gray testified that he replicated
Dr. Israel’s results exactly and then
calculated what Dr. Israel had omitted—
95% confidence intervals around the
estimates of the value of an additional
minute of programming by category
type. Gray WDT ¶ 29. Dr. Gray
determined that at the 95% confidence
level, the JSC share could have been as
low as .05%, far less than the 37.5%
share derived by Dr. Israel through his
point estimate, but consistent with the
0% share for the JSC estimated by the
SDC’s economic expert, Dr. Erdem.
Accordingly, Dr. Gray opined that Dr.
Israel’s regression is both ‘‘imprecise’’
and ‘‘unreliable.’’ Gray CWRT ¶ 29.
Dr. Israel rejected Dr. Gray’s criticisms
in this regard. Specifically, Dr. Israel
maintained that it was uninformative
that Dr. Gray’s sensitivity analysis
diminished the statistical significance of
the former’s estimates because statistical
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significance is ‘‘a measure . . . [of] how
certain we are that the estimate is
different from zero.’’ 3/12/18 Tr. 2840
(Israel). Further, when a modeler or
critic adds many additional variables,
the regression will generate lower
statistical significance. Thus, according
to Dr. Israel, Dr. Gray’s sensitivity
analysis necessarily created the loss of
statistical significance, by introducing
too many new variables that were
unrelated to the core variables (program
categories) that must be isolated and
measured in this proceeding.
Dr. Israel also defended this large
interval with what the Judges see as a
non sequitur—that he nonetheless still
ranked the JSC first. See id. at 3011.
When confronted with the additional
fact that injecting the DMA effect into
the regression resulted in a regression
with the highest R2 among his proffered
and sensitivity regressions, Dr. Israel
testified that when ‘‘you add a bunch of
DMA fixed effects, you’re going to get a
higher R-squared. The notion of
choosing a regression to maximize Rsquared is given zero credit in
economics.’’ Id. The Judges agree with
Dr. Israel on this narrow point because,
as discussed supra with regard to the
Crawford regression analysis, goodnessof-fit as measured by the R2 calculation
is not dispositive when evaluating a
regression intended to measure specific
effects rather than to predict a result.
The Judges also agree with Dr. Israel
that the replicated model created by Dr.
Gray did not necessarily discredit Dr.
Israel’s analysis, given the addition of
several variables in that replication.
However, the Judges agree with Dr.
Gray that the large confidence intervals
around Dr. Israel’s estimated
coefficients—and therefore around his
shares—are troubling, especially when
compared to the narrow confidence
intervals and low standard errors in
Professor Crawford’s regression
analysis. The Judges recognize, as in the
2004–05 Determination, that wide
confidence intervals and large standard
errors call into doubt the ‘‘precision of
the results [and] caution against
assigning ‘too much weight’ to their
corroborative value.’’ See also ATA
Airlines, 665 F.3d at 896 (confidence
interval can be so wide that ‘‘there can
be no reasonable confidence’’ sufficient
for reliance by fact finder.).80
b. Choice of Linear Functional Form and
Inclusion of Minimum Fee CSOs
Dr. Gray took issue with Dr. Israel’s
use of a linear relationship between
royalties paid and minutes of
programming, rather than using a log of
royalties paid. Rather, and by
comparison, Dr. Gray found that
Professor Crawford’s use of a log-linear
relation was ‘‘a more realistic economic
function for the functional form of the
relationship,’’ particularly as ‘‘between
minutes and royalties,’’ because the
logarithmic calculation revealed the
percentage impact that retransmitted
minutes have on royalties. Gray CWRT
¶ 30.81
In response to Dr. Gray’s criticism of
his use of a linear form, Dr. Israel
testified that ‘‘taking the log is kind of
a technical thing . . . .’’ 3/12/18 Tr.
2856 (Israel). Further, he did not utilize
any econometric tests to determine
whether the linear form was
appropriate, particularly compared to
the log-linear form.
Dr. Gray combined his log
transformation of Dr. Israel’s linear
approach with another of Dr. Gray’s
criticisms—the use of data from CSOs
that only pay the minimum fee (as he
also discussed in his criticism of
Professor Crawford’s regression).
Adjusting for these two purported
defects, Dr. Gray found that Dr. Israel’s
reworked regression produced the
following radically different estimates,
compared to Dr. Israel’s unadjusted
regression:
TABLE 8—IMPACT OF ACCOUNTING FOR MINIMUM FEES REQUIREMENT ON ISRAEL ROYALTY SHARES, 2010–2013
Claimant category
Israel royalty
shares
(%)
Israel-modified
royalty shares
(%)
Distant viewing royalty
shares
(%)
(1)
(2)
(3)
CCG .............................................................................................................................................
CTV ..............................................................................................................................................
Devotionals ..................................................................................................................................
Program Suppliers .......................................................................................................................
PTV ..............................................................................................................................................
JSC ..............................................................................................................................................
0.00
22.16
0.00
26.82
13.48
37.54
4.15
27.20
0.64
44.27
19.55
4.19
3.70
13.50
1.44
45.43
33.04
2.89
Gray CWRT ¶ 31 Table 4.
In response to Dr. Gray’s criticism of
Dr. Israel’s use of data from CSOs
paying only the minimum fee, Dr. Israel
stated that such data should not simply
be disregarded, because it provides
useful information regarding the
carriage decisions of those CSOs. He
also noted that Dr. Waldfogel’s
regression, relied upon by the Judges in
the most recent Allocation/Phase I
proceeding, likewise applied the data
from CSOs who paid only the minimum
fee. 3/12/18 Tr. 2830 (Israel).
The Judges agree with Dr. Israel that
the data regarding the carriage decisions
of CSOs who pay only the minimum fee
should not be disregarded, and adopt
their findings relating to this issue in
connection with Professor Crawford’s
regression. See section II.B.3.b, supra.
To summarize, even when a CSO is
obligated to pay the minimum royalty
fee, it still has the incentive to select
stations for distant retransmission that it
believes will maximize the benefits (or,
in economic terms, utility) to the CSO.
80 The Judges emphasize that Dr. Israel’s
confidence intervals are problematic especially
because they are wide relative to those in Professor
Crawford’s regression. The Judges are not finding
that wide confidence intervals, standing alone,
automatically serve to discredit a regression
analysis. See generally Fisher, 80 Colum. L. Rev.,
at 716 (even when the standard errors are relatively
large and the confidence intervals relatively wide,
that ‘‘does not mean that the true coefficient is
likely to be any part of that range,’’ but rather ‘‘the
estimated coefficient’’ remains ‘‘[t]he single most
probable figure . . . .’’) (emphasis added).
81 Dr. Gray stated that he used a ‘‘Box-Cox’’ test
to confirm that a percentage-based relationship was
a preferred specification over an assumed linear
relation and better fit the data. However, Dr. Gray
did not support that statement with a citation to his
work or to literature that would be supportive. Gray
WRT ¶ 30 n. 10. When a rebuttal expert purports
to do a deeper dive into a model than the expert
whose work he or she is criticizing, support for that
deeper analysis should be provided in the written
rebuttal testimony. However, Professor Crawford
also undertook (and provided a succinct
explanation of) a Box-Cox test for his regression
analysis and found the results ‘‘strongly favoring
the log-linear over the linear model.’’ Crawford
CWDT ¶ 115.
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However, because carriage decisions are
not tied even indirectly to a
contemporaneous discretionary decision
to pay royalties (beyond the mandatory
minimum 1.064% for the first DSE),
they strike the Judges as potentially less
informative than discretionary decisions
by CSOs to incur an additional royalty
expense in order to distantly retransmit
particular stations. Nonetheless, as
explained supra in the Judges’
consideration of this issue in connection
with Professor Crawford’s regressions,
the Judges find no basis in the record by
which they could or should make a
reasonable ‘‘relative value’’ adjustment
based on whether a CSO did or did not
pay only the minimum fee.
c. Negative Coefficients
Dr. Gray further attacked the
usefulness of Dr. Israel’s regression by
criticizing as ‘‘nonsensical’’ the negative
coefficients Dr. Israel estimated for
Canadian and Devotional programming.
According to Dr. Gray, negative
coefficients are implausible because a
program category cannot have a negative
market value. Gray CWRT ¶ 35.
In response, Dr. Israel did not dispute
that the coefficients themselves
(whether positive or negative) should be
understood as the value per minute, or,
equivalently, as the ‘‘implied price’’ of
a minute of programming. 3/12/18 Tr.
2832–36 (Israel). Dr. Israel understood
the negative coefficients to indicate that
the inclusion of such programming on a
station lineup (i.e., a bundle) correlated
with a lower station value compared to
programming that generated a ‘‘positive
coefficient’’ in the regression. 3/12/18
Tr. 2832–33 (Israel). However, Dr. Israel
conceded that even programming with
negative coefficients nonetheless have
positive value when retransmitted, and
he therefore declined to assign zero
value to such categories.
However, the Judges find that Dr.
Israel’s concession proves too much. If
programs could have positive economic
value despite the negative value of the
coefficient identified by the regression,
then the coefficient does not reflect
absolute market value per minute.
Rather, the coefficient must represent
something else. Dr. Israel identified that
something else as the contribution of a
program category to the value of the
royalty pool as compared with, that is,
relative to, the value of other program
categories.82 Of course, this ‘‘something
82 For a simpler example, consider a restaurant
patron offered a three-flavor ice cream dessert.
Assume for that patron chocolate adds a utility
measure (‘‘utils’’ in econo-speak) of 5, vanilla adds
a util measure of 4, strawberry adds a util measure
of 3, and kiwi adds a util measure of 2. A threeflavor combination of chocolate, vanilla and
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else’’ is something that the Judges must
determine in this proceeding—the
relative value of a program from a given
category to a CSO when packaged in a
station bundle, i.e., relative to the
inclusion of a program in another
category.
Accordingly, the Judges do not find
the presence of negative coefficients to
be ‘‘nonsensical.’’ However, because of
Dr. Israel’s explanation of the negative
coefficients, the Judges disagree with his
decision to reset those negative
coefficients to zero.83 And, because
negative coefficients do not mean that
the programs lacked any absolute value
as contributors to the sum of royalties
paid, any negative values for program
categories derived from a regression
would need to be adjusted to reflect the
absolute value of such programming,
given that it indeed was retransmitted
on some cable systems.84
d. Criticisms by Dr. Jeffrey Stec
Dr. Jeffrey Stec, another economic
expert witness for Program Suppliers,
leveled several criticisms at Dr. Israel’s
regression. First, he added to the chorus
of witnesses who opined that the
regulated nature of the market renders
inapposite any purported statistical
relationship between royalties and
program categories. Amended Written
Rebuttal Testimony of Jeffrey Stec, Trial
Ex. 6016, at 15 (Stec AWRT).
Nonetheless, the Judges find regression
in such circumstances to be a useful tool
to ascertain relative differences in value
among program categories,
strawberry has a total util value of 12 (5 + 4 + 3).
If kiwi is substituted for strawberry, the total util
value is now only 11 (5 + 4 + 2). Thus, kiwi, relative
to strawberry in this combination, has a value in
utils of ¥1 (reducing the value of the dessert from
12 to 11)—even though its absolute value in utils
is +2. This negative value reflects the opportunity
cost or relative value of substituting kiwi for
strawberry in the bundle, but not the absolute
market value of kiwi as an unbundled ice cream
flavor. Applying this example to a market, the
coefficient represents the value in a market
populated by such bundles, not a value in a market
without bundles. Clearly, how the ‘‘hypothetical
market’’ is understood in terms of bundled
programs therefore determines whether the negative
coefficients make sense and also affects the extent
to which the coefficients are of assistance in
allocating the royalties.
83 Dr. Israel’s explanation of the reason for a
negative coefficient is substantively similar to
Professor George’s explanation of negative
coefficients, discussed infra, as well as to Professor
Crawford’s explanation of negative coefficients for
duplicative network programming, as discussed
supra.
84 However, because the Judges find that only Dr.
Crawford’s regression is sufficiently credible and
because it does not contain negative coefficients for
the categories of interest, the conundrum of
negative coefficients does not affect the Judges’
reliance on regression analysis in this
determination.
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notwithstanding the regulated nature of
the marketplace.
Dr. Stec also criticized Dr. Israel’s
regression because it suggests that two
different distantly retransmitted signals
could be associated with the same
royalty level despite transmitting
different combinations of content. Stec
AWRT at 25–27. The Judges do not find
this to be a valid criticism. Dr. Israel’s
regression identifies values for each
program category and multiplies those
values by the number of minutes
transmitted for each category. These
categorical values certainly could be
summed up for any given signal, as Dr.
Stec’s criticism assumes. However, there
is no reason why different signals
retransmitted on different cable systems
to different subscriber groups (of various
sizes) could not generate the same level
of royalties notwithstanding that they
contain different mixes of program
categories. This criticism
misapprehends that the purpose of a
section 111 allocation proceeding is not
to value the signals as a whole, but
rather to value the constituent program
categories across the signals.
4. The SDC’s Criticisms
a. Criticisms by John Sanders
John Sanders, a media valuation
expert who testified on behalf of the
SDC, criticized Dr. Israel’s regression
from a non-statistical perspective. First,
he opined that the concept of correlating
royalty generation with program
categories is ‘‘conceptually flawed.’’
Written Rebuttal Testimony of John
Sanders, Trial Ex. 5006, at 6 (Sanders
WRT). He opined that marketplace
value, or fair market value, is identified
by evaluating actual transactions that
are ‘‘modulat[ed]’’ by price and
quantity. Accordingly, he asserted that a
higher market value could be associated
with programming that represents a
relatively small amount of airtime.
Amended Direct Testimony of John
Sanders, Trial Ex. 5001, at 21.
The Judges agree with Mr. Sanders
regarding the potential for programming
to possess a relative value greater than
would be suggested by relatively low
total viewership and airtime.85
85 Royalty distribution parties have proposed fee
generation valuation methodologies in the past and
the Judges and their predecessors have generally
discounted them as appropriate for determining
overall relative values. See, e.g., 2000–03
Distribution Order, 75 FR at 26800–01. In that
order, the Judges noted that the CRT had criticized
the fee generation approach, but then resorted to fee
generation reasoning in excluding PTV from a
distribution from the 3.75% Fund. Id. at 26803. The
Judges later reaffirmed their declination of fee
generation valuation in the 2004–05 distribution
proceeding, noting that the fees cable systems pay
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that is not a reasonable criticism of the
regression by Dr. Israel in particular or
of the Waldfogel-type regressions in
general. Such regressions, for example,
have assigned a relative value to the JSC
programming that is greater than its
total minutes of airtime would suggest.
See, e.g., Gray CWRT ¶ 31 & Table 4
(Israel regression estimated a 37.5% JSC
share whereas a viewing analysis
provided only a 2.8% JSC share).
Mr. Sanders also found fault with Dr.
Israel’s regression because other
evidence suggested that SDC
programming had a positive value not
captured by that regression.
Specifically, Mr. Sanders noted that
when WGNA removed certain
programming from its retransmitted
feed, it would frequently replace that
local programming with SDC
programming, suggesting that the latter
has significant value. Sanders WRT at
13.86 While this may be indicative,
anecdotally, of the value of SDC
programming as ‘‘programming inserts
on WGNA,’’ it does not suggest to the
Judges any defect in Dr. Israel’s
regression analysis.
Finally, Mr. Sanders noted that CSO
program selection cannot be viewed as
a voluntary market-related decision in
all instances, because the record reflects
that WGNA’s parent company, Tribune
Media Services (Tribune Co. in 2010),
had a practice of requiring CSOs to
agree to transmit multiple stations that
it owned if a CSO wanted to transmit a
particular Tribune station. See Direct
Testimony of Sue Ann R. Hamilton,
Trial Ex. 6008, at 7 (Hamilton WDT).87
Thus, Mr. Sanders argued, Tribune’s
forced bundling diminished the
assumption that a CSO’s station-bystation retransmission decision was
made by consideration of the
programming categories within the
station signal. Rather, he opined that in
certain instances, CSOs may well have
retransmitted WGNA and its mix of
categorical programming because those
CSOs wanted to include other Tribune
stations in the channel lineup.
Dr. Israel did not address this issue in
his Written Rebuttal Testimony.
are statutorily determined and do not necessarily
reflect relative value. See 2004–05 Distribution
Order, 75 FR at 57072.
86 Though making a point about relative value,
Mr. Sanders acknowledged that substituted
programming inserts on the WGNA national feed
are not compensable in this proceeding because
they do not constitute retransmitted local
programming. Sanders WRT at 13.
87 Ms. Hamilton did not have direct knowledge of
the existence of this Tribune Co. policy after 2007
when she left her position with Charter, a CSO.
Rather, she opined that such tying would have
likely been a factor thereafter ‘‘primarily due to
legacy carriage considerations.’’ Hamilton WDT at
7.
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However, another JSC witness, Allan
Singer, a Charter Communications
executive from 2011 through 2016,
testified that ‘‘during [2010–2013], an
annual average of approximately 86
Charter Form 3 systems made the
decision to carry WGNA on a distant
basis each year, and on average
approximately 69 of those systems did
not carry any other Tribune station in
addition to WGNA [and] approximately
11 Charter Form 3 systems carried
Tribune-owned stations on a local basis,
but did not carry WGNA.’’ Written
Rebuttal Testimony of Allan Singer,
Trial Ex. 1009, ¶¶ 1, 5. Likewise,
another JSC witness, Daniel Hartman, a
former satellite television programming
executive, testified that industry data
showed ‘‘that in 2010–13 . . . 169 Form
3 cable systems carried a Tribune signal
other than WGN (on a local or distant
basis) while not carrying WGN during
the same period . . . and . . . 725 Form
3 cable systems carried WGN as a
distant signal while not carrying another
Tribune signal during the same period.’’
Written Rebuttal Testimony of Daniel
Hartman, Trial Ex. 1011, ¶ 25 (Hartman
WRT).
The Judges find that the record does
not support Mr. Sanders’ or Ms.
Hamilton’s claim that there were tyingbased reasons for the distant
transmission of WGNA that would have
diminished the probative value of
WGNA data as regression inputs.
Additionally, to the extent any tyingbased pressures may have existed, they
were not quantified and thus this factor
could not serve to alter the regression
estimates.88
b. Criticisms by Dr. Erdem
Dr. Erdem, on behalf of the SDC,
leveled several criticisms at Dr. Israel’s
regression. Dr. Erdem opined that Dr.
Israel’s regression was especially
sensitive to: (1) The inclusion of
additional variables, (2) changes in the
regression model specifications, and (3)
data points that Dr. Erdem identified as
‘‘influential observations’’ 89 that, in his
88 Of course, Ms. Hamilton’s tying-based
argument would be equally unavailing as against
either the Crawford or George regression analyses.
89 An ‘‘influential observation,’’ also known as an
‘‘influential data point,’’ is defined as ‘‘[a] data
point whose addition to a regression sample causes
one or more estimated regression parameters to
change substantially.’’ Rubinfeld, supra note 36, at
465. An ‘‘outlier,’’ by contrast, is ‘‘[a] data point that
is more than some appropriate distance from a
regression line that is estimated using all the other
data points in the sample.’’ Id. at 466 (emphasis
added). Although some authorities equate all
‘‘influential observations’’ with ‘‘outliers,’’ Dr.
Rubinfeld’s more careful distinction makes it clear
that an ‘‘influential’’ observation or data point is not
to be disregarded unless it is outside an
‘‘appropriate distance’’ from the regression line.
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opinion, were statistical outliers. Erdem
WDT at 14–18.
i. Sensitivity to Additional Variables
Dr. Erdem testified that much of the
variation within Dr. Israel’s regression
could be explained by introducing the
number of distant subscribers as an
independent (explanatory) variable
rather than applying it in the regression
as a control variable. When Dr. Erdem
applied this subscriber count data in
this manner, he claimed that ‘‘all of the
implied royalty shares’’ in Dr. Israel’s
regression became zero percent, and that
some coefficients turned from positive
to negative. Erdem WDT at 15–16.
Overall, he found that, with this one
sensitivity adjustment, the coefficients
for the program categories necessarily
were no longer statistically significant.
Id.
In rebuttal, Dr. Israel focused on a
database issue, arguing that Dr. Erdem
had misunderstood ‘‘the nature of the
CDC data’’ he used to calculate distant
subscribers, resulting in double-counted
subscribers. Israel WRT ¶ 24 n.22. This
is the same criticism made of Dr.
Erdem’s data analysis pertaining to the
number of distant subscribers. As noted,
Dr. Erdem acknowledged his error, and
the Judges denied the SDC’s out-of-time
motion for leave to correct his
testimony.
Accordingly, the Judges find that,
given the acknowledged deficiency in
Dr. Erdem’s application of distant
subscriber data, his criticism of Dr.
Israel’s regression for failure to utilize
that data as an independent
(explanatory) variable rather than a
control variable cannot support Dr.
Erdem’s claims regarding the lack of
statistical significance in Dr. Israel’s
coefficients.
ii. Specification of the Functional Form
of the Regression
With regard to Dr. Erdem’s second
criticism, he hypothesized that ‘‘royalty
payments may not have a linear
relationship with several potential
variables.’’ Erdem WDT at 16. Therefore,
he transformed Dr. Israel’s regression
from linear form to non-linear form to
test for further sensitivity. Specifically,
Dr. Erdem made log transformations to:
(1) The total number of subscribers, (2)
the number of distant subscribers, (3)
the number of activated channels, and
(4) the number of broadcast channels.
The experts’ dueling positions (with citations to
other outside authority) on whether the ‘‘influential
observations’’ identified by Dr. Erdem in Dr. Israel’s
regression are ‘‘outliers’’—and thus must be ignored
in the regression—are discussed infra.
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Id. These transformations indicated to
him that the estimated coefficients for
the program categories changed
substantially. Id. at 17.
In response, Dr. Israel asserted that he
found Dr. Erdem’s log transformation/
exponential versions of the former’s
level variables to be something he had
‘‘never seen . . . before.’’ Israel WRT
¶ 24, n.22. Rather, Dr. Israel
characterized this transformation as
‘‘simply ‘fishing’ for a specification that
changes my result—throwing variables
into a model until the result changes.’’
Id. Dr. Israel indicated that such
additions to the variables and such
transformations are ‘‘not informative’’
because they lack ‘‘economic
justification.’’ Id.
At the hearing, Dr. Israel elaborated,
flatly rejecting the contention that Dr.
Erdem had merely tested for nonlinearities. Rather, he testified that Dr.
Erdem had ‘‘added an extra set of
variables to the regression.’’ 3/12/18 Tr.
2993 (Israel). He further elucidated that
the proper way for Dr. Erdem to have
tested for another functional form, i.e.,
a non-linear function, would have been
to use a log form on the right side (the
explanatory variable side) of Dr. Israel’s
regression, not for Dr. Erdem to pile log
variables on top of linear variables. Id.
at 2994.
Finally, Dr. Israel testified that he
decided to use a linear function in order
to be consistent with the previous
Waldfogel regression. Id. at 2955–56. As
with the Judges’ discussion regarding
Professor Crawford’s regression
analysis, the Judges do not find that Dr.
Israel’s use of a linear relationship
between royalties paid and these
additional variables diminished the
value of his regression analysis.
Additionally, as discussed in
connection with Professor Crawford’s
regression, the Judges do not find it was
necessary or appropriate for a modeler
to treat the number of subscribers,
distant or otherwise, as anything other
than control variables because, in this
proceeding, the economic and
regulatory purpose is to estimate the
relative values of different program
categories on the level of royalties rather
than to predict or explain all of the
causes or correlations between other
independent (explanatory) variables and
the level of royalties.
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iii. ‘‘Influential Observations’’
Dr. Erdem identified 200
observations, out of Dr. Israel’s 5,465
observations, that he labeled as
‘‘influential observations.’’ However, Dr.
Erdem did not propose that these
influential observations constituted
outliers that should have been removed
from Dr. Israel’s regression analysis.
Quite the contrary, Dr. Erdem testified
that these influential observations
‘‘shouldn’t be excluded’’ for any
economic reason, but rather
demonstrate that, from an econometric
perspective, Dr. Israel’s ‘‘regression is
sensitive to influential observations and
only that there ‘‘could be subsets of data
. . . that may require additional
investigation . . . .’’ 3/8/18 Tr. 2708
(Erdem). Dr. Erdem further posited that
the influential observations might
reflect a ‘‘geographic effect’’ that
influenced Dr. Israel’s coefficients, a
problem that, Dr. Erdem further opined,
was not present in Professor Crawford’s
regression analysis because he used
‘‘system accounting period fixed
effects’’ that have ‘‘indirect geography
implications.’’ 3/8/18 Tr. 2708–09
(Erdem). In fact, Dr. Erdem further
contrasted Professor Crawford’s
approach with Dr. Israel’s approach by
noting that ‘‘Dr. Crawford’s model does
not exhibit sensitivity to outliers.’’
Erdem WRT at 20 n.17.
In response, Dr. Israel testified that
Dr. Erdem was fundamentally wrong to
suggest exclusion of what he
characterized as ‘‘influential
observations.’’ More particularly, Dr.
Israel asserted that ‘‘[t]he purpose of this
regression analysis is to study the
relationship established by the full set of
data, representing all Form 3 CSOs.’’
(emphasis added). Moreover, Dr. Israel
pointed out that ‘‘even the authors Dr.
Erdem cited for this statistical practice,
Israel WRT ¶ 24 n.22, themselves state
that ‘‘influential data points, of course,
are not necessarily bad data points; they
may contain some of the most
interesting sample information.’’ D.
Belsley, D. E. Kuh, and R. E. Welsch,
Regression Diagnostics: Identifying
Influential Data and Sources of
Collinearity at 3 (1980). Dr. Israel noted
that the data Dr. Erdem characterized as
distorting influential observations, i.e.,
outliers, actually revealed an important
influence, viz., the impact of the
relatively large size of the CSOs and
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Prorated DSEs that were associated with
these observations. More broadly, Dr.
Israel noted that ‘‘every regression that
has ever been run is going to be
sensitive to the removal of influential
observations,’’ indicating that the mere
presence of such observations begs the
question of whether they provide
valuable or anomalous data points. 3/
12/18 Tr. at 2996 (Israel).
The Judges agree with Dr. Israel that
it would be inappropriate on this record
to disregard the 200 observations that
Dr. Erdem labeled as influential
observations/outliers. The Judges find
that, from this record, absent any
compelling explanation as to why the
data from these 200 observations are not
relevant, simply ignoring those data
would not necessarily paint a more
accurate picture of the population as a
whole with respect to the relationship
between royalties paid and program
categories on local stations
retransmitted by CSOs. The dueling
positions taken by Drs. Israel and Erdem
indicate that the difference between
informative influential observations and
uninformative outliers is a matter of
degree, and deciding where an
observation crosses from one type to the
other is a matter of expert judgment. Dr.
Erdem, who raised this issue, did not
provide a sufficient argument to support
his criticism that the impact of these
data points should preclude or diminish
reliance on Dr. Israel’s regression
analysis. In fact, on the present record,
disregarding Dr. Israel’s regression
analysis because he failed to discard
‘‘influential’’ data seems to the Judges to
be more likely to risk a cherry-picking
of the data rather than an identification
of demonstrable anomalies. The Judges
note, however, that Professor Crawford’s
regression analysis is superior to Dr.
Israel’s in that the former is not subject
even to potential distortion from
influential observations.
c. Limited Impact of Dr. Erdem’s
Adjustments
The Judges note that, notwithstanding
the merits of Dr. Erdem’s specific
criticisms, there is not a wide gulf
between the share values that he
identified after reworking Dr. Israel’s
regression to remove the alleged
influential observations, as noted by the
following comparison:
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TABLE 9—COMPARISON OF ISRAEL REGRESSION AND ERDEM’S ADJUSTED ISRAEL REGRESSION
Israel
regression
(%)
Joint Sports Claimants .............................................................................................................................................
Program Suppliers ...................................................................................................................................................
Commercial TV ........................................................................................................................................................
Public TV .................................................................................................................................................................
Devotional ................................................................................................................................................................
Canadian ..................................................................................................................................................................
Israel WDT ¶ 39 & Table V–3.; Erdem
WDT at 18, Ex. 13. As for the bottom
two ranked program categories,
Devotional and Canadian, Dr. Israel was
unsurprised that his regression could be
less accurate in estimating the shares for
these categories. See 3/12/18 Tr. 2881,
2960 (Israel) (acknowledging ‘‘negative
coefficients for Canadian [and]
Devotional,’’ explaining that ‘‘in my
experience, regressions of this type
often struggle to match at the lower
end.’’).
Dr. Erdem acknowledged as well that
his allocations set forth in the above
table are ‘‘very broadly comparable to
the results from both the Bortz and
Horowitz surveys,’’ although he
hastened to opine that ‘‘there are strong
reasons to doubt that comparability of
the results is much more than a
coincidence . . . .’’ Id.90
5. Dr. Israel’s Sensitivity Analyses
Dr. Israel is on shakier ground when
it comes to defending the results of his
own sensitivity analyses of his
regression. Specifically, in his
sensitivity analysis set forth in his own
Model 3 (in which Dr. Israel controlled
by geography by including an indicator
variable ‘‘by DMA’’), Dr. Israel estimated
coefficients for Program Suppliers and
PTV that were approximately 50%
higher for each category than in the
regression on which he has asked the
Judges to rely. 3/12/18 Tr. 3002–04
(Israel). When confronted on crossexamination with this quantitative
change, Dr. Israel responded by saying
that he did not view that quantitative
difference ‘‘as changing the overall
90 The economic expert witness for the CCG,
Professor Lisa George, weighed in with a defense of
Dr. Israel’s regression. She asserted that Dr. Erdem’s
argument that Dr. Israel’s regression technique
produced ‘‘unstable’’ results reflects a fundamental
misunderstanding of the regression process. George
WRT at 6–7 (‘‘[V]ariables that do not affect royalty
payments are not needed, since they typically will
just worsen precision of the estimates. Changes to
Dr. Israel’s regression advocated by Settling
Devotional Claimants run counter to the goals of
causal inference, tending to increase bias and
reduce precision.’’).
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Erdem’s adjusted Israel
regression
(%)
37.5
26.8
22.2
13.5
0.00
0.00
45.0
22.6
21.6
7.0
3.8
0.0
rankings of the corroboration [of the
Bortz Survey].’’ 3/12/18 Tr. 3004 (Israel)
The Judges are troubled by Dr. Israel’s
fixation on ‘‘relative ranks’’ over the
substantial ‘‘quantitative difference’’ in
shares. The present proceeding is
intended, by statute, precedent, and
consensus, to allocate a dollar quantity
of royalties. The rank ordering of those
allocations is not an end in itself.
Moreover, the fact that one could rank
the claimant categories in that process is
obvious—yet legally, economically, and
practically of no importance.
A simple example is useful. Assume
three program categories, A, B and C,
seeking to split a $100 million royalty
pool. A CSO survey might estimate the
following allocation of royalties:
Category A: 60%, i.e., $60 million
Category B: 30%, i.e., $30 million
Category C: 10%, i.e., $10 million
By contrast, a regression might estimate
the following allocation of this $100
million royalty pool:
Category A: 35%, i.e., $35 million
Category B: 33%, i.e., $33 million
Category C: 32%, i.e., $32 million
The rankings are identical in both the
survey and the regression: A, B, and C
in descending order. However,
copyright owners in Categories C
certainly would not agree that the
regression results ‘‘corroborate’’ the
survey result, when the regression
produces $22 million more in royalties
for them than the survey. Similarly,
copyright owners in Category A would
be unlikely to find their $35 million
payout under the regression to be
‘‘corroborative’’ of the $60 million
payout they would otherwise receive
pursuant to the survey. Even copyright
owners in Category B would likely chafe
at the notion that the survey results
would take precedence over the
regression results—resulting in a $3
million loss—based on the strained idea
that a $33 million regression allocation
corroborates a $30 million payout.91
In fact, under questioning by Program
Suppliers’ counsel, Dr. Israel
acknowledged that an over-reliance on
the rankings established by a regression
as opposed to the values estimated by
the regression could be of limited use.
See 3/12/18 Tr. 3101 (Israel) (‘‘mere
ranking’’ only ‘‘one indicator generated
by his regression’’). For the foregoing
reasons, the Judges do not place much
weight on the relative rankings of the
program categories in Dr. Israel’s
regression as evidence of relative value,
or as a basis to find his sensitivity
analysis supported his regression
results.
91 Alternately stated, this exercise is not
analogous to Olympic competition, where the
difference in rankings—gold, silver and bronze
medals—makes all the difference. Here, copyright
owners in any claimant category would prefer more
gold (royalty money) than less. Therefore, any
analysis that assumes that value attaches to being
ranked more highly would be absurd.
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6. Conclusion Regarding Dr. Israel’s
Regression Analysis
The Judges give no weight to Dr.
Israel’s regression analysis, for a number
of reasons. First, he did not break out
his proposed allocations on an annual
basis, making his average allocations
inapplicable in the present proceeding.
Second, he did not perform any analysis
of data for the final year (2013) of the
period at issue. Third, his regression
analysis produced large standard errors,
making his estimates less reliable than
Professor Crawford’s estimates and
potentially unreliable. Fourth, and
relatedly, Dr. Israel failed to produce the
confidence intervals around his
proposed coefficients which, when
calculated, were shown to be extremely
wide. Fifth, his regression analysis
produced negative coefficients for
several program categories, which he
arbitrarily reset to zero. Finally, even Dr.
Israel did not wholeheartedly advocate
for the Judges’ adoption of his
regression results as independent proof
of reasonable royalty shares; rather, he
proposed that the Judges accept his
results as corroboration of the Bortz
survey results. Perhaps no single one of
these failings would have been
sufficient to justify the Judges’ decision
to give no weight to Dr. Israel’s
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regression analysis. However, in
combination, and in comparison to Dr.
Crawford’s better constructed regression
analysis, the Judges find themselves
unable to rely on Dr. Israel’s regression
analysis.
D. Professor George’s Regression
Analysis
The CCG proffered a valuation
estimate based on the regression
analysis of their economic expert,
Professor Lisa George. As a general
matter, Professor George testified that
she believed the regression approach
was superior to other attempts to
measure relative value because it infers
value from decisions actually made by
market participants. George CWDT at 2.
She noted further that inferring value
from observed market decisions, known
as the ‘‘revealed preference’’ method,
has been an established feature of
economic analysis. George CWDT at 3
n.1. Like Drs. Crawford and Israel, she
undertook a Waldfogel-type regression.
George CWDT at 6. However, she
modified that approach in a manner that
she understood better focused on
Canadian programming. See id. at 5.
Professor George understood that her
task was to estimate, via her regression
approach, the relative value of the
several program categories, in a
hypothetical market in which no
compulsory license existed. See id. at 6.
She assumed that: (1) The supply side
of the market was not relevant, because
distant retransmission does not affect
local carriage decisions; (2) the cable
television market is imperfectly
competitive; (3) CSOs focus on
incremental revenue and cost, in the
form of royalties, transmission costs,
and the opportunity costs of
transmitting (or retransmitting) any
given program or signal rather than any
other program or signal; (4) distantly
retransmitted programs that are
differentiated from other programs
transmitted by the CSO will have greater
value; and (5) the transactions by which
the distant retransmissions would be
agreed to would be between the CSO, as
buyer, and the station (or groups of
stations), as sellers. Id. at 7–9.
Professor George testified that in her
regression the coefficients for the
Canadian program category should be
interpreted as a ‘‘value per unit’’ or,
equivalently, as an ‘‘implicit price.’’ Id.
at 10, 12.92 With regard to the functional
92 In her regression, Professor George used signal
carriage and royalty data provided by cable systems
on Form 3 Statements of Account as provided by
CDC. George CWDT at 51–54; Written Direct
Testimony of Jonda Martin, Trial Ex. 4009, at 23
(Martin WDT). Professor George obtained program
categorization information that was assembled by
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form, Professor George selected a linear
model because the coefficient in
interest, the value of the programming
by category, is itself linear, i.e., it is
measured in dollars per minute. See id.
at 11.
Anticipating that past criticisms of
Waldfogel-type regressions would be
repeated in this proceeding, Professor
George met those points head-on. First,
she noted that the presence of price
regulation not only does not diminish
the usefulness of a regression, but in fact
is the type of situation in which a
regression approach to the estimation of
value is appropriate. See id. at 18. She
distinguished market prices from
market decisions, noting that the latter
are sufficient, standing alone, to
estimate values through regression
analysis. See id. at 13.93 More
particularly, she opined that the CSO
must decide whether the revenues to be
realized from retransmission are
sufficient to warrant incurring the costs
associated with retransmission
(including royalties, transmission cost,
and opportunity costs). With regard to
the systems paying only the minimum
fee, Professor George noted that their
decision to carry any particular signal
rather than other potential signal
provides useful information regarding
relative value. See id. at 16. From a
technical point of view, Professor
George explained that her regression
‘‘accounts for minimum fee systems by
specifying a separate average (intercept)
term 94 for systems carrying less than
one distant signal equivalent and paying
minimum fees,’’ which she further
noted was similar to the procedure
followed by Dr. Waldfogel in his 2004–
2005 regression. George CWDT at 16.
Professor George explained that,
although she followed the basic
specifications of the Waldfogel-type
regressions, she made two important
changes. First, she estimated only the
relative market value of Canadian
programming compared with the
combined value of all other program
claimant categories. See id. at 23.
Second, Professor George made her
Danielle Boudreau from program content logs filed
with the Canadian Radio-television and
Telecommunications Commission (CRTC) by
Canadian broadcasters. George CWDT at 53;
Corrected Written Direct Testimony of Danielle
Boudreau, Trial Ex. 4001, at 3 (Boudreau CWDT).
93 And, to state the obvious, if market prices were
available, no analysis of any sort would be
necessary.
94 The ‘‘intercept’’ is defined as ‘‘the value of the
y variable when the x variable is zero,’’ and,
accordingly, it is ‘‘the parameter in a multiple linear
regression model that gives the expected value of
the dependent variable when all the independent
variables equal zero.’’ Wooldridge, supra note 34,
at 864. The intercept parameter ‘‘is rarely central’’
to a regression analysis. See id. at 25.
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3579
estimates only for the region in which
Canadian signals may be retransmitted.
See id. at 23. According to Professor
George, applying these two
modifications rendered her regression
both more precise and less subject to
downward bias. See id. at 25.
As in the other Waldfogel-type
regressions, Professor George included
control variables in her regression, in
order ‘‘to isolate the role of the
independent variables of interest
holding all else equal.’’ Id. In particular,
Professor George’s control variables
controlled for: (1) Average income, (2)
population, (3) the number of local
stations, (4) the number of subscribers,
and (5) the number of active channels.
See id. The model also included
‘‘indicator variables for binary system
attributes such as for minimum fee
systems carrying less than one distant
signal equivalent.’’ Id.
Her regression estimated that, within
its regulatory geographic region,
Canadian programming’s share of the
royalties was 24.22%, 24.08%, 25.92%
and 27.4% for each year, respectively,
from 2010–2013. Corrected Amended
Written Direct Statement of Lisa George,
Tr. Ex. 4006, at 6–7 (George CAWDT).
Professor George then considered the
proportion of total U.S. royalties that
were generated within this narrow
region, in order to estimate the
Canadian Claimants’ share of the total
royalty pool across the 2010–2013 fouryear period. When making this
calculation, Professor George utilized
revised data updating compensable
minutes that were contained in
Professor Crawford’s regression
analysis.95 She estimated the following
shares for Canadian programming:
6.55% for 2010, 6.61% for 2011, 7.47%
for 2012 and 7.85% for 2013. George
CAWDT at 4, 7.
Professor George noted that her
regression produced a negative
coefficient within the Canadian region
for Program Suppliers’ and the SDC’s
programs aired on Canadian signals. As
noted supra, she explained that a
negative coefficient in this context
meant that the marginal presence of
such programming ‘‘does not allow
cable systems to charge higher prices for
signal bundles, or to attract and retain
subscribers,’’ relative to program
categories with positive coefficients,
such as Canadian programming on the
Canadian distant signals. Id. at 32.
95 Professor George had originally made her
calculations for the entire 2010–2013 period
without breaking down her estimates by year. After
she reviewed data contained in Professor
Crawford’s CWDT, Professor George was able to
update her estimates and express them on an
annual basis. George CAWDT at 2.
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1. The JSC’s Criticisms
a. Collapsing Non-Canadian
Programming
The JSC’s expert, Dr. Israel, took issue
with Professor George’s unique decision
to collapse all other claimant categories
into a single catch-all category to
compare with the category of interest to
her client: Canadian programming on
Canadian signals in the Canadian zone.
Israel WRT ¶ 12. He explained that
when he altered her model to control for
the categories individually, her point
estimate for Canadian programming fell
to 1.48% of the total royalty fund,
which was more consistent with the
Bortz Survey share of 0.5% for Canadian
programming. See id. at A–2 to A–3.
Further, Dr. Israel opined that his
alteration to control for other program
categories individually was necessary
because Professor George’s collapsing of
all other programming into a collective
category distorted her results by
subjecting her estimation of those
collapsed minutes to ‘‘noise’’ for which
she failed to account. That is, he
claimed that Professor George’s
Canadian share result was ‘‘driven by
many important variables on the
number of minutes by each other
category, thus subjecting her regression
to omitted variable bias.’’ Israel WRT
¶ 75 (emphasis added).96
At the hearing, Professor George
explained that she chose to collapse all
U.S. programming into one category
because of the ‘‘limited data’’ available
to her, precluding her from engaging in
a ‘‘detailed breakdown of programming
on U.S. distant signals.’’ 3/5/18 Tr. 2022
(George). However, she did not
adequately respond to Dr. Israel’s
assertions regarding the impact of this
decision on the statistical reliability of
her regression. See 3/5/18 Tr. 2055
(George) (criticizing Dr. Israel’s
rerunning of her model for several
reasons, but without sufficiently
explaining why her collapsing of all
U.S. programming into a single category
would not be problematic). The Judges
96 ‘‘Omitted variable bias’’ can arise ‘‘when a
relevant variable is omitted from the regression.’’
Wooldridge, supra note 34, at 866. More
particularly, omitted variable bias arises ‘‘because a
variable that is a determinant of Y [the dependent
variable] and is correlated with a regressor
[independent variable] has been omitted from the
regression.’’ Stock & Watson, supra note 32, at 822.
The cumulative effect of any excluded variables
‘‘shows up as a random error term in the regression
model. . . . An important assumption in multiple
regression analysis is that the error term and each
of the explanatory variables are independent of
each other.’’ ABA Econometrics, supra note 22, at
10 n.21. Thus, Dr. Israel’s criticism is that the
‘‘noise’’ in Professor George’s regression reflects a
bias arising from her failure to include important
data from each programming category. Id. at 160.
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are troubled by the absence of an
adequate response to this criticism, and
find insufficient her testimony as to the
limited nature of her data. Accordingly,
the Judges find that this criticism serves
to diminish the weight they give to
Professor George’s regression results.
b. Applying Negative Coefficients
Dr. Israel also claimed error in
Professor George’s treatment of the
negative coefficient she estimated in her
regression for Program Suppliers and
the SDC. Whereas Professor George
simply used the negative coefficient as
an input for her calculation of relative
values per minute, as noted supra, when
Dr. Israel’s own regression estimated
negative coefficients, he reset them to
zero, on the theory that a coefficient
intended to measure the value of
programming could not be negative.
Thus, he opined that Professor George’s
application of the negative coefficients
‘‘distort[ed] the royalty shares for
categories with positive coefficients.’’
Israel WRT ¶ 76.
In response, Professor George testified
that her negative coefficient is ‘‘telling
us that [Program Suppliers’
programming] is effectively dragging
down the value of the Canadian
signals.’’ 3/5/18 Tr. 2031 (George).
Alternately stated, she explained that, in
her opinion, the negative coefficient
indicates that ‘‘if we could replace the
Program Supplier content on Canadian
signals in a sort of hypothetical world
. . . with Joint Sports or Canadian
Claimant programming, the value of the
signal would be higher. . . . So it’s not
surprising to me that more Program
Supplier minutes on a Canadian signal
reduces the value of the signal.’’ Id. at
2031–32 (George) (emphasis added).
Thus, she opined that the negative
coefficient does not reflect a negative
monetary value for such programming,
but rather reflects the opportunity cost
arising from the inclusion of
programming from such categories in
the bundle of programs on the
retransmitted signal compared with
programs from other categories with
positive coefficients. 3/5/18 Tr. 2117
(George).
Accordingly, because Professor
George finds valuable information in the
negative coefficient, she rejected Dr.
Israel’s criticism that she should have
reset the negative coefficient to zero. See
id. at 2043 (George) (‘‘[My] . . . negative
valuation, which is precisely estimated,
so within standard confidence intervals
. . . makes sense from theory. [I]t is
completely arbitrary to replace a
coefficient in a regression model with
another . . . number. It is just bad
econometric practice.’’).
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As discussed in connection with Dr.
Israel’s regression, the Judges find (as
Professor George opined) that negative
coefficients are reasonably wellexplained by the fact that they reflect
the relative impact on the value of the
signal 97 of different categories of
programming rather than the absolute
value of programming-by-category.
Again, though, this explanation of the
negative coefficients underscores that
the coefficients represent the relative
value in a market for programs by
categories as inputs to a bundle (the
signal)—economically relevant to the
task at hand (allocating the royalty pool
by category) but not reflective of
absolute market prices.
c. Weighting Results by the Number of
Subscribers
Dr. Israel asserted that Professor
George’s regression is inconsistent with
the specifications of the Waldfogel-type
regression because she weighted her
compensable minutes by the number of
subscribers of each CSO, whereas Dr.
Waldfogel estimated royalty payments
per CSO, not royalty payments per
subscriber. See Israel WRT ¶ 76.
Moreover, Dr. Israel asserted that this
deviation from Dr. Waldfogel’s approach
was improper because it was
inconsistent with the functional form of
her regression, which was otherwise of
the Waldfogel-type. See id.
In response to Dr. Israel, Professor
George acknowledged that her approach
was ‘‘quite different,’’ yet she did not
adequately explain how or why her
modification made her results more
precise or otherwise improved the
quality of her regression. See 3/5/18 Tr.
2055 (George). The Judges find Professor
George’s vague statement to be an
insufficient response to Dr. Israel’s
criticism.98
2. The SDC’s Criticisms
a. The Regulated Nature of the Market
Dr. Erdem criticized Professor
George’s regression approach because,
as she acknowledged, it did not reflect
the prices that CSOs and stations would
negotiate in an unregulated market.
However, Dr. Erdem did note that her
‘‘observed data’’ revealed that distant
retransmission occurred when
‘‘incremental benefits are higher than
incremental costs’’ for the retransmitting
CSOs. Erdem WRT at 20 (citing George
97 Indeed, Professor George twice referred to the
value of the program categories in the context of the
‘‘value of the signal’’ containing a bundle of
programs offered to a CSO. 3/5/18 Tr. 2031–32
(George).
98 However, this issue was also raised by Dr.
Erdem and, in response, Professor George provided
a more compelling defense, as discussed infra.
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CWDT at 8–9, 20). The Judges note that
this criticism is a variant of the repeated
refrain that the regulated nature of the
market precluded the use of a
Waldfogel-type regression. In the
context of the present criticism as well,
the Judges find that the relative
preferences of CSOs for different
categories of programs are revealed
through such a regression and that
Professor George’s regression analysis is
not subject to appropriate criticism in
this regard.
b. Compensable Minutes
Dr. Erdem also criticized Professor
George’s approach for using actual
compensable minutes for Canadian
signals, but estimated compensable
minutes for U.S. signals in the Canadian
zone. Dr. Erdem suggested that such an
approach ‘‘is likely less precise.’’ Erdem
WRT at 21. Moreover, like Dr. Israel, Dr.
Erdem criticized Professor George for
using Professor Crawford’s data, based
on all U.S. distant signals, as a proxy for
compensable minutes in the Canadian
zone. Dr. Erdem asserted that there was
no basis in the record for Professor
George to make this assumption. See id.
Professor George did not offer a
sufficient response to this criticism.
Accordingly, the Judges find Dr.
George’s regression analysis is
compromised by this unexplained
criticism. However, there is no
sufficient evidence in the record that
reflects the dimensions of this
assumption or the impact it may have
on Professor George’s proposed
allocations. The Judges find, as noted
supra, that Professor George’s lack of
disaggregated data across other program
categories is insufficient to justify her
less precise approach.
c. The Number of Broadcast Hours
Next, Dr. Erdem asserted that
Professor George also assumed without
substantiation that ‘‘all stations
broadcast the same number of hours
throughout the day,’’ which, according
to Dr. Erdem, ‘‘seems to contradict the
actual data . . . used in Professor
George’s analysis’’. Erdem WRT at 21–
22.
Once again, Professor George did not
offer a sufficient substantive response to
this criticism. Thus, the Judges find her
assumption to be unsupported by the
record and her regression analysis
therefore is compromised. However,
there is no sufficient evidence in the
record that reflects the dimensions of
this assumption or the impact it may
have on Professor George’s proposed
allocations.
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d. Negative Coefficients
Dr. Erdem (like Dr. Israel) is troubled
by the negative coefficient produced by
Professor George’s regression for
Program Suppliers’ minutes. However,
his concern is not aimed at Professor
George’s defense of such a negative
coefficient. In fact, he agreed with
Professor George regarding a ‘‘likely’’
reason for the presence of the negative
coefficient, i.e., that it ‘‘suggests that on
Canadian signals, Program Supplier
content is a close substitute for other
cable system offerings from the
standpoint of viewers [and] the presence
of Program Supplier programming on
Canadian distant signals does not allow
cable systems to charge higher prices for
signal bundles, or to attract or retain
subscribers.’’ Erdem WRT at 22
(approvingly quoting Professor George).
Rather, Dr. Erdem contended that the
negative coefficient in the context of the
Canadian signal ‘‘likely does not factor
in the complex decision making process
of U.S. cable operators, who are
maximizing overall profits across all
regions combined.’’ Id. However, this
criticism was speculative, unsupported
by a factual basis and otherwise
undeveloped, and the Judges do not find
it to diminish the value of Professor
George’s regression analysis.
e. Joinder of the Program Supplier and
SDC Categories
Next, Dr. Erdem attempted a
sensitivity analysis of Professor George’s
results. In particular, he separated the
Program Supplier and SDC minutes and
input this separated data into an
updated model. He found meaningful
changes in the resulting coefficients,
including a ‘‘coefficient for [SDC]
distant minutes [that was] positive and
statistically significant.’’ Id. at 22.
In response, Professor George testified
that she had combined these two
program categories because the amount
of SDC programming was so low and
therefore the data would not generate
enough variation. Further, she asserted
that when Dr. Erdem split apart the data
for Program Suppliers and the SDC, he
created ‘‘multicollinearity problems’’
because the variables for each program
category are functions of each other. 3/
5/18 Tr. 2042 (George). However,
Professor George did not point to
evidence that would indicate the
presence of such multicollinearity.
Moreover, she acknowledged she had
combined the two categories to obtain
sufficient variation in the SDC minutes
across CSOs that would be lacking if the
SDC category was analyzed separately.
That in itself was an artifact, because
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3581
SDC programming is not Program
Supplier programming.
Accordingly, the Judges find that the
probative value of Professor George’s
regression analysis is compromised to
an extent by her artificial joinder of the
Program Supplier and SDC categories.
f. Subscriber-Weighted Compensable
Minutes
Dr. Erdem, like Dr. Israel, criticized
Professor George’s decision to multiply
the coefficients by ‘‘the subscriber
weighted compensable distant
minutes.’’ Erdem WRT at 23
(‘‘Conceptually, weighting by
subscribers may not be appropriate in
Waldfogel-type regressions which
model the decisions of cable operators
(i.e., decision to carry a signal or signals
with minutes of different types of
content in return for royalty payments
implied by the formula.’’)). Dr. Erdem
replaced Professor George’s weighted
compensable distant minutes with
unweighted compensable distant
minutes and found that Professor
George’s use of the weighted minutes
approach caused ‘‘[t]he share for the
Canadian category [to] increase[ ]
significantly.’’ Id.
In response, Professor George
explained her reason for using
subscriber-weighted compensable
minutes: ‘‘[W]e are counting up the
subscribers who have access to this
programming to give us a better feel,
because counting just systems doesn’t
give you really a full picture of how
many people are exposed to
programming.’’ 3/5/18 Tr. 2078 (George)
(emphasis added).
The emphasized language above
indicates that Dr. George engaged in
such weighting for the same reasons that
Professor Crawford used minutes at the
subscriber group level and Dr. Israel
used prorated DSE data—to better
identify which subscribers actually
received the distantly retransmitted
local signal. Accordingly, the Judges
find Professor George’s weighting to be
an acceptable deviation from the
Waldfogel approach in the same way as
Professor Crawford’s subscriber group
approach and Dr. Israel’s Prorated DSE
approach represent appropriate
adaptions of the Waldfogel-type
regression to available and more
granular data.
3. Program Suppliers’ Criticisms
a. Negative Coefficients
Dr. Gray criticized Professor George
for failing to reset her negative
coefficient for her combined Program
Supplier/SDC minutes to zero, as did
Dr. Israel. Dr. Gray asserted that these
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negative coefficients implied that these
two program categories would be
required to pay royalties to CSOs,
clearly an absurd result. See Gray CWRT
¶ 35. However, as the Judges have
explained, supra, these negative
coefficients do not represent negative
values for programs in the categories,
but rather represent, on average,
reductions in the value of a program
bundle (i.e., a station) in comparison
with other program categories.
b. The Minimum Fee Issue
Dr. Gray also criticized Professor
George’s regression for the same reason
he criticized all the Waldfogel-type
regressions in this proceeding—the
failure to distinguish between CSOs
paying only the minimum fee and those
who intentionally incurred additional
incremental costs by paying more than
the minimum to distantly retransmit
additional local stations. See id. ¶ 37.
Dr. Gray’s reworking of Professor
George’s regression applying only the
subset of CSOs paying greater than the
statutory minimum fee found no
statistically significant relationship
between CCG programming minutes and
royalty fees paid in the Canadian region,
which would support an estimate of 0%
for the Canadian share (presumably
because the null hypothesis 99 was not
disproven). See Gray CWRT App. D.
In response, Professor George testified
that even the station retransmission
choices by CSOs paying only the
minimum fee provide relevant
economic information. 3/5/18 Tr. 2038–
39 (George). However, she
acknowledged that incorporating the
minimum-fee-paying CSOs in an
integrated analysis does add some
‘‘uncertainty . . . to our estimates [and]
we do lose some precision from having
some minimum fee systems.’’ 3/5/18 Tr.
2039 (George). Further, Professor George
did not contest the statistical
correctness of Dr. Gray’s estimate of a
0% share for Canadian programming
regarding the relative value for
Canadian programming arising from an
analysis of only those CSOs paying
more than the minimum fee. 3/5/18 Tr.
2044–45 (George).
99 ‘‘An expert’s expectation or contention that a
particular independent variable does not have a
correlation with a particular dependent variable is
called a null hypothesis, because the expected
outcome of the analysis would show the absence of
a correlation. . . . Often, the null hypothesis is
stated in terms of a particular regression coefficient
equal to zero.’’ ABA Econometrics, supra note 22,
at 17 (emphasis added). See also Rubinfeld, 85
Colum. L. Rev. at 1054 n.20 (‘‘If the evidence is not
sufficiently strong, the null hypothesis is sometimes
presumed to be correct, but a more accurate
description would simply say that the evidence was
not sufficiently strong to allow for its rejection.’’).
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The Judges find, as noted supra, that
an analysis of the CSOs paying only the
minimum fee might provide some
useful information. However, as also
noted supra, the record does not
provide an adequate basis to incorporate
any ‘‘relative value’’ differences based
on a distinction between CSOs that do
and do not pay only the minimum fee.
4. Conclusion Regarding Professor
George’s Regression Analysis
In sum, the Judges find that Professor
George’s regression analysis is of limited
value. Her collapsing of all nonCanadian programming into a single
category was the consequence of the
unavailability of data, not a choice
intended to enhance the reliability of
her estimates. Also, her negative
coefficients within the Canadian zone of
compensable programming categories
rendered her analysis indeterminate and
thus in need of adjustment.
III. CSO Surveys
Another analytical approach
presented in this proceeding for
determining relative value of the
program types retransmitted by cable
operators is analysis of data from
surveys administered to CSOs, the
entities that buy the compensable
programming (bundled as distant
signals). In essence, the surveys ask the
CSOs to place a relative value on the
types of programming they license for
retransmission to their subscribers.
CSO survey results have long played
a central role in assisting adjudicators in
assessing relative market value of cable
programming. The JSC presented the
first survey report, designed by the
predecessor of Bortz Media & Sports
Group, Inc. (Bortz), to establish the
relative value of the various categories
of programming at issue in 1983. See
Bortz Survey,100 Trial Ex. 1001 at A–2.
Over the years, Bortz refined its survey
design to address issues raised by the
triers of fact. The goal of the surveys
was to answer the question of relative
value of the competing program
categories as seen through the eyes of
CSOs. Id. at A–3—A–4. In the present
proceeding, the JSC and the SDC
support an analysis based on the work
of Bortz for the relevant royalty years.
Program Suppliers offer an alternative
survey 101 designed by Horowitz
Research (Horowitz Survey), which they
offered as a critique of the Bortz survey
100 The full title of the Bortz Survey is ‘‘Cable
Operator Valuation of Distant Signal Non-Network
Programming: 2010–13.’’
101 Program Suppliers also advocated using
viewing statistics as the optimal measure of relative
market value of the participating program category
groups. See infra, section IV.
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results.102 In addition, the CCG
presented a third survey focused on
Canadian signals (Ringold Survey).
Other participants offered criticisms of
the surveys.
All of the surveys the parties
proffered in this proceeding were
conducted by telephone and purported
to inquire of the individual at the
responding CSO who was responsible
for signal carriage decisions. Each
proponent constructed its survey as a
constant sum survey; that is,
respondents were asked to value each
program category relative to the other
categories and as a portion of 100%.
The JSC contended that the Bortz
Survey responses are a sound measure
of the relative value of programming, by
category. See Bortz Survey, Trial Ex.
1001 at 7. Program Suppliers contended
that CSO survey responses are
[d]one well, such a survey may illuminate
the criterion (sic.) by which to allocate
royalties. . . . [W]hatever the reasoned
judgment of executives . . . , any cable
operator survey should not be considered a
substitute for behavioral data on viewing.
Corrected Written Direct Testimony of
Howard Horowitz, Trial Ex. 6012 at 21–
22 (Horowitz CWDT). The Ringold
Survey focuses on CCG programming
within the Canadian broadcast region.
The CCG claimed the Ringold Survey
provides a better measure of the relative
value of compensable Canadian
programs distantly retransmitted in the
U.S.
A. Bortz Survey
As in the past, the JSC have engaged
Bortz to develop and implement a
methodology to ascertain relative
market value of categories of distantly
retransmitted television
programming.103 See Bortz Survey at A–
1. Bortz made ‘‘refinements’’ to the
present survey to address concerns
expressed by the CRT, CARP, and more
recently, the Judges. Specifically, Bortz
refined the way in which it (1) assessed
the level of pertinent knowledge of the
individual survey respondent (i.e., the
person ‘‘most responsible for
programming decisions’’), (2) conformed
program category definitions to those
adopted for royalty distribution
proceedings, (3) selected cable systems
to participate by excluding any that did
102 Notwithstanding his survey results, Mr.
Horowitz opined that ‘‘the Horowitz Survey is not
a substitute for behavioral data such as viewing.’’
Corrected Written Direct Testimony of Howard
Horowitz, Trial Ex. 6012, at 3 (Horowitz CWDT).
103 Bortz retained THA Research to conduct the
2010–13 telephone surveys. Id. at 19. Criticisms of
the Bortz Survey focused on construct and content;
no party criticized the Bortz selection of THA
Research.
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not distantly retransmit eligible nonnetwork programming, and (4) closed
the time gap 104 between the royalty year
at issue and the conduct of the survey
relating to that year. Id. at A–5—A–12.
With regard to the survey contents,
Bortz attempted to focus respondents on
the actual distant signals at issue using
information from the CSOs’ Statements
of Account filed with the Copyright
Office. Id. at 12. To address a criticism
regarding asking respondents to allocate
‘‘value,’’ Bortz asked them to think
about relative value of the categories
and subsequently to provide estimates
for each. The interviewers then went
through the list of program categories to
give respondents an opportunity to
reconsider the relative values the
respondent placed on the categories. Id.
at 13. Bortz also reported other
refinements responsive to criticisms of
the triers of fact and opposing parties in
prior proceedings.105
The CARP determination regarding
allocation of 1998–99 cable royalties
noted that the Bortz Survey focused on
the demand side of a typical market, i.e.,
what CSOs are willing to pay to
broadcasters, which it concluded is
more likely to reflect relative values of
the programming categories. In essence,
according to the CARP, in the relevant
hypothetical market the supply of
programming would be fixed and value
would be determined only by the CSOs’
demand as reflected in their willingness
to pay. See 1998–99 Librarian Order, 69
FR at 3613–15. In any event, beginning
with its 2009 survey, Bortz included a
question asking respondents to rank the
relative cost of the programming
categories, which it alleged gave
respondents a cue to consider the
supply side of the valuation. Bortz
Survey at A–14—A–15.
Bortz surveyed a stratified, random
sample of ‘‘Form 3’’ cable systems,106
but excluded systems that did not carry
distant signals and those whose only
distant signals were PTV or Canadian
signals, or both. Id. at 13–14. Bortz
made five adjustments for the 2010–13
survey questionnaires to address
criticisms of their studies from earlier
104 To
avoid any criticism that there was a delay
in conducting an annual survey that could result in
‘‘recall bias,’’ Bortz conducted all but the 2010
survey beginning in the summer following the
royalty year at issue. Bortz conducted the 2010
survey in December 2011. See Bortz Survey at A–
11.
105 Other criticisms noted by the triers of fact and
opposing parties included, e.g., breaking up the
survey and completing it through multiple
callbacks, and asking for critical conclusions in a
short survey of approximately ten minutes’ length.
106 Form 3 cable systems are the largest systems
by gross receipts and account for over 98% of
section 111 royalty deposits. Id. at 10.
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proceedings. Specifically, Bortz (1)
identified compensable programming on
WGNA, the most widely carried distant
signal; (2) reduced the number of signals
about which they inquired; (3) did not
offer ‘‘sports’’ as a category in the
constant sum question for CSOs that did
not retransmit programming within the
Sports Programming category
established in this proceeding; (4)
modified the ‘‘warm-up’’ questions; and
(5) omitted reference to attracting and
retaining subscribers to broaden the
concept of value to CSOs. Id. at 2.
Initially, Bortz confirmed that the
respondent self-identified as the
individual responsible for signal
carriage decisions for the cable system.
Then Bortz identified the distant signals
at issue and asked each respondent to
rank by ‘‘importance’’ to the system the
non-network programming on those
distant signals by categories ‘‘intended
to correspond’’ to the programming
categories adopted in the present
proceeding. Id. at 15–16. Bortz next
asked respondents to estimate the cost
to acquire programming within the
identified categories if the cable system
had been required to purchase the
programming in the marketplace. Id. at
16. Respondents were then asked to
assign relative values to the relevant
programming; that is, to assign a share
of 100% of value to each category.107
The influence of superstation WGN
America (WGNA) was a major factor in
valuing compensable programming
during 2010 to 2013. Bortz concedes
that survey respondents might have
lacked information detailed enough to
distinguish between compensable
programming and content WGN
substituted for contemporaneous
broadcasts and transmitted to WGNA
subscribers.108 Bortz modified its prior
survey questions to attempt to address
the WGNA content issue. According to
Bortz, for cable systems that only
retransmit WGNA as a distant signal,
survey questions regarding WGNA
programming described only
compensable programming, by agreed
category as nearly as possible.109 In this
way, Bortz sought to address criticism
107 The relative value question read: ‘‘Assume you
[system] spent a fixed dollar amount in [year] to
acquire all the non-network programming actually
broadcast during [year] by the stations . . . listed.
What percentage, if any, of the fixed dollar amount
would your system have spent for each category of
programming?’’ Id. at 18.
108 Only programming that airs simultaneously on
WGN-Chicago (the local feed) and WGNA (the
satellite feed) is compensable under the section 111
license.
109 Questioners offered to send respondents a
guide to compensable WGNA programming and
instructed respondents that they could call back if
the respondent needed more time to consider the
compensable program list. Bortz Survey at 30.
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that its prior survey results contained
skewed values because Bortz’ survey
questions failed to distinguish between
compensable and non-compensable
WGNA retransmissions. Id. at 19.
Comparing the 2004–05 survey results
(which formed the basis of the 2010–13
survey) to those for the time period
relevant to the present proceeding
compensable programming
retransmitted by WGNA decreased by
about half, from approximately 30% of
the signal to under 15%. JSC-, CTV-,
and SDC-represented programming
increased in relative value from the
2004–05 survey to the 2010–13 survey,
while Program Suppliers’ content
declined in relative value. Bortz
attributes these changes to a reduction
in compensable retransmissions of
Program Suppliers’ programming. Id. at
29.
PTV 110 and the CCG 111 criticized the
Bortz results because the survey
excluded cable systems for which
public television and/or Canadian
programming were the systems’ only
distantly retransmitted signals. Bortz
conceded that both PTV and CCG
categories are likely undervalued
because of the survey’s exclusion of
PTV-only and CCG-only systems and
because of the relatively small number
of Form 3 systems that retransmit PTV
and CCG signals. Bortz Survey at 46–47.
Respondents for multiple signal systems
that included PTV and Canadian
programming valued public television
programming on multiple signal
systems at an average of between 7.8%
and 10.3% and valued Canadian signals
at an average of between 2.4% and 7.9%
during the relevant period. Id. The Bortz
Survey aggregate values for PTV and
CCG during the period were
substantially lower because of the
exclusion of PTV-only and CCG-only
systems.112
Notwithstanding the refinements
Bortz implemented in its survey for
2010–13, Mr. Trautman still professed
110 McLaughlin and Blackburn augmented the
2004–05 Bortz survey results by inserting stations
whose only distant signal was PTV, using the same
response rates reported by Bortz. See 3/7/19 Tr. at
2457–59 (McLaughlin). They concluded that
response bias depressed the PTV values claimed in
the Bortz Survey. See Written Rebuttal Testimony
of Linda McLaughlin and David Blackburn, Trial
Ex. 3002, at 4 (McLaughlin/Blackburn WRT).
111 See, e.g., Corrected Written Rebuttal
Testimony of Frederick Conrad, Trial Ex. 4003, at
7–8 (Conrad CWRT) (assuming stations with
Canadian-only distant signals would assign 100%
relative value to CCG programming creates response
bias).
112 The Bortz Survey measured all programming
on Canadian signals as one category. See Bortz
Survey at 46–47. The CCG concedes that some of
the programming on Canadian signals is
compensable in other categories, such as Devotional
or Program Suppliers.
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that the Judges should consider the
value estimates for the Program
Suppliers and Devotional Programming
categories as a ‘‘ceiling’’ or upper bound
for the allocation to those categories.
Mr. Trautman reached this conclusion
largely because he was not confident
that even the modified survey
accurately accounts for noncompensable programming on WGNA,
most of which he asserted falls within
those two program categories. Id. at 18.
Further, Mr. Trautman conceded that
‘‘some adjustment’’ upward of
allocations to the PTV and CCG
categories is appropriate. Id. 7–8;
Trautman WRT ¶ 4.113 Professors
McLaughlin and Blackburn adjusted the
2010–13 Bortz Survey results to increase
the share of value allocated to PTV and
CCG programming, but Mr. Trautman
argued that the McLaughlin/Blackburn
adjustments should be considered a
‘‘ceiling’’ on the values of those two
categories, because they relied in part
on Horowitz Survey results. Mr.
Trautman contended the Horowitz
results were invalid because ‘‘most’’ of
the respondents with PTV-only or CCGonly distant retransmissions valued the
compensable programming at less than
100%. Trautman WRT ¶ 3.
The initial relative valuations from
the 2010–13 Bortz Survey results are:
TABLE 10—INITIAL BORTZ SURVEY RESULTS
2010
(%)
Category
CCG .................................................................................................................
CTV ..................................................................................................................
Devotional ........................................................................................................
PS ....................................................................................................................
PTV ..................................................................................................................
Sports ...............................................................................................................
2011
(%)
0.10
18.70
4.00
31.90
4.40
40.90
2012
(%)
0.20
18.30
4.50
36.00
4.70
36.40
2013
(%)
0.60
28.80
4.80
28.80
5.10
37.90
1.20
22.70
5.00
27.30
6.20
37.70
(Columns might not add to 100% because of rounding.)
See Bortz Survey at 3. Referring to the
calculations performed by Ms.
McLaughlin and Dr. Blackburn, Mr.
Trautman adjusted the allocations in the
Bortz Survey, to increase the relative
values of PTV and CCG programming at
the expense of the relative values of the
remaining categories:
TABLE 11—MCLAUGHLIN/BLACKBURN AUGMENTED BORTZ SURVEY RESULTS
2010
(%)
Category
CCG .................................................................................................................
CTV ..................................................................................................................
Devotional ........................................................................................................
PS ....................................................................................................................
PTV ..................................................................................................................
Sports ...............................................................................................................
2011
(%)
1.6
17.8
3.8
30.3
7.5
39.0
2012
(%)
1.8
17.2
4.2
33.8
8.7
34.2
2013
(%)
1.2
22.3
4.6
28.1
6.9
37.0
2.1
21.7
4.8
26.1
9.1
36.1
(Columns might not add to 100% because of rounding.)
See Table A–2, Trautman WRT, App. A
at A–3.
After reviewing the McLaughlin/
Blackburn analysis, Mr. Trautman
adjusted the Bortz Survey results in two
ways. First, he adjusted the Bortz
Survey results using the McLaughlin/
Blackburn augmented results, derived
by adding PTV-only and Canadian-only
distant signals and assuming CSOs
would have set the relative value of the
PTV and Canadian signals at 100%. Mr.
Trautman then referred to the Horowitz
Survey results, opining that it was error
for McLaughlin/Blackburn to assume
CSOs would assign 100% relative value
to PTV programming on PTV-only
signals.
113 Mr. Trautman criticized the Horowitz Survey
results that valued Program Suppliers and
Devotional programming higher than the Bortz
Survey. He contended Horowitz failed to account
for the amount of non-compensable programming
on WGNA, i.e., the substituted syndicated or
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Program Suppliers retained Horowitz
Research, Inc. to evaluate the Bortz
Survey and to design a proprietary
survey to improve on the Bortz Survey.
Horowitz attempted to replicate and
improve upon the methods and
procedures of the Bortz Survey used in
the ‘‘Phase I’’ or allocation phase of the
2004–05 cable royalty distribution
proceeding.114 See Horowitz WDT at 3.
The Horowitz Survey sought to measure
the relative value of programming
categories in attracting and retaining
subscribers. Id. In rebuttal, Horowitz
evaluated the Bortz Survey covering
royalty years 2010–13. See Written
Rebuttal Testimony of Howard
Horowitz, Trial Ex. 6013, at 2 (Horowitz
WRT).
Horowitz also conducted its own
survey, fashioned on the Bortz Survey,
but with amendments Horowitz
considered necessary. The Horowitz
Survey, among other things, addressed
the PTV and CCG programming the
Bortz Survey omitted. The Horowitz
Survey questionnaire provided category
descriptions to assist respondents in
allocating relative value, identified
examples of programming that might fit
the category description, and created a
separate ‘‘Other Sports’’ category to
clarify that the definition of ‘‘sports
programming’’ for purposes of the
valuation survey did not include all
sports broadcasts, but only included
devotional programs WGNA adds to its lineup
when it is not simultaneously retransmitting WGN
programming. Trautman WRT ¶ 1. Mr. Trautman
argued that Horowitz further inflated Program
Suppliers, because it attributed all programming in
the allegedly inflated ‘‘Other Sports’’ category to
Program Suppliers. Id. ¶ 2.
114 Horowitz employed Global Marketing
Research Services, Inc. to conduct the telephone
surveys. Horowitz WDT at 8.
B. Horowitz Survey
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those live college and professional team
sports fitting the category definition
operative in CRB royalty distribution
proceedings. Horowitz WDT at 5–6. The
2010–13 Bortz Survey eliminated from
the valuation questions references made
in prior Bortz surveys to attraction and
retention of subscribers. See Bortz
Survey at 15.115 Horowitz opined that
omitting references to subscriber
acquisition and retention ‘‘distracted
survey respondents from the purpose of
allocating a fixed budget . . . by leaving
out all references to subscriber value
. . . the ‘primary consideration’ for
allocating value.’’ Horowitz WRT at 2.
According to Horowitz, between 79%
and 85% of CSO survey respondents
ranked programming popular with and
important to current and potential
subscribers as the most important factor
in their carriage decisions. By contrast,
only between 4% and 35% ranked
importance to the cable system as the
primary factor influencing carriage
decisions.116
The Horowitz Survey results,
weighted by Dr. Martin Frankel,
indicate relative market values of the
programming categories at issue 117 in
this proceeding as:
TABLE 12—HOROWITZ SURVEY RESULTS
2010
(%)
Category
CCG .................................................................................................................
CTV ..................................................................................................................
Devotional ........................................................................................................
PS ....................................................................................................................
PTV ..................................................................................................................
Sports ...............................................................................................................
‘‘Other Sports’’ .................................................................................................
2011
(%)
0.01
12.38
3.78
37.43
7.69
31.94
6.77
2012
(%)
1.00
12.85
5.92
28.99
13.31
27.13
10.80
0.87
15.72
5.74
28.11
15.05
25.50
9.02
2013
(%)
0.35
9.54
3.48
28.65
15.39
35.28
7.40
categories of programming on
retransmitted Canadian signals as well
as retransmitted superstation and
independent station signals.120 The
Ringold Survey was conducted by
telephone and used a constant sum
construct.
The CCG criticized both the Bortz and
the Horowitz studies and presented its
own limited survey (Ringold Survey).
See Report of Gary T. Ford and Debra
J. Ringold, Trial Ex. 4010 (Ringold
WDT).118 The Ringold Survey attempted
to establish a value for eligible programs
distantly retransmitted by cable systems
in the United States, segregating
Canadian-produced programs
comprising the CCG and other programs
included in the Devotional, Program
Suppliers, and Sports categories.
Valuation of CCG programming is
complicated by the legal prohibition on
retransmission of Canadian
programming outside a geographic zone
lying along the U.S. northern border. 17
U.S.C. 111(c)(4). The CCG argued that
the relative value of CCG programming
inside its retransmission zone is
necessarily diluted when measuring the
relative value of other claimant groups’
programming over the entirety of the
United States. See Written Rebuttal
Testimony of Lisa George, Trial Ex.
4007, p. 8 (George WRT). In addition,
the CCG argued that its category is an
‘‘unnatural’’ category of programming,
because the Canadian signals include
programming compensable in other
categories, viz., the JSC, Program
Suppliers, and Devotional Programming
categories.
The CCG commissioned a ‘‘double
blind’’ 119 survey of cable systems
sampled from the Form 3 systems that
retransmit Canadian signals distantly.
To further guard against response bias,
Professors Ringold and Ford constructed
the survey to include questions
regarding the relative values of various
115 In the 2004–05 Bortz Survey, the warmup
questions focused respondents on subscriber
acquisition and retention by asking which
categories were most ‘‘popular’’ with subscribers.
See Bortz Survey at 39. Responding to a Judges’
observation that acquisition and retention of
subscribers might be too narrow a notion of value,
Bortz replaced the popularity question with one
intended to establish distant signals’ importance to
the respondent’s system.
116 See Horowitz WDT at 17. Horowitz surveyed
a sample of 300 systems, inquiring about factors
influencing carriage decisions. The response
categories were (1) programming popular and
important to current and potential subscribers, (2)
programming important to the cable system, and (3)
other. Respondents could choose multiple factors.
117 The numbers for Program Suppliers (PS) are
derived by adding responses for syndicated series
and movies. ‘‘Other Sports’’ are left as a separately
valued type of programming because the Horowitz
Survey did not and could not specify whether nonJSC sports programming should be categorized as
Program Suppliers or CTV.
118 The report of results of the Canadian Survey
included Emeritus Professor Gary Ford as an
author, but only Professor Ringold signed the
report; consequently, for simplicity, the Judges refer
to the report as Ringold WDT. Professors Ford and
Ringold had conducted similar surveys since 1996
and Professor Ringold presented a longitudinal
study showing the results from 1996 through 2013.
See Trial Ex. 4011. A longitudinal study analyzes
data collected using the same methodology to ask
the same population of respondents the same
question(s) over time. Such studies can prove useful
in evaluating the stability and/or robustness of an
estimate. Ringold WDT at 4–5.
119 Ford and Ringold referred to their survey,
conducted by Target Research Group, as ‘‘double
blind’’ in that neither the interviewers nor the
respondents were aware of the sponsor of the
survey. Written Direct Testimony of Gary Ford and
Debra Ringold, Trial Ex. 4010 at 7 (Ford/Ringold
WDT).
120 Drs. Ringold and Ford used responses relating
to superstations and independent stations both to
disguise the survey sponsor and as comparators to
substantiate their results.
See Horowitz WDT at 16; Written Direct
Testimony of Martin R. Frankel, Trial
Ex. 6010 at 7 (Frankel WDT).
Mr. Horowitz’s decisions to (1) rely on
acquisition and retention of subscribers
and (2) create a separate ‘‘Other Sports’’
category came under criticism, as did
his methodological choice to provide
examples of shows that might fall
within the categories.
C. Ringold Survey
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The Ringold Survey differed from
both the Bortz and Horowitz surveys in
two significant aspects. Unlike in the
Bortz Survey, interviewers in the
Ringold Survey asked respondents to
assign relative values to program
categories that included programming
on Canadian signals. Unlike both the
Bortz Survey and the Horowitz Survey,
Ringold Survey interviewers asked each
respondent to rank programming on
only one retransmitted signal at a time.
The Ringold Survey measured the
average relative value of CCG
programming on retransmitted Canadian
signals as:
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TABLE 13—RINGOLD SURVEY RESULTS: RELATIVE VALUE OF CCG PROGRAMMING ON CANADIAN SIGNALS
2010
(%)
Category
CCG .................................................................................................................
Program Suppliers (U.S.) .................................................................................
Sports (JSC) ....................................................................................................
‘‘Other’’ .............................................................................................................
See Ringold WDT at 15, Table 1.121 In
other words, the Ringold Survey results
indicated that Canadian-produced
programming accounted for
approximately 61%, 64%, 61%, and
56%, respectively, of the value of all
programming shown on surveyed
systems’ Canadian signals for the years
2010–2013. Ringold WDT, at 5, 11; 15,
Table 1. Ringold found that live
professional and college sports were
generally valued higher on independent
and superstations than on Canadian
signals. Ringold WDT at 12; 16, Table 2;
17, Table 3; see Fig. 4. Ringold also
found that movies and syndicated series
were always valued higher on
independent and superstations than on
Canadian signals. Ringold WDT at 12,
16, Table 2; 17, Table 3; see Fig. 5.
Scaling the relative value of Canadian
signals within the Canadian zone, CCG
concluded Canadian signals should
command the following portions of each
annual fund.
TABLE 14—RINGOLD SURVEY RESULTS: RELATIVE VALUE OF CCG
PROGRAMMING OVERALL
Year
2010
2011
2012
2013
Base rate fund
(%)
......................................
......................................
......................................
......................................
5.59
5.36
5.95
6.18
Written Direct Statement of Canadian
Claimants Group at 1.122 CCG does not
claim any portion of the overall royalty
funds for programming on Canadian
signals that is compensable in the
Program Suppliers or Joint Sports
Claimants groups. Id. At the hearing,
CCG did not controvert testimony by
SDC’s witness, Mr. Sanders that some
Canadian programming is or should be
compensable as Devotional
121 The values for the CCG category are the
aggregate of relative values CSOs assigned to
Canadian-produced news, public affairs, religious,
and documentary programs (both network and
station-produced); Canadian-produced sports
programming; Canadian-produced series, movies,
arts and variety shows, and specials; and Canadianproduced children’s programming.
122 The table recreated here omits the column
headed ‘‘3.75% Fund.’’ The Judges consider the
3.75% Fund separately.
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61.45
11.40
26.67
0.48
Programming. See 3/6/18 Tr. at 2410
(Sanders).
D. Criticisms of the Survey Instruments
1. Survey Construct
The surveys the parties presented in
this proceeding had some construct
similarities. Each of the surveys was
directed to CSO executives who selfidentified as the person responsible for
carriage decisions for the cable systems
about which the surveyor inquired. All
of the surveys were conducted by
telephone 123 by experienced survey
entities. Each survey inquired of a
sample of potential respondents drawn
from the universe of Form 3 cable
systems.
a. Sampling
Professor Martin Frankel, who was
retained by Program Suppliers,
criticized Bortz for including in its
sampling Form 3 cable systems that did
not carry a distant signal and not
correcting for the overinclusion. See
Amended Rebuttal Testimony of Martin
Frankel, Trial Ex. 6011, at 3 (Frankel
AWRT). In fact, Bortz sampled from all
Form 3 systems but dropped, i.e., did
not interview, systems in the sample
with zero distant signals. See 2/15/18
Tr. at 247 (Trautman). In live testimony,
Professor Frankel submitted that Bortz,
while not ‘‘wrong,’’ conducted its
survey on a ‘‘suboptimal’’ sample frame.
See 3/6/18 Tr. at 2267, 2288 (Frankel).
Professor Frankel also criticized the
Bortz Survey for disadvantaging cable
systems with only PTV, CCG, or PTV
and CCG distant signals by excluding
them and ‘‘affording them no value
when producing . . . weighted results.’’
Frankel AWRT at 3.
In his amended rebuttal testimony,
Professor Frankel corrected for the
suboptimal sampling and for the
exclusion of PTV and CCG signals in the
Bortz Survey. Even so, Professor Frankel
declined to endorse even the corrected
Bortz results. Id. at 15. Professor Frankel
123 Professor Steckel criticized telephone
questioning, contending that the issues were too
complex for the respondents to weigh and analyze
over the telephone. See Written Direct Testimony
of Joel Steckel, Trial Ex. 6014, at 36–37 (Steckel
WDT). Telephone surveys have been the norm for
allocation proceedings.
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(%)
2012
(%)
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21.11
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0.00
61.47
12.20
24.67
1.67
2013
(%)
56.36
21.82
20.91
0.91
advocated reliance on the Horowitz
Survey, which used his improved
sample frame and included distantly
retransmitted PTV and CCG claimant
programming. Id. at 16.
Professor Frederick Conrad, testifying
on behalf of CCG, criticized both the
Bortz Survey and the Horowitz Survey
on the basis of their sampling.124 See
Written Rebuttal Testimony of Frederick
Conrad, Trial Ex. 4003 passim (Conrad
WRT). Because so few cable systems
retransmit Canadian stations, the small
sample size caused Professor Conrad to
question the validity of the results as
they relate to the CCG. Id. at 4. Further,
Bortz excluded from its survey systems
whose only distantly retransmitted
signal was Canadian, Public Television,
or some combination of those. Bortz
then assigned a value of zero to CCGand PTV-only systems, without
accounting for the regulatory constraints
limiting retransmission of Canadian
signals to a geographic zone in the
northern tier of states. Exclusion of the
CCG and PTV programming from the
Bortz Survey resulted in agreement
among the parties that the Bortz results
would need an unquantified adjustment
to reflect the actual relative value of
CCG and PTV programming.
Professor Conrad recognized that the
Horowitz Survey corrected for this
omission by Bortz. Id. at 6. Inclusion of
the ‘‘missing’’ stations did not, however,
address all of the issues troubling
Professor Conrad. Notably, when
Horowitz asked CSOs whose only
distantly retransmitted signal was
Canadian, for example, the CSO
nevertheless stated the relative value of
the Canadian programming at less than
100%. Id. at 7. According to Professor
Conrad, this purported anomaly
suggests a problem with the construct of
the survey or a problem of
communicating the task to either the
124 Professor Conrad criticized the Bortz and
Horowitz Surveys on four bases: Sample size, i.e.,
the number of participants that actually carry a
distant Canadian signal; assigning a value of zero
to Canadian programming for systems that do not
have the option to carry Canadian signals;
incompatibility of programming categories; and
flaws in either survey design or execution. See
Written Rebuttal Testimony of Frederick Conrad,
Trial Ex. 4003, passim (Conrad WRT).
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interviewers or the respondents.125
Given that Canadian signals include less
than 100% Canadian content, the Judges
reject this particular criticism.
b. Respondents
All three surveys sought to elicit
responses from the individual at each
cable system that had primary
responsibility for signal carriage
decisions. In the Bortz Survey, the
questioners asked several questions at
the outset to establish that they were
speaking with the appropriate
individual. See, e.g., Trautman WDT at
14–15.
Testimony at the hearing was in
conflict regarding carriage decisionmakers. Horowitz Research, Inc.
employed a cable system executive to
screen respondents to assure that they
were the appropriate respondents, viz.,
the respondents responsible for making
carriage decisions at the system level.
See Horowitz WDT at 8. Fact witnesses
disagreed about the level at which
carriage decisions are made. Compare 2/
21/18 Tr. at 930 (Burdick) (carriage
decisions at Schurz Communications
decentralized to local CSOs) with 2/22/
18 Tr. (Singer) at 1082–84 (carriage
decisions made at system level, not at
corporate headquarters), 1144–45
(respondents intimately familiar with
categories and signals they carry). Ms.
Sue Ann Hamilton testified that cable
programming decisions 126 are generally
centralized at the corporate level in an
increasingly consolidated cable
industry. 3/19/18 Tr. at 4295
(Hamilton). She opined that
respondents to the Bortz Survey were
insufficiently ‘‘sophisticated . . . ,
programming-focused and experienced’’
to understand the categories at issue in
this proceeding. Id. at 4311.
c. Constant Sum Methodology
All three surveys were structured as
‘‘constant sum’’ surveys; that is,
respondents were asked to allocate
value among the programming
categories at issue, with the sum of
those values to equal 100%. An increase
in valuation of one category must result
in a decrease in value in one or more
other categories.
Among the many criticisms of the
three surveys,127 Professor Joel Steckel,
125 Professor
Conrad criticized both surveys for
lacking independent pre-testing to detect confusion
or anomalies. 3/5/18 Tr. at 1969–70 (Conrad).
126 Ms. Hamilton also testified that distant signal
programming was an insignificant consideration in
cable systems’ programming decisions. 3/19/18 Tr.
at 4306.
127 Professor Steckel asserted two standards to
which a survey must conform: Reliability, i.e., the
ability to replicate the survey’s results, and validity,
i.e., the conclusion that the survey measures what
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a witness for Program Suppliers,
criticized in general the use of the
constant sum survey structure. See
Written Direct Testimony of Joel
Steckel, Trial Ex. 6014, at 34–35
(Steckel WDT). Professor Steckel
criticized Professor Mathiowetz’s
touting of the suitability of a constant
sum construct in this context. He noted
that she cited prior testimony that relied
on academic literature from the 1960s
and 1970s. See Written Rebuttal
Testimony of Joel Steckel, Trial Ex.
6015, at 21 (Steckel WRT). Countering
the perceived endorsement of constant
sum survey methodology by the
CARP,128 Professor Steckel cited recent
academic studies that conclude that a
measurement based on paired
comparisons, i.e., comparisons across
only two categories, out-predict
constant sum surveys by 22 percentage
points. Id. at 36 (citations omitted).
On rebuttal, Professor Steckel
reviewed the changes in the Bortz
Survey between the 2004–05 proceeding
and the present proceedings. While he
conceded some improvement, he
concluded that the changes were
insufficient to bestow construct validity
on the Bortz Survey. See Steckel WRT
at 26. Viewing the Horowitz Survey as
an augmented Bortz Survey, Professor
Steckel also noted some improvements,
but concluded that those improvements
in form were insufficient to reorient the
Horowitz Survey to the question of
interest in this proceeding, viz., relative
value of program categories.129
Professor Mathiowetz endorsed the
constant sum survey method used by
Bortz in the present proceeding.
Professor Mathiowetz concluded,
however, that the Horowitz Survey did
not employ a valid constant sum
construct because of the differences
Horowitz introduced as alleged
it purports to measure. See 3/13/18 Tr. at 3269
(Steckel). He opined that neither the Bortz Survey
nor Horowitz Survey measures what it purports to
measure nor what the statute requires the Judges to
determine. He concluded that both, therefore, lack
construct validity. See Steckel WRT at 21.
128 Professor Mathiowetz did cite multiple royalty
allocation decisions that relied on Bortz surveys.
See Written Rebuttal Testimony of Nancy
Mathiowetz, Trial Ex. 1007, at 5–6 (Mathiowetz
WRT). She did not contend those decisions were an
endorsement of the constant sum methodology;
rather she cited those decisions as support for the
conclusion that the Bortz Survey addresses the
relevant question of interest in these proceedings.
Id.
129 Given the task to choose the lesser of the two
evils, Professor Steckel concluded that the Horowitz
Survey was a slightly better instrument because,
inter alia, it included PTV and CCG stations and
programming, it broke out ‘‘other sports’’ categories
from those represented by the JSC, and its
interviewers did a better job of reminding
respondents of program categories, stations at issue.
Steckel WDT at 38.
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improvements to the Bortz Survey. See
Mathiowetz WRT at 16. Professor
Mathiowetz opined that the Horowitz
changes in fact rendered the Horowitz
Survey both unreliable and invalid. Id.
at 26. For example, Professor
Mathiowetz opined that Horowitz’s
inclusion of program examples and
‘‘such as’’ descriptions rendered the
questions misleading. Id. Similarly,
incorrect information in program
category descriptions resulted in invalid
valuations for the various program
categories. Id. at 17–18. Professor
Mathiowetz criticized Horowitz’s
creation of an ‘‘Other Sports’’ category
when no such category is a part of this
proceeding. She faulted Horowitz’s
failure clearly to identify
noncompensable programming on
WGNA. Id. at 19.
In the Bortz Survey, interviewers
asked respondents about a maximum of
eight distant signals even if their
systems carried more. See Bortz Survey
at 31. Professor Mathiowetz criticized
the Horowitz decision to ask a single
respondent to answer on behalf of all
distantly retransmitted signals for the
surveyed system, rather than limiting
those to a manageable number.
Respondents to the Horowitz Survey
were asked to evaluate from one to
‘‘over fifty’’ discrete signals. See
Mathiowetz WRT ¶ 48. According to
Professor Mathiowetz, this inclusion of
so many signals for valuation rendered
the survey burdensome and invalid, as
respondents would not or could not
make fine distinctions between the
distantly retransmitted program lineups
at multiple systems. Id.
Dr. Jeffery Stec, an economic expert
called by Program Suppliers, performed
reliability analyses of the Bortz Survey
results by comparing responses of CSOs
for consistency over time. He concluded
that the Bortz Survey responses were
not reliable as they were not consistent
over time, notwithstanding Mr.
Trautman’s assertions that the Bortz
results were consistent over time. See
Amended Written Rebuttal Testimony
of Jeffery Stec, Trial Ex. 6016, at 30–34
(Stec AWRT).
2. Survey Content
a. Programming Categories
Surveyors inquired about
programming on retransmitted distant
signals using the category designations
adopted in the present proceeding.
CSOs, however, do not acquire
categories of programs for
retransmission; by law they must
acquire entire signals which often
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bundle together multiple categories of
programming.130
Professor Steckel criticized the Bortz
and Horowitz surveys for requiring
CSOs, unaided and in the course of a
brief telephone survey, to disaggregate
signals and reconfigure the
programming from each into
compensable categories. See Steckel
WDT at 29–30. Professor Steckel opined
that, because of the perceived
complexity of the survey construct,
respondents were compelled to
satisfice 131 with shortcuts and
heuristics to create a defensible answer
to the overly complicated questions. Id.
at 31–32; 3/13/18 Tr. at 3298 (Steckel).
More than one witness downplayed
Professor Steckel’s complexity criticism,
asserting that the survey respondents
are experienced professionals
thoroughly familiar with the
programming categories copyright
owners utilize in CRB distribution
proceedings. See, e.g., 3/13/18 Tr. at
3176 (Hartman) (CSOs negotiate for
linear channels, but channels fall into
categories. ‘‘It’s our day-to-day job to
. . . know those, that type of
programming.’’); 2/22/18 Tr. at 1144–45
(Singer). Participants proffering survey
results as a measure of relative value
also asserted that cable system
executives could accurately allocate
program category values by reference to
the ‘‘dominant impression’’ of each
signal’s content or the ‘‘signature
programming’’ of a given signal. See 2/
15/18 Tr. at 281, 334 (Trautman); 2/22/
18 Tr. at 1001 (Singer).
Ms. Sue Ann Hamilton testified that
the programming categories adopted in
royalty distribution proceedings are
unique and ‘‘quite different from the
industry understanding of what
programming typically falls in a
particular programing genre.’’ Id. at 10;
see 3/19/18 Tr. at 4309, 4312
(Hamilton); Hamilton WRT at 17–18.
For example, she testified that ‘‘most
cable operators’’ would not recognize
that pre- and post-game interviews and
highlight compilation telecasts would
fall into the Program Suppliers category,
or that locally produced high school
team sports would fall into the
Commercial Television category. Id. at
11. Other industry witnesses disagreed.
See 2/22/18 Tr. at 1046–47 (Singer)
(categories ‘‘straightforward’’). Ms.
Hamilton further opined that cable
operators were not likely to differentiate
between network and non-network
130 PTV and, to a lesser extent, CCG signals are
exceptions to this bundling phenomenon.
131 Satisfice means ‘‘to choose or adopt the first
satisfactory option that one comes across.’’ See
www.dictionary.com, last visited 07/19/2018.
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sports telecasts and that migration of
live team sports programming to
regional cable networks further
complicates the equation. See Hamilton
WRT at 17–18; 3/19/18 Tr. at 4315
(Hamilton).
Dr. Stec gave weight to Ms.
Hamilton’s testimony. See Stec AWRT
at 23–25. According to Dr. Stec, the
Horowitz Survey results, gained after
the surveyors provided category
descriptions and program examples,
demonstrate the fallacies of the Bortz
Survey and its reliance on CSO
executives’ familiarity with the program
categories. Id. at 27. The Horowitz
category descriptions and examples
were also roundly criticized,
however.132 Nothing in Dr. Stec’s
analysis supports his contention that
there is a causal relationship between
changes in an interviewer’s category or
program descriptions in the two major
surveys, from which Dr. Stec concludes
that the Horowitz results are more valid
than the Bortz results.
A related criticism from Professor
Conrad was that the categories about
which respondents were questioned
were not comparable. Id. at 10–11. In
other words, all programming categories
other than CCG and PTV are
characterized by homogeneity in types
of program content. The CCG and PTV
categories, on the other hand, are based
on program origin and include programs
that span the categories making them, in
this context, ‘‘unnatural categories.’’ See
3/5/18 Tr. at 1965 (Conrad). Even
though cable systems might retransmit
PTV signals, all of which are
compensable entirely from the PTV
category, PTV stations might broadcast
children’s programming, nationally
produced specials or series, or locallyproduced programming. On the other
hand, some of the CCG programs might
be allocable to another category but
some might not.133
b. Augmentation of Categories
Professor Mathiowetz criticized
aspects that distinguish the Horowitz
Survey from the Bortz Survey. Her two
most significant criticisms related to Mr.
Horowitz’s use of program examples
and the creation of an ‘‘Other Sports’’
category.134
132 See
discussion at section § III.D.2.b.
example, Mr. Trautman acknowledged
that the Bortz Survey did not differentiate by
category programming transmitted on Canadian
signals even though some of the programs should
be compensated not in the CCG group, but in other
categories. 2/20/18 Tr. at 629 (Trautman).
134 Professor Mathiowetz also opined that the
Horowitz Survey was not a valid constant sum
survey because some of the Horowitz respondents,
the PTV-only and CCG-only systems, could be
asked about only one category of programming, and
133 For
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Professor Mathiowetz asserted that a
questioner’s volunteering of examples
tends to bias survey results. See 2/20/18
Tr. at 699 (Mathiowetz); but see 3/5/18
Tr. at 1967–68 (Conrad) (examples can
hurt or help or have no effect on
responses). According to Professor
Mathiowetz, Respondents assume a
questioner has valid information or
knows something that is important to
the survey outcome. See 2/20/18 Tr. at
699 (Mathiowetz). Thus, even a
knowledgeable respondent might be
influenced by a questioner’s prompting.
As she noted, in a relative valuation, a
shift in one category affects potentially
the value of every other category. Id. at
727.
Furthermore, according to Professor
Mathiowetz, some of the examples used
in the Horowitz Survey were simply
erroneous. 2/20/18 Tr. 700
(Mathiowetz). Use of erroneous
examples illustrated Professor
Mathiowetz’s criticism of Mr.
Horowitz’s creation of an ‘‘Other
Sports’’ category. In an effort to
differentiate live team college and
professional sports, i.e., the programs to
be compensated from JSC’s share of the
royalty funds, interviewers introduced
‘‘other sports programming.’’ For
WGNA-only systems, the category
description ended with ‘‘Examples
include horse racing.’’ Id. at 27.
According to Professor Mathiowetz, in
2013, WGNA carried only a single horse
race. Accord Trautman WRT 20–21.135
For WGNA and PTV systems, the
interviewers prompted, ‘‘Examples
include NASCAR auto races,
professional wrestling, and figure
skating broadcasts.’’ Horowitz WDT
(App. A) at 26. WGNA retransmitted no
programming fitting the description of
the examples. 2/20/18 Tr. at 703
(Mathiowetz). Professor Mathiowetz
also expressed doubt that non-JSC
sports broadcasts accounted for
sufficient distantly retransmitted airtime
to warrant a separate category, even for
survey inquiry purposes. Id. at 702. As
she noted in another context, in a
constant sum survey, variation in one
thus not requiring a sum of percentages at all. 2/
20/18 Tr. at 511 (Mathiowetz). While correct as to
PTV-only systems, this opinion disregards the fact
that Canadian stations transmit both CCGcompensable programs and, for example,
Devotional programs compensable from the SDC
royalty funds.
135 Mr. Trautman further argued that cable
systems retransmit a ‘‘substantial amount’’ of other
sports programming, most of which is noncompensable under the section 111 license.
Trautman WRT at 16. He contended that,
notwithstanding the examples of rare compensable
sports broadcasts, CSO respondents likely confused
the volume of non-compensable sports programs as
belonging in the unfamiliar Other Sports category
inserted by Mr. Horowitz. Id.
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category necessarily effects the relative
value of other categories. See 2/20/18
Tr. at 727 (Mathiowetz).
Professor Conrad agreed with the
criticism of enumerating examples of
‘‘other sports’’ or any program category.
3/5/18 Tr. at 1967(Conrad). According
to Professor Conrad, citing examples
might cut either way. If the example is
typical of the category, then citing it
will have no effect. An atypical example
might help a respondent ‘‘think outside
the box’’ and trigger a broader, more
accurate response. For other
respondents, however, an atypical
example might narrow focus to
incidents closely related to the
particular example and therefore
confine the respondent’s thinking too
narrowly. Id. at 1968. Professor Conrad
cautioned that a ‘‘rare example’’ will
bias downward the counts for more
typical choices. Id.
Mr. Horowitz assigned all ‘‘Other
Sports’’ points to Program Suppliers.
See Horowitz WDT at 3, 5. This
allocation ignores the possibility that a
portion of ‘‘other sports’’ might be
attributable to CTV. Without evidence to
support the assignment of all ‘‘other
sports’’ value to Program Suppliers, the
category becomes even more
problematic.
c. Value Measurement
Dr. Jeffery Stec, criticized the Bortz
Survey on several grounds. See Stec
AWRT at 11–12. His primary criticism
is that the Bortz Survey measures, at
best, only a CSO’s willingness to pay.
Id. at 17. Dr. Stec disputes the assertion
by Mr. Trautman and Bortz that CSO
respondents are familiar with the rates
charged for programming and that their
responses are, therefore, a reflection of
the ‘‘supply side.’’ Id. at 18; see 3/13/18
Tr. at 3432–50 (Stec). Dr. Stec contends
that a CSO’s willingness to pay is also
influenced by its own market factors,
e.g., local market demand or
competition from other CSOs. Id. at 19–
20. According to Dr. Stec, relative
willingness to pay is not the same as
relative market value. Id. at 22.
An underlying assumption in each
survey is that cost is the equivalent of
value. Economists do not measure such
a subjective trait as value. According to
Professor Steckel, value, in an economic
sense, can only be surmised by
reference to external indicators of value.
Steckel WDT at 36–40; but see
Mathiowetz WRT ¶¶ 4, 11–12 (Steckel
incorrect; CARP precedent accepted
Bortz as measure of relative market
value). Professor Steckel opined that
resource allocation does not equate to
value and that marketplace value is
measured by a CSO’s return on
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investment. Steckel WDT at 21. Because
of the cable television market structure,
i.e., program acquisition in a bundle,
CSOs are unable to assess market
returns by program category. Id.
Professor Steckel proposed—as a
possible alternative to surveying CSO
executives’ best guesses about supplyside relative values—a survey of
demand-side program consumers.
Steckel WDT at 40–41 (‘‘customers are
the best judges of what customers want,
value, and will do.’’). Alternatively,
Professor Steckel recommended relying
on viewership to establish relative
values. See Steckel WRT at 4.
Mr. Horowitz also criticized Bortz for
asking a cost question, opining that cost
is not the equivalent of value. Horowitz
WDT at 7. He testified that the Bortz
Survey erroneously mixed the concepts
of value and cost. 3/16/18 Tr. at 4146–
47 (Horowitz). Mr. Horowitz contended
that by asking about expense in a
warmup question, Bortz conflated the
concepts of cost and value.136 Mr.
Horowitz noted that the Bortz Survey
did not define ‘‘relative value’’ and
made no mention of subscriber
attraction and retention.137 Id. Further,
Mr. Horowitz criticized the form of the
budget allocation (constant sum)
question as ambiguous. The question
asked how much the respondent’s
system ‘‘would have spent’’ during the
relevant year. See, e.g., Bortz Survey at
B–5 (Question 4a.). Mr. Horowitz
maintains this sentence structure is
open to interpretation. Id. Treatment of
PTV, CCG, and WGNA.
d. PTV and Canadian Measures
Various parties criticized the
treatment of PTV and CCG claimant
groups in almost every relative value
measure, including the surveys. As
noted, Ms. McLaughlin and Dr.
Blackburn criticized both the survey
and regression methodologies, but
applied their ‘‘changed
circumstances’’ 138 analysis to estimate
the relative value of PTV programming
and PTV’s relative claim to royalties
deposited in the Basic Fund.139
136 Question 3 of the Bortz Survey asked
respondents as a warmup question to rank how
‘‘expensive’’ it would be to acquire the
programming in each category if the system had to
acquire the programming ‘‘in the marketplace.’’ See,
e.g., Bortz Survey at B–4.
137 See supra note 110 and accompanying text.
138 See infra section 200E;VI. McLaughlin and
Blackburn used the Judges’ 2004–05 distribution
determination as their starting point. See Testimony
of Linda McLaughlin & David Blackburn, Trial Ex.
3012 at 9 (McLaughlin/Blackburn WDT).
139 PTV does not participate in the 3.75% Fund
or the Syndex Fund. McLaughlin and Blackburn
were careful, therefore, to relate their valuations to
the Basic Fund. See McLaughlin/Blackburn WDT,
passim.
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Professor Conrad opined that it was a
‘‘strange practice’’ to assign a value of
zero to Canadian programming for
respondents who did not retransmit any
Canadian signals. See 3/5/18 Tr. at
1964–65 (Conrad). He testified that the
better practice would have been to
characterize Canadian programming for
non-CCG signals as ‘‘missing data’’ and
to impute values from data actually
collected. Id. at 1965.
Mr. Trautman acknowledged a slight
participation bias in the Bortz Survey,
but testified that the number of PTVonly and CCG-only cable systems
(approximately 60 systems in the
aggregate) was insignificant and that
including them would have made little
difference in his results. See 2/15/18 Tr.
at 507 (Trautman). The triers of fact for
these royalty allocation proceedings
have long recognized that the results of
the survey methodology employed by
Bortz exhibited a bias against PTV and
Canadian claimants. The Judges in the
2004–04 proceeding acknowledged that
the participation bias affecting results
for both PTV and CCG was troubling,
but that
[i]t would be inappropriate to overstate the
impact of this problem. No one in this
proceeding maintains that it substantially
affects more than a small portion of the total
royalty pool . . . . Nor has it been shown that
the Bortz survey’s remaining non-PTVCanadian estimates were thrown outside the
parameters of their respective confidence
intervals solely because of this problem. That
is, the PTV-Canadian problem does not
substantially affect any of the remaining
categories in some disproportionate way.
2004–05 Distribution Order, 75 FR at
57067. Nonetheless, on rebuttal, Mr.
Trautman adjusted the Bortz Survey
results based on the McLaughlin/
Blackburn testimony that supported a
greater valuation of the PTV and CCG
claimant groups and by referring to the
Horowitz Survey responses to further
adjust the augmentation proposed by
McLaughlin/Blackburn. See Trautman
WRT at 47–48; 2/20/18 Tr. at 523–24
(Trautman).140
Further, in the present proceeding,
the Judges have the advantage of
competing surveys such as the Ringold
Survey commissioned by the CCG that
dealt with PTV and Canadian
programming, and other methodologies
that did not suffer from the participation
bias that discounts the Bortz Survey
results.
140 Mr. Trautman made the further adjustment by
reference to the Horowitz Survey actual responses
from PTV-only cable systems. See 2/2/0/18 Tr. at
525–26 (Trautman).
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e. Impact of WGNA
Participants in the present proceeding
wrangled with valuation of WGN
programming distantly retransmitted on
the WGN ‘‘Superstation,’’ WGN America
(WGNA).141 WGNA did not offer for
retransmission, a program lineup
identical to the one broadcast locally on
WGN. Only those programs carried
simultaneously on WGN and WGNA are
compensable under the section 111
license. WGNA substituted syndicated
or devotional programming for elements
of the WGN signal. In the 2004–05
proceeding, the Judges criticized the
Bortz Survey for failing to measure and
value accurately the compensable
programs retransmitted on WGNA. In
fact, Bortz acknowledged this failure to
differentiate compensable from
noncompensable programs on WGNA
and conceded that the survey results for
Program Suppliers (the category most
frequently retransmitted on WGNA) and
Devotional Programming should be
considered the ceiling for those
categories. See 75 FR at 57067. In the
2004–05 determination, the Judges cited
repeatedly the lack of record evidence
regarding the quantitative adjustment
for over-valuing noncompensable
programming retransmitted on WGNA.
See, e.g., id.
In the present proceeding, Bortz
employed a separate questionnaire form
to survey cable systems that
retransmitted only the WGNA signal.
Bortz created a WGNA programming list
that identified compensable
programming and provided the list to
survey respondents before continuing
with the questions. See Bortz Survey at
30. Bortz continued to use its standard
questionnaire for cable systems that
carried WGNA along with other distant
signals. See Bortz Survey at B–2 (‘‘This
Appendix provides examples of the
survey instruments used to interview
respondents at systems that carried
distant signals in addition to or other
than WGN during the relevant survey
year.’’) (emphasis added).
The Horowitz Survey’s questions
relating to WGNA directed respondents
not to assign any value to
noncompensable programming,
describing noncompensable programs as
‘‘substituted for WGN’s blacked out
programming.’’ Mr. Trautman opined
that the ‘‘blacked out’’ instruction in the
Horowitz Survey was meaningless
because respondents would ‘‘have no
reason to be aware of which
[programming is substituted].’’ See
2/20/18 Tr. at 535 (Horowitz).
141 According to the Bortz Survey, approximately
three-fourths of cable systems retransmitting distant
signals retransmitted WGNA. Bortz Survey at 25.
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WGNA was the most widelyretransmitted station in the U.S. during
the period at issue in this proceeding.142
In the 2010–2013 timeframe WGNA was
retransmitted by approximately threefourths of the cable systems
retransmitting distant signals and
reached over 41 million distant
subscribers. See Wecker Report, ¶ 23;
Bortz Survey at 25. Bortz attempted to
improve on the measure of WGNA
retransmissions criticized in the 2004–
05 proceeding. Horowitz also addressed
the issue from the 2004–05 Bortz
survey, but with less specificity than
Bortz achieved in its 2010–13 survey for
WGNA-only cable systems.
E. Conclusions Regarding Surveys
Surveys of cable system programming
executives provide insight into the
value those executives assign to the
categories of programs eligible to receive
a portion of the retransmission royalties
cable systems deposit with the
Copyright Office. No participant in any
television royalty proceeding has
developed a method to measure the
actual market value of a content
creator’s product as bundled into a
broadcast signal. Indeed, the value of a
content creator’s product will vary
depending on the nature of the bundle
and the buyer of that bundle; every
creator and every viewer is likely to
place a different value on every product.
As buyers of the broadcast signals, CSO
executives’ valuations reflect their
conclusions regarding the extent to
which the category of programming
contributes to the return on that
investment; i.e., helps the cable system
attract and retain subscribers.143
Surveys of CSO executives admittedly
measure only the demand side of a
value calculation. Several witnesses in
the present proceeding criticized the
focus only on a demand-side valuation.
See, e.g., 3/13/18 Tr. at 3433 (Stec) As
noted in the discussion of relative value
in allocation proceedings, the Judges
accept that there are valid reasons for
focusing on the demand side in this
proceeding. See 1998–99 Librarian
Order, 69 FR at 3615 (in relevant
hypothetical marketplace, supply of
142 For purposes of the royalty years at issue in
this proceeding, WGNA as a superstation cast a long
shadow on valuation methodologies. Following the
period at issue in the present proceeding, WGNA
began the process of converting to a cable network,
which would, in time, remove it from consideration
in royalty allocation proceedings.
143 Subscribers are a major source of revenue for
cable systems; consequently, CSOs focus on
retention of subscribers. In some instances, a CSO
might relicense a signal with less viewed, niche
programming to avoid losing a subscriber to a
competing system. See 3/19/18 Tr. at 4297–99
(Hamilton).
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broadcast programming is fixed and
does not determine value). Indeed, in
the present proceeding, both the
regression and viewership
methodologies also attempt to measure
value from a demand-side perspective:
Regressions by measuring various
demand variables, such as subscribers,
and the viewership study by measuring
consumption of programming by
viewers. In the current regulated market
structure, CSOs’ purchase of broadcast
signals as bundles reflects a derived
demand, one step removed from the
supply and demand measured at the
station acquisition level. CSOs deposit
royalties based on distant signal
equivalents (or a minimum fee) that is
divorced from the individual program
content copyright owner. In this
context, the buyers’ demand, as
measured primarily by revealed
preferences, is the only equitable
measure of compensation to copyright
owners.
Bortz, Horowitz, and Ringold used a
constant sum construct, asking
respondents to value program categories
by percentages and requiring that their
allocations totaled 100%. The Bortz
Survey muddled the concepts of cost
and value by means of its warm-up
question that asked survey respondents
to rank program categories by how
expensive it would have been for the
CSO to acquire them. This may have
injected some confusion into the
respondent’s estimation of relative
value. The question of interest in this
proceeding is not cost; rather, it is
relative value. It is unclear how, if at all,
the injection of a cost question furthers
that inquiry.
Further, as in past surveys Bortz did
not survey cable systems that carried
only PTV and/or CCG signals; those
systems thus had no opportunity to
allocate any of their hypothetical
budgets to PTV or CCG programming.
See id. The Horowitz Survey included
PTV- and CCG-only systems, but threw
a curve ball by including an ‘‘Other
Sports’’ category when there may have
been little to no ‘‘other sports’’ content,
and assigning the entire value of that
category to Program Suppliers. Horowitz
also may have introduced bias by
providing program examples for some of
the program categories. The examples,
at best, would have had no effect on the
results; but at worst, could have skewed
results unnecessarily.
For all of the reasons highlighted by
critics of the survey valuation method,
the Judges agree that surveys are not a
perfect measure. Nonetheless, survey
results have been cited in prior royalty
distribution proceedings as a generally
acceptable starting point to measure
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relative program category value.
Previous allocation determinations have
relied heavily and almost exclusively on
Bortz surveys. That reliance serves as
precedent for the current Judges.144
Adoption of a methodological precedent
does not, however, preclude the Judges’
consideration of current evidence.145 In
the present proceeding, the Judges have
three CSO surveys to consider. The
methodological precedent thus gives
rise to additional evidence to guide the
Judges’ treatment of the survey
methodology. Notwithstanding the
differences in approach, the results
derived from the Bortz Survey and the
Horowitz Survey are compatible.
Further, the relative valuations of CSO
executives do not vary wildly from the
valuations derived from participants’
regression analyses.
The Judges conclude that the
allocation measures resulting from the
Horowitz Survey, with adjustments, are
the survey results that most closely
reflect the relative value of the agreed
categories of programming in the
hypothetical, unregulated market.
Regardless of proffered evidence to the
contrary, the Judges find that the
surveyed cable system executives were
sufficiently familiar with the
compensable content on the signals
their respective systems retransmit.146
The doubly regulated nature of
compensable Canadian programming
complicates assignment of a value to
that category. The clarity of the Ringold
Survey, with its comparisons to
superstations and independent stations,
establishes the relative value of
Canadian and non-Canadian
programming on Canadian signals to
cable systems retransmitting within the
Canadian zone of the U.S. The Ringold
Survey takes the relative values of
Canadian programming on Canadian
signals to cable operators that retransmit
them within the Canadian zone. The
CCG did not provide any means of
converting those results into a royalty
share for the CCG category (or any other
program category). The Ringold survey
is thus of minimal assistance to the
Judges.
Horowitz did not exclude from its
sample systems that distantly carried
only PTV and/or Canadian signals. The
Judges conclude that Horowitz’s use of
examples to ‘‘aid’’ respondents, while
flawed, was not likely to skew
significantly results in any of the
established categories. Horowitz.
Horowitz’s inclusion of Other Sports
created a value where none, or next to
none, existed and allocated all Other
Sports value to Program Suppliers.
For all the reasons described above,
particularly the acknowledged
systematic bias against PTV and CCG
programming, the Judges accord
relatively less weight to the
‘‘Augmented’’ Bortz Survey. On balance,
the Judges find the Horowitz Survey
results to be more reflective of CSOs
actual valuations of the program
categories defined by agreement and
adopted in this proceeding. However,
the Judges cannot accept allocation of
100% of the Other Sports relative value
to Program Suppliers. For that reason,
the Judges conclude that the most
appropriate treatment of the Other
Sports ‘‘points’’ is to reallocate them in
proportion to the relative values
established outside the Other Sports
category. The Judges’ calculations are
illustrated in Table 15.147
TABLE 15—HOROWITZ SURVEY RESULTS AFTER REALLOCATING ‘‘OTHER SPORTS’’ TO REMAINING CATEGORIES
2010
(%)
CTV ..................................................................................................................
Program Suppliers ...........................................................................................
JSC ..................................................................................................................
SDC .................................................................................................................
PTV ..................................................................................................................
CCG .................................................................................................................
2011
(%)
13.28
40.15
34.26
4.05
8.25
0.01
2012
(%)
14.41
32.50
30.41
6.64
14.92
1.12
17.28
30.90
28.03
6.31
16.54
0.96
2013
(%)
10.30
30.94
38.10
3.76
16.62
0.38
With regard to the ultimate question
of interest in the present proceeding, the
Judges conclude that survey results offer
one acceptable measure of relative
value, particularly for Sports, Program
Suppliers, Commercial TV, and
Devotional programming. With regard to
PTV and Canadian programming,
adjustments resulting from the
McLaughlin/Blackburn evidence and
the Ringold Survey assure a reasonable
relative value of PTV and Canadian
signals, respectively. Considering all of
the evidence presented in this
proceeding, the Judges conclude that the
constant sum survey methodology, with
adjustments, provides relevant
information relating to the relative value
for each of the six categories remaining
at issue. Considering the more
persuasive regression analyses,
however, the Judges afford less
evidentiary power to the values derived
from these adjusted survey results. The
Judges conclude that Dr. Crawford’s first
(duplicate minutes) regression analysis
is a stronger base on which to make the
category allocation determination.
144 In the 1998–99 CARP determination, the Panel
concluded that the Bortz Survey was the most
‘‘robust’’ and ‘‘powerfully and reliably predictive’’
model for determining relative value . . .’’ for all
categories except PTV, Canadian Programming, and
Music Claimants. Report of the Copyright
Arbitration Royalty Panel to the Librarian of
Congress, Docket No. 2001–8 CARP CD 98–99, at 31
(Oct. 21, 2003) (1998–99 CARP Report); see also
1998–99 Librarian Order, 69 FR at 3609. For PTV,
the Panel acknowledged the inherent bias against
PTV in the Bortz Survey, but found the changed
circumstances and fee-generation evidence
proffered by PTV to be unpersuasive and declined
to increase the PTV allocation percentage from the
1990–92 determination. Id. at 3616.
145 For Canadian Claimants, the CARP had no
Bortz results so it used a fee-generation
methodology. Id. at 3618. In the 2000–03
determination involving only the Canadian
Claimants, the Judges distinguished the
precedential mandate of a fee-generation
methodology and applicable changed circumstances
evidence. See 2000–03 Distribution Order, 75 FR at
26807.
146 Further, the categories endorsed by the Judges
in the present proceeding have not changed for
decades, giving CSOs time to acquaint themselves
fully with the programming comprising each agreed
category, whether or not they routinely agree with
the programming characterizations at issue in these
proceedings. The Judges do not gainsay that there
have been changes in CSO personnel over the years,
but it is nonetheless not unreasonable to think that
even with changes in personnel, the CSOs have
maintained an institutional memory of the
requirements of these proceedings.
147 For example, for 2010, eliminating the relative
value of Other Sports from the 100% constant sum
leaves an allocation of 93.23% of the total assessed
value. Recasting that 93.23% as the whole, the
3.78% relative value assigned to Devotional
programming in 2010 would translate to 3.52%
(3.78% of 3.78 × 93.23 = 100x; x = 3.52).
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IV. Viewership Measurement
Program Suppliers, unique among all
participants in this proceeding,
proposed an allocation methodology
based on the relative amount of
aggregate viewing of the programs in
each of the agreed program categories.
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They presented this methodology
through the report and testimony of
economist Dr. Jeffrey Gray.148
A. Viewership as a Measure of Value
Dr. Gray posited a hypothetical
market structure divided into a primary
market and a secondary market. In the
primary market broadcasters would
purchase from copyright owners the
right to broadcast programs in their
local market (as is currently the case)
and would at the same time obtain the
right to retransmit the programs into
distant markets. In the secondary market
the broadcasters would sell their entire
signal to cable operators, most likely as
part of retransmission consent
negotiations. In the hypothetical
primary market the broadcaster would
pay the copyright owner both a royalty
to broadcast the program in the local
market and a surcharge for the right to
retransmit each program into distant
markets. The broadcaster would recoup
that surcharge as part of its transaction
with the cable operator in the secondary
market. See 3/14/18 Tr. at 3682–84,
3779–81 (Gray); Hamilton WDT at 14.
Dr. Gray stated that ‘‘[i]t is axiomatic
that consumers subscribe to a CSO to
watch the programming made available
via their subscriptions’’ and that ‘‘[t]he
more programming a subscriber
watches, the happier the subscriber is,
and the more likely she will continue to
subscribe, all else equal.’’ Gray CAWDT
¶ 13. He concluded, therefore, that ‘‘a
measure of the happiness, or ‘utility,’ an
individual subscriber gets from a
specific program is the number of
minutes that subscriber spent viewing
the program offered to him or her by the
CSO’’ and ‘‘[a] measure of the utility all
subscribers get, in total, from a specific
program is the total level of subscriber
viewing of the program.’’ Id.
148 Dr. Gray also performed an analysis of the
relative ‘‘volume’’ (i.e., total number of minutes) of
the different categories of programming, which he
described as ‘‘useful’’ but not ‘‘sufficient’’
information concerning the relative value of
programming. See Corrected Amended Direct
Testimony of Jeffrey S. Gray, Ph.D., Trial Ex. 6036,
¶¶ 17–18, 32–34 (Gray CAWDT); 3/14/18 Tr. at
3696–97 (Gray); 3/15/18 Tr. at 3834–36 (Gray). As
Dr. Gray himself conceded that his volume analysis
was an insufficient basis for determining relative
value of programming, the Judges will not rely on
it. See also Written Rebuttal Testimony of Dr. Mark
A. Israel, Trial Ex. 1087, ¶ 38 (Israel WRT)
(‘‘measures of volume do not translate directly into
value’’). The Judges need not consider, therefore,
criticisms concerning the accuracy of Dr. Gray’s
volume analysis. See Analysis of Written Direct
Testimony of Jeffrey S. Gray, Ph.D., Trial Ex. 1089,
at ¶¶ 11–17 (Wecker Report); 2/22/18 Tr. at 1169
(Harvey); Written Rebuttal Testimony of
Christopher J. Bennett, Trial Ex. 2007, ¶¶ 36–43
(Bennett WRT); 3/1/18 Tr. at 1861–64 (Bennett).
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Applying this economic principle to
the hypothetical market, Dr. Gray
opined that expected viewing in the
distant market would determine the
value of the programming in the distant
market. See 3/14/18 Tr. at 3684–85,
3873–74. Program Suppliers assert that
actual and projected subscriber viewing
information would be critical to
negotiations between cable operators
and broadcasters for the right to
retransmit broadcast signals in an
unregulated market. See PS PFF ¶ 17;
Hamilton WDT at 14; 3/19/18 Tr. at
4317–19 (Hamilton). Consequently,
Program Suppliers argue that subscriber
viewing information is the most
reasonable metric for determining
relative market value. See PS PFF ¶ 18;
Hamilton WDT at 14–15; 3/19/18 Tr. at
4317–19 (Hamilton); 3/14/18 Tr. at
3822–23, 3873–74 (Gray).
B. Implementation of the Viewing Study
In the broadest sense, Dr. Gray’s
methodology for determining the
relative value of programming in the
various program categories was to assign
all compensable distantly retransmitted
programs on a sample of stations to
appropriate program categories,
aggregate the quarter hours of expected
viewing for every program in each
category, and divide the total number of
expected quarter hours of viewing for
each program category by the sum of
expected quarter hours of viewing for all
categories. See Gray CAWDT ¶ 22; 3/14/
18 Tr. at 3684–85, 3689–90 (Gray).
To accomplish this, Program
Suppliers obtained, at Dr. Gray’s
direction, data on cable systems and
retransmitted television signals from
Cable Data Corporation (CDC),149
television programming data from
Gracenote,150 program logs for Canadian
television stations from the Canadian
Radio-television and
Telecommunications Commission
149 CDC data is a compilation of information
provided by cable systems to the Copyright Office
on their semi-annual statements of account (SOAs).
It includes information about the number of distant
signals that each cable system carries, the number
of subscribers receiving each distant signal, and the
amount of royalties paid. See Gray CAWDT ¶ 28;
Martin WDT at 5. From this information, CDC
provided, inter alia, an analysis of which counties
fall within a television station’s local service area.
See Martin WDT at 5–6.
150 Gracenote (formerly Tribune) provides a
compilation of information about each television
program airing throughout each day, including the
station on which the program aired; whether the
program was local, network or syndicated; the
program and episode titles; and the type of
program. See Gray CAWDT ¶ 27; 3/14/18 Tr. at
3686–87 (Gray).
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(CRTC),151 and viewing data from
Nielsen’s National People Meter (NPM)
database.152 See 3/14/18 Tr. at 3685–88
(Gray). Due to cost considerations, Dr.
Gray created a sample of approximately
150 distantly retransmitted stations for
each year and instructed Program
Suppliers to obtain program and
viewership data only for those stations
included in his sample. See Gray
CAWDT at 24 App. B; 3/14/18 Tr. at
3686–89 (Gray).
Dr. Gray did not calculate viewing
shares directly from the Nielsen viewing
data. Instead, he used the Nielsen data
as inputs to a regression algorithm that
permitted him to calculate expected
distant viewing for each program in
each quarter-hour throughout each year
based on a number of independent
variables including what Dr. Gray
described as ‘‘a measure of local
ratings.’’ See Gray CAWDT ¶¶ 36–38; 3/
14/18 Tr. at 3692 (Gray).153 Dr. Gray
stated that he employed regression to
compensate for the high incidence of
non-recorded viewing in the Nielsen
data, as well as instances where viewing
data were missing. Id. at 3690–91.
Regression analysis allowed Dr. Gray to
estimate positive viewing even in
instances where there was zero observed
viewing in the Nielsen data, by
increasing low estimates and decreasing
high estimates. Dr. Gray described this
as ‘‘data smoothing,’’ and opined that
‘‘[i]t’s a desirable outcome in general
when estimating based upon other
estimates, in particular.’’ Id. at 3691. In
addition, regression allowed Dr. Gray to
‘‘fill in the blanks’’ where Nielsen data
was missing. Id.
Based on his regression analysis Dr.
Gray derived the following viewing
shares:
151 The CRTC program logs include station call
signs, program title, actual starting and ending time,
and country of origin for each program broadcast on
Canadian television stations. Dr. Gray used them to
determine the country of origin of programs
broadcast on Canadian stations, since U.S.-origin
programs are excluded from the Canadian Claimant
category. See Gray CAWDT ¶ 29.
152 A ‘‘people meter’’ is a device attached to a
television set that passively detects the channel to
which the television is tuned, and includes a means
for each household member to identify him- or
herself as the person watching the TV. The NPM
database is derived from a national sample of
households equipped with people meters and is
used for measuring national broadcast and cable
networks. See Direct Testimony of Paul B.
Lindstrom, Trial Ex. 6017, at 4 (Lindstrom WDT);
3/14/18 Tr. at 3496–97, 3505–07 (Lindstrom).
153 The other independent variables include the
time of day that the program aired and the program
type. See 3/14/18 Tr. at 3692 (Gray).
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TABLE 16—GRAY VIEWING SHARES
Royalty share
Claimant
2010
(%)
2011
(%)
2012
(%)
2013
(%)
Canadian Claimants ........................................................................................
Commercial Television .....................................................................................
Devotionals ......................................................................................................
Program Suppliers ...........................................................................................
Public Television ..............................................................................................
JSC ..................................................................................................................
1.96
15.83
1.18
50.94
27.96
2.13
3.93
12.06
2.44
49.92
29.09
2.57
3.58
15.48
1.07
36.17
41.64
2.06
5.16
10.61
1.10
45.09
33.29
4.76
Total ..........................................................................................................
100
100
100
100
Gray CAWDT ¶ 38, Table 2.
Program suppliers propose that Dr.
Gray’s viewing shares serve as one end
of a range of reasonable royalty
allocations (the other end being
determined by the Horowitz survey). PS
PFF ¶ 355.
C. Criticism of Dr. Gray’s Viewing Study
Program suppliers’ proposed use of
Dr. Gray’s viewing analysis as a basis for
allocating royalty shares was roundly
criticized by nearly all other
participants through their respective
experts. The criticism ranged from
general disagreement with the
underlying premise that viewership is
an appropriate measure of relative
value, to specific critiques of how Dr.
Gray executed his study.
1. Viewership Not an Appropriate
Measure
Several economists testified that
viewership is not an appropriate
measure of relative value, at least when
apportioning value among different
program types.154 See, e.g., Written
Direct Testimony of Michelle Connolly,
Trial Ex. 1005, ¶ 33, and citations to
designated prior testimony therein
(Connolly WDT); Israel WRT ¶ 42; see
also 3/7/18 Tr. at 2474 (McLaughlin)
(‘‘We can look at viewing, which I don’t
see as a measure of value itself . . . .’’).
For example, Dr. Mark Israel, an
economist testifying for the JSC, opined
that Dr. Gray’s viewing analysis
‘‘provides no reliable basis for
determining the relative valuation’’ of
the agreed categories of programs,
primarily because ‘‘it treats all viewing
minutes as the same and thus does not
account for the fact that minutes of
different types of programming have
different values.’’ Israel WRT ¶ 42. Dr.
154 Dr. Erdem, an economist testifying on behalf
of the SDC, conceded that, in past proceedings, he
had found viewership to be a reasonable basis for
apportioning royalties among claimants within the
same program category. See 3/8/18 Tr. at 2791–93
(Erdem); accord Amended Written Direct
Testimony of John S. Sanders, Trial Ex. 5001, at 22.
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Israel argues that it is not valid to treat
all minutes of viewing equally without
considering the number of minutes of
each type of content that is available. ‘‘If
the same number of minutes of all types
of content were available, then the total
amount of each that viewers choose to
consume could indicate their relative
value. But given the smaller number of
available minutes of Sports
programming, one cannot support such
a conclusion.’’ Id.
Professor Crawford, an expert witness
for CTV, sought to demonstrate the lack
of a one-to-one correlation between
viewing minutes and relative value by
examining the affiliate fees cable
operators pay in an unregulated market
to carry cable channels with different
types of content. His analysis showed
that cable systems pay far more for
sports content than non-sports content
with the same level of viewership. See
Written Rebuttal Testimony of Gregory
S. Crawford, Ph.D., Trial Ex. 2005, ¶ 36
& Fig. 1 (Crawford WRT).
Dr. Israel posited that many viewers
may choose to view a given category of
programming only as a second choice
because their first choice is not
available. See Israel WRT ¶ 42. Stated
differently, a raw viewing measurement
conveys no information about the
intensity of the viewers’ preferences for
particular types of programming. See
Connolly WDT ¶ 29. In its pursuit of
greater subscription revenues, ‘‘the
perceived intensity of subscriber
preferences’’ would be a key
consideration for cable operators. Id.
¶¶ 29–30.
Several economists found Dr. Gray’s
focus on subscribers’ viewing patterns
to be misplaced because it is cable
operators, not subscribers, who pay for
programming to fill their channel
lineups. See, e.g., Israel WRT ¶ 43;
Written Rebuttal Testimony of Matthew
Shum, Trial Ex. 4004, ¶ 7 (Shum WRT).
‘‘Naturally, the value of distant signals
to CSOs derive [sic] in part from the
value that existing and potential
subscribers place on them. . . .
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Nevertheless, as a principle, the relative
market values for distant signal
programming depend on the CSOs’
valuations of the programming, and not
on subscribers’ valuations. Shum WRT
¶ 7. According to CCG expert Professor
Shum, viewing is, at best, ‘‘a measure of
subscribers’ valuations’’ rather than
CSOs’. Id. ¶ 8.
Dr. Gray’s critics assert that
viewership is not a primary
consideration for cable operators. A
cable operator’s goal in selecting distant
signals is to grow subscriber revenue by
attracting new subscribers, retaining
existing subscribers, and increasing
subscription fees. See Connolly WDT
¶¶ 29, 31–32. Cable operators seek to
increase profits by offering bundles of
channels that will appeal to subscribers
with varying tastes, including tastes for
niche programming. See Shum WRT
¶¶ 10–11; Connolly WDT ¶¶ 31–32.
According to JSC expert Professor
Connolly, ‘‘the economics of bundling
suggests that the most profitable
addition to a cable system’s
programming is for content that is
negatively correlated with content
already offered by the cable system[,]’’
thus, ‘‘in the context of the economic
value of individual programming within
a bundle to a CSO, neither simple
viewership data nor volume of
programming is an appropriate metric
for the relative market value of
programming on distant signals.’’
Connolly WDT ¶¶ 32, 31; accord
Crawford CWDT ¶ 7 (‘‘channels that
appeal to niche tastes are more likely to
increase cable operator profitability due
to the likelihood that household tastes
for such programming are negatively
correlated with tastes for other
components of cable bundles’’). As
Professor Shum explained:
[N]iche programming, which may have small
viewership numbers, may actually have
higher incremental value for CSOs relative to
mass appeal programs with larger
viewerships. . . . While this may seem
paradoxical, the reason is that many mass
appeal programs (e.g., gameshows or sitcom
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reruns) are close substitutes for each other,
and hence if many viewers watch a mass
appeal program on a distant signal, that
merely subtracts from, or ‘‘displaces,’’ the
viewership of similar programs on nondistant signals. Thus adding a distant signal
station with mass appeal programming
merely shuffles existing viewers between the
added stations and other stations already
carried by the CSO and does not attract new
viewers to the CSO’s offerings. The rational
CSO would have no value for such a distant
signal. In contrast, the viewership of niche
programs, no matter how small, represent
‘‘new eyeballs’’ for the CSOs, as those
viewers would not find similar programs on
other channels in the CSO’s bundles. These
viewers would be among the ‘‘new
subscribers’’ who may otherwise not initiate
service with the CSO if distant signal
programming were not available.
Shum WRT ¶ 12 (footnotes omitted).
Parties critical of using viewing as a
measure of value point to empirical
evidence to corroborate arguments
based on economic theory. Dr. Wecker
and Mr. Harvey demonstrate (based on
Dr. Gray’s analysis) that paid
programming (i.e., infomercials) had a
higher viewing share than JSC
programming in three of the four years
covered by this proceeding. See Wecker
Report ¶ 44 & Table 7. The JSC point out
that, according to Dr. Gray’s theory
equating viewership with value, cable
operators would place a higher value on
paid programming than live sports
broadcasts, even though Mr. Allan
Singer, a former cable industry
executive and JSC witness, testified that
content such as infomercials actually
detracts from the value of a signal.
Singer WRT ¶ 7. Mr. Singer also testified
that there is ‘‘clearly not’’ a ‘‘one-to-one
correlation between audience viewing
levels and value,’’ though it is a
‘‘component’’ of value. 2/22/18 Tr. at
1047–48 (Singer). Mr. Daniel Hartman, a
media consultant and former DirectTV
executive testifying for the JSC, stated
that ratings were ‘‘definitely not a
determinative factor’’ in a multi-channel
video program distributor’s (MVPD’s)
negotiations with suppliers of
programming. 3/12/18 Tr. at 3155–56
(Hartman). Nor do ratings figure into the
rates that MVPD’s pay or the contractual
terms and conditions they agree to when
they negotiate with suppliers of
programming. Id. at 3156–57. CTV
argues that, while Program Suppliers’
witness Sue Ann Hamilton testified to
the importance to cable operators of
prospective viewing by subscribers, she
also stated that she did not obtain
Nielsen data on viewing of distant
signals. CTV PFF ¶¶ 147–148 (citing
Hamilton WDT at 5–6; 3/19/18 Tr. at
4326 (Hamilton)).
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Program Suppliers responded by
holding to the position that viewership
is the most direct measurement of
relative value of programming for the
reasons articulated supra,155 relying
primarily on Dr. Gray’s and Ms.
Hamilton’s testimony in support of Dr.
Gray’s viewing study. See, e.g., PS Reply
PFF ¶ 129.
2. Reliance on Incomplete Nielsen Data
On January 22, 2018, two weeks
before the scheduled commencement of
the allocation hearing in this
proceeding,156 Program Suppliers filed a
‘‘Third Errata’’ to Dr. Gray’s written
direct testimony. See Third Errata to
Amended and Corrected Written Direct
Statement and Second Errata to Written
Rebuttal Statement Regarding
Allocation Methodologies of Program
Suppliers (Jan. 22, 2018) (Third Errata).
The stated reason for this Third Errata
was that Dr. Gray had discovered that
the Nielsen viewing data he had been
provided for his analysis did not
include any data for distant viewing of
WGNA. Id. at 1; see also 3/14/18 Tr. at
3518 (Lindstrom). WGNA, the national
satellite feed for WGN-Chicago, was the
most widely retransmitted distant signal
in the U.S. during the years covered by
this proceeding.
The SDC moved to exclude the Third
Errata from evidence, arguing that
Program Suppliers were seeking to
introduce ‘‘substantial revisions to its
proposed allocation methodology’’ and
not ‘‘mere corrections of errors.’’
Settling Devotional Claimants’ . . .
Motion to Strike MPAA’s Purported
‘‘Errata’’ to the Testimony of Dr. Jeffrey
Gray at 9 (Jan. 25, 2018). The SDC
argued that, in addition to using a
Nielsen dataset that included WGNA
viewing data, Dr. Gray proposed ‘‘an allnew regression in addition to the
regression [he] previously proposed,
and a new sample weighting
methodology underlying all of its
computations.’’ Id. The Judges granted
the SDC’s motion and excluded the
Third Errata, reasoning that it was too
late to introduce a new analysis. See 2/
15/18 Tr. at 232 (Barnett, C.J.); accord
Order Granting MPAA and SDC Motions
to Strike IPG Amended Written Direct
Statement and Denying SDC Motion for
Entry of Distribution Order, Docket Nos.
2012–6 CRB CD 2004–09 (Phase II),
155 See
supra, section IV.A.
hearing had been scheduled to begin on
February 5. The Judges granted Program Suppliers’
motion to delay the start of the hearing until
February 14 for reasons unrelated to Dr. Gray’s
Third Errata. See Order Continuing Hearing and
Permitting Amended Written Rebuttal Statements,
Denying Other Motions, and Reserving Ruling on
Other Requests (Jan. 26, 2018).
156 The
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2012–7 CRB SD 1999–2009 (Phase 2), at
5 (Oct. 7, 2016) (striking Amended
Written Direct Statement that was filed
without leave and that introduced a
substantially modified regression
specification).
As a result of the Judges’ exclusion of
the Third Errata, the version of Dr.
Gray’s viewing analysis in the record is
based on a Nielsen dataset that does not
include viewing data for WGNA. While
it is undisputed that the use of this
incomplete dataset almost certainly
affected Dr. Gray’s computations, the
record does not reveal the magnitude of
the effect on each participant’s viewing
share.
Dr. Gray testified that, in spite of the
missing WGNA data, his viewing
analysis produced viewing shares that
were within a ‘‘zone of reasonable
consideration.’’ 3/14/18 Tr. at 3764
(Gray). He based his opinion on ‘‘a
dramatic decline in compensable
programming carried on WGNA and a
dramatic decline in viewing of WGNA
programming, such that it had become
increasingly less important over time.’’
Id. at 3763; see also 3/14/Tr. at 3522
(Lindstrom) (‘‘I haven’t quantified it, but
based on past experience, I would say
that . . . there wasn’t much that was, in
fact, compensable programming that
was on.’’). In addition, Program
Suppliers argue that Dr. Gray’s
computed viewing shares were based on
accurate Nielsen data as to viewing on
the remainder of the approximately 150
stations in his sample for each year and
were reliable as to those stations. See PS
PFF ¶ 109; 3/14/18 Tr. at 3525, 3537–38
(Lindstrom). Moreover, Dr. Gray
testified that the Crawford and Israel
fee-based regression analyses, as
modified by Dr. Gray, support his
estimated viewing shares as being
within a zone of reasonableness. See 3/
14/18 Tr. at 3744–45 (Gray).
Other participants dispute this. The
JSC point to evidence that, while
compensable Program Suppliers’
programming declined in the 2010 to
2013 time frame (and as between that
period and the 2004–05 period), the
amount of compensable JSC
programming remained stable. See
Cable Operator Valuation of Distant
Signal Non-Network Programming
2010–13, Trial Ex. 1001, at 28 Table III–
2 (Bortz Report); see also Hartman WRT
¶ 14, Table III–1 (telecasts of JSC
programming on WGNA remain
relatively constant during 2010–13 and
between 2010–13 and 2004–05). The
JSC argue that the omission of the
WGNA data thus disproportionately
affected the JSC, as compared to
Program Suppliers. JSC PFF ¶ 162.
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The SDC, through the testimony of
their economist Dr. Erdem, similarly
argue that the absence of WGNA data is
likely to disproportionately bias the
results against claimant categories with
smaller distant viewership. See Erdem
WRT at 32.
Several experts testified that the
imputed zero distant viewing values
that Dr. Gray input into his regression
for the missing WGNA data necessarily
affected the predicted viewing that the
regression produced. See Wecker Report
¶ 33 (‘‘choosing to code zero distant
viewing for large stations such as
WGNA . . . created counterintuitive
associations within the data where
stations with extremely large distant
subscribers are predicted to have low
numbers of viewers’’); 2/22/18 Tr. at
1299–1300 (Harvey). Dr. Gray appears to
have conceded this point. See 3/15/18
Tr. at 4054–55 (Gray).
3. Reliance on Unweighted Nielsen
NPM Data
The Nielsen data on which Dr. Gray
relied was an extract from Nielsen’s
NPM database. See 3/14/18 Tr. at 3685–
88 (Gray). The NPM data are derived
from a geographically stratified sample
of about 22,000 television households
that is ‘‘designed in such a way so that
every household in the United States
has a probability of being selected’’ and
represents approximately 110 million
U.S. television households. Id. at 3507,
3539–40 (Lindstrom); 2/22/18 Tr. at
1179 (Harvey); National Reference
Supplement 2010–2011, Trial Ex. 2021,
at 1–1 (Nielsen Supplement). A subset
of the NPM data, known as Local People
Meter (LPM) data, is used for measuring
viewership in the top 25 local markets.
3/14/18 Tr. at 3556 (Lindstrom);
Sanders WRT ¶ 6.viii. Nielsen
disproportionately oversamples the
(mostly urban) LPM markets, with 600
to 1000 metered households in each.
See Nielsen Supplement at 1–1; Erdem
WRT at 27.
a. Use of Nielsen NPM Data
Several witnesses opined that the
NPM database is the wrong tool for
measuring local and distant viewing to
individual television stations because
the NPM data are not designed to
measure viewership in local or regional
markets. See Corrected Written Rebuttal
Testimony of Susan Nathan, Trial Ex.
1090, at 3, 5–6 (Nathan CWRT); 2/22/18
Tr. at 1180–81, 1213 (Harvey); Written
Rebuttal Testimony of Ceril Shagrin,
Trial Ex. 2009, ¶ 24 (Shagrin WRT). Ms.
Shagrin contended that an appropriate
sample to measure distant viewing
would need to oversample small
markets, and the NPM does not
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3595
oversample small markets.
Consequently, the NPM data could not
produce a proper measure of distant
signal viewing. Shagrin WRT at ¶¶ 18,
22, 24; 3/1/18 Tr. at 1778 (Shagrin).
The CCG and SDC both argued that
their program categories are
underrepresented in the NPM sample
design. See CCG PFF ¶ 200; SDC PFF
¶¶ 130–131. By statute, Canadian
television stations may only be carried
by cable systems within 150 miles of the
U.S.-Canada border or north of the fortysecond parallel. 17 U.S.C. 111(c)(4).
Many communities within that
‘‘Canadian Zone’’ are not included in
the NPM sample. 3/15/18 Tr. at 4071–
73 (Gray); Sanders WRT, App. E;
Boudreau CWDT at 87. Similarly, the
SDC claim that many portions of the
‘‘Bible Belt’’ are not included in the
NPM sample. See Sanders WRT, ¶ 6.xi,
Apps. E–F.
More generally, some experts argued
that Dr. Gray’s use of the NPM data
resulted in a high number of instances
of zero recorded viewing in the data he
fed into his regression algorithm.
Viewing of distantly-retransmitted
signals is a relatively small
phenomenon, and in many regions the
NPM had an insufficient number of
metered households to measure that
viewing. See Nathan CWRT at 5–6, 8;
Wecker Report ¶¶ 21–22 & Table 4; 2/
22/18 Tr. at 1180–81, 1183–84, 1252–54
(Harvey); Gray CAWDT ¶ 35. Ninetyfour percent of the quarter hour
observations in Dr. Gray’s dataset
showed zero recorded viewing, and only
0.96% of the observations reported two
or more distant viewing households. See
Wecker Report ¶¶ 18, 21–22 & Table 4;
Shum WRT ¶ 17; see also Bennett WRT
¶ 49 & Fig. 16. Approximately 20% of
the distantly-retransmitted stations in
Dr. Gray’s sample have no recorded
local or distant viewing in the Nielsen
data. See Shum WRT ¶ 18.
Dr. Gray, and Mr. Lindstrom of
Nielsen,157 defended the use of NPM
data for measuring viewership of
programs on distant signals. Dr. Gray
testified that he consulted with Nielsen
concerning his selection of data and the
uses to which he intended to put it, and
Nielsen found his approach to be
reasonable. See 3/14/18 Tr. at 3932–33
(Gray); 3/15/18 Tr. at 3846 (Gray). He
relied on his regression analysis to
project distant viewership values to
b. Application of Improper Sample
Weights to the Nielsen Data
In order to project viewing data from
sample households to the broader
television audience, Nielsen employs
sophisticated weighting schemes. ‘‘The
weights measure the number of people
in the population that are represented
by each member of the sample. For
example, if [a] sample member has a
weight of 20,000 for a selected day, this
157 Mr. Lindstrom retired in June 2017 after nearly
40 years at Nielsen. See 3/14/18 Tr. at 3495–96
(Lindstrom). Prior to his retirement, Mr. Lindstrom
was a Senior Vice President in charge of custom
research and custom analysis for Nielsen’s media
business. See id. at 3496. He testified in this
proceeding with Nielsen’s ‘‘full cooperation and
support.’’ Id. at 3495.
158 Program Suppliers also sought to cast doubt
on the experience and expertise of the witnesses
who criticized Dr. Gray’s use of the NPM database
for his viewing study. See, e.g., PS Reply PFF ¶ 66
(‘‘Ms. Shagrin testified that she had never worked
on custom analysis projects while at Nielsen, and
that she did not understand how Dr. Gray used
Nielsen’s custom analysis in his methodology.’’).
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quarter hours on stations in his sample,
including those stations in portions of
the country that were not included in
the Nielsen NPM sample. See id. at
4073. Mr. Lindstrom testified that
Nielsen recommended the NPM
database because ‘‘it is recognized that
the meter is by far the best technology
and best method for being able to
measure television usage.’’ 3/14/Tr. at
3506 (Lindstrom). Mr. Lindstrom also
testified that, while the NPM is a
measurement of nationwide viewing,
‘‘all national viewing is inherently
aggregations of local usage. . . . It’s all
based on viewing built up from a very
localized level. . . . [I]f you believe in
sampling—and I’m a big believer in
sampling—and the core methodology
behind it, that you are getting a very
good measure of the viewing going on
in those homes and that when looked at
in aggregate, it is a very solid number.’’
Id. at 3508–10.
Regarding the ‘‘zero viewing’’
criticisms, Dr. Gray testified that
instances of no recorded viewing are to
be expected, and constitute
‘‘information regarding the level of
viewing for the Nielsen sample.’’ 3/15/
18 Tr. at 3973 (Gray); see Gray CAWDT
¶ 35; 3/14/18 Tr. at 3717 (Gray).
Similarly, Mr. Lindstrom explained that,
given Nielsen’s sampling rates and the
levels of distant viewing, one would
expect a large number of individual
quarter-hour observations to show no
recorded viewing. He emphasized that it
is necessary to aggregate and average the
observations to get an accurate picture
of viewing. See 3/14/18 Tr. at 3527–28
(Lindstrom). ‘‘[I]f you believe in
sampling, then the aggregation is, in
fact, going to give you solid results . . .
. [I]f you’re going to look at the
individual pieces, then the individual
pieces are highly subject to criticism
because you’re not supposed to look at
individual pieces.’’ Id. at 3529.158
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means that on that day the sample
member represents 20,000 in the
population.’’ Nathan CWRT at 5
(quoting Nielsen online tutorial on
weighting (internal quotations and
footnote omitted)). Dr. Gray was
supplied with Nielsen’s national
weights, but not with weights that
would permit accurate projection to
local or regional markets. See 3/14/18
Tr. at 3711, 3715–16 (Gray). He chose to
use the unweighted Nielsen data, rather
than weights that would project to a
national audience. Dr. Gray testified that
he was concerned that using the
national weights would produce
anomalous results, where numbers of
projected viewers for a distant signal
would, in some cases, exceed the
number of cable households that receive
the signal on a distant basis. See id. at
3715–16.
Ms. Susan Nathan, a media research
consultant, agreed that it would have
been inappropriate for Dr. Gray to apply
the NPM national weights to data
concerning distant viewing. See Nathan
CWRT, at 9. However, Ms. Nathan also
found Dr. Gray’s use of unweighted
Nielsen data inappropriate:
In arriving at his distant viewing estimates,
Dr. Gray treats each NPM sample household
as equal—even though each NPM sample
household is not equal in Nielsen’s sample
design. Rather, each household is
representative of a different number of
potential viewers. Simply estimating the
number of sample participants that might
view a given program is not an accurate
means of estimating viewership. By ignoring
the weighting and assuming one people
meter household is the same as another, Gray
also applies the unweighted data in a manner
for which it was not intended.
Id. Mr. Gary Harvey, a statistician and
applied mathematician, similarly
criticized Dr. Gray’s use of unweighted
data: ‘‘[B]ecause Dr. Gray doesn’t take
into account any weighting . . . you
don’t know how important that
household is . . . for your particular
area.’’ 2/22/18 Tr. at 1182 (Harvey); see
id. at 1201–02.
Dr. Gray responded that his decision
to use the unweighted Nielsen data was
the best of three options available to
him. He could have used the sample
weights in the NPM database, which
project each quarter-hour observation
out to the number of households in the
NPM survey that particular Nielsen
household represented on that
particular day. Dr. Gray was concerned
that this would produce anomalous
results, where the predicted number of
viewing households could exceed the
number of distant subscribers with
access to that distant signal. See 3/14/
18 Tr. at 3714–15 (Gray). He could have
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used sample weights that project each
observation to the particular distant
viewing market, but those weights were
not available from Nielsen, and would
have been impracticable for him to
develop. Id. at 3715–16. Or he could
have taken the approach that he
ultimately settled on and used the
unweighted Nielsen data. See id. at
3716. Dr. Gray pointed out that Nielsen
used unweighted data in a similar
fashion in a previous proceeding and
noted that, in any event, he was not
interested in the absolute number of
viewer quarter hours, but the relative
level of viewing among the parties. See
id. He concluded that performing a
regression on the unweighted Nielsen
viewing numbers was ‘‘a reliable
methodology to do so.’’ Id.
4. Sample of Stations Biased Results
Dr. Gray selected his sample of
stations using a statistical technique
called stratified random sampling. He
ranked the universe of distantlyretransmitted stations by numbers of
distant subscribers, divided the stations
into strata proportionate to the number
of distant subscribers reached by the
signal, and randomly selected stations
from each stratum. 3/14/18 Tr. at 3686
(Gray). He selected stations from the
stratum containing the stations with the
most distant subscribers with 100%
probability (i.e., he selected all of them).
The probability of selecting any given
station declined with each succeeding
stratum, with the probability of
selecting a given station in the final
stratum ranging from approximately
2.4% (i.e., 19 in 792) to approximately
3.5% (i.e., 22 in 632). See Bennett WRT
¶ 28, Figs. 6–9. In order to account for
the differing probabilities of selection
between the different strata, Dr. Gray
had to weight the viewing data. Data
pertaining to the largest stations, which
were selected with 100% probability
received a weight of 1. Data pertaining
to stations with a lower probability of
selection received a higher sample
weight (the reciprocal of the probability
of selection). See 3/15/18 Tr. at 3964–
65 (Gray). The stations with the fewest
number of distant subscribers, which
had the lowest probability of being
selected, received the highest sample
weight, ranging from 28.73 to 41.68. See
Bennett WRT ¶ 28, Figs. 6–9.
Use of a stratified random sample
(with appropriate weighting) can allow
oversampling of elements with a given
characteristic (in this case stations with
larger numbers of distant subscribers),
while still being able to make statistical
inferences about the universe of
elements as a whole. However, Dr.
Bennett, an economist and
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econometrician who testified for CTV,
criticized this approach, arguing that Dr.
Gray’s sampling design is prone to error
and bias and that Dr. Gray made a
number of errors implementing his
sample. See generally Bennett WRT.
a. Sample Design Led to a Biased
Sample
Dr. Bennett describes Dr. Gray’s
sample design as an example of ‘‘cluster
sampling’’ because Dr. Gray sampled
stations (which air multiple programs)
rather than sampling programs directly.
See Bennett WRT ¶¶ 15–16. Cluster
sampling, according to Dr. Bennett, is
‘‘more prone to bias than simple random
samples of equal size’’ because
‘‘individual clusters often contain a
non-random and relatively homogenous
set of units.’’ Id. ¶ 17, 18 & Fig.1. In the
context of television programming, Dr.
Bennett observed that programs
assigned to particular claimant
categories are often concentrated by
station type (i.e., Canadian, educational,
network, independent, or low power).
Over- or under-sampling of stations of a
particular type could thus have a
substantial impact on the volume and
viewership share of the categories of
programming that are
disproportionately carried on those
stations. Id. ¶ 18. If the sample of
stations is not proportionately
representative of the station types in the
population, the program types will not
be representative of the population of
television programs.
Dr. Bennett argues that Dr. Gray’s
samples of stations were, in fact, not
representative of the station types in the
population. See id. ¶ 29. Dr. Bennett
offers as evidence of
unrepresentativeness the proportion of
educational stations in Dr. Gray’s
samples in each year as compared to the
proportion of educational stations in the
population. He notes that Dr. Gray
consistently under-sampled educational
stations in 2010, 2011, and 2013, and
oversampled educational stations in
2012. See id. ¶ 32 & Fig. 10. Conversely,
he finds that Dr. Gray over-sampled
independent stations in 2010, 2011, and
2013, and under-sampled them in 2012.
See id. ¶ 34 & Fig. 11. Since
independent stations carry a greater
proportion of Program Suppliers’
programs than other station categories,
Dr. Bennett concludes that Dr. Gray’s
computations of volume and viewership
overstate those values for Program
Suppliers’ programming. See id. ¶¶ 39–
42. Dr. Bennett opines that Dr. Gray
should have included station type as a
stratification variable to avoid potential
bias. See id. ¶ 19.
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Dr. Gray acknowledged that it would
have been possible, as Dr. Bennett
suggested, to stratify with respect to
program type. See 3/14/18 Tr. at 3771
(Gray). However, he argued that not
performing that stratification did not
render his sample biased. ‘‘I’m
appealing to randomness. I think bias is
a strong word.’’ Id. He also
acknowledged that he could have done
some ‘‘post-sampling weighting, which
would have changed [the] estimate
slightly,’’ but did not do so. Id.
b. Sampling Frame and Sampling
Weights Were Incorrect
Dr. Bennett points out (and Dr. Gray
confirms) that some duplicate stations
were included in Dr. Gray’s samples.
See id. ¶¶ 21–25 & Fig. 3; 3/15/18 Tr. at
3859–63 (Gray). This occurred, for
example, when the CDC data Dr. Gray
received listed certain stations twice—
once with a ‘‘DT’’ suffix after the call
sign and once without (e.g., CBUT and
CBUT–DT). See Bennett WRT ¶ 24 &
Fig. 4.
As a result of these duplicates, Dr.
Bennett found that Dr. Gray’s sampling
frame included more stations than were
in his target population.159 Bennett
WRT ¶ 22. Dr. Bennett argues that the
mismatch of Dr. Gray’s sampling frame
and the population of distantlyretransmitted stations rendered the
sampling frame unsuitable to represent
the target population. Id. ¶ 21. Dr.
Bennett argues that ‘‘Dr. Gray’s failure to
remove duplicate stations . . . distorts
his count of unique stations, his
assignment of stations to individual
strata, and the sampling weights that he
calculates based on his incorrect station
count,’’ which could affect Dr. Gray’s
analysis in several ways:
a. Double-counting some stations in the
sampling frame, which changed the
likelihood of selection for all stations outside
the top stratum; and
b. Where both versions of the duplicative
station were selected, such as for CBUT . . .
2010, overrepresentation of the duplicate
station in the sample, and the exclusion of
a non-duplicate station from the sample; and
c. Incorrect sampling weights being
applied to sampled stations in strata with one
or more of the duplicative stations.
Id. ¶ 25.
Dr. Bennett argued that ‘‘the errors in
Dr. Gray’s sampling weights are further
compounded by the fact that Dr. Gray
has dropped sampled stations that did
not have coverage in the Gracenote
Data.’’ Id. ¶ 26. Over the four years at
159 ‘‘A
sampling frame is an enumeration of the
items from which a sample is selected. Ideally, the
sampling frame will be identical to—and therefore
representative of—the target population that one
seeks to study.’’ Bennett WRT at ¶ 21.
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issue in this proceeding, Dr. Gray had to
drop between five and eight sampled
stations per year (for a total of 24 of his
609 sampled stations) because
Gracenote could not provide
programming information for them. See
id. ¶ 27. The omitted stations were
distributed unevenly across the sample
strata and subject to different sample
weights. Dr. Bennett opines that Dr.
Gray should have adjusted his
weighting to account for the number of
missing stations across the strata for
each year. See id. ¶ 28. In addition, Dr.
Bennett testified that Dr. Gray failed to
apply his sample weights in performing
his regression analysis, leading to biased
results. See id. ¶¶ 58–59.
Dr. Gray acknowledged the existence
of duplicate stations in his sample. See
3/15/18 Tr. at 3859 (Gray). He explained
that at the time that he drew the sample
there were a number of stations that had
the same call signs with different
suffixes, and, after consultation with
CDC and Nielsen, he was unable to
determine whether or not they were the
same or different signals. See 3/14/18
Tr. at 3719–20. He opted to treat them
as different stations because, if he had
treated them as the same station and
they proved to be different stations he
would have had to discard the sample
and start over. Id. Having duplicate
stations in the sample effectively
resulted in a smaller sample and a
higher margin of error. See id. at 3721;
3/15/18 Tr. at 3853–56 (Gray). Dr. Gray
testified, however, that the existence of
duplicate stations did not render his
viewing estimates biased or incorrect.
See 3/15/18 Tr. at 3859 (Gray).
Dr. Gray also acknowledged that the
existence of duplicate stations resulted
in the application of different sample
weights to different subscriber groups
that received the same signal. See id. at
3861–62. He maintained, however, that
applying differing sample weights did
not ‘‘make the make the estimated
viewing biased or wrong.’’ Id. at 3861.
Regarding his sampling weights, Dr.
Gray acknowledged that he should have
recalculated them to reflect the removal
of certain stations from the sample for
which data were unavailable. See id. at
3867. He opined that the difference
would be de minimis, ‘‘given the types
of stations that did not have
programming data.’’ Id. ‘‘[E]very . . .
sensitivity analysis I ever did with
respect to viewing had . . . almost de
minimis impacts. . . . I would not
expect it to impact the overall
calculated shares.’’ Id. at 3867–68.
Contrary to Dr. Bennett’s assertion,
Dr. Gray testified that he applied his
sample weights to the Nielsen data and
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3597
maintained that ‘‘it’s an unbiased
measure of viewing.’’ Id. at 3861–62.
c. Erroneous Application of Random
Sample to Geographic Stratified Sample
Dr. Erdem criticized Dr. Gray’s
sampling technique because it
superimposed a random selection on a
geographically-stratified sample.160 He
argued that the two sampling schemes
are incompatible, because ‘‘[t]here is no
guarantee that the stations in Dr. Gray’s
sample were broadcast or retransmitted
in the . . . geographic areas sampled by
Nielsen.’’ Erdem WRT at 26. As a result,
‘‘[l]ocal or distant viewership would be
underreported or completely missing if
geographies where a particular station is
retransmitted are not sampled by
Nielsen.’’ Id. Consequently, Dr. Erdem
considered Dr. Gray’s data source to be
‘‘practically unusable,’’ and concluded
that ‘‘no reliable conclusions can be
drawn on the basis of the sample that
Dr. Gray uses.’’ Id. at 25.
Dr. Gray responded that Dr. Erdem’s
criticism ‘‘would have been a concern,
had [he] not used regression analysis.’’
3/14/18 Tr. at 3718 (Gray). He conceded
that ‘‘Dr. Erdem has a legitimate point’’
and that it is not ‘‘ideal’’ to superimpose
a random sample on top of a geographic
sample. Id. He testified, however, that
he had overcome that criticism by using
regression analysis to predict viewing
‘‘even in those areas of
underrepresentation by Nielsen.’’ Id. at
3718–19. As a consequence, he was not
concerned about Dr. Erdem’s criticism.
Id. at 3719.
5. Other Methodological Errors
Experts for the other parties lodged a
barrage of criticisms of a variety of
methodological choices that Dr. Gray
made in performing his analysis.
a. Imputation of Zeroes for Missing
Nielsen Data
The NPM data that Nielsen provided
to Dr. Gray included only observations
of positive viewing. See 3/14/18 Tr. at
3712 (Gray). For several million station/
quarter-hour pairings during the
relevant period there was no record of
positive viewing in the NPM data. See
Wecker Report ¶ 21. Dr. Gray added
zero-viewing records for these station/
quarter-hour pairings and used these
zero values as input in his regression
analysis. See id.; Bennett WRT ¶ 53 &
Fig. 17.
160 Nielsen’s sample is a tiered sample of
geographic areas, see Erdem WRT at 25; see also 3/
14/18 Tr. at 3507, 3539–40 (Lindstrom), unlike Dr.
Gray’s sample, which was stratified by the number
of distant subscribers. See 3/14/18 Tr. at 3686
(Gray).
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Dr. Bennett and Mr. Harvey both
criticized this practice. Dr. Bennett
argued that ‘‘Dr. Gray’s practice of
equating missing records with zero
viewing 1acks foundation and
undermines the reliability of his
regression analysis. . . . Dr. Gray offers
no logical explanation for why zero
might be the correct value to use in
place of a missing record.’’ Bennett
WRT ¶ 54. Dr. Bennett posited the
existence of an apparent contradiction:
‘‘[E]ither the missing values truly
correspond to zero viewing and the
regressions serve no purpose—why
estimate a known quantity—or the true
values of the missing records potentially
differ from zero, in which case Dr. Gray
has imposed an incorrect assumption
that biases the estimated relationship
between distant and local viewing.’’ Id.
Mr. Harvey argued that Dr. Gray failed
to demonstrate that a sufficient number
of NPM households received a given
distantly transmitted signal to conclude
that the absence of viewership data
indicated zero viewing. 2/22/18 Tr. at
1203–07 (Harvey). ‘‘[Y]ou might have
zero people meters, in which case [a
zero viewing observation] is useless
data. . . .’’ Id. at 1335. In Mr. Harvey’s
view, ‘‘there is no possible way to come
up with some metric . . . for these
smaller samples without knowing the
number of people meters. . . .’’ Id.
Dr. Gray explained that ‘‘[t]here was
[sic] never any zeros in the Nielsen data.
They only have recorded viewing and
non-recorded viewing.’’ 3/24/18 Tr. at
3712 (Gray). The data that Nielsen
provided to Dr. Gray were ‘‘all recorded
viewing values.’’ Id. He testified that the
absence of an entry for recorded viewing
for a given quarter hour meant that
‘‘there was no Nielsen household in the
sample viewing’’ that channel at that
particular time. Id. In those cases he
added an entry with a zero-household
count. See id. at 3712–13. Dr. Gray
distinguished between instances zero
local viewing and data that was
‘‘missing’’ because local viewing for that
channel was not measured by Nielsen.
See id. at 3895–97; 3/14/18 Tr. at 3717–
18. In the latter instance, he imputed a
local rating based on the average local
rating for programs of the same type
during that particular quarter hour. See
id.; 3/15/18 Tr. at 3897–3900 (Gray).
b. Incorrect Measure of Local Ratings
As an input for his regression
analysis, Dr. Gray used a ‘‘measure of
local ratings’’ that he constructed by
dividing local viewing (as measured by
Nielsen) by the size of the market—i.e.,
‘‘the number of subscribers reached by
the particular signal.’’ See 3/14/18 Tr. at
3693 (Gray). Dr. Bennett clarifies that,
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by number of subscribers, Dr. Gray
refers to the total number of local and
distant subscribers who receive the
signal. See Bennett WRT ¶ 56.
Dr. Bennett faults Dr. Gray’s inclusion
of the number of distant subscribers in
the denominator when calculating his
measure of local ratings. ‘‘Dr. Gray’s
inclusion of distant subscribers in his
‘measure’ of local viewing means that,
all else equal, he will assign higher local
viewing to a station with the fewest
distant subscribers, and vice versa.’’ Id.
Dr. Gray maintained that, after
consultation with Nielsen, he found his
measure of local ratings to be
reasonable. See id. at 3932–33.
c. Regression-Based Estimates in Lieu of
Nielsen Observations of Positive
Viewing
Dr. Gray computed his viewing shares
based solely on the estimates he
computed using his regression analysis.
He used the observations of positive
viewing in the Nielsen NPM data solely
as an input into the regression analysis,
not in the final computation of viewing
shares. Dr. Bennett described this
procedure as being ‘‘without . . .
support’’ and argued that Dr. Gray’s
reliance on estimated viewing ‘‘further
undermines the reliability of his
viewing analysis.’’ Id. ¶¶ 50–51.
Specifically, Dr. Bennett argued that,
as compared with the observations of
positive viewing in the Nielsen NPM
data, Dr. Gray’s estimates are biased in
favor of Program Suppliers and PTV
programming, and biased against CTV
and CCG programming. See id. ¶ 64 &
Figs. 21–22; 3/1/18 Tr. at 1874–75
(Bennett). Professor Shum reiterates the
same point with respect to CCG
programming, arguing that Dr. Gray’s
analysis systematically lowered
estimates of distant viewing of Canadian
signals because (a) the regression
undercounted local viewing by
excluding local viewing in Canada; (b)
Canadian stations were
underrepresented in Dr. Gray’s 2010
sample; and (c) Canadian signals cannot
be carried outside the Canadian Zone.
See Shum WRT ¶¶ 25–38. Professor
Shum proposes adjustments to Dr.
Gray’s viewing shares to account for the
first two purported defects, but he was
unable to propose an adjustment to
account for the third. See id. ¶¶ 29–30,
33–35, 38.
Dr. Gray maintained that basing his
viewing shares on the predicted viewing
he computed through his regression
analysis was both reasonable and
superior to using Nielsen’s viewing
estimates for that purpose. See 3/15/18
Tr. at 3940–41, 3943, 3948 (Gray). In
particular, he argued that, while
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Nielsen’s measurements were of
‘‘geographically-focused areas,’’ his
regression analysis produces estimates
of relative viewing ‘‘throughout the
United States.’’ Id. at 3949. He
acknowledged that his regression would
not produce particularly good estimates
of the level of distant viewing, but
opined that his estimates were ‘‘more
accurate on a relative basis for the
United States.’’ Id.; see id. at 3946, 3948.
d. Miscategorized Programs
Dr. Bennett asserts that Dr. Gray
incorrectly assigned thousands of
programs to the wrong claimant
categories. For example, he states that
Dr. Gray’s algorithm failed to consider
Gracenote’s title and program type fields
when assigning programs to the CCG
category and, as a result, incorrectly
assigned JSC programming on Canadian
signals to the CCG category. Bennett
WRT ¶¶ 44–45; see also Wecker Report
¶ 12 (Dr. Gray included nearly all MLB,
NHL, NBA, and NFL broadcasts on
Canadian signals in the CCG category);
2/22/18 Tr. at 1169–70 (Harvey) (‘‘Dr.
Gray was very clear in his testimony
that he intended to code Canadian
broadcasts of Major League Baseball
games and football games into the JSC
Category, but he did not do that.’’);
Bennett WRT ¶ 18, n.11 (‘‘obvious
program categorization errors’’ in table
showing 20 CTV programs on Canadian
stations and 5 Devotional programs on
Educational stations). In addition, Dr.
Bennett states that Dr. Gray didn’t
consider whether a program coded as
‘‘religious’’ was syndicated before he
assigned it to the Devotional category.
Dr. Bennett asserts that nonsyndicated
religious programming belongs in the
CTV category. Id. ¶ 46.
Dr. Gray compared the category
classification that he performed to Dr.
Bennett’s. He found that their respective
algorithms assigned programs to the
same category 93.5% of the time. See
Gray CWRT ¶ 50. As to the programs
where Dr. Gray’s categorization differed
from Dr. Harvey’s, Dr. Gray was unable
to determine which categorization was
correct with undertaking a program-byprogram review.161 See id. Instead, Dr.
Gray performed a sensitivity analysis to
determine whether using Dr. Bennett’s
categorizations would have an impact
on his (Dr. Gray’s) share calculations.
See id. ¶ 51. Using Dr. Bennett’s
program categorizations resulted in a
modest increase in Program Suppliers’
161 Dr. Gray testified about a number of specific
instances in which his categorization differed from
Dr. Bennett’s, and, on further review, he stood by
his categorization. However, he did not perform a
comprehensive review. See 3/14/18 Tr. at 3721–23
(Gray).
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viewership share in each royalty year,
‘‘consistent with no bias in intent on the
part of Dr. Bennett or me.’’ Id. ¶ 52.
D. Analysis
1. Relevance and Impact of Prior
Decisions
Program Suppliers’ use of viewing
data to propose allocations of cable
royalties among program categories has
a long, if not illustrious history. MPAA
(to use the Program Suppliers’
contemporaneous designation) first
offered a Nielsen study in the Copyright
Royalty Tribunal’s (CRT) adjudication of
1979 cable royalties. See 1979 Cable
Royalty Distribution Determination, 47
FR 9879, 9880 (Mar. 8, 1982). At that
time the CRT found Nielsen’s
viewership study to be the ‘‘single most
important piece of evidence in [the]
record.’’ Id. at 9892. Over time,
however, decision makers’ (first the
CRT, then the CARPs) reliance on
Nielsen studies waned. See 1998–99
CARP Report, supra note 144, at 33
(recounting history of use of Nielsen
studies by CRT and CARPs). In 2003 a
CARP, with the approval of the
Librarian of Congress (Librarian)
declined to use the Nielsen study as a
direct measure of relative value of
programming to cable operators:
[T]he Nielsen study does not directly address
the criterion of relevance to the Panel. The
value of distant signals to CSOs is in
attracting and retaining subscribers, and not
contributing to supplemental advertising
revenue. Because the Nielsen study ‘‘fails to
measure the value of the retransmitted
programming in terms of its ability to attract
and retain subscribers,’’ it can not be used to
measure directly relative value to CSOs. The
Nielsen study reveals what viewers actually
watched but nothing about whether those
programs motivated them to subscribe or
remain subscribed to cable.
Id. at 38 (citations omitted). Or, as the
Librarian summarized pithily, ‘‘[t]he
Nielsen study was not useful because it
measured the wrong thing.’’ 1998–99
Librarian Order, 69 FR at 3613.
More recently the Judges have relied
upon evidence of viewership in a pair
of ‘‘Phase II’’ distribution cases.162 In
the 2000–03 cable Phase II distribution
case, the Judges concluded that
162 Prior to the cases to determine allocation and
distribution of 2010–13 cable and satellite royalties
the Judges and their predecessors referred to the
process of dividing royalties among program
categories as ‘‘Phase I,’’ and the process of dividing
royalties allocated to a program category among the
claimants within that category as ‘‘Phase II.’’ When
the Judges decided to conduct both processes
simultaneously for 2010–13 cable and satellite
royalties they decided to refer to them as the
‘‘allocation phase’’ and ‘‘distribution phase,’’
respectively, to avoid any expectation that the
processes would be carried out sequentially.
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‘‘viewership, as measured after the
airing of the retransmitted programs is
a reasonable, though imperfect proxy for
the viewership-based value of those
programs.’’ Distribution of 2000, 2001,
2002 and 2003 Cable Royalty Funds, 78
FR 64984, 64995 (Oct. 30, 2013) (2000–
03 Cable Phase II Decision) (footnote
omitted). The Judges agreed with
Program Suppliers’ expert in that
case 163 that ‘‘viewership can be a
reasonable and directly measurable
metric for calculating relative market
value . . . . Indeed, the Judges
conclude that viewership is the initial
and predominant heuristic that a
hypothetical CSO would consider in
determining whether to acquire a
bundle of programs for distant
retransmission . . . .’’ Id. at 64996.
Similarly, in the 1998–99 Phase II
proceeding, the Judges found a
viewership analysis to be an ‘‘acceptable
‘second-best’ measure of value’’ for
distributing funds allocated to the
devotional programming category
among claimants in that category. See
Distribution of 1998 and 1999 Cable
Royalty Funds, 80 FR 13423, 13432–33
(Mar. 13, 2015) (1998–99 Cable Phase II
Decision).
The Copyright Act mandates that the
Judges act
on the basis of a written record, prior
determinations and interpretations of the
Copyright Royalty Tribunal, Librarian of
Congress, the Register of Copyrights,
copyright arbitration royalty panels (to the
extent those determinations are not
inconsistent with a decision of the Librarian
of Congress or the Register of Copyrights),
and the Copyright Royalty Judges (to the
extent those determinations are not
inconsistent with a decision of the Register
of Copyrights that was timely delivered to the
Copyright Royalty Judges pursuant to section
802(f)(1)(A) or (B), or with a decision of the
Register of Copyrights pursuant to section
802(f)(1)(D)), under this chapter, and
decisions of the court of appeals. . . .
17 U.S.C. 803(a)(1). In interpreting a
nearly identical provision under the
CARP system,164 the Librarian stated
that ‘‘[w]hile the CARP must take
account of Tribunal precedent, the
Panel may deviate from it if the Panel
provides a reasoned explanation of its
decision to vary from precedent.’’
Distribution of 1990, 1991 and 1992
Cable Royalties, 61 FR 55653, 55659
(Oct. 28, 1996) (1990–92 Librarian
Order) (citation omitted). In a
163 Then, as now, the Program Suppliers’
principal witness regarding the analysis of Nielsen
viewership data was Dr. Gray.
164 The earlier provision, former section 802(c) of
the Copyright Act, stated that CARPs ‘‘shall act on
the basis of . . . prior decisions of the Copyright
Royalty Tribunal, prior copyright arbitration panel
determinations, and rulings of the Librarian . . . .’’
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subsequent decision, the Librarian
observed that ‘‘prior decisions are not
cast in stone and can be varied from
when there are (1) changed
circumstances from a prior proceeding
or; (2) evidence on the record before it
that requires prior conclusions to be
modified regardless of whether there are
changed circumstances.’’ 1998–99
Librarian Order, 69 FR at 3613–14.
As an initial matter, the Judges find
that the 1998–99 CARP Report and the
1998–99 Librarian Order are relevant
‘‘precedent’’ 165 that the Judges must
consider in connection with Dr. Gray’s
analysis of Nielsen viewing data; the
1998–99 Cable Phase II Decision and the
2000–03 Cable Phase II Decision are not.
The task of distributing royalties among
a reasonably homogeneous group of
programs differs from that of allocating
royalties among heterogeneous
categories, and different considerations
apply to each. See Indep. Producers
Grp. v. Librarian of Congress, 792 F.3d
132, 142 (DC Cir. 2015) (IPG v.
Librarian); Distribution of 1993, 1994,
1995, 1996 and 1997 Cable Royalty
Funds, 66 FR 66433, 66453 (Dec. 6,
2001).
In considering Dr. Gray’s viewing
study, therefore, the Judges are mindful
of the earlier decisions that found
viewership studies unhelpful in
allocating royalties among program
categories. In particular, the Judges
examine whether there is record
evidence that would compel a different
conclusion in the present case.166
2. Rejection of Viewership as a Measure
of Relative Value
Although the record supports a
conclusion that viewership is a measure
of value, the weight of the evidence
demonstrates that it is an incomplete
measure of value.
The Judges agree in principle with Dr.
Gray that the focus of the relative
market value inquiry is on the
hypothetical market in which copyright
owners license programs to broadcasters
for retransmission by cable operators.
See 3/14/18 Tr. at 3683–84 (Gray).
Experts from multiple parties agreed
that, in the hypothetical market, cable
operators would continue to acquire
165 The decision whether or not to accept a
methodology for determining relative market value
is factually-dependent, so it is a misnomer to
describe a previous decision declining to rely on
viewership as ‘‘precedent’’—i.e., controlling under
the principle of stare decisis. Nevertheless, it is a
‘‘prior determination’’ ‘‘on the basis of ’’ which
Congress has directed the Judges to act (along with
the written record and other items enumerated in
the statute). See 17 U.S.C. 803(a)(1).
166 No party has alleged changed circumstances
that would bear on the Judges’ reliance, vel non, on
viewing data.
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entire signals, rather than individual
programs. See id. at 3683; 2/28/18 Tr. at
1377–78 (Crawford); 3/5/18 Tr. at 2157–
58 (George). In this market structure
copyright owners’ compensation (the
object of this proceeding) would flow
from broadcasters to copyright owners,
and would be recouped through the
retransmission fee charged by the
broadcaster to the cable operator. See 3/
14/18 Tr. at 3682–84, 3779–81 (Gray).
That market does not exist in a world
with a compulsory license, so there is
no evidence of the surcharge that
broadcasters would pay to copyright
owners for the right to license distant
retransmissions. Most parties have used
the transaction in which a cable
operator acquires the right to retransmit
programming as a proxy. Program
Suppliers, by contrast, focus on the
consumer demand for programs as
measured by viewership.
At bottom, Dr. Gray’s study is
premised on the truism that, ultimately,
programming is acquired to be viewed.
See Gray CAWDT ¶ 13. Consumers
subscribe to cable in order to watch the
programming carried on the various
channels provided by the cable
operator. Cable operators acquire
broadcast and cable channels that carry
programming their subscribers want to
view. Broadcasters acquire programs
that will attract viewers.167 Viewing is
the engine that drives the entire
industry. It is an example of the
economic concept of derived demand.
The demand for programming at each
step in the chain is derived from
demand further along the chain, all the
way to the television viewer. Program
Suppliers corroborated Dr. Gray’s
economic insight with evidence that at
least some MVPDs consider viewership
metrics in making program acquisitions.
Consequently, based on the evidence
presented in this proceeding, the Judges
disagree with the Librarian’s statement
that viewership studies are not useful
because they ‘‘measure [ ] the wrong
thing.’’ 1998–99 Librarian Order, 69 FR
at 3613. Viewership is no less relevant
to the question of how a copyright
owner would be compensated by a
broadcaster in the hypothetical market
than to the question of what a cable
operator would be willing to pay to a
broadcaster. Both are relevant because
the copyright owner’s compensation
would be a function of downstream
demand in the hypothetical market.
However, even accepting that
viewership is relevant to the question of
167 Broadcasters’ reasons to attract viewers are
driven by advertising-revenue considerations rather
than subscriber attraction and retention
considerations.
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value doesn’t end the inquiry. There is
record evidence supporting the
contention that, in the analogous market
for cable channels, cable operators will
pay substantially more for certain types
of programming than for other
programming with equal or higher
viewership. See Crawford WRT ¶ 36 &
Fig. 1.168 These empirical data support
economic arguments about the role of
bundling and ‘‘niche’’ programming in
cable operators’ decision making. See
Shum WRT ¶¶ 10–12; Connolly WDT
¶¶ 31–32; Crawford CWDT ¶ 7. It is
clear to the Judges that relative levels of
viewership do not adequately explain
the premium that certain types of
programming can demand in the
marketplace. In short, viewing doesn’t
provide the whole picture.
The Judges conclude, therefore, that
viewership, without any additional
evidence to account for the premium
that certain categories of programming
fetch in an open market, is not an
adequate basis for apportioning relative
value among disparate program
categories.
3. Rejection of Dr. Gray’s Study due to
Incomplete Data
The Judges also must reject Dr. Gray’s
study because he computed his
predicted distant viewing on the basis of
incomplete data. Specifically, the use of
erroneous zero viewing observations for
compensable WGNA programming
rendered Dr. Gray’s results unreliable.
WGNA was, by far, the most widely
retransmitted signal in the U.S. during
the period covered by this proceeding,
reaching over 40 million distant
subscribers. See Wecker Report, ¶ 23.
That provided an opportunity for any
compensable program retransmitted on
WGNA to be viewed by a substantial
number of households. Yet nearly none
of those compensable programs were
credited with any positive distant
viewing on WGNA in Dr. Gray’s
regression. The Wecker Report,
moreover, demonstrates that there were
significant amounts of positive distant
viewing in Nielsen’s NPM database for
programs carried on WGNA. See id. ¶ 26
& App. G. As Dr. Wecker and Mr.
Harvey demonstrated, the numerous
zeros for distant viewing on WGNA that
were input into Dr. Gray’s regression,
combined with the use of the number of
distant subscribers as a variable in the
regression specification, created an
erroneous negative correlation between
distant subscribership and distant
viewing. See id. ¶¶ 33; 2/22/18 Tr. at
1299–1300 (Harvey); see also 3/15/18
168 See also discussion of Dr. Israel’s ‘‘cable
content analysis,’’ supra, section V.
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Tr. at 4054–55 (Gray) (appearing to
concede point).
The aggregate effect of the missing
WGNA data on Dr. Gray’s predictions of
distant viewing, and on the viewing
shares he computed therefrom, cannot
be determined with any certainty from
the record. It was incumbent on
Program Suppliers to demonstrate that
the effect of the missing WGNA data did
not have a substantial influence on Dr.
Gray’s results. They failed to do so.
Program Supplier’s efforts to argue,
essentially, that the omission of the
WGNA data was harmless error are
unavailing. The JSC rebutted Dr. Gray’s
assertion that compensable
programming on WGNA had declined
significantly, showing that JSC
programming on WGNA remained
stable during the 2010–2013 period. See
Bortz Report, at 28 Table III–2. The
Wecker Report rebutted Dr. Gray’s
assertion that his computed viewing
shares were accurate as to the nonWGNA stations in his sample. See
Wecker Report, ¶¶ 33. As for Dr. Gray’s
assertion that his viewing analysis
produced viewing shares that were
within a ‘‘zone of reasonable
consideration,’’ 3/14/18 Tr. at 3764
(Gray), the ‘‘zone of reasonableness’’ is
a legal construct that is solely within the
purview of the Judges. Dr. Gray’s views
on what lies within or without a zone
of reasonableness are immaterial.
4. Other Asserted Methodological
Defects
As recounted above,169 several
experts identified what they found to be
methodological errors in Dr. Gray’s
analysis, including his decision to use
Nielsen NPM data and not to apply
Nielsen’s weighting to that data; his
sample design and application of
sampling weights; his program
categorization; his imputation of zero
viewing values to quarter hours not
represented in the Nielsen data; and his
substitution of regression-based
predicted distant viewing values for the
observed distant viewing in the Nielsen
data. Because the Judges have found an
adequate basis for rejecting Dr. Gray’s
viewing study based on its failure to
provide a complete measurement of
value, and its reliance on incomplete
data, the Judges do not need to evaluate
the remaining critiques.
E. Conclusion Concerning Viewing
Study
Dr. Gray’s viewing study provides an
incomplete and therefore inadequate
measure of relative market value of
disparate categories of distantly169 See
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retransmitted programming. While
viewing is relevant to value, it does not
adequately measure the premium that
cable operators are willing to pay for
certain types of programming in the
analogous market for cable channels.
Even if viewing were an adequate
basis for apportioning value among
program categories, Dr. Gray’s study is
fatally flawed by its reliance on Nielsen
data that omitted distant viewing on
WGNA—the most widely retransmitted
station in the United States.
For the foregoing reasons, the Judges
will not rely on Dr. Gray’s viewing
study for apportioning royalties among
the program categories represented in
this proceeding.
V. Cable Content Analysis
Dr. Israel also undertook an analysis
that he characterized as a ‘‘Cable
Content Analysis’’—focusing on the
dollar amount paid by CSOs to carry
sports and other programming during
the years 2010–13. More particularly,
for the years 2010–13 he considered the
amounts that cable networks spent per
hour of programming televised in
relation to total household viewing
hours (HHVH). Israel WDT ¶ 45. As
explained in more detail, infra, Dr.
Israel concluded that CSOs place a high
value per hour on live sports
programming compared with other
program categories. He further opined
that his Cable Content Analysis
presented results that were consistent
3601
with the share estimates determined by
the Bortz Survey. Israel WDT ¶ 46.
More particularly, according to Dr.
Israel, his Cable Content Analysis
demonstrated that in each year of the
2010–13 period, CSOs networks paid
significantly more per hour for JSC
programming than for any other
category of programming. Making this
point in an alternative manner, Dr.
Israel testified that the JSC’s
programming share of CSO expenditures
was larger than the JSC programming
share of CSO broadcast minutes or
HHVH. Israel WDT ¶ 46.
Table V–5 of Dr. Israel’s WDT, set
forth below, compares total program
hours, total HHVH, and total CSO
expenditures for JSC programming with
all other categories of programming on
the top twenty-five cable networks:
TABLE 17—CABLE CONTENT ANALYSIS 2010–2013, SUMMARY OF TOP 25 NETWORKS
Category
Total
programming
hours
%
Total HHVH
(000)
%
Expenditures
($M)
%
Expenditures
per hour of
programming
Expenditures
per hour of
viewing
[A]
[B]
[C]
[D] =
[C] / [A]
[E] =
[C] / [B]
$1,350,517.6
49,268.2
27.41
........................
$0.826
0.086
9.60
........................
JSC ......................................................................................
Non-JSC ...............................................................................
JSC / Non-JSC ....................................................................
JSC % of Total .....................................................................
Israel WDT ¶ 47 Table V–5.
As this table shows, for the top
twenty-five cable networks, JSC
programming represents approximately
1% of all programming in terms of
hours transmitted and less than 3% of
total HHVH. Nonetheless, these top
twenty-five cable networks applied
more than 22% of their programming
budgets to acquire the rights to transmit
JSC programming.
Dr. Israel further highlighted the
importance of JSC programming to these
cable networks, relative to other
categories, by expressing the data on a
per hour basis. Dividing total
expenditures by total hours of
programming per category, he showed
that expenditures per hour of JSC
programming are worth more than 27
times other programming categories. Dr.
Israel also calculated these expenditures
per hour of household viewing and
found that JSC programming was worth
almost 10 times more per hour of
viewing than all other programming
categories on the top twenty-five cable
networks. Israel WDT ¶ 47; Table 17,
supra.
Dr. Israel also looked more granularly
at two cable networks, TBS and TNT,
which he noted (without opposition)
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9,274.0
866,726.0
0.01
1.06
15,164,368.9
496,492,970.2
0.03
2.96
carried a mix of JSC and other program
categories. His analysis showed patterns
that were similar to what he had found
with regard to the top twenty-five cable
networks, viz., that JSC programming
was far more valuable than all other
program categories. Specifically, during
the years 2010–13, JSC programming
accounted for approximately 2% of the
total programming hours transmitted by
TBS, and about 3% of the total
programming hours transmitted by TNT.
In terms of viewership, the JSC
generated roughly 5.5% of total HHVH
on TBS during the four-year period and
about 7.9% on TNT. In contrast to these
relatively small percentages of
programming and viewing hours, TBS
spent 44.4% of its 2010–13
programming budget on JSC
programming, and TNT quite similarly
spent 45.5%. Once again, expressing
these choices on an hourly basis,
expenditures per hour of JSC
programming were more than 40 times
greater than expenditures per hour of all
other programming on TBS, and
expenditures per hour of JSC
programming were almost 30 times
greater than expenditures per hour of all
other kinds of programming on TNT. In
terms of expenditures per HHVH, TBS
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$12,524.7
42,702.0
0.29
22.68
spent more than 13 times as much on
JSC programming than on other program
categories, and TNT spent almost 10
times as much compared with its
spending on other program categories.
Israel WDT ¶ 48 & Table V–6.
According to Dr. Israel, these absolute
and relative differences are reflected in
‘‘the significantly higher license fees
that cable systems and other MVPDs
[Multichannel Video Programming
Distributors] pay to carry these
networks.’’ Israel WDT ¶ 51. Dr. Israel
presented data to support this point,
analyzing the 97 nationally and
regionally distributed cable networks
with a minimum of 50 million
subscribers in 2013. Of these 97
networks, he found that 14 offered
telecasts of JSC events and 83 did not.
Over the full 2010–13 period, Dr. Israel
found that the average license fee for the
14 cable networks that offered JSC
programming (along with other
programming) was $0.753 per subscriber
per month, whereas for the 83 cable
networks that did not offer JSC
programming, the average license fee
over the four year period was much
lower, $0.174 per subscriber per month.
Israel WDT ¶ 51.
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In opposition, Program Suppliers
asserted that this analysis ‘‘is irrelevant
to this proceeding.’’ PSPFF ¶ 354. In
support of this argument they rely on
Dr. Gray’s assertion that ‘‘consistent
with Professor Crawford’s economic
arguments, after negotiating
programming deals with cable networks
carrying live team sports programming,
CSOs may then have a sufficient
quantity of that type of programming to
bundle for its current and potential
subscribers [such that] live team sports
programming would be less valuable to
CSOs than other types of programming.’’
Gray CWRT ¶ 60.
In response to this opposition, the JSC
asserted that Dr. Gray had misapplied
Professor Crawford’s explanation that
CSOs have an incentive to add
differentiated distant signal
programming to their bundles ‘‘because
it can help to attract and retain
subscribers.’’ JSC RPFF ¶ 46 & n.174
(and record citations therein). More
particularly, the JSC argued that
Program Suppliers’ argument regarding
program-type saturation would not
apply only to JSC programming. As they
asserted: ‘‘[T]hat argument would apply
equally to [Program Suppliers] (and
others), whose content likewise is on
cable networks in addition to local and
distant signals; it provides no basis to
ascribe a lower relative value to JSC.’’
JSC PFF ¶ 50 (and record citations
therein).
The Judges understand Dr. Israel’s
Cable Content Analysis to be in the
nature of an assertion that a similar
market provides relevant and
meaningful information regarding the
relative values of distantly retransmitted
local programs in a hypothetical market
in which the statutory royalty structure
did not exist. As such, Dr. Israel’s
approach is similar to the ‘‘benchmark’’
approach that is a hallmark of the sound
recording and musical works rate
proceedings within the Judges’
jurisdiction. That is, parties in those
proceeding regularly present economic
evidence regarding royalty rates in other
markets, urging the Judges to find
sufficient comparability between the
‘‘benchmark’’ market and the
hypothetical market at issue. When
Judges decide whether and how to
weigh such benchmark evidence, they
begin with the following foundational
analysis that is equally applicable here:
In choosing a benchmark and determining
how it should be adjusted, a rate court must
determine [1] the degree of comparability of
the negotiating parties to the parties
contending in the rate proceeding, [2] the
comparability of the rights in question, and
[3] the similarity of the economic
circumstances affecting the earlier
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negotiators and the current litigants, as well
as [4] the degree to which the assertedly
analogous market under examination reflects
an adequate degree of competition to justify
reliance on agreements that it has spawned.
VI. Changed Circumstances
The Judges and their predecessors
have looked at a ‘‘changed
circumstances’’ analysis in prior
proceedings. In the 1998–99 cable
distribution proceeding, the CARP
recommended allocation to the four
largest categories strictly based on the
Bortz survey results.170 Because PTV
and CCG were undervalued by the Bortz
survey, the CARP recommended
adjustment of allocations to those
categories, giving ‘‘some weight’’ to the
remarkable increases in relative fee
generation and in ‘‘changed
circumstances’’ as measured by an
increase in subscriber instances.171 See
Final Order, Distribution of 1998 and
1999 Cable Royalty Funds, 69 FR 3606,
3617 (Jan. 26, 2004). In the 2000–03
distribution proceeding, the Judges
salvaged consideration of changed
circumstances by differentiating a fee
generation methodology from a changed
circumstances evidentiary
consideration. See Distribution
Order, 172 75 FR 26798, 26805–07 (May
12, 2010) (2000–03 Distribution Order).
Ultimately, the CARP concluded that
changed circumstances, as measured by
changes in subscriber instances alone,
revealed a change in programming
volume, which did not necessarily
translate to a change in programming
value. 1998–99 Librarian Order, 69 FR at
3616.
In the present proceeding, PTV
retained Ms. Linda McLaughlin and Dr.
David Blackburn, who filed joint written
testimony. See Trial Ex. 3012. The
McLaughlin/Blackburn report focused
on the share of royalties that would
reflect the relative value of PTV
programming only. See 3/7/18 Tr. at
2446 (McLaughlin). McLaughlin and
Blackburn began with the PTV share
from the 2004–05 distribution
proceeding, which was based largely on
Bortz survey results. See Amended
Testimony of McLaughlin and
Blackburn, Trial Ex. 3007 at 7
(McLaughlin/Blackburn AWDT). Using
primarily data from the Cable Data
Corporation (CDC), they analyzed not
just changes in subscriber instances, but
external changes in various unit
measures from 2005 to the relevant
period, 2010–13, viz., distant subscriber
instances, distant signal transmissions,
and the balance of programming types
distantly retransmitted. See id. at 7–8.
Each of their unit measures indicated an
increase in the PTV relative share, and
all of their unit measures indicated a
basis for an increase in PTV’s relative
share for the period at issue in this
proceeding. As Ms. McLaughlin
testified, however, an increase in unit
measures does not compel a conclusion
that value also increased. 3/7/18 Tr. at
2648 (McLaughlin).
For valuation, McLaughlin and
Blackburn analyzed survey results,
regression analyses, and viewership
studies. For survey analysis, they used
the 2004–05 Bortz survey as a starting
point. The Bortz Survey omitted
respondents whose distantly
retransmitted signal carried only PTV or
only CCG or only PTV and CCG
together.173 McLaughlin and Blackburn
added those omitted stations to the
Bortz Survey results, using the overall
Bortz response rates by stratum, and by
assuming, for example, that the PTVonly systems would assign a relative
value to PTV of 100%.174 They then
170 SDC did not challenge the relative share
indicated by the Bortz results. 1998–99 Librarian
Order, 69 FR at 3609 n.15.
171 A ‘‘subscriber instance’’ as used in these
proceedings relating to distant signal retransmission
means one subscriber having access to one distant
signal.
172 The 2000–03 Distribution Order was a ‘‘Phase
I’’ or category allocation determination.
173 Ms. McLaughlin estimated that the average
number of omitted stations over the period 2010–
13 was 16 per year. See 3/5/18 Tr. at 2457
(McLaughlin).
174 Ms. McLaughlin also assumed that CCG-only
systems would assign a relative value of CCG at
100%. 2/20/18 Tr. at 719–20 (Mathiowetz); 3/6/18
Tr. at 2291 (Frankel). In fact, not all Canadian
programming falls within the CCG category for
In re Pandora Media, 6 F. Supp. 3d 317,
354 (S.D.N.Y. 2014), aff’d sub nom.,
Pandora Media, Inc. v. ASCAP, 785 F.3d
73 (2d Cir. 2015).
In the present case, Dr. Israel has not
attempted to make such a structured
analysis. Rather, the Judges understand
his argument to be based on the
assumption that the rights at issue are
comparable (i.e., the programs can be
categorized in a similar manner) and the
buyers/licensees (the CSOs) are
identical in both markets. However, in
all other respects—regarding economic
circumstances, competitive positions,
and the nature of the seller/licensor—
the relative similarities or differences
are unexplored.
Accordingly, the Judges are reluctant
to put much weight on Dr. Israel’s Cable
Content Analysis. At most, the Judges
rely on his Cable Content Analysis as
demonstrating that JSC programming
enjoys a level of demand out of
proportion to its broadcast minutes, not
inconsistent with the results of his
regression analysis and Dr. Crawford’s
regression analysis.
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recalculated the Bortz Survey relative
value for PTV, by stratum, using the
relative values she determined.
McLaughlin and Blackburn noted that
the increase resulting from their
augmentation of the Bortz Survey
yielded a smaller PTV relative value
(9.9%) than did the Horowitz Survey
(15.8%), which included PTV- and
CCG-only systems from the outset. They
attributed this discrepancy to the
participation bias evident in the Bortz
data, i.e., that fewer eligible systems
carrying PTV responded to the Bortz
Survey than the Horowitz Survey. See
Rebuttal Testimony of McLaughlin and
Blackburn, Trial Ex. 3002, at 4
(McLaughlin/Blackburn WRT).
On rebuttal, McLaughlin and
Blackburn noted that their own
calculations augmenting the Bortz
survey probably also underestimated the
relative value of PTV, because they
originated with the 2004–05 Bortz
survey, which was tainted with
participation bias. See id. at 4.
McLaughlin and Blackburn asserted that
participation bias also discounted the
value of the 2010–13 Bortz Survey as an
accurate measure of the relative value of
PTV programming. Id. at 5.
McLaughlin and Blackburn looked at
Professor Crawford’s econometric study
to confirm that marginal value per
minute of distantly retransmitted
programs changed in a like manner to
her unit measurements. She noted
increases in relative value from Dr.
Waldfogel’s 2004–05 regression
analysis, on the one hand, and Professor
Crawford’s and Dr. Israel’s regression
analyses on the other: 20.8% under
Professor Crawford’s analysis and 15%
using Dr. Israel’s analysis. 3/7/18 Tr. at
2472–73 (McLaughlin). As Ms.
McLaughlin testified, the Crawford
study establishes a price, from which
value may be ascertained: ‘‘value is . . .
a quantity times a price. . . . ’’ 3/7/18
Tr. at 2653 (McLaughlin).
Ms. McLaughlin opined that
viewership is just another unit measure,
not a valuation. Nonetheless, she
contended that the results of Dr. Gray’s
viewership analysis were consistent
with the survey and regression analyses,
indicating a PTV relative market value
of 12.6%. See McLaughlin/Blackburn
WDT at 23.
royalty purposes. CCG conceded that, for example,
some programming broadcast on Canadian stations
should rightfully be attributed to the SDC. 3/7/18
Tr. at 2675 (Erdem); Boudreau CWDT at 3–4, 10.
The volume of mischaracterized programming is
not great, but, as Professor Mathiowetz pointed out,
a change in the relative allocation to any one
category necessarily changes the allocation to other
categories. 2/20/18 Tr. at 701 (Mathiowetz).
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The Judges find that quantifying
changes in various unit measures, while
not without corroborative value, is not
a definitive approach to relative
valuation, especially in comparison to
other more probative approaches, such
as regression analyses. Apparently, PTV
ultimately made the same assessment.
See PTV PFF ¶ 11 (‘‘[Professor]
Crawford’s econometric framework is
the best suited methodology to
determine the claimants’ shares in this
proceeding for the years 2010 through
2013.’’). Accordingly, the Judges
consider PTV to have adopted Professor
Crawford’s regression analysis as the
methodology on which it has relied in
this proceeding.
VII. Nonparticipation Adjustment for
PTV
In its proposed findings of fact and
conclusions of law, PTV raised the issue
of Basic Fund allocation adjustment to
account for PTV not being a participant
in the 3.75% Fund. See PTV PFF/PCL
at ¶¶ 43–45. Although there was
mention of the 3.75% Fund in the
record of the proceeding, no party
addressed the issue comprehensively.
The Judges issued an order seeking
additional briefing, including an inquiry
about both the 3.75% Fund and the
Syndex Fund. See Order Soliciting
Further Briefing (Jun. 29, 2018) (June 29
Order). Specifically, the Judges asked
[w]hether the interrelationship between and
among the Basic Fund, the 3.75% Fund, and
the Syndex Fund affects the allocations
within the Basic Fund, if at all, and, if so,
how that affect should be calculated and
quantified.
June 29 Order at 1. The Judges expressly
asked for legal analysis of the issue. The
Judges refused to allow introduction of
any new evidence but agreed to accept
affidavits, if appropriate, to clarify the
record evidence of any witness. Id. at 2.
In their responses, the parties agreed
that only Program Suppliers were
entitled to any royalties in the Syndex
Fund and that the size of the fund was
so insignificant in context that the
Judges should not make any adjustment
to allocations in the Basic Fund to
compensate for any party’s exclusion
from the Syndex Fund. See, e.g., SDC
Brief at 1 n.1; SDC Responsive Brief at
5 (‘‘given the minuscule amount of
money in the Syndex Fund, any
calculation to compensate for that fund
would constitute nothing more than a
rounding error to a second or third
decimal place. . . .’’). The parties
offered analysis and argument regarding
the 3.75% Fund.
The essence of the Judges’ question is
whether the record evidence was
intended to propose an allocation of all
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3603
royalty funds in all three funds, which
might imply an adjustment to the Basic
Fund allocations for parties that did not
participate in the other two funds.
Program Suppliers submitted affidavits
from their witnesses asserting that their
analysis focused on the Basic Fund
only. Accordingly, according to the
Program Suppliers’ argument, the
Judges should simply scale the Basic
Fund allocation by eliminating PTV
from the calculation of allocation
percentages for the 3.75% Fund. See
Program Suppliers’ Responsive Brief at
6. PTV and the SDC both argued
contrariwise that the Judges should
scale the Basic Fund up for PTV. PTV/
SDC derived their argument from prior
allocation determinations. See PTV
Brief at 5–7; SDC Brief at 1–5.
All parties agree that the PTV category
is ineligible for an allocation of royalties
assigned to the 3.75% Fund.175 The
Judges found, however, that the parties
did not agree whether PTV’s
nonparticipation in the 3.75% Fund
affects the allocations within the Basic
Fund. Moreover, the Judges found that
the arguments and evidence presented
by the parties was insufficient for the
Judges to resolve the issue. That
problem was compounded by the fact
that prior determinations, regarding
how the 3.75% Fund allocations might
affect the Basic Fund allocation, were
themselves contradictory and did not
address all the issues the Judges have
concluded are relevant. Consequently,
on June 29, 2018, the Judges entered an
Order soliciting further briefing
regarding:
Whether the interrelationship between and
among the Basic Fund, the 3.75% Fund, and
the Syndex Fund affects the allocations
within the Basic Fund, if at all, and, if so,
how that affect should be calculated and
quantified.
Order Soliciting Further Briefing (Jun.
29, 2018) (3.75% Fund Order).176 In
175 The five parties eligible to share the royalties
allocated to the 3.75% Fund (CCG, CTV, JSC,
Program Suppliers, and the SDC) agree that, to
reflect PTV’s nonparticipation in the 3.75% Fund,
the Judges must adjust each eligible group’s share
of that fund in proportion to its respective share of
the Basic Fund. See 2004–05 Distribution Order, 75
FR at 57071; Declaration of Howard Horowitz ¶ 4
(Jul. 13, 2018); Declaration of Jeffrey S. Gray ¶ 8 (Jul.
16, 2018); see also JSC Initial Brief at 3–4. The
Judges apply this approach in allocating shares in
the 3.75% Fund in the present proceeding.
176 The parties agreed that Program Suppliers are
entitled to receive 100% of the remaining royalties
from the Syndex Fund. Further, the amount in that
Fund, less than $10,000 per six-month accounting
period, see JSC Initial Brief at 2 n.1, is so low that,
even assuming arguendo allocations to the Syndex
Fund would require an adjustment to the Basic
Fund, such an adjustment would be
‘‘inconsequential.’’ CTV Initial Brief at 11 n.20; see
also SDC Initial Brief at 1 n.1 (the Syndex Fund
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accordance with the 3.75% Fund Order,
the parties filed briefs and responding
briefs on these issues, The Judges
weighed the parties’ arguments and
based on their analysis, the Judges do
not adjust PTV’s share of the Basic Fund
to reflect its nonparticipation in the
3.75% Fund or to reflect any alleged
inconsistencies between the record
evidence, on the one hand, and the
separate allocations to the Basic Fund
and the 3.75% Fund, on the other.
A. Arguments of the Parties
The parties disagree as to how, if at
all, the scaling of the 3.75% Fund
allocations might affect allocations in
the Basic Fund. PTV argues that it is
entitled to an ‘‘Evidentiary
Adjustment,’’ 177 whereby its share of
the Basic Fund is ‘‘bumped up’’ 178 to
offset its nonparticipation in the 3.75%
Fund. PTV Initial Brief at 1–2. PTV
alleges that this increase is necessary
because ‘‘[t]he surveys and econometric
estimates of value to CSOs determine
shares of the Combined Royalty Funds
for each of the programming claimants’’
and that ‘‘[a]s a result, in order for PTV
to receive the share of total value to
CSOs estimated by the . . . experts, it
must receive a larger share of the Basic
Fund, since it will receive no share from
the [3.75% Fund].’’ Id. at 7 (quoting
McLaughlin/Blackburn WDT at 24–25).
In addition, PTV maintains that it is
entitled to this Evidentiary Adjustment
regardless of whether the Judges allocate
the Basic Fund shares based on survey
evidence, regression evidence, or
viewing evidence. PTV Responding
Brief at 12–21. PTV also argues that this
result is supported by precedent and by
the record in this proceeding. PTV
Initial Brief at 10–16.179
JSC, CTV, and the SDC agree that
prior rulings support PTV’s assertion
comprises ‘‘only about 0.01% of total royalties paid
in 2010–2013.’’). Accordingly, the discussion in this
section is limited to the impact, if any, of the
allocations to the 3.75% Fund on the allocations in
the Basic Fund.
177 PTV broadly defines the phrase ‘‘Evidentiary
Adjustment’’ as the process by which ‘‘the Judges
must . . . convert the [evidentiary] studies’
estimated shares based on the ‘Combined Royalty
Funds’ [i.e., estimated without explicit regard to an
itemization among the three specific funds] to
shares tailored to the particular funds from which
the parties are entitled to recover.’’ Id. at 1. For the
sake of clarity, the Judges utilize the phrase
‘‘Evidentiary Adjustment’’ more narrowly in this
Determination, to mean only the potential bump up
of PTV’s share of the Basic Fund to account for its
nonparticipation in the 3.75% Fund.
178 Of course, because the Basic Fund is finite,
any bump up in PTV’s share would necessitate a
decrease in the percentage allocations to the other
five claimant groups proportionate to their relative
shares (inter se) of the Basic Fund.
179 The Judges discuss the relevant prior rulings,
infra, section 0.
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that it is entitled to a bump up in its
Basic Fund share, but only to the extent
the Judges tie the Basic Fund allocations
to the Bortz Survey results and no other
allocation methodology.180 Those
parties maintain that the language in
prior rulings supports such an
adjustment only to that limited extent.
See JSC Initial Brief at 7–8; CTV Initial
Brief at 10; SDC Initial Brief at 9–10.
By contrast, CCG argues that, in light
of the evidence presented, PTV’s Basic
Fund shares should be adjusted upward,
regardless of the allocation methodology
employed by the Judges, to account for
PTV’s non-participation in the 3.75%
Fund. See CCG Initial Brief at 6.
At the other extreme, Program
Suppliers oppose any increase in PTV’s
Basic Fund share, arguing that such an
increase ‘‘effectively, albeit indirectly,
compensates PTV for royalties to which
it is not entitled.’’ Program Suppliers
Initial Brief at 2. Further, Program
Suppliers argue that relevant prior
rulings that may have suggested PTV
was entitled to this upward adjustment
were based on incorrect reasoning and
that none of them ‘‘rises to the level of
controlling precedent.’’ Id. at 7; see
Program Suppliers Responding Brief at
2. Finally, arguing in the alternative,
Program Suppliers assert that, even
under PTV’s view of the relevant prior
rulings, PTV would not be entitled to
the Evidentiary Adjustment it seeks
unless ‘‘PTV’s Basic Fund share was
derived solely from the Bortz Survey.’’
Program Suppliers Initial Brief at 7.
B. Analysis
1. Statutory Law and Regulations
Any upward adjustment of PTV’s
share of the Basic Fund to account for
its non- participation in the 3.75% Fund
would be inconsistent with the
regulations that established the 3.75%
Fund because CSOs are expressly
exempted from paying into the 3.75%
Fund for the distant retransmission of
noncommercial educational stations.
See 37 CFR 387.2(c)(2).181
180 In prior rulings by the Judges and the
Librarian (in the CARP era), the Bortz survey was
the only survey of CSO representatives given any
credence. In the present case, the Horowitz Survey
also surveyed CSO representatives. The Judges find
no basis to treat these two surveys differently in
connection with the issue of whether PTV should
receive an increase in its Basic Fund share to
account for its nonparticipation in the 3.75% Fund.
181 The original regulatory text was located in 37
CFR, part 308. See 37 CFR 308.2(c)(2). In 2016, the
Judges recodified this provision in Part 387,
without changing the relevant language. See
Adjustment of Cable Statutory License Royalty
Rates, 81 FR 24523 (April 26, 2016); Adjustment of
Cable Statutory License Royalty Rates 62812 (Sept.
13, 2016) (Note that the CFR version of Part 387
erroneously lists the second Federal Register page
cite as page 62813.).
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More particularly, the CRT
established the 3.75% Fund in 1982 to
offset the negative economic effects on
owners of copyrights on commercial
programming arising from the FCC’s
elimination of its rule setting a ceiling
on the number of distant commercial
stations a CSO could retransmit. See
Final Rule, Adj. of the Royalty Rate for
Cable Sys., 47 FR 52146 (Nov. 19, 1982).
The regulation implements
Congressional policy as expressed in 17
U.S.C. 801(b)((2)(B), which provides
that ‘‘[i]n the event that the . . . [FCC]
. . . permit[s] the carriage by cable
systems of additional television
broadcast signals beyond the local
service area . . . the royalty rates
established by section 111(d)(1)(B) may
be adjusted to ensure that the rates for
the additional [DSEs] resulting from
such carriage are reasonable in light of
the changes effected by the [FCC]
. . . . ’’). See also Malrite T.V. of New
York, Inc. v. FCC, 652 F.2d 1140, 1148
(2d Cir. 1981) (‘‘The plain import of
§ 801 is that the FCC, in its development
of communications policy, may increase
the number of distant signals that cable
systems can carry and may eliminate the
syndicated exclusivity rules, in which
event the [CRT] is free to respond with
rate increases.’’).182
Thus, any upward adjustment in the
Basic Fund by the Judges to
‘‘compensate’’ PTV—i.e., noncommercial stations—would constitute
an unlawful back-door attempt to
modify this regulation and would be
inconsistent with the statutory
provision on which it is based. See
generally 5 U.S.C. 706(2)(A) and (C)
(agency action unlawful if ‘‘not in
accordance with law’’ or ‘‘in excess of
statutory jurisdiction, authority, or
limitations, or short of statutory right.’’).
2. Administrative Process
Even assuming arguendo that
applicable statutory law permits the
adjustment PTV seeks, any such
adjustment would amount to an
adjudicatory change to an economic
policy that was created through a
separate administrative rulemaking
proceeding initiated for the express
purpose of protecting only those
copyright owners who, as a result of
FCC action, lost the protection afforded
by the ceiling on the number of a CSO’s
distant retransmissions of commercial
broadcasts. See 47 FR 52146. The Judges
182 In economic terms, the new 3.75% Fund
royalties substitute a tariff for a quota, in order to
maintain some form of protection of the value of
copyrights on local commercial programs in
markets into which CSOs would now be able to
retransmit an unlimited number of commercial
stations from distant locales.
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will not shoehorn a de facto change in
the regulations in this adjudicatory
proceeding by permitting PTV to share
in the royalty revenue collected by the
levy of the ‘‘penalty rate’’ 183 of 3.75%
of gross receipts.
3. Unauthorized Redistribution of
Wealth and Income
Any adjustment upward to PTV’s
Basic Fund allocation to account for its
nonparticipation in the 3.75% Fund
would amount to a redistribution of
wealth and income by the Judges that is
not authorized by law or regulation.
That is, any reduction in the Basic Fund
royalties paid to owners of copyrights
on programs distantly retransmitted on
commercial stations to ‘‘compensate’’
PTV for its nonparticipation in the
3.75% Fund would constitute the
imposition of an economic loss on the
former and an economic windfall on the
latter, in terms of the value of the
program copyrights (a redistribution of
wealth) and the flow of royalties
realized from such ownership (a
redistribution of income). The Judge
find no basis in law to support such a
transfer of wealth or income.
PTV argues though that ‘‘[n]othing
could be further from the truth’’ than
the characterization of its position as
seeking to share in the 3.75% Fund.
PTV Responding Brief at 5. In point of
fact, PTV’s argument is tantamount to an
attempt to appropriate value from the
3.75% Fund. Although PTV does not
seek a ruling that it is legally entitled to
share in the 3.75% Fund, it seeks a
ruling that it is economically entitled to
appropriate value from the Basic Fund,
as measured by its non-participation in
the 3.75% Fund. The Judges are as
concerned with the economic incidence
of the application of the so-called
Evidentiary Adjustment as they are with
the legal incidence of PTV’s attempt to
appropriate wealth and income from a
fund that, by law, belongs to other
claimants.184
183 See, e.g., PTV Initial Brief at 4 (3.75% rate
‘‘sometimes called the ‘Penalty Rate’ ’’ because it
applies higher royalty rate ‘‘to the retransmission of
additional distant signals beyond the limited
number that cable systems could carry under the
[f]ormer FCC Rules.’’).
184 The distinction between economic incidence
and legal incidence is typically exemplified in the
analysis of sales taxes. The seller bears the legal
incidence by writing a check to the governmental
unit assessing the tax, but the seller and the
consumer share the economic incidence of the sales
tax, the latter paying a portion of the tax in the form
of a higher prices for the taxed item, with the
allocation of the economic incidence between
merchant and consumer determined by the
elasticity of demand for the taxed item. See R.
Posner, Economic Analysis of Law at 491–495 (6th
ed. 2003). Analogously, the economic incidence of
PTV’s argument is transparent; although the legal
incidence of its argument—bumping up its Basic
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In the face of the foregoing points,
PTV and all the other parties except
Program Suppliers nonetheless argue
that two factors—evidence and
precedent—support the subsidy sought
by PTV. The two arguments are
considered below.
4. The Evidence-Based Argument
As an initial matter, the Judges note
that the evidence-based argument
asserted by PTV and other parties in
support of the Evidentiary Adjustment
cannot overcome the legal points,
discussed above, that make it legally
impermissible to bump up PTV’s share
of the Basic Fund.
Additionally, the Judges find the
evidence-based argument made by and
on behalf of PTV, standing alone, to be
insufficient. Broadly, PTV and other
parties assert that the Evidentiary
Adjustment is necessitated by the
purported nature of the survey evidence
and the regression evidence.185 The
Judges reject this argument.
a. The Survey Evidence
With regard to the survey evidence,
PTV notes that the survey questions did
not explicitly ask the respondents to
‘‘differentiat[e] between the Basic,
3.75% and Syndex Rates,’’ and ‘‘their
responses presumably were based on
their past payments at all rates into the
Combined Royalty Funds.’’ PTV Initial
Brief at 10–11 (emphasis added); see
also CTV Initial Brief at 6 (survey
responses measure relative value of
distant signals ‘‘without regard to the
royalty rate paid for any particular
signal’’). According to this argument,
the survey responses could not reflect
the effects, if any, of the higher royalty
rate of 3.75% of gross receipts paid by
CSOs into the eponymous 3.75% Fund.
Rather, according to this argument, the
survey responses reflected relative value
in the combined royalty funds.
Therefore, PTV asserts that it is entitled
to the Evidentiary Adjustment, bumping
up its Basic Fund allocation to offset the
economic effect of its nonparticipation
in the 3.75% Fund.
The Judges find this argument to lack
sufficient merit. The two surveys were
designed to allow for the selection of
respondents to the surveys who were
the individuals most responsible for
programming carriage decisions at the
Fund share—is not expressly prohibited, 100% of
the economic incidence of its argument is a shift to
itself wealth and income from the lawful
participants in the 3.75% Fund.
185 Again, PTV makes the same argument with
regard to the viewing evidence. However, that issue
is moot, because, as explained supra, the Judges do
not apply the viewing evidence in making
allocations.
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CSO. See Bortz Survey at 14–15 & App.
B; Horowitz WDT at 9, 24; see also
2/15/18 Tr. 254 (Trautman); 3/16/18 Tr.
4109 (Horowitz). Neither survey was
designed to question whether the
individuals who self-reported in fact
possessed this knowledge, or to test the
extent or specific aspects of
respondents’ knowledge.
The Judges decline to presume, in the
context of this 3.75% Fund dispute, that
the survey respondents lacked
knowledge as to the variable royalties
paid for distantly retransmitted stations,
when the accepted survey evidence
upon which the Judges rely (the same
type of survey evidence on which their
predecessors have consistently relied)
presumes the opposite, i.e., that the
respondents are indeed knowledgeable
regarding this sector of the cable
industry.186 Indeed, the argument that
the Judges should presume that the
survey respondents were ignorant of the
impact on royalty costs of retransmitting
a given number of distant local
stations 187 also proves too much,
because it would call into question any
reliance on the survey evidence.
Moreover, the Bortz Survey includes
a question—Question #3—in which the
respondents are directed to consider the
costs associated with the retransmission
of categories of programs. Although the
question is linked to the cost of program
categories rather than the cost of
retransmitting entire stations, the
question was designed as a ‘‘warm-up’’
question that would encourage
186 The Judges part company with the CARP
determination (adopted by the Librarian), allocating
royalties for 1998 and 1999, in which the CARP
stated that the adjustment is warranted because
‘‘the Bortz respondents . . . presumably did not
know that PTV would not be eligible to receive part
of their budget allocation . . . . ’’ Distribution of
1998–1999 Cable Royalties, at 26 n.10 (Oct. 21,
2003), adopted by the Librarian 69 FR 3606 (Jan. 26,
2004). When the Judges have qualified and relied
upon expert survey witnesses, the Judges cannot,
without contrary evidence, inject a presumption
inconsistent with their qualifications. The Judges
consider that and other prior rulings infra.
187 The Judges find no reason to presume that
survey respondents who were otherwise deemed by
the survey experts, based on answers to
introductory questions, to be knowledgeable about
their programming and carriage decisions, would
not also be aware that they could add an
educational station without incurring the higher
3.75% royalty, whereas the addition of a
commercial station in certain instances did trigger
the 3.75% royalty. All parties accepted, and the
Judges agreed, that the individuals responsible for
making distant retransmission decisions for the
cable systems understood that the CSO paid the
minimum fee of 1.064%, regardless of whether they
distantly retransmitted any local stations. It would
be inconsistent to presume, on the one hand, that
CSO executives were cognizant of a 1.064%
minimum fee, but were ignorant of the 3.75% rate—
more than 300% greater than that minimum fee—
when the responsible executives answered the
surveys.
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respondents to be cognizant of the costs
associated with their decisions to
distantly retransmit stations containing
the categories represented in this
proceeding. See Bortz Survey, App. at
15. Thus, the Bortz Survey evidence
tends further to support the assumption
that the respondents were cognizant of
the costs, including the royalty costs,
associated with retransmitting distant
local stations.188
For these reasons, the Judges cannot
adopt a presumption that the survey
respondents, deemed knowledgeable in
all other pertinent respects regarding
distant retransmissions of local stations,
were ignorant of the royalty costs
associated with the number and type of
local stations they carried. Thus, there
is not a sufficient evidentiary predicate
for the application of the Evidentiary
Adjustment.189
b. The Regression Evidence
Turning to the Crawford and Israel
regressions, PTV’s arguments fare no
better. As the SDC explained in its
briefing: ‘‘Each regression includes an
indicator for retransmission of a 3.75%
signal [with] statistically significant
coefficients for the indicator variables
suggest[ing] that there is a systematic
difference in the amount of royalties
paid by systems and subscriber groups
that retransmit 3.75% signals and those
that do not.’’ SDC Initial Brief at 4.
Thus, the Crawford and Israel regression
analyses demonstrated a correlation
between the amount of royalties paid by
a CSO and its participation in the 3.75%
Fund. This correlation is essentially
tautological. CSOs who pay the higher
3.75% royalty rate for the distant
retransmission of one or more
additional commercial local stations
(previously ‘‘non-permitted’’ under the
188 Although Question #3 referred to program
categories, it is still relevant to the 3.75% Fund
issue, because only the five other claimant
categories (i.e., other than PTV) could have
triggered the higher royalty cost. Thus, a
knowledgeable survey respondent could not be
presumed to lack knowledge of the different impact
on value from adding an educational station rather
than a commercial station.
189 In response to the Judges’ 3.75% Fund Order,
Program Suppliers submitted a Declaration by
Howard Horowitz, who designed the Horowitz
Survey, in which he stated that it is ‘‘appropriate’’
to apply the allocation of the Horowitz Survey
shares ‘‘to any fund in which all parties
participate.’’ Declaration of Howard Horowitz ¶ 4
(July 16, 2013). This statement would support the
Judges’ decision, but the Judges give no weight this
declaration, for two reasons. First, Mr. Horowitz did
not offer any such testimony during the proceeding;
therefore his declaration is impermissible new
testimony (not clarifying testimony). Second, in the
absence of persuasive hearing testimony, Mr.
Horowitz cannot opine as to what would be the
‘‘appropriate’’ allocation of the Horowitz Survey
shares. What is an appropriate allocation in this
context is a question of law reserved to the Judges.
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since-repealed FCC ‘‘ceiling’’ regulation)
will pay higher royalties than CSOs that
pay no more than 1.064% to retransmit
such stations. See id. (correlation is ‘‘not
surprising, considering that
retransmission of a 3.75% signal by
definition carries a higher rate’’).
Moreover, Dr. Crawford confirmed that
the coefficient for the 3.75 control
variable in his regression analysis was
both large and statistically significant.
Crawford WDT at App. B Fig. 22.190
Likewise, Dr. Israel ‘‘[s]imilar to Dr.
Waldfogel,’’ included an indicator
variable ‘‘for whether a CSO pays the
special 3.75 percent fee,’’ and he held
this factor ‘‘constant’’ in order to
determine the extent of any correlation
between royalty payments and
additional minutes of programming
category content. Israel WDT ¶¶ 33–34.
In his regression model, Dr. Israel
estimated a coefficient of 41,918 for his
‘‘Indicator for Special 3.75% Royalty
Rate,’’ multiple times the coefficients he
estimated for any other variable. Id.
¶ 36, Table V–1.
Thus, the regression evidence in the
hearing records provides independent
support for distinguishing the
allocations in the 3.75% Fund from the
allocations in the Basic Fund.
Accordingly, the regression evidence
provides substantial support for
rejecting PTV’s proposed bump-up in its
Basic Fund allocation to offset its nonparticipation in the 3.75% Fund.191
190 CTV, on whose behalf Dr. Crawford undertook
his regression analysis, argues in its briefing that Dr.
Crawford’s 3.75% Fund coefficient ‘‘may already be
accounted for to some degree’’ in his overall
regression analysis. CTV Responding Brief at 7
(emphasis added). Not only is this statement highly
conditional (as noted by the italicized language,
CTV also did not submit a supporting declaration
from Dr. Crawford properly clarifying how his
hearing testimony supported this assertion, despite
the Judges’ invitation in the 3.75% Fund Order to
submit witness statements. Instead, CTV referred to
Dr. Crawford’s hearing testimony on an unrelated
issue in which he stated, with regard to a different
control variable, that its coefficient estimate should
be included in a regression analysis when there are
‘‘good’’ economic and statistical reasons to do so.
See 2/28/18 Tr. 1643 (Crawford). The Judges do not
dispute this point, but it is not relevant to the task
at hand. As an indicator (dummy) variable in a
regression designed to generate estimates for
relative value results among program categories, the
3.75% Fund variable was designed to control for
the influence of the 3.75% Fund impact on those
relative values. Dr. Crawford further testified that
any control variable that would correlate
significantly with the dependent variable should be
included in the regression model so that it does not
bias the coefficients of interest (the program
categories’ coefficients in the present case), Id. at
1644 (Crawford). Thus, the excerpt from Dr.
Crawford’s testimony, when considered in context,
does not demonstrate that the impact of
participation in the 3.75% Fund is already
‘‘accounted for’’ in his overall regression analysis in
a manner relevant to the present issue.
191 The Judges emphasize a distinction between
their consideration of the 3.75% Fund regression
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5. The Effect of Prior Decisions
The second argument raised by PTV
and supported by several other parties,
is that the Judges are bound by prior
decisions of CARP panels, the Librarian,
and the Judges, in which the
Evidentiary Adjustment was either
applied or found to be generally valid.
PTV Initial Brief at 10–12; PTV
Responding Brief at 9–12; JSC Initial
Brief at 4–6; CTV Brief at 1–6; SDC
Initial Brief at 1–7. That is, they argue
that prior rulings, by the force of their
reasoning or as controlling law, require
the Judges to bump up PTV’s share of
the Basic Fund to account for its nonparticipation in the 3.75% Fund.
More particularly, PTV and other
parties make this argument in several
alternative forms, from broad to narrow.
PTV and CCG argue that prior rulings
support increasing PTV’s share of the
Basic Fund to reflect not only the
survey-based allocations but also the
regression-based allocations, whereas
JSC, CTV, and the SDC assert that PTV’s
survey-based allocations should be
bumped-up, only to the extent the
Judges apply the survey share
percentages in making their overall
allocations.
The Judges conclude that there is
neither controlling law nor any prior
determination or other ruling that binds
them on this issue. Further, the Judges
do not agree with the explanations in
two prior rulings that applied or
legitimized the application of the
Evidentiary Adjustment. To the extent
those prior rulings might, arguendo,
constitute controlling law or might,
arguendo, have properly applied or
legitimized the Evidentiary Adjustment
on the record in those cases, the Judges
find those rulings distinguishable, based
on the particular facts of the present
case.
a. The 1986 CRT Determination
In a 1986 determination regarding the
distribution of 1983 royalties, the CRT
ruled that public television (represented
by PBS in that proceeding) was not
entitled to participate in the 3.75%
coefficients and their evaluation of the various
coefficients relied on by Dr. Erdem to predict the
level of royalty payments. The Judges discounted
Dr. Erdem’s emphasis on coefficients relating, for
example, to the number of CSO subscribers, because
such coefficients, as Dr. Crawford testified, simply
re-created the royalty formula. However, now the
Judges are called upon to distinguish and apply a
separate royalty formula—the formula for the 3.75%
Fund—from the formula for the Basic Fund. In this
latter context, the coefficients related to the 3.75%
Fund are indeed relevant. Accordingly, what
constituted vice in the critique of the Crawford
regressions with regard to allocations among the
program categories is virtue in distinguishing
between two different categories of rate formulas.
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Fund because ‘‘non-commercial
educational stations could be carried on
an unlimited basis prior to FCC
deregulation, and . . . no cable operator
paid the 3.75% rate to carry any
noncommercial stations.’’ 1983 Cable
Royalty Distribution Proceeding, 51 FR
12792, 12813 (Apr. 15, 1986), aff’d sub
nom. Nat’l Ass’n of Broadcasters v.
CRT, 809 F.2d 172, 179 n.7 (2d Cir.
1986) (‘‘because cable carriage of
noncommercial educational stations
was not limited by the old distant signal
rules, PBS is not eligible for royalties at
the new 3.75% rate’’). Further, there
was no argument by the parties, and no
discussion in the 1986 determination,
with regard to the issue at hand, viz.
whether PTV should receive an upward
adjustment to its Basic Fund allocation
to account for its non-participation in
the 3.75% Fund. See 51 FR 12792 et
seq.
Accordingly, the Judges find no
aspect of the 1986 determination to be
on point with regard to whether PTV is
entitled to an upward adjustment in its
Basic Fund share to offset its nonparticipation in the 3.75% Fund.
Indeed, the 1986 determination would
be consistent with the rejection of such
an adjustment.
b. The 1992 CRT Determination
The next CRT determination
concerned distribution of cable
television royalties for the 1989 year.
1989 Cable Royalty Distribution
Proceeding, 57 FR 15286 (Apr. 27,
1992). PBS was again denied any share
of the 3.75% Fund ‘‘because PBS
stations are not paid for at the 3.75%
rate . . . . ’’ 57 FR at 15303.
In this 1992 case, public television
claimants, through PBS, requested the
bump up in their adjustment to the
Basic Fund that is at issue in the present
proceeding, i.e., ‘‘to back out the 3.75%
portion’’ from the Basic Fund. See 57 FR
at 15300. The CRT rejected this
proposed adjustment, relying on the
testimony of Paul Bortz (president of the
entity that administered the Bortz
Survey), who stated that ‘‘there was
nothing in his survey to suggest that
respondents were considering their
1989 copyright payment as the fixed
budget they were allocating.’’ Id.
The Judges find this rationale to be
cryptic at best, because there is no
obvious logical link between Mr. Bortz’s
description of the mindset of the CSO
survey respondents and its impact on
whether PBS’s share of the Basic Fund
should have been adjusted upward to
reflect the survey evidence. In fact, Mr.
Bortz’s testimony could be construed as
supportive of the upward adjustment in
the public television claimants’ share of
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the Basic Fund. Accordingly, the Judges
do not find any controlling or
persuasive authority in the 1992
determination that can serve as
guidance in the present proceeding.
c. The 1990–92 CARP Report and the
Librarian’s Order
In the proceeding to allocate royalties
for the 1990–1992 period, PTV argued
on behalf of public television claimants
for an Evidentiary Adjustment to its
share of the Basic Fund, as that share
was estimated by the CARP’s reliance
on the Bortz Survey.192 The CARP
ruled, with regard to the question of
whether to adjust PTV’s share of the
Basic Fund:
PTV also contends that a further
adjustment should be made in its award
because its total share of the adjusted Bortz
Survey must come entirely from the Basic
Fund and the Bortz survey does not
differentiate between the Basic fund and the
3.75 fund in which PTV does not participate.
. . .
PTV’s proposed further adjustment to
allow for its non-participation in the 3.75
fund is rejected for the same reason given by
the [CRT] in the 1989 proceeding. Mr. Bortz
specifically disavowed any intention or
implication in his survey to have
respondents answer based on their royalty
payments.
1990–92 CARP Phase I Distribution
Report 120, 124 (Jun. 3, 1996) (1990–92
CARP Report). The Judges find that the
CARP’s reliance on the prior reasoning
of the CRT only serves to repeat the
cryptic nature of that prior ruling, and
does not offer any basis on which the
Judges may rely to resolve the issue in
this proceeding.
When Congress instituted the CARP
process, it also charged the Librarian
with the duty to accept or reject, in
whole or in part, the decision of a
CARP, and charged the Register with the
duty to provide recommendations to the
Librarian. 17 U.S.C. 802(f) (2003)
(superseded). Discharging her duty in
that 1990–92 proceeding, the Register
made specific recommendations to the
Librarian regarding the issues pertaining
to the 3.75% Fund, all of which the
Librarian adopted. The Register
described, and the Librarian agreed, that
the CARP’s reasoning supporting its
distribution of the 3.75% Fund was ‘‘at
best, terse.’’ Distribution of 1990, 1991
and 1992 Cable Royalties, 61 FR 55653,
55662 (Oct. 28, 1996) (Librarian’s
Order).
In her recommendations, the Register
more specifically addressed the issue at
192 While this proceeding was pending, Congress
abolished the CRT. The proceeding continued
under the auspices of the CARP appointed to
distribute the royalties.
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hand, rejecting PTV’s request for the
Evidentiary Adjustment.
The Panel did not act arbitrarily in
rejecting PBS’s 193 Bortz adjustment for the
same reasons articulated by the [CRT] in
1989. . . . [T]he approach used in the Bortz
survey itself remained unchanged. As in the
1989 proceeding, Bortz did not ask cable
operators to base their program share
allocation according to the royalties they
actually paid. Thus, in awarding PBS
programming a specific share, a [CSO] did
not take into account that its stated share
only applied to the Basic Fund and not the
3.75% fund. . . . The Bortz survey numbers
therefore do not necessarily require the
adjustment demanded by PBS. Thus, the
Panel was reasonable in adopting the [CRT’s]
1989 rationale because PBS’s argument, and
the design parameters of the Bortz survey,
were fundamentally the same.
Id. at 55668. However, for the first time
in a distribution proceeding, the door
was opened to an argument that this
Evidentiary Adjustment might be
appropriate in certain contexts, as the
Register further recommended:
The Panel did not state that it was using
PBS’s Bortz numbers as the sole means of
determining its award. In fact, the Panel
awarded PBS a share that is less than the
unadjusted Bortz survey numbers. Had the
Panel stated that it was attempting to award
PBS its Bortz share, then PBS’s argument
might have some validity. However, since the
Panel did not, it did not act arbitrarily in
denying PBS’s requested adjustment.
Id. (emphasis added).
d. The 2003 CARP Determination and
the Librarian’s Order
In 2003, for the first time, public
television claimants, through PTV, were
successful in obtaining a ruling that
supported the application of the
Evidentiary Adjustment. Specifically, a
CARP adopted PTV’s argument that it
was entitled to the Evidentiary
Adjustment, whereby its share of the
Basic Fund was increased to offset the
impact of its non-participation in the
3.75% Fund. The CARP Report was
adopted by the Librarian, upon the
recommendation of the Register. 1998–
99 CARP Report, supra note 144, at 26,
n.10, adopted by the Librarian, 69 FR
3606.
The 1998–99 CARP found that, based
on the evidence, PTV’s ‘‘raw Bortz
figure’’ was 2.9% for both 1998 and
1999, prior to the application of the
Evidentiary Adjustment. 1998–99 CARP
Report at 26 n.10. The CARP then, over
JSC’s opposition, bumped up this ‘‘raw’’
percentage ‘‘to account for PTV’s nonparticipation in the 3.75% . . . fund[ ].’’
Id. The CARP explained its rationale:
193 The Librarian identified the public television
claimants as the PBS claimants, rather than the PTV
claimants as had the CARP.
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The Adjustment makes sense in the context
of a CSO Survey where the respondents are
allocating a fixed budget among the various
claimant groups—unless JSC can
demonstrate that the respondents already
understood that PTV does not participate in
the 3.75% Fund. JSC has made no such
showing.
Id.
The CARP also sought to distinguish
the prior rejections of this Evidentiary
Adjustment by the CRT and the 1990–
92 CARP panel.
The Panel is aware that the 1989 CRT
rejected this Adjustment to Bortz and the
1990–1992 CARP adopted that rejection
. . . . The Panel believes the 1989 CRT and
1990–92 CARP did not fully appreciate the
logic supporting this Adjustment. It is
precisely because the Bortz respondents did
not answer based on their actual royalty
payments and presumably did not know that
PTV would not be eligible to receive part of
their budget allocation that the Adjustment is
warranted.
Id. (citation omitted) (boldface added).
However, the 1998–99 CARP Report did
not make an upward adjustment to
PTV’s overall Basic Fund allocation or
to any measure of its relative share of
the Basic Fund other than the Bortz
Survey percentage, concluding:
[W]e disagree with PTV’s assertion that it is
entitled to such an Adjustment no matter
which methodology is employed. . . . We
view PTV’s position that the adjustment
should be made for any methodology merely
as an attempt to circumvent mathematically
the legal precedents established by the CRT,
and PTV has presented no legal justification
for reversing these precedents.
5.49125%, the same level as in the prior
proceeding. Id. at 69; see 69 FR 3606,
3610, 3616 & n.32.
The Librarian, upon the
recommendation of the Register,
accepted the CARP Report in its
entirety. 69 FR at 3606. However,
neither the Register nor the Librarian
made any specific recommendations or
findings regarding the Evidentiary
Adjustment applied by the CARP to
increase PTV’s allocation floor from
2.9% to 3.2%. See 69 FR at 3616–17[.
In the present proceeding, Program
Suppliers assert that, because the CARP
set PTV’s Basic Fund share above the
3.2% floor, it had not actually applied
the Evidentiary Adjustment to the Bortz
Survey results. Therefore, Program
Suppliers argue that the CARP’s
analysis regarding the Evidentiary
Adjustment was mere dicta, rather than
a controlling endorsement of the
Evidentiary Adjustment. Program
Supplier’s Responding Brief at 3–4. The
Judges disagree with Program Suppliers’
characterization of that ruling. The fact
that PTV’s ultimate Basic Fund Share
exceeded the floor does not call into
question the ruling by the CARP or the
Librarian that the Evidentiary
Adjustment, in their opinion, should be
applied.194
e. The Judges’ 2010 Determination
In 2010, the Judges determined the
allocation of royalties for the 2004 and
2005 distribution years.195 See 2004–05
Distribution Order. There, the Judges
Id. Consistent with this limitation, the
applied the Evidentiary Adjustment on
1998–99 CARP did not apply the
behalf of PTV, as proposed by the
Evidentiary Adjustment to the
‘‘Settling Parties.’’ 196 Id. at 57070.
regression approach utilized by Dr.
However, the Judges did not engage in
Gregory Rosston, an economic expert
any analysis of the Evidentiary
who presented a regression analysis on
Adjustment (and indeed did not even
behalf of another party. See 1998–99
describe that adjustment or identify it
CARP Report, supra note 144, at 45–51
by name). Rather, they simply adopted
(discussing Rosston regression
approach). However, although the CARP as a ‘‘starting point’’ the augmented
Bortz Survey ‘‘which includes
did not apply the Evidentiary
Adjustment, it did not explicitly state its appropriate adjustments to the PTV
share’’ and then referred to paragraph
reasoning, nor did the CARP provide
317 of the ‘‘Settling Parties’’ Proposed
any specific rationale for not applying
Findings of Fact. That paragraph stated:
the Evidentiary Adjustment to the
Rosston regression approach, other than ‘‘Because PTV receives payments from
to refer to the general discussion in that only the Basic fund, an adjustment to
same report.. See id. at 48 n.21 & 59 n.29 the augmented survey results is needed
to produce PTV’s share of the Basic
(citing p. 26 n.10).
In the end, the CARP applied the
194 However, as discussed infra, for other reasons,
Evidentiary Adjustment by increasing
the Judges do not conclude that the decisions by the
PTV’s Basic Fund minimum allocation,
CARP and the Librarian to apply the Evidentiary
or ‘‘floor,’’ as derived from the Bortz
Adjustment are dispositive in the present
proceeding.
Survey, from 2.9% to 3.2%. 1998–99
195 Congress replaced the CARP system with the
CARP Report, supra note 144, at 25–26,
Judges in 2004 (effective 2005). Copyright Royalty
& n.10. The final allocation to PTV
and Distribution Reform Act of 2004, Public Law
though was based on additional
108–419, 118 Stat. 2341 (Nov. 30, 2004).
evidence, which led the CARP to
196 The ‘‘Settling Parties’’ were comprised of: JSC,
establish PTV’s share above this floor, at CTV, PTV, and Music Claimants. Id. at 57064.
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fund, as recognized by the CARP in the
1998–99 Proceeding.’’ Id.
In the present proceeding, PTV
further notes that, in that 2010
proceeding, Professor Waldfogel
asserted that his regression approach,
like the Bortz survey approach, had not
differentiated between the Basic Fund
and the 3.75% Fund, thus purportedly
supporting an application of the
Evidentiary Adjustment to the
regression allocations. PTV Initial Brief
at 14–15. PTV further asserts that
Professor Waldfogel’s testimony was
consistent with Dr. Rosston’s testimony
in the prior proceeding, supporting the
application of the Evidentiary
Adjustment to Basic Fund allocations
based on regression analyses. Id. at 13–
14. Notwithstanding that testimony, in
neither of those cases did the CARP, the
Librarian, or the Judges find that the
Evidentiary Adjustment should be
applied to the regression results. See
JSC Responding Brief at 7, 9.
6. The Prior Decisions Are Not Binding
The Judges do not find the foregoing
findings and conclusions sufficient to
overcome the analysis they undertake in
this proceeding. First, none of the prior
cases considered the dispositive
statutory or regulatory issues discussed
herein. Second, the prior cases are
factually distinguishable, because
neither the survey evidence nor the
regression evidence support the
application of the Evidentiary
Adjustment to PTV’s share of the Basic
Fund. Third, as explained below, as a
matter of law, the Judges are not duty
bound to apply the Evidentiary
Adjustment on behalf of PTV as it
relates to the survey evidence,
notwithstanding the conclusions in the
two most recent distribution cases.
The Copyright Act does not equate
relevant prior rulings with binding legal
precedent. Rather, the Act provides only
that the Judges shall ‘‘act on the basis
. . . of prior determinations and
interpretations . . . .’’ 17 U.S.C.
803(a)(1) (emphasis added). As the D.C.
Circuit has explained, this provision
does not mandate that the Judges abide
by specific findings in prior rulings,
provided the Judges set forth a
‘‘reasoned explanation’’ for a departure
from those findings. See Program
Suppliers v. Librarian of Congress, 409
F.3d 395, 402 (D.C. Cir. 2005). In the
present determination, the Judges have
explained the legal, administrative,
policy, economic, and factual reasons
why an application of the Evidentiary
Adjustment on behalf of PTV is
unwarranted. The two prior rulings that
applied the Evidentiary Adjustment did
not address these multiple factors, and
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certainly did not consider the issue at
the depth warranted by the
supplemental briefing required in this
proceeding.
Further, the prior decisions reveal
that the relevant tribunals went through
an evolution, from prohibiting the
application of the Evidentiary
Adjustment, to acknowledging its
potential application and, then, to
supporting its application. Thus, the
‘‘controlling’’ aspect of those prior
decisions, if any, appears to be the
proposition that this thorny issue needs
to be considered in detail, and that no
prior decision should be extended if the
successor tribunal, through reasoned
explanation, finds good cause to render
a decision different from the one that
immediately preceded it.
7. The Waiver Argument
In its Responding Brief, PTV asserts,
for the first time, that Program
Suppliers, the SDC, and JSC, each
‘‘waived’’ its right to contest the
application of the Evidentiary
Adjustment. PTV Responding Brief at
21–26.197 PTV makes two basic
arguments in support of its theory of
waiver. First, it argues that Program
Suppliers, the SDC, and JSC ‘‘knowingly
and intentionally’’ did not ‘‘submit
evidence or advance arguments’’
regarding the Evidentiary Adjustment,
seeking to depart from or to distinguish
the prior determinations that adopted
PTV’s construction of the Evidentiary
Adjustment. Id. at 21. Second, PTV
notes that none of these parties raised
the issue of the application of the
Evidentiary Adjustment in closing
arguments. Id. at 22. PTV acknowledges
that Program Suppliers did address the
issue previously, but only in response to
PTV’s PCL addressing the Evidentiary
Adjustment issue. See PTV Initial Brief
at 9 (citing Program Suppliers’ RPCL
¶ 12. Accordingly, PTV, relying on four
decisions,198 asserts that Program
197 There is an element of irony in PTV’s assertion
of waiver for the first time in its Responding Brief.
By not making this legal argument of waiver in its
July 16, 2018 Initial Brief, PTV prevented adverse
parties from addressing the issue of waiver. See,
e.g., U.S. v. Layeni, 90 F.3d 514, 522 (D.C. Cir.
1996); In re Brand Name Prescription Drugs
Antitrust Litig. 186 F.3d 781, 790 (7th Cir. 1999)
Although PTV might claim that it could not have
been certain it had the right to assert the waiver
argument until it had reviewed these parties’ Initial
Briefs, such a position would be belied by the fact
that PTV’s waiver argument is based on the alleged
absence from the hearing record of adverse facts
relating to facts or arguments concerning the
impact, if any, of the 3.75% Fund allocations on the
allocations of the Basic Fund. Thus, PTV appears
to have waived its waiver argument. Nonetheless,
the Judges consider and reject PTV’s waiver
argument on the merits.
198 The cases are cited at PTV’s Responding Brief
at 22 n.85 and discussed below.
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Suppliers, the SDC, and JSC waived
their arguments against the Evidentiary
Adjustment.
The Judges find PTV’s waiver
argument to be inapposite, given the
procedural posture of the proceeding.
The Judges found the hearing record
and legal arguments to be incomplete
with regard to the impact, if any, of
allocations in the 3.75% Fund on the
allocations in the Basic Fund. That
deficiency extended to PTV’s briefing as
well as to the briefing of the other
parties. In an attempt to cure the
incompleteness, the Judges, sua sponte,
entered the 3.75% Fund Order, which
specifically noted the insufficiency of
the facts (‘‘exhibits [and] witness
testimonies’’) and the law (‘‘legal
arguments’’), which could be remedied
by supplemental ‘‘memoranda of law,’’
as well as new affidavits that
‘‘clarif[ied]’’ the extant record. Id. at 1.
In sum, the deficiencies in the factual
presentations and legal briefings of the
parties were the bases for the Judges’
ordering of supplemental briefing.199 It
199 The Judges regularly exercise discretion to
seek supplemental briefing in order to address an
issue that had not been sufficiently addressed
during the hearing. A judicial order directing the
filing of supplemental papers is the preferred
method by which judges should address issues they
find to have been insufficiently considered. See
United States Nat’l Bank of Oregon v. Ind. Agents
of America, 508 U.S. 439 (1991) (affirming D.C.
Circuit’s sua sponte raising of unaddressed issue
and ordering supplemental briefing). Moreover,
supplemental briefing provides the parties a full
and fair opportunity to address relevant issues that
were insufficiently developed and argued. Trest v.
Cain, 522 U.S. 87, 92 (1997) (‘‘We do not say that
a court must always ask for further briefing when
it disposes of a case on a basis not previously
argued . . . [but] often . . . that somewhat longer
(and often fairer) way ‘round is the shortest way
home.’’) (dicta); see also R. Offenkrantz & A.
Lichter, Sua Sponte Actions in the Appellate
Courts: The ‘‘Gorilla Rule’’ Revisited, 17 J. App.
Prac. 113, 120 (Spring 2016) (noting the Supreme
Court’s ‘‘preference for ordering supplemental
briefing when a new issue is raised sua sponte
. . . . ’’); B. Miller, Sua Sponte Appellate Rulings:
When Courts Deprive Litigants of an Opportunity to
be Heard, 39 San Diego L. Rev. 1253, 1281–82,
1297–1300 (2002) (courts more likely to raise, sua
sponte, ‘‘questions of law,’’ and ‘‘routinely ask the
parties for supplemental briefs when deciding a
new issue.’’); R. Ginsburg, The Obligation to Reason
Why, U. Fla. L. Rev. 205. 214–15 (1985) (in D.C.
Circuit, if judges identify a potentially dispositive
point not raised by the parties, they generally invite
supplemental briefs).
In the present case, the Judges also have wide
statutory discretion to cure deficiencies in the legal
or factual record to mitigate the harm that might
otherwise necessitate a finding of waiver. See 17
U.S.C. 801(c) (‘‘The . . . Judges may make any
necessary procedural . . . rulings in any proceeding
under this chapter. . . . ’’). The ordering of
supplemental briefing is one example of the
exercise of that discretion, and its invocation
renders moot a claim that legal arguments had been
waived.
The parties’ supplemental briefing ultimately did
not address all of the legal reasons in the full detail
that the Judges now rely upon to conclude that they
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3609
would be anomalous for the Judges to
now reverse course and find that the
arguments relevant to this issue had
been waived prior to the submission of
supplemental filings, when those
deficiencies had themselves engendered
the 3.75% Fund Order.
The four cases PTV string cites in its
responding brief,200 are not on point,
and do not alter the Judges’ analysis.
U.S. v. Laslie,201 American Wildlands v.
Kempthorne,202 and U.S. v. L.A. Tucker
Truck Lines, Inc.,203 all involved
litigants who raised issues for the first
time during judicial review of action by
a trial court or administrative agency,
and thus had engaged in an ‘‘intentional
relinquishment of a known right,’’
which is the essence of an act of waiver.
Laslie, 716 F.3d at 614. These cases are
clearly distinguishable because: (1) The
arguments raised with regard to the
impact, if any, the 3.75% Fund has on
allocation of the Basic Fund relate to an
issue still before the tribunal hearing the
matter; (2) the Judges have called for
supplemental briefing on the very issue;
and (3) the Judges’ have concluded that
the issue can and should be decided as
a matter of law.
The final case cited by PTV is
Intercollegiate Broadcast. Sys., Inc., v.
Copyright Royalty Bd., 574 F.3d 748
(D.C. Cir. 2009). There, the D.C. Circuit
declined to consider an argument,
raised by an appellant for the first time
‘‘[n]early a year after appealing the
Judges’ order, and almost three months
after filing its opening brief. . . . ’’ Id. at
755. Although the D.C. Circuit accepted
the supplemental briefing and permitted
responsive briefing, the court expressly
noted that it was allowing that briefing
‘‘without prejudice’’ as to whether it
would consider the delinquent issue on
appeal. Id. The D.C. Circuit ultimately
ruled that it would not consider the
cannot bump-up PTV’s share of the Basic Fund to
offset its non-participation in the 3.75% Fund.
However, as Nat’l Bank of Oregon further holds, a
court can rule sua sponte even if the parties fail to
address in their supplemental briefing the issue on
which the court sought such briefing. Id. at 447.
Moreover, in that decision, the Supreme Court held
that lower courts may reframe the legal issues posed
by the parties, in order to ensure that the law is
correctly applied, lest the parties force the court to
misstate the law. Nat’l Bank of Oregon at 446–47.
In the same vein, ‘‘[a] court should apply the right
body of law even if the parties fail to cite their best
cases.’’ Palmer v. Bd. Of Educ., 46 F.3d 682, 684
(7th Cir. 1995 (Easterbrook, J.). Here, a fortiori,
because PTV did not make its legal waiver
argument until it filed its Responding Brief (the
very tactic of which it accuses Program Suppliers
regarding the substantive Evidentiary Adjustment
issue), the adverse parties had no opportunity to
cite any cases.
200 See PTV Responding Brief at 22 n.85.
201 716 F.3d 612 (D.C. Cir. 2013).
202 530 F.3d 991 (D.C. Cir. 2008).
203 344 U.S. 33 (1952).
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issue, noting that, notwithstanding its
discretionary ‘‘power’’ to consider the
delinquently briefed issue, it chose not
to exercise that discretion, in part
because of the incomplete nature of the
briefing and the far-reaching
consequences of the delinquently raised
issue. Id. at 755–56.
Intercollegiate is clearly not on point.
To the extent the D.C. Circuit’s
procedure for weighing whether to
consider a delinquently raised issue is
analogous to the present case, the D.C.
Circuit emphasized that it was a matter
of discretion. Likewise, the Judges have
the discretion, pursuant to 17 U.S.C.
801(c), to make procedural rulings in
furtherance of their statutory duties. The
fact that the D.C. Circuit chose in
Intercollegiate to allow supplemental
briefing—without prejudice to its
ultimate ruling that the delinquently
asserted issue would not be heard—in
no way suggests that the Judges in this
proceeding are barred (by an assertion of
waiver, or otherwise) from exercising
their statutory discretion by deciding
the issue at hand, after ordering
supplemental briefing.
C. Conclusion Regarding
Nonparticipation Adjustment
For the foregoing reasons, the Judges
do not apply an Evidentiary Adjustment
to or otherwise adjust PTV’s share of the
Basic Fund to reflect PTV’s
nonparticipation in the 3.75% Fund.
VIII. Conclusions and Award
As many witnesses testified in this
proceeding, no one methodology can be
a perfect measure of relative market
value of categories of television
programs distantly retransmitted by
cable television systems. That is
inevitable, because the market value of
distantly retransmitted programs cannot
be measured directly: Cable systems do
not buy retransmission rights from the
program copyright owners and cable
systems do not acquire retransmission
rights to broadcast stations in
marketplace transactions. In the
applicable scheme, prices are set by
statute. Neither the copyright owners’
valuations nor the general laws of
supply and demand apply in all their
particulars in setting prices as they
would in an unregulated market. Use of
different methodologies can assist the
Judges by illuminating different aspects
of the buyers’ valuation.
In this proceeding, the participants,
through their respective expert
witnesses, took a variety of approaches
to estimate how cable systems value
programming on distant signals. Some
witnesses looked to survey evidence in
which CSOs estimated relative value of
programming by category. Cable system
fact witnesses also considered whether
the value of the distantly retransmitted
programs is generated more by
acquisition of new subscribers or by
retention of niche viewers.
A broadcast station’s valuation of
programming is driven by each show’s
popularity among viewers: Viewership
translates to advertising income for the
broadcast station. Program Suppliers
advocated looking at that viewership to
determine relative value. While
viewership is important for
broadcasters, the Judges conclude, based
on the evidence and arguments
presented, that viewership, without
more, is an inadequate measure of
relative value of different categories of
programming distantly retransmitted by
cable systems. The Judges, consistent
with the past several allocation
decisions, give no weight to viewership
evidence in allocating royalties among
the various program categories.
Several participants’ econometricians
who testified in this proceeding
analyzed value from the perspective of
what CSOs actually had done in terms
of deciding which distant signals to
retransmit on their systems. The essence
of their regression approaches was the
same as the fundamental correlation in
the Waldfogel regression analysis in the
2004–05 proceeding—the correlation
between royalties paid and minutes of
programming in each program category
on each distant signal. As discussed, the
Judges place primary reliance on
Professor Crawford’s regression
analysis, and rely on his duplicated
minutes approach, as to which he
expressed no methodological
reservations during his testimony.
After considering all the
methodologies and supporting evidence
presented by the copyright owner
groups, the Judges are struck by the
relative consistency of the results across
the accepted methodologies.204 In this
proceeding, the Judges conclude that the
Horowitz Survey responses and
Professor Crawford’s duplicate minutes
regression analysis, adjusted to account
for methodological limitations in these
approaches, are the best available
measures of relative value of the
program categories.
The Bortz and Horowitz Surveys,
together with the McLaughlin
‘‘Augmented Bortz’’ results and the
Crawford and George regressions, taking
into account the confidence intervals
(when available) surrounding the point
estimates, define the following ranges of
reasonable allocations for each program
category in each year:
TABLE 18—RANGES OF REASONABLE ALLOCATIONS
2010
Min.
(%)
JSC ..................................
CTV ..................................
Program Suppliers ...........
PTV ..................................
SDC ..................................
CCG .................................
2011
Max.
(%)
26.73
13.28
23.88
6.70
0.48
0.01
Min.
(%)
41.85
20.48
40.15
17.46
4.20
6.55
24.82
14.41
22.10
7.90
0.33
1.12
2012
Max.
(%)
Min.
(%)
39.42
23.91
35.70
21.21
6.64
6.61
2013
Max.
(%)
28.03
14.25
19.56
6.10
0.25
0.70
43.81
23.30
30.90
21.61
6.31
7.47
Min.
(%)
30.12
10.30
17.27
8.30
0.23
0.38
Max.
(%)
45.88
22.60
30.94
29.39
5.20
7.85
Within these ranges, the Judges use
Professor Crawford’s point estimates as
the starting point for most categories
because the Judges find the Crawford
(duplicate minutes) analysis to be the
most persuasive methodology overall on
this record. For two specific categories,
however, the Judges deviate from the
Crawford analysis based on other record
204 As noted, Dr. Israel’s Cable Content Analysis,
although not a methodology that the Judges
adopted, provided information on JSC-related
expenditures in a related market sufficient to lend
some support for the award of a significant share
to JSC (as indicated by the methodologies that the
Judges have adopted), even though the shares are
disproportionate to the number of programming
hours retransmitted. Similarly, the McLaughlin/
Blackburn ‘‘changed circumstances’’ adjustments
bolster the results of methodologies valuing PTV
programming above the lower bound set by
regression analyses.
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evidence. Specifically, the Judges make
a modest upward adjustment to
Professor Crawford’s allocation for the
SDC category based on the Horowitz
survey results and the Augmented Bortz
survey results, together with testimony
concerning the ‘‘niche’’ value of
devotional programming. Similarly, the
Judges make a modest upward
adjustment to the CCG category based
on Professor George’s analysis and
testimony that Professor Crawford’s
analysis (as well as the survey evidence)
undervalues Canadian programming to a
degree. The Judges adjust the Crawfordbased allocations for the remaining
categories to account for the increased
allocations to the SDC and CCG
categories, and to ensure that the
percentages total 100% after rounding.
The resulting allocations are:
TABLE 19—BASIC FUND ALLOCATIONS
2010
(%)
2011
(%)
2012
(%)
2013
(%)
JSC ..................................................................................................................
CTV ..................................................................................................................
Program Suppliers ...........................................................................................
PTV ..................................................................................................................
SDC .................................................................................................................
CCG .................................................................................................................
32.9
16.8
26.5
14.8
4.0
5.0
30.2
16.8
23.9
18.6
5.5
5.0
33.9
16.2
21.5
17.9
5.5
5.0
36.1
15.3
19.3
19.5
4.3
5.5
Total ..........................................................................................................
100.0
100.0
100.0
100.0
As discussed in section VII, the
Judges considered and rejected PTV’s
arguments that the allocations of Basic
Fund royalties must be adjusted to
account for PTV’s non-participation in
the 3.75% Fund. Consequently, the
allocations for the Basic Fund set forth
in Table 1 are identical to the
allocations set forth in Table 19. To
arrive at the allocations for the 3.75%
Fund set forth in Table 1, the Judges
have reallocated the PTV share from
Table 19 proportionally among the
categories that participate in that fund.
In accordance with the consensus view
of the parties, the Judges have allocated
100% of the funds remaining in the
Syndex Fund (after distribution of the
Music Claimants’ share) to Program
Suppliers.
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The allocations described in Table 1
at the outset of this Determination
reflect the Judges’ weighing of the
evidence and their findings regarding
allocation to each category of
programming within the respective
ranges of reasonable allocations.
The Register of Copyrights may
review the Judges’ Determination for
legal error in resolving a material issue
of substantive copyright law. The
Librarian shall cause the Judges’
Determination, and any correction
thereto by the Register, to be published
in the Federal Register no later than the
conclusion of the 60-day review period.
October 18, 2018.
So ordered.
Suzanne M. Barnett,
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Chief United States Copyright Royalty Judge.
David R. Strickler,
United States Copyright Royalty Judge.
Jesse M. Feder,
United States Copyright Royalty Judge.
The Register of Copyrights closed her
review of this Determination on January
28, 2019, with no finding of legal error.
Dated: January 29, 2019.
Suzanne M. Barnett,
Chief United States Copyright Royalty Judge.
Approved by:
Carla B. Hayden,
Librarian of Congress.
[FR Doc. 2019–01544 Filed 2–11–19; 8:45 am]
BILLING CODE 1410–72–P
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[Federal Register Volume 84, Number 29 (Tuesday, February 12, 2019)]
[Notices]
[Pages 3552-3611]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2019-01544]
[[Page 3551]]
Vol. 84
Tuesday,
No. 29
February 12, 2019
Part II
Library of Congress
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Copyright Royalty Board
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Distribution of Cable Royalty Funds; Notice
Federal Register / Vol. 84 , No. 29 / Tuesday, February 12, 2019 /
Notices
[[Page 3552]]
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LIBRARY OF CONGRESS
Copyright Royalty Board
[Docket No. CONSOLIDATED 14-CRB-0010-CD (2010-2013)]
Distribution of Cable Royalty Funds
AGENCY: Copyright Royalty Board (CRB), Library of Congress.
ACTION: Final allocation determination.
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SUMMARY: The Copyright Royalty Judges announce the allocation of shares
of cable and satellite royalty funds for the years 2010, 2011, 2012,
and 2013 among six claimant groups.
ADDRESSES: The final distribution order is also published in eCRB at
https://app.crb.gov/.
Docket: For access to the docket to read background documents, go
to eCRB, the Copyright Royalty Board's electronic filing and case
management system, at https://app.crb.gov/and search for CONSOLIDATED
docket number 14-CRB-0010-CD (2010-2013). For older documents not yet
uploaded to eCRB, go to the agency website at https://www.crb.gov/or
contact the CRB Program Specialist.
FOR FURTHER INFORMATION CONTACT: Anita Blaine, CRB Program Specialist,
by phone at (202) 707-7658 or by email at crb@loc.gov.
SUPPLEMENTARY INFORMATION:
Final Determination of Royalty Allocation
The purpose of this proceeding is to determine the allocation of
shares of the 2010-2013 cable royalty funds among six claimant groups:
The Joint Sports Claimants, Commercial Television Claimants, Public
Television Claimants, Canadian Claimants Group, Settling Devotional
Claimants, and Program Suppliers.\1\ The parties have agreed to
settlements regarding the shares to be allocated to the Music Claimants
and National Public Radio (NPR). Public Television Claimants Proposed
Findings of Fact and Conclusions of Law (PFFCL) ] 1.
---------------------------------------------------------------------------
\1\ The program categories at issue are as follows: Canadian
Claimants Group: All programs broadcast on Canadian television
stations, except (1) live telecasts of Major League Baseball,
National Hockey League, and U.S. college team sports and (2)
programs owned by U.S. Copyright owners; Joint Sports Claimants:
Live telecasts of professional and college team sports broadcast by
U.S. and Canadian television stations, except programming in the
Canadian Claimants category; Commercial Television Claimants:
Programs produced by or for a U.S. commercial television station and
broadcast only by that station during the calendar year in question,
except those listed in subpart (3) of the Program Suppliers
category; Public Television Claimants: All programs broadcast on
U.S. noncommercial educational television stations; Settling
Devotional Claimants: Syndicated programs of a primarily religious
theme, but not limited to programs produced by or for religious
institutions; and Program Suppliers: Syndicated series, specials,
and movies, except those included in the Devotional Claimants
category. Syndicated series and specials are defined as including
(1) programs licensed to and broadcast by at least one U.S.
commercial television station during the calendar year in question,
(2) programs produced by or for a broadcast station that are
broadcast by two or more U.S. television stations during the
calendar year in question, and (3) that are comprised predominantly
of syndicated elements, such as music videos, cartoons, ``PM
Magazine,'' and locally hosted movies. Public TV PFFCL at ] 4;
Notice of Participant Groups, Commencement of Voluntary Negotiation
Period (Allocation), and Scheduling Order, Docket No. 14-CRB-0010-
CD, at Ex. A (Nov. 25, 2015). The categories are mutually exclusive
and, in aggregate, comprehensive.
---------------------------------------------------------------------------
Between 2012 and 2015, the Judges ordered partial distributions of
the 2010-2013 cable funds to the ``Phase I'' participants (including
Music Claimants and NPR) according to allocation percentages agreed
upon by the participants. Order Granting Phase I Claimants' Motion for
Partial Distribution of 2010 Cable Royalty Funds, Docket No. 2012-4 CRB
CD 2010 (Sept. 14, 2012), Order Granting Phase I Claimants' Motion for
Partial Distribution of 2011 Cable Royalty Funds, Docket No. 2012-9 CRB
CD 2011 (Mar. 13, 2013), Order Granting Motion of Phase I Claimants for
Partial Distribution, Docket No. 14-CRB-0007 CD (2010-12) (Dec. 23,
2014); Order Granting Motion of Phase I Claimants for Partial
Distribution, Docket No. 14-CRB-0010 CD (2013) (May 28, 2015).
In December 2016, the Judges ordered the final distribution of the
settled shares from the remaining funds to Music Claimants and National
Public Radio. Amended Order Granting Motion for Final Distribution of
2010-2013 Cable Royalty Funds to Music Claimants (Aug. 23, 2017); Order
Granting Motion for Final Distribution of 2010-2013 Cable Royalty Funds
to National Public Radio (Aug. 23, 2017). When the Judges ultimately
order the final distribution of the remaining 2010-13 cable royalty
funds, they will direct the Licensing Division of the Copyright Office
to adjust distributions to each participant to account for partial
distributions and to apply the allocation percentages determined
herein.
Based on the record in this proceeding, the Judges make the
following allocation of deposited royalties.\2\
---------------------------------------------------------------------------
\2\ In reviewing responses to Program Suppliers' request for
rehearing, the Judges became aware of an error in the Initial
Determination. The Judges used an incorrect base figure in
calculating the royalty shares for 2012 and 2013. The Judges
detailed that correction in the Order on Rehearing. The corrected
values appear in this Final Determination.
Table 1--Royalty Allocations
----------------------------------------------------------------------------------------------------------------
2010 (%) 2011 (%) 2012 (%) 2013 (%)
----------------------------------------------------------------------------------------------------------------
Basic Fund:
Canadian Claimants.......................... 5.0 5.0 5.0 5.5
Commercial TV............................... 16.8 16.8 16.2 15.3
Devotional Programs......................... 4.0 5.5 5.5 4.3
Program Suppliers........................... 26.5 23.9 21.5 19.3
Public TV................................... 14.8 18.6 17.9 19.5
Sports...................................... 32.9 30.2 33.9 36.1
3.75% Fund:
Canadian Claimants.......................... 5.9 6.1 6.1 6.8
Commercial TV............................... 19.7 20.6 19.7 19.0
Devotional Programs......................... 4.7 6.8 6.7 5.3
Program Suppliers........................... 31.1 29.4 26.2 24.0
Public TV................................... 0.0 0.0 0.0 0.0
Sports...................................... 38.6 37.1 41.3 44.9
Syndex Fund:
Program Suppliers........................... 100 100 100 100
----------------------------------------------------------------------------------------------------------------
[[Page 3553]]
Program Suppliers filed a timely request for rehearing on November
2, 2018 (Rehearing Request). The Judges issued their ruling on the
Rehearing Request on December 13, 2018 (Order on Rehearing), denying
rehearing on any basis asserted by Program Suppliers in the Rehearing
Request. The Initial Determination is, therefore, the Judges' Final
Determination in this proceeding.
I. Background
A. Legal Context
In 1976, Congress granted cable television operators a statutory
license to enable them to clear the copyrights to over-the-air
television and radio broadcast programming which they retransmit to
their subscribers. The license requires cable operators to submit semi-
annual royalty payments, along with accompanying statements of account,
to the Copyright Office for subsequent distribution to copyright owners
of the broadcast programming that those cable operators retransmit. See
17 U.S.C. 111(d)(1). To determine how the collected royalties are to be
distributed among the copyright owners filing claims for them, the
Copyright Royalty Judges (Judges) conduct a proceeding in accordance
with chapter 8 of the Copyright Act. This determination is the
culmination of one of those proceedings.\3\ Proceedings for determining
the distribution of the cable license royalties historically have been
conducted in two phases. In Phase I, the royalties were divided among
programming categories. The claimants to the royalties have previously
organized themselves into eight categories of programming retransmitted
by cable systems: Movies and syndicated television programming; sports
programming; commercial broadcast programming; religious broadcast
programming; noncommercial television broadcast programming; Canadian
broadcast programming; noncommercial radio broadcast programming; and
music contained on all broadcast programming. In Phase II, the
royalties allotted to each category at Phase I were subdivided among
the various copyright holders within that category.\4\ In the current
proceeding, the Judges broke with past practice by combining Phase I
and Phase II into a single proceeding in which the functions of
allocating funds between program categories and distributing funds
among claimants within those categories would proceed in parallel.\5\
This determination addresses the Allocation Phase for royalties
collected from cable operators for the years 2010, 2011, 2012 and 2013.
---------------------------------------------------------------------------
\3\ Prior to enactment of the Copyright Royalty and Distribution
Reform Act of 2004, which established the Judges program, royalty
allocation determinations under the Section 111 license were made by
two other bodies. The first was the Copyright Royalty Tribunal,
which made distributions beginning with the 1978 royalty year, the
first year in which cable royalties were collected under the 1976
Copyright Act. Congress abolished the Tribunal in 1993 and replaced
it with the Copyright Arbitration Royalty Panel (``CARP'') system.
Under this regime, the Librarian of Congress appointed a CARP,
consisting of three arbitrators, which recommended to the Librarian
how the royalties should be allocated. Final distribution authority,
however, rested with the Librarian. The CARP system ended in 2004.
See Copyright Royalty Distribution and Reform Act of 2004, Public
Law 108-419, 118 Stat. 2341 (Nov. 30, 2004).
\4\ The Judges last adjudicated an allocation (Phase I)
determination for royalty years 2004-05. See Distribution of the
2004 and 2005 Cable Royalty Funds, Distribution Order, 75 FR 57063
(Sept. 17, 2010) (2004-05 Distribution Order). In the Phase I cable
proceeding relating to royalties deposited between 2000 and 2003,
the parties stipulated that the only unresolved issue would be the
Phase I share awarded to the Canadian Claimants Group. The remaining
balance would be awarded to the Settling Parties. See Distribution
of the 2000-2003 Cable Royalty Funds, Distribution Order, 75 FR
26798-99 (May 12, 2010) (2000-03 Distribution Order). The Judges
adopted the stipulation.
\5\ Second Reissued Order Granting In Part Allocation Phase
Parties' Motion to Dismiss Multigroup Claimants and Denying
Multigroup Claimants' Motion For Sanctions Against Allocation Phase
Parties, Docket No. 14-CRB-0010-CD (2010-13) (Apr. 25, 2018). The
Judges discontinued use of the terms Phase I and Phase II and use
the terms Allocation Phase and Distribution Phase instead. Id. at
n.4. This determination addresses the Allocation Phase of the
proceeding.
---------------------------------------------------------------------------
The statutory cable license places cable systems into three classes
based upon the fees they receive from their subscribers for the
retransmission of over-the-air broadcast signals. Small- and medium-
sized systems pay a flat fee. See 17 U.S.C. 111(d)(1). Large cable
systems (``Form 3'' systems) \6\--whose royalty payments comprise the
lion's share of the royalties distributed in this proceeding--pay a
percentage of the gross receipts they receive from their subscribers
for each distant over-the-air broadcast station signal they
retransmit.\7\ The amount of royalties that a cable system must pay for
each broadcast station signal it retransmits depends upon how the
carriage of that signal would have been regulated by the Federal
Communications Commission (``FCC'') in 1976, the year in which the
current Copyright Act was enacted.
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\6\ ``Form 3'' cable systems, so named because they account to
the Copyright Office for retransmissions and royalties on ``Form
3.'' The Form 3 filing is required because they have semiannual
gross receipts in excess of $527,600. These systems must submit an
SA3 Long Form to the U.S. Copyright Office. They are the only
systems required to identify which of the stations they carry are
distant signals. Royalty payments from Form 3 systems accounted for
over 90% of the total royalties that cable systems paid during 2010-
2013. Corrected Testimony of Christopher J. Bennett ] 10 n.2
(Bennett CWDT).
\7\ The cable license is premised on the Congressional judgment
that large cable systems should only pay royalties for the distant
broadcast station signals that they retransmit to their subscribers
and not for the local broadcast station signals they provide.
However, cable systems that carry only local stations are still
required to submit a statement of account and pay a basic minimum
fee. See 2000-03 Distribution Order, 75 FR at 26,798 n.2.
---------------------------------------------------------------------------
The royalty scheme for large cable systems employs a statutory
device known as the distant signal equivalent (DSE), which is defined
at 17 U.S.C. 111(f)(5). The cable systems, other than those paying the
minimum fee, pay royalties based upon the number of DSEs they
retransmit. The greater the number of DSEs a cable system retransmits
the larger its total royalty payment. The cable system pays these
royalties to the Copyright Office. These fees comprise the ``Basic
Fund.'' See 17 U.S.C. 111(d)(1)(B). In addition to the Basic Fund,
large cable systems also may be required to pay royalties into one of
two other funds that the Copyright Office maintains: The Syndex Fund
and the 3.75% Fund.
As noted above, the utilization of the cable license is linked with
how the FCC regulated the cable industry in 1976.\8\ FCC rules at the
time restricted the number of distant broadcast signals a cable system
was permitted to carry (``the distant signal carriage rules'').
National Cable Television Assoc., Inc. v. Copyright Royalty Tribunal,
724 F.2d 176, 180 (D.C. Cir. 1983). FCC rules also allowed local
broadcasters and copyright holders to require cable systems to delete
(or blackout) syndicated programming from imported signals if the local
station had purchased exclusive rights to the programming (``syndicated
exclusivity'' or ``syndex'' rules). Id. at 187. In 1980, the FCC
repealed both sets of rules. Id. at 181.
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\8\ FCC regulation of the cable industry was impacted by passage
of the 1976 Copyright Act that created the compulsory license for
cable retransmissions codified in section 111. See Report and Order,
Docket Nos. 20988 & 21284, 79 F.C.C. 663 (1980), aff'd sub nom.
Malrite T.V., v. FCC, 652 F.2d 1140, 1146 (2d Cir. 1981).
---------------------------------------------------------------------------
The Copyright Royalty Tribunal (CRT) initiated a cable rate
adjustment proceeding to compensate copyright owners for royalties lost
as a result of the FCC's repeal of the rules. Adjustment of the Royalty
Rate for Cable Systems; Federal Communications Commission's
Deregulation of the Cable Industry, Docket No. CRT 81-2, 47 FR 52146
(Nov. 19, 1982). The CRT adopted two new rates applicable to large
cable systems making section 111 royalty
[[Page 3554]]
payments. The first, to compensate for repeal of the distant signal
carriage rules, was a 3.75% surcharge of a large cable system's gross
receipts for each distant signal the carriage of which would not have
been permitted under the FCC's distant signal carriage rules. Royalties
paid at the 3.75% rate--sometimes referred to by the cable industry as
the ``penalty fee''--are accounted for by the Copyright Office in the
``3.75% Fund,'' which is separate from royalties kept in the Basic
Fund. See id.; see also 17 U.S.C. 111(d); 37 CFR, part 387.The second
rate the CRT adopted, to compensate for the FCC's repeal of its
syndicated exclusivity rules, is known as the ``syndex surcharge.''
Large cable operators were required to pay this additional fee for
carrying signals that were or would have been subject to the FCC's
syndex rules. Syndex Fund fees are accounted for separately from
royalties paid into the Basic Fund.\9\
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\9\ In 1989, in response to changes in the cable television
industry and passage of the Satellite Home Viewer Act of 1988, the
FCC reinstated syndicated exclusivity rules. The reinstated rules
differed from the original syndex rules, giving rise to a petition
to the CRT for adjustment or elimination of the syndex surcharge.
See Final Rule, Adjustment of the Syndicated Exclusivity Surcharge,
Docket No. 89-5-CRA, 55 FR 33604 (Aug. 16, 1990).
The CRT held that the syndicated exclusivity surcharge paid by
Form 3 cable systems in the top 100 television markets is
eliminated, except for those instances when a cable system is
importing a distant commercial VHF station which places a predicted
Grade B contour, as defined by FCC rules, over the cable system, and
the station is not ``significantly viewed'' or otherwise exempt from
the syndicated exclusivity rules in effect as of June 24, 1981. In
such cases, the syndicated exclusivity surcharge shall continue to
be paid at the same level as before. Id.
See Final Rule, 54 FR 12,913 (Mar. 29, 1989), aff'd sub nom.
United Video, Inc. v. FCC, 890 F.2d 1173 (D.C. Cir. 1989); 47 CFR
73.658(m)(2) (1989); 47 CFR 76.156 (1989). The present proceeding
deals only with allocation of those royalties among copyright owners
in the various program categories.
---------------------------------------------------------------------------
Royalties in the three funds--Basic, 3.75%, and Syndex--are the
royalties to be distributed to copyright owners of non-network
broadcast programming in a Section 111 cable license distribution
proceeding. See 37 CFR, part 387.\10\
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\10\ The CRB last adjusted cable Basic, 3.75%, and Syndex rates
in 2016, for the period January 1, 2015, through December 31, 2019.
See Final Rule, Adjustment of Royalty Fees for Cable Compulsory
License, Docket No. 15-CRB-0010-CA, 81 FR 62,812 (Sept. 13, 2016).
This adjustment was pursuant to a negotiated agreement.
---------------------------------------------------------------------------
Cable system operators are required to file Statements of Account
with the Copyright Office detailing subscription revenues and specific
television signals they retransmit distantly, and to deposit section
111 royalties calculated according to the reported figures. Ex. 2004,
Testimony of Gregory S. Crawford ] 74 & n.37. As cable system operators
merged they created contiguous cable systems that were required to file
consolidated Statements of Account. The consolidated systems were
required to pay royalties calculated on the aggregate subscription
income of the corporate operator, even though not all the systems under
the corporate umbrella, not even the contiguous systems, carried or
retransmitted compensable distant signals.
Between the time of the last adjudicated cable royalty allocation
proceeding and the present proceeding, Congress passed the Satellite
Television and Localism Act of 2010 (STELA).\11\ Before STELA, cable
operators were required to pay for the carriage of distant signals on a
system-wide basis, even though each signal was not made available to
every subscriber in the cable system. U.S. Copyright Office, Frequently
Asked Questions on the Satellite Television Extension and Localism Act
of 2010. Distant broadcast signals that subscribers could not receive
were called ``phantom signals.'' Id. STELA addressed the phantom-signal
issue by amending section 111(d)(1) of the Copyright Act, which details
the method by which cable operators can calculate royalties on a
community-by-community or subscriber-group basis. Id. From the 2010/1
accounting period and all periods thereafter, cable operators have been
required to pay royalties based upon where a distant broadcast signal
is offered rather than on a system-wide basis.\12\ Id. As discussed
below, this statutory change permitted the participants to analyze
relative value at the subscriber-group level. See, e.g., Corrected
Written Direct Testimony of Gregory Crawford, Ex. 2004 (Crawford CWDT)
] 66.
---------------------------------------------------------------------------
\11\ Public Law 111-175, 124 Stat. 1218 (May 27, 2010),
reauthorized by Public Law 113-200, 128 Stat. 2059 (Dec. 4, 2014),
\12\ CSOs continue to be liable to pay a ``minimum fee'' for
systems that do not retransmit distant signals. See 17 U.S.C.
111(d)(1)(B)(i). Calculation of royalties at subscriber group levels
segregates minimum fee systems from systems that pay royalties based
on retransmission of distant signals in excess of one DSE.
---------------------------------------------------------------------------
B. Posture of the Current Proceeding
In December 2014, the Copyright Royalty Board (CRB) published
notice in the Federal Register announcing commencement of proceedings
and seeking Petitions to Participate to determine distribution of 2010,
2011, and 2012 royalties under the cable and satellite licenses.\13\ On
June 5, 2015, the CRB published a notice in the Federal Register
announcing commencement of a proceeding to determine distribution of
2013 royalties deposited with the Copyright Office under the cable
license and the satellite license.\14\ The Judges determined that
controversies existed with respect to distribution of the cable (and
satellite) retransmission royalties deposited for 2013, and directed
interested parties to file Petitions to Participate.\15\ On September
9, 2015, the Judges consolidated the proceedings regarding the cable
license for the years 2010, 2011, 2012, and 2013. See Notice of
Participants, Notice of Consolidation, and Order for Preliminary Action
to Address Categories of Claims.
---------------------------------------------------------------------------
\13\ Docket Nos. 14-CRB-0007-CD (2010-12) and 14-CRB-0008-SD
(2010-12), 79 FR 76396 (Dec. 22, 2014). The CRB received Petitions
to Participate from: ASCAP/BMI (joint), Canadian Claimants, Major
League Soccer, PBS for Public Television Claimants, Certain
Devotional Claimants aka certain Devotional Claimants or Settling
Devotional Claimants (SDC), Joint Sports Claimants, MPAA for Program
Suppliers, Multigroup Claimants, NAB for Commercial Television
Claimants, NPR, SESAC, and Spanish Language Producers. Major League
Soccer subsequently withdrew its petition to participate.
\14\ Docket Nos. 14-CRB-0010-CD (2013) and 14-CRB-0011-SD
(2013), 80 FR 32182 (June 5, 2015).
\15\ The Judges received petitions from: ASCAP/BMI (joint),
Canadian Claimants, SDC, Joint Sports Claimants, Major League
Soccer, MPAA for Program Suppliers, Multigroup Claimants, NAB for
Commercial Television Claimants, NPR, Professional Bull Riders, PBS
for Public Television Claimants, SESAC, and Spanish Language
Producers. Professional Bull Riders and Major League Soccer
subsequently withdrew their Petitions to Participate. Major League
Soccer withdrew its Petition to Participate in the Joint Sports
Category for 2010-2013 but maintained its 2013 satellite and cable
claims in the Program Suppliers category and indicated it would be
represented by MPAA. Major League Soccer LLC Withdrawal of Certain
Claims Relating to the Distribution of the 2010-2013 Cable and
Satellite Royalty Funds (Sept. 21, 2016). Multigroup Claimants,
which had sought to participate in the Allocation and Distribution
phases of the proceeding failed to file a written direct statement
in the Allocation Phase and was dismissed from participating in that
phase of the proceeding. [Second Reissued] Order Granting in Part
Allocation Phase Parties' Motion to Dismiss Multigroup Claimants and
Denying Multigroup Claimants' Motion for Sanctions Against
Allocation Phase Parties (April 25, 2018).
---------------------------------------------------------------------------
On November 25, 2015, the Judges issued a Notice of Participant
Groups, Commencement of Voluntary Negotiation Period (Allocation), and
Scheduling Order, in which the Judges identified eight categories of
claimants for the proceeding: (1) Canadian Claimants, (2) Commercial
Television Claimants; (3) Devotional Claimants, (4) Joint Sports
Claimants, (5) Music Claimants, (6) National Public Radio, (7) Program
Suppliers, and (8) Public Television Claimants. National Public Radio
and Music Claimants reached settlements with the other claimants groups
and received respective final distributions. Order Granting Motion for
[[Page 3555]]
Final Distribution of 2010-2013 Cable Royalty Funds to Music Claimants
(Aug. 11, 2017) and Order Granting Motion for Final Distribution of
2010-2013 Cable Royalty Funds to National Public Radio (Aug. 23, 2017).
With the settlement of the Music Claimants' share, only the Program
Suppliers claimant group has an interest in the royalties in the Syndex
Fund. Program Suppliers Proposed Conclusions of Law ] 2 & n.3 and
references cited therein. Public TV Claimants claim a share only of the
Basic Fund. Public TV PFFCL ] 43.
The hearing in the present proceeding commenced on February 14,
2018, and concluded on March 19, 2018.\16\ During that period, the
Judges heard live testimony from 23 witnesses and admitted written and
designated testimony from a number of additional witnesses. The Judges
admitted into the record more than 200 exhibits. Participants made
closing arguments on April 24, 2018, after which time the Judges closed
the record.
---------------------------------------------------------------------------
\16\ The Judges also held a hearing on June 15, 2016, to address
concerns the parties raised about changes to the historical
bifurcation of proceedings into a first and a second phase.
---------------------------------------------------------------------------
After reviewing the record, the Judges identified a controversy
among the parties relating to the allocation of royalties held in the
3.75% Fund and requested additional briefing from the parties. Order
Soliciting Further Briefing (June 29, 2018) (3.75% Order). Responding
to the Judges' order, the parties submitted additional briefs and
responses to address the issue framed by the Judges:
Whether the interrelationship between and among the Basic Fund,
the 3.75% Fund, and the Syndex Fund affects the allocations within
the Basic Fund, if at all, and, if so, how that affect should be
calculated and quantified.
Id. The Judges' disposition of the 3.75% Fund and Syndex Fund issues is
set forth at section VII, infra. The allocation described in Table 1 of
this Determination incorporates the Judges' resolution of this issue.
C. Allocation Standard
Congress did not establish a statutory standard in section 111 for
the Judges (or their predecessors) to apply when allocating royalties
among copyright owners or categories of copyright owners. However,
through determinations by the Judges and their predecessors (the
Copyright Royalty Tribunal, the CARPs, and the Librarian of Congress),
the allocation standard has evolved, and the present standard is one of
``relative marketplace value.'' \17\ See Distribution Order, 75 FR
57063, 57065 (Sept. 17, 2010) (2004-05 Distribution Order).
---------------------------------------------------------------------------
\17\ In this proceeding, the Judges distinguish between
``relative values'' (to describe the allocation shares), and
absolute ``fair market values.'' Because the royalties at issue in
this proceeding are regulated and not derived from any actual market
transactions, they do not correspond with absolute dollar royalties
that would be generated in a market and thus would not reflect
absolute ``fair market value.''
---------------------------------------------------------------------------
``Relative marketplace values'' in these proceedings have been
defined as valuations that ``simulate [relative] market valuations as
if no compulsory license existed.'' 1998-99 Librarian Order, 69 FR at
3608. Because such a market does not exist (having been supplanted by
the regulatory structure), the Judges are required to construct a
``hypothetical market'' that generates the relative values that
approximate those that would arise in an unregulated market. 2004-05
Distribution Order, 75 FR at 57065; see also Program Suppliers v.
Librarian of Congress, 409 F.3d 395, 401-02 (D.C. Cir. 2001) (``[I]t
makes perfect sense to compensate copyright owners by awarding them
what they would have gotten relative to other owners . . . .'').
In the present proceeding, the parties disagree as to the
appropriate specification of the sellers in the hypothetical market.
Program Suppliers assert that the hypothetical sellers are the owners
of the copyrights in the retransmitted programs. See Corrected Written
Rebuttal Testimony of Jeffrey S. Gray, Trial Ex. 6037, ] 11 (Gray
CWRT). Other parties assert that the sellers are the local stations
offering for licensing the entire bundle of programs on the
retransmitted signal. See Corrected Written Direct Testimony of Gregory
S. Crawford, Trial Ex. 2004, ] 45 (Crawford CWDT) and Corrected Written
Direct Testimony of Lisa George, Trial Ex. 4005, at 8 (George CWDT).
After considering the record and arguments in this proceeding, the
Judges find that, from an economic perspective, this is a disagreement
without a difference, and therefore, consistent with prior rulings,
identify the local stations as the hypothetical sellers. If the
hypothetical sellers (licensors) were assumed to be the owners of the
individual programs (instead of the local stations), then (as a matter
of elementary economics) they, like any sellers, would attempt to
maximize the royalties they receive from licensing the retransmission
rights to CSOs.\18\ Because the CSOs are assumed to be the buyers
(licensees), they would each negotiate one-to-one with owners of the
program copyrights. The corollary to the assumption that the
hypothetical sellers are the individual program copyright owners is the
assumption that the CSOs, as buyers, would need to create one or more
new channels to bundle these programs for retransmission. That raises
the economically important question of whether the transaction costs
\19\ that a CSO would incur to negotiate separate contracts with
individual copyright owners would be so prohibitive as to preclude one-
to-one negotiations from going forward. Transaction costs are
relatively ubiquitous in the licensing of copyrighted products to
licensees, resulting in the creation of a collective to represent the
licensees, and in blanket or standardized licenses to reduce
transaction costs further. See Watt, supra note 19, at 17, 164-67.
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\18\ Because the programs already exist, production costs have
been ``sunk,'' and the copyright owners incur no marginal physical
cost in the retransmission of their programs. Thus, the copyright
owners would seek only to maximize marginal revenue (but would still
consider marginal ``opportunity cost'' if applicable, e.g., if
retransmission would cannibalize their profits from local
broadcasting of the identical program or another program owned by
the copyright owner). In a more dynamic long-run model, copyright
owners might consider even the costs of production to be variable
and would then also seek to recover an appropriate portion of
production costs from retransmission royalties, thereby maximizing
long-term profits (rather than only shorter-term revenue), with
respect to retransmission royalties. However, because
retransmissions of local broadcasts are ``only a very small fraction
of a typical CSO's programming budget,'' it is unlikely that, in the
hypothetical market, owners of copyrights to the retransmitted
programs would have the market power to compel CSOs to contribute to
the long-run program production costs. See Rebuttal Testimony of Sue
Ann R. Hamilton, Trial Ex. 6009, at 14 (Hamilton WRT). Thus, the
Judges agree with the pronouncement in prior determinations that the
royalties that would be paid in the hypothetical market would
essentially be a function only of the CSOs' demand and the copyright
owners' costs, and their supply curves (if any) would not be
important determinants of the market-based royalty. See, e.g.,
Distribution of 1998 and 1999 Cable Royalty Funds, Final Order, 69
FR 3606, 3608 (Jan. 26, 2004) (1998-99 Librarian Order).
\19\ Transaction costs are ``pure reductions in the total amount
of resources to be distributed that are necessary to achieve and
maintain any given allocation.'' Richard Watt, Copyright and
Economic Theory at 15 (2000).
---------------------------------------------------------------------------
But in the present case, a ``collective'' of sorts already exists--
the broadcaster who bundles programs for transmission within a single
signal. Therefore, it remains reasonable to consider the local stations
that have bundled the programs into their respective signals to be the
hypothetical sellers.
As noted supra, the values of the programs in the several
categories that are determined in this proceeding are ``relative
values,'' i.e., values relative to each other, from the perspective of
the CSOs, when the programs from these different categories are offered
for
[[Page 3556]]
distant retransmission in the form of bundles from local stations.
Relative value is based on the preferences of the CSOs (derived from
those of their subscribers). Because relative preferences are
components of market demand, the CSOs' choices represent important
elements of a market transaction. See generally P. Krugman & R. Wells,
Microeconomics, 284-85 (2d ed. 2009) (relative ``preferences'' lead to
buyers' ``choices'' and an ``optimal consumption bundle''); A.
Schotter, Microeconomics: A Modern Approach (2009) (revealed
``preferences'' allow for an analysis of how buyers ``behave in
markets,'' and those preferences are building blocks for ``individual
and market demand''). Thus, any methodology based on the identification
of the relative preferences and values of CSOs is indeed a market-based
approach to the allocation of royalties in this proceeding.
Because the pricing of the licenses is regulated, however, it is
not possible to identify the actual royalties that would be established
by these ranked preferences. To identify such royalties would require
an application of game theoretic/bargaining power considerations and
the extent and allocation of costs attributable to the licensed
programs--facts that are not in the record and likely are not
reasonably or accurately ascertainable.\20\ Nonetheless, the raison
d'[ecirc]tre of this section 111 proceeding is to allocate royalties
that have already been paid in a manner that reflects relevant market
factors. To do so, it is sufficient to relate CSOs' revealed
preferences among program categories, whether through a CSO survey or a
regression analysis, to the sum of all royalties paid. Prior
determinations may have described the allocations that resulted as the
``relative market value,'' \21\ but there is no doubt that royalties
determined in these ways reveal ``relative values'' that are based on
the critical market factor of identified preferences.
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\20\ For example, in a hypothetical market, a copyright owner
could refuse to grant distant retransmission rights to a local
station unless the local station (and the retransmitting CSO) agreed
to pay an additional royalty (to cover a share of sunk costs and/or
additional profit). The ability of the copyright owner to obtain
such value would be a function of his or her market and bargaining
power. (Because the costs are sunk, the copyright owner would not
rationally walk away from a retransmission agreement as long as some
positive royalty would be paid.) Even at the level of the
``collective,'' a local station in the hypothetical market could use
its market/bargaining power to maximize royalty payments, assuming
it had the economic incentive to do so.
\21\ Actually, in the 2004-05 Determination, the Judges
recognized that neither a survey approach nor a regression approach
(both of which they nonetheless relied upon) identified all aspects
of actual market values as opposed to relative values based on
market forces. See 2004-05 Distribution Order, 75 FR at 57066, 57068
(noting that a CSO survey ``is certainly not a fully equilibrating
model of supply and demand in the relevant hypothetical market,''
and a regression does not ``necessarily identif[y]'' all of ``the
determinants of distant signal prices in a hypothetical free market
. . . .'').
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In the present proceeding, the parties presented five discrete
analytical methodologies for the Judges to consider in determining
relative market value of the programming types at issue: Regression
analyses, CSO survey results, viewership measurements, a changed
circumstances analysis, and a cable content analysis.
II. Regression Analyses
Regression analysis, when properly constructed and applied, ``is an
accurate and reliable method of determining the relationship between
two or more variables, and it can be a valuable tool for resolving
factual disputes.'' \22\ A particular approach, multiple regression
analysis, ``is the technique used in most econometric studies, because
it is well suited to the analysis of diverse data necessary to evaluate
competing theories about the relationships that may exist among a
number of explanatory facts.'' ABA Econometrics, supra note 22, at 4.
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\22\ American Bar Association, Econometrics 1-2 (2005) (ABA
Econometrics).
---------------------------------------------------------------------------
A regression can take one of several forms. The linear form is the
most common form, though not the most appropriate for all analyses. As
one court has explained:
[A] linear regression is an equation for the straight line that
provides the best fit for the data being analyzed. The ``best fit''
is the [regression] line that minimizes the sum of the squares of
the vertical distance between each data point and the line . . . .
The regression equation that generates that line can be written as
Y = a + bX + u
Where Y is the dependent variable, a is the intercept [with the
vertical axis], X the independent variable, b the coefficient of the
independent variable (that is, the number that indicates how changes
in the independent variable produces changes in the dependent
variables), and u the regression residual--the part of the dependent
variable that is not explained or predicted by the independent
variable . . . or, in other words, what is ``left over.''
ATA Airlines, Inc. v. Fed. Express Corp., 665 F.3d 882, 890 (7th Cir.
2011) (Posner, J.), cert. denied, 568 U.S. 820 (2012).\23\ See Crawford
CWDT ]] 94-95.
\23\ In a multiple linear regression, the equation would be
expanded, for example as Y = a + bX + cZ + u- with Z an additional
independent variable and c its coefficient.
---------------------------------------------------------------------------
An economist testifying in the present proceeding, Professor Lisa
George, explained how the regression approach may be useful to test
economic theories, describing regression analysis as ``a tool for
understanding how variations in an outcome of interest . . . depends on
various factors affecting that outcome . . . when the factors of
interest are not separately priced or traded.'' George CWDT at 2.
Professor George noted a basic difference between regression analysis
and survey methodology. Regression analysis, unlike survey methodology,
``infers value for decisions actually made in a market.'' Id.
Although regression analysis is a powerful tool, it is important to
appreciate the subtle distinction between econometric correlation
identified by a regression, on one hand, and economic causation
explained by economic theory, on the other:
Econometrics provides a means for determining whether a
correlation, which may reflect a . . . causal relationship, may
exist between various events that involve complex sets of facts. The
principle value of econometrics . . . lies in its use for developing
an empirical foundation in order to prove or disprove assertions
that are based on a particular economic theory . . . . [E]conometric
evidence coupled with economic theory [may] show the likelihood of a
causally-driven correlation between two events or facts. . . .
[Thus] [c]orrelation is distinct from causation. . . . [T]he
correlation is simply circumstantial confirmation of a hypothesized
relationship. If the hypothesized relationship does not make
theoretical sense, the existence of a correlation between the two
variables is irrelevant.
ABA Econometrics, supra note 22, at 1, 3, 5 (emphasis added).
In the present proceeding, the economic theory that the experts put
to the test via regression analysis is whether or not royalties paid
are a function of (caused by) the types of program categories bundled
in distantly retransmitted local stations.
A. Waldfogel-Type Regressions
Professors Crawford, Israel, and George each used a regression
approach based on the regression approach undertaken by Dr. Joel
Waldfogel, an economist who appeared in the 2004-05 proceeding on
behalf of the joint ``Settling Parties,'' including three of the
present parties: The JSC, Commercial Television Claimants (CTV), and
PTV. 2004-05 Distribution Order, 75 FR at 57064. The Judges' findings
concerning his regression (Waldfogel regression) are instructive with
regard to the Judges' analysis in the present proceeding of the
``Waldfogel-type'' regressions proffered
[[Page 3557]]
by Professor Crawford, Professor George, and Professor Israel.
Several features characterize a Waldfogel-type regression. Most
importantly, such an approach attempts to correlate ``variation in the
[program category] composition of distant signal bundles along with
royalties paid to estimate the relative marketplace value of
programming.'' George CWDT at 6. Specifically, Dr. Waldfogel
``regress[ed] observed royalty payments for the bundle on the numbers
of minutes in each programming category. . . . '' Israel WDT ] 22. He
also employed `` `control variables' . . . to hold other drivers of CSO
payments constant.'' Id. Dr. Waldfogel's control variables included the
number of subscribers, local median income, and the number of local
channels. Id.
In the 2004-05 allocation proceeding, the Judges found the
Waldfogel regression ``helpful to some degree'' in assisting the Judges
``to more fully delineate all of the boundaries of reasonableness with
respect to the relative value of distant signal programming. 2004-05
Distribution Order, 75 FR at 57068. The Judges described the Waldfogel
regression as an ``attempt [ ] to analyze the relationship between the
total royalties payed by cable operators for carriage of distant
signals . . . and the quantity of programming minutes by programming
category . . . .'' Id. Conceptually, the Judges found that, ``Dr.
Waldfogel's regression coefficients do provide some additional useful,
independent information about how cable operators may view the value of
adding distant signals based on the programming mix on such signals.''
Id. The Judges also found Dr. Waldfogel's methodology ``generally
reasonable.'' Id. They cautioned, however, that the wide confidence
intervals around Dr. Waldfogel's coefficients limited the usefulness of
his analysis in corroborating survey-based evidence in that proceeding.
Id.\24\
---------------------------------------------------------------------------
\24\ The Judges noted that ``Dr. Waldfogel's specification was
similar in its choice of independent variables to a regression model
utilized by Dr. Gregory Rosston to corroborate the Bortz survey
results in the 1998-99 CARP proceeding. Id. See Report of the
Copyright Arbitration Royalty Panel to the Librarian of Congress,
Docket No. 2001-8 CARP CD 98-99 (1998-99 CARP Report) at 46 (Oct.
21, 2003).
---------------------------------------------------------------------------
The SDC challenge the use of Waldfogel-type regressions in this
proceeding, thus raising as a preliminary question whether or not the
Judges' past acceptance of this regression approach is binding on the
Judges in the present proceeding as a matter of what has been loosely
described as ``precedent.''
The Librarian and the Register considered the extent to which a
CARP should be bound by prior determinations of acceptable royalty
allocation methodologies in the 1998-99 Phase I cable distribution
proceeding.\25\ The Register acknowledged that ``[t]he concept of
`precedent' . . . plays an important role in [these] proceedings,'' but
observed that ``prior decisions are not cast in stone and can be varied
from when there are (1) changed circumstances from a prior proceeding
or; (2) evidence on the record before it that requires prior
conclusions to be modified regardless of whether there are changed
circumstances.'' 1998-99 Librarian Order, 69 FR at 3613-14 (citations
omitted). The Register also referred to a prior Librarian's decision in
which the Register had stated that a CARP ``may deviate from [a prior
decision] if the Panel provides a reasoned explanation of its decision
to vary from precedent . . . .'' Id.
---------------------------------------------------------------------------
\25\ The CARPs were governed by a statutory provision regarding
precedent that was nearly identical to the current section
803(a)(1). See 17 U.S.C. 802(c) (2003) (repealed). Consequently, the
1998-99 Librarian Order remains relevant in spite of the intervening
statutory amendments abolishing the CARP system and creating the
Judges.
---------------------------------------------------------------------------
The Judges understand that they have the authority and, indeed, the
duty, to consider all appropriate factual presentations regarding the
establishment of value in this proceeding in order to allocate
royalties among the several program categories. The Judges consider the
loose use of the term ``precedent'' in this context to be unhelpful.
The concept of ``precedent'' typically relates to judicial deference to
prior legal determinations, not factual ones.\26\
---------------------------------------------------------------------------
\26\ Legal precedents provide stare decisis effect to ``legal
issues . . . prescribing the norms that apply and consequences that
attach to'' facts presented at trial. See A. Larsen, Factual
Precedents, 162 U. Pa. L. Rev. 59, 68 (2013).
---------------------------------------------------------------------------
However, the 1998-99 Librarian Order clearly indicates that factual
challenges to previously-accepted methodologies shall be subject to a
particular evidentiary standard. Specifically, the Judges have been
directed that they may disregard or modify prior methodologies only in
the event of ``changed circumstances'' or because of evidence in the
record that ``requires'' such a change. See Program Suppliers v.
Librarian of Congress, 409 F.3d 395, 402 (D.C. Cir. 2005). The Judges
understand this instruction to be in the nature of a ``precedent''
setting forth the legal standard for the evaluation of fact evidence.
Accordingly, the Judges consider the challenges in this proceeding
to the application of Waldfogel-type regressions by considering whether
there have been either ``changed circumstances'' or the presentation of
other record evidence that ``requires'' a departure from considering
the Waldfogel-type regressions introduced into the record in this
proceeding. Absent evidence of relevant ``changed circumstances'' or
other new evidence in the record specifically identified as such by any
critics of the Waldfogel-type regression approach, the Judges will
evaluate the proffered Waldfogel-type regressions consistent with their
treatment of Dr. Waldfogel's analysis in the 2004-2005 allocation
proceeding.
In the current proceeding, the SDC's economic expert, Dr. Erkan
Erdem, leveled broad criticisms at the use of Waldfogel-type
regressions by Professor Crawford, Professor George, and Dr. Israel,
notwithstanding the Judges' prior contrary conclusions in the 2004-05
Determination. See Written Rebuttal Testimony of Erkan Erdem, Trial Ex.
5007, at 5-6 (Erdem WRT).\27\ Dr. Erdem opined that, conceptually,
``Waldfogel-type regressions do not measure relative market value'' for
two reasons. First, according to Dr. Erdem, CSO royalty payments are
uninformative because they are determined by a statutory formula, not
through free-market negotiations between CSOs and content owners; \28\
and, second, in Dr. Erdem's view, the volume of programming does not
necessarily equate to value. Written Direct Testimony of Erkan Erdem,
Trial Ex. 5002, at 14 (Erdem WDT). Dr. Erdem thus concluded that
``[o]verall, the Waldfogel-type regressions say little about relative
market value'' and at most are ``marginally informative'' as
corroborative evidence. . . . .'' Id. at 18.
---------------------------------------------------------------------------
\27\ Dr. Erdem referred to the Crawford, Israel, and George
analyses as ``Waldfogel-type'' regressions because they ``attempted
to estimate the marginal effect of each minute of programming for
claimant categories using regression analysis in which the dependent
variable is the royalty fees paid by a system and independent
variables include minutes of programming for each claimant category
and other control variables.'' Id.
\28\ Another SDC witness, Mr. John Sanders (a valuation expert
rather than an economic expert), echoed this criticism, as discussed
infra. A Program Supplier economic expert witness, Dr. Jeffrey Gray,
criticized the regression approach to the extent it included minimum
fee-paying CSOs in the analysis, as also discussed infra.
---------------------------------------------------------------------------
The Judges have found previously that Waldfogel-type regressions
are relevant in cable distribution proceedings and find nothing in Dr.
Erdem's testimony in the current proceeding to support changing that
position. Therefore, the Judges reject Dr. Erdem's broad argument that
Waldfogel-
[[Page 3558]]
type regressions are not useful in establishing relative value in this
proceeding.\29\ Of course, this point does not mean that the Judges
therefore necessarily accept all aspects of the application of the
Waldfogel-type regressions by Professor Crawford, Professor George, and
Dr. Israel in this proceeding. Rather, the Judges analyze infra the
more granular critiques of those regressions leveled by various
witnesses, to determine the weight to be accorded to each such
regression.
---------------------------------------------------------------------------
\29\ In this determination, when the use of a particular
Waldfogel-type regression is challenged on one of these broad bases,
the Judges address those specific challenges.
---------------------------------------------------------------------------
B. Crawford Regression Analysis
1. General Principles
CTV called Professor Gregory Crawford as an economic expert
witness. Professor Crawford undertook a Waldfogel-type regression,
which he opined was an appropriate approach for estimating relative
market value among the six allocation-phase categories. Crawford CWDT ]
5. Professor Crawford envisaged a hypothetical market consistent with
the actual market for cable channel carriage in general. Crawford CWDT
]] 8, 36. In Professor Crawford's hypothetical market, the owners of
the distantly retransmitted stations (i.e., broadcasters) are the
sellers of bundles of programming (their respective program lineups),
and the CSOs are the buyers. Crawford CWDT ] 6.\30\ Professor Crawford
opined that CSOs are more likely to retransmit ``distant signals that
carry more highly-valued programming.'' Id. ] 7. Although this
reasoning appears self-evident (ceteris paribus, re-sellers prefer to
sell products that are more valuable), according to Professor Crawford,
this point also has a subtler meaning in connection with CSO decision-
making. Id. ] 46. Specifically, he opined that, because such stations
bundle various types of programming, there can exist across subscribers
a ``negative correlation'' in their ``Willingness to Pay'' (WTP) (in
other words, making the bundle relatively less preferable when a
program from one category is added to the bundle, as opposed to one
from another category). Id. ] 6 (emphasis added).
---------------------------------------------------------------------------
\30\ Professor Crawford does not hypothesize that in this ersatz
market the CSO could replace advertising that was included in the
local broadcast with advertising targeted to the distant market in
which it has been retransmitted. Crawford CWDT ] 37. The Judges find
this approach reasonable because they did not identify any evidence
that would sufficiently support the hypothesis that CSOs would
insert replacement advertising into distantly retransmitted
stations.
---------------------------------------------------------------------------
Accordingly, Professor Crawford concluded that when deciding
whether to enlarge its channel lineup by distantly retransmitting a
television station, a rational CSO would consider the variety, or mix,
of programming on that channel in light of the existing programming mix
offered by the CSO to subscribers across the channel lineup. According
to Professor Crawford, to achieve an optimal programming mix a CSO
would recognize that ``niche taste[ ] channels are more likely to
increase CSO profitability due to the likelihood that household tastes
for such programming are `negatively correlated' with tastes for other
components of cable bundles.'' Id. ] 7. For example, if a channel
lineup were saturated with programming from five of the six program
categories, but had little or no programming in the sixth category,
e.g., PTV, then a CSO might enhance its profitability through fees from
new subscribers, by adding PTV programming, which may have a following
among subscribers who have little or no taste for marginal increases in
programming in other categories.
Professor Crawford's regression adopted the general concept from
the Waldfogel-type regressions. Specifically, Professor Crawford
concluded that the ``most suitable'' econometric regression would
``relat[e] existing distant signal royalty payments to the minutes of
programming of different types carried on distant signals under the
compulsory license . . . .'' Id. ] 46. He favored a regression model
because it is a standard econometric approach utilized to establish the
discrete prices of different elements in a bundle of goods, or the
value of a bundle of attributes in a single good. Id. ] 47.\31\
---------------------------------------------------------------------------
\31\ Despite his advocacy for a regression approach, and for his
particular regression, Professor Crawford acknowledged the
possibility ``for economists to apply alternative approaches to this
problem.'' Id.
---------------------------------------------------------------------------
Thus, Professor Crawford inferred the ``average marginal value'' of
content type (by program category), based on the decisions CSOs made.
2/28/18 Tr. 1400-02 (Crawford). More precisely, as in any Waldfogel-
type regression, he related the relative variation in royalties across
categories to the relative variation in minutes of different categories
of programming. Crawford CWDT ]] 53-54.
In econometric terms, Professor Crawford related the natural log
\32\ of royalties: (1) To the minutes of claimed programming by
category; and (2) to other ``control'' variables.\33\ Id. ] 91.
Professor Crawford's regression looked for a correlation in a
subscriber group between changes in the number of minutes of
programming the subscribers watched by categories and changes in the
percentage of royalties the subscriber group paid while holding
constant other potential explanatory variables (called control
variables).\34\ The variables Professor Crawford controlled for
included the numbers of local and distant stations, the number of
activated cable channels, and the size of the CSO. Id. ] 118 & App. A.
---------------------------------------------------------------------------
\32\ The ``natural log'' (shorthand for logarithm) is ``[a]
mathematical function defined for a positive argument; its slope is
always positive but with a diminishing slope tending to zero,'' and
it ``is the inverse of the exponential function X = ln(ex).'' J.
Stock & M. Watson, Introduction to Econometrics 821 (3d ed. 2015).
For purposes of applied econometrics, using the logarithmic
functional form, showing percentage changes in the variables, may be
more practical.
\33\ A ``control variable'' is an independent (explanatory)
variable that ``is not the object of interest in the study; rather
it is a regressor included to hold constant factors that, if
neglected, could lead the estimated . . . effect of interest to
suffer from omitted variable bias.'' Stock & Watson, supra note 32,
at 280.
\34\ By investigating the change (effect) in percentage terms on
royalties (the dependent variable) from a change in the number of
minutes per program category (the independent variable), Professor
Crawford adopted what is known as a ``log-level'' (a/k/a ``log-
linear'') functional form. See, e.g., J. Wooldridge, Introductory
Economics 865 (3d ed. 2006). This approach allowed Professor
Crawford to compare the effect of a change in the number of program
category minutes to the percent increase in subscriber group
royalties of different sizes. For example, a 100-minute increase in
Program Supplier minutes for a subscriber group in which 10,000 such
minutes are retransmitted represents a 1% increase in such minutes,
whereas the same 100-minute increase for a subscriber group in which
only 1,000 such minutes are retransmitted would represent a 10%
increase. See Crawford CWDT ]] 113-114.
---------------------------------------------------------------------------
Professor Crawford first estimated the average marginal value per
minute of each type of programming by subscriber group. Id. ] 128.\35\
Econometrically, these values are referred to as the coefficients for
each program-category parameter.\36\ Professor Crawford then summed the
marginal value of the compensable minutes each subscriber group
retransmitted. Id. ] 131. Finally, Professor Crawford divided the total
[[Page 3559]]
value of each given programming category by the total value of all
compensated minutes, which produced a percentage reflecting the
relative value of each program category as produced by his regression.
---------------------------------------------------------------------------
\35\ The royalty data on which Dr. Crawford relied came from the
Licensing Division of the Copyright Office via the Cable Data
Corporation (CDC), and were provided to Dr. Christopher Bennett,
another CTV economic witness, who directed the preparation of the
data for Professor Crawford's regression analysis. Crawford CWDT ]
73. Dr. Bennett also obtained and compiled the data relating to the
minutes of different programming types, using raw data obtained from
FYI Television. Crawford CWDT ]] 78-79.
\36\ A ``parameter'' is ``[a] numerical characteristic of a
population or a model,'' whereas a ``coefficient'' is ``an estimated
regression parameter.'' D. Rubinfeld, Reference Guide on Multiple
Regression, reprinted in Reference Manual on Scientific Evidence
463, 466 (2011). The ``true'' value of the parameter is ``unknown,''
but can be estimated, and the coefficient is that estimate. See
Peter Kennedy, A Guide to Econometrics 4 (5th ed. 2003).
---------------------------------------------------------------------------
The percentage totals estimated by Professor Crawford, and the
standard errors \37\ associated with those estimates, by year and
averaged across all four years, were as follows (with standard errors
in parentheses):
---------------------------------------------------------------------------
\37\ The ``standard error is ``[a]n estimate of the standard
deviation of the regression error . . . calculated as an average of
the squares of the residuals associated with a particular multiple
regression analysis.'' Rubinfeld, supra note 36, at 467. The
standard error measures the probability distribution for the
estimates of each parameter in the regression if ``the expert
continued to collect more and more samples and generated additional
estimates . . . .'' ABA Econometrics, supra note 22, at 404.
Table 2--Implied Shares of Distant Minutes by Claimant Categories
--------------------------------------------------------------------------------------------------------------------------------------------------------
Program Commercial TV
Year suppliers (%) Sports (%) (%) Public TV (%) Devotional (%) Canadian (%)
--------------------------------------------------------------------------------------------------------------------------------------------------------
2010.................................................... 27.66 (1.89) 34.29 (3.78) 17.48 (1.50) 15.44 (1.01) 1.02 (0.27) 4.10 (0.33)
2011.................................................... 25.44 (1.67) 32.12 (3.65) 17.93 (1.49) 19.77 (1.22) 0.71 (0.19) 4.02 (0.32)
2012.................................................... 22.84 (1.64) 36.09 (3.86) 17.29 (1.52) 19.03 (1.29) 0.55 (0.15) 4.19 (0.35)
2013.................................................... 20.31 (1.52) 38.00 (3.94) 16.08 (1.45) 20.51 (1.44) 0.51 (0.14) 4.59 (0.39)
2010-13................................................. 23.95 (1.68) 35.19 (3.82) 17.18 (1.49) 18.75 (1.25) 0.69 (0.18) 4.23 (0.35)
--------------------------------------------------------------------------------------------------------------------------------------------------------
Id. ] 141 and Fig. 17.
Professor Crawford did not use these values, however, as his only
estimates of relative market value across the six programming
categories. Rather, he identified an issue with regard to network (and
to a lesser extent, non-network) programming that he believed to
require a further adjustment. Specifically, Professor Crawford noted
that on some distantly retransmitted stations there existed programming
that duplicated programming on the local channels in that market. Id.
at ] 87. According to Professor Crawford, ``[n]etwork duplication is a
non-trivial issue, accounting for 4.6% of minutes carried on distant
broadcast signals . . . .'' Id. This issue, he noted, is particularly
applicable to Big 3 (ABC, CBS, and NBC) network programming, because a
number of local markets to which Big 3 affiliate stations were
distantly retransmitted by a CSO already had a local Big 3 network
affiliate, rendering the retransmitted network programming duplicative.
Professor Crawford understood the relative percentages attributable to
the six categories of programming--because they were averaged across
all minutes of programming--to be distorted by these duplicative
minutes. Id. ]] 81, 85-87, 143. Accordingly, even though network
programming is not compensable in this proceeding, Professor Crawford
made this adjustment as a ``deaveraging'' device, stating: ``I am
attributing the full value of the positive non-duplicate programming
just to the non-duplicate programming (and the zero value of the
duplicate programming to the duplicate programming).'' Id. ] 147.
Assuming a zero value for the duplicative network programming,
Professor Crawford instructed his data analysts to remove the duplicate
network programming.\38\ With those duplications removed, Professor
Crawford re-ran his regression and averaged the relative values of the
six program categories at issue in this proceeding.
---------------------------------------------------------------------------
\38\ Professor Crawford assumed that duplicated programming,
whether or not it was blacked out upon retransmission, had zero
value because the programming was already available on a local
station. Id. ]] 86, 144-145. The Judges find this assumption
reasonable because identical network programs that are broadcast
locally and retransmitted distantly into the same local market are
essentially perfect substitutes. Why are they essentially perfect
and not just perfect substitutes? Because they are on different
channels, the search cost might be different for viewers. For
example a viewer might find a show on local channel 4, but the same
show on a distantly retransmitted station might appear on channel
157, which is not included in the viewer's usual ``channel
surfing.''
---------------------------------------------------------------------------
After making this adjustment, Professor Crawford estimated the
following percentage allocations (with the associated standard errors
set forth below each allocation):
Table 3--Implied Shares of Distant Minutes by Claimant Categories: Non-Duplicate Minutes Analysis
--------------------------------------------------------------------------------------------------------------------------------------------------------
Program Commercial TV
Year suppliers (%) Sports (%) (%) Public TV (%) Devotional (%) Canadian (%)
--------------------------------------------------------------------------------------------------------------------------------------------------------
2010.................................................... 27.06 (1.97) 34.02 (3.96) 19.76 (1.48) 14.01 (1.00) 1.05 (0.25) 4.10 (0.36)
2011.................................................... 24.67 (1.73) 31.78 (3.82) 20.18 (1.45) 18.64 (1.25) 0.73 (0.18) 4.00 (0.35)
2012.................................................... 22.50 (1.72) 35.93 (4.06) 19.64 (1.51) 17.17 (1.27) 0.56 (0.14) 4.20 (0.38)
2013.................................................... 19.74 (1.60) 38.56 (4.17) 18.44 (1.48) 18.09 (1.41) 0.53 (0.13) 4.65 (0.44)
2010-13................................................. 23.40 (1.76) 35.13 (4.02) 19.49 (1.48) 17.02 (1.23) 0.71 (0.17) 4.24 (0.38)
--------------------------------------------------------------------------------------------------------------------------------------------------------
Id. ] 153 & Fig. 20.
2. The SDC Criticisms of Dr. Crawford's Analysis
a. Alleged Flaw in the Algorithm
Dr. Erkan Erdem, the SDC's economist, claimed to have identified a
flaw in the algorithm Professor Crawford used to allocate royalties to
minutes of programming across categories. Dr. Erdem testified that,
because of this alleged flaw, Professor Crawford's model was highly
sensitive to the sequencing in which data was inputted and sorted into
his regression model. Erdem WRT at 2, 14.
However, Dr. Erdem acknowledged receiving additional data from CTV
that pertained to this issue. When Dr. Erdem re-ran the updated data
using Professor Crawford's regression model, Dr. Erdem found only
``slightly different'' results with regard to ``implied shares of
distant minute royalties by claimant categories for both the initial
and nonduplicated analyses . . . presented by Professor Crawford.''
Erdem WRT at 15 n.13.
Dr. Erdem further testified that he did not review and test
Professor Crawford's
[[Page 3560]]
algorithm fully because it would have taken him a week to do so. Id. at
14. Additionally, neither Dr. Erdem nor the SDC pursued this point
further, either in Dr. Erdem's further testimony or in post-hearing
filings and arguments.
Based on the foregoing, the Judges find this criticism to be
insufficient to invalidate or call into question the evidentiary value
of Professor Crawford's regression.
b. Economic Principles Allegedly Not Embodied in Crawford Regression
Analysis
Dr. Erdem noted approvingly certain general economic points that
Professor Crawford made. First, he agreed with Professor Crawford that
it is reasonable to posit that a rational CSO would likely tend to
select stations for distant retransmission that maximize the difference
between anticipated revenue and the cost of acquiring the
retransmission rights. Second, Dr. Erdem agreed with Professor Crawford
that a ``negative correlation'' rationally should exist among
subscribers between different categories of programs, leading CSOs to
engage in strategic bundling of program categories. Id. at 12.
However, Dr. Erdem faulted Professor Crawford for failing to
incorporate these economic observations into the latter's regression
model. With regard to the first point--maximizing the spread between
revenues and costs--Dr. Erdem noted that the royalty fees are set by
statute, so this concept is not applicable in the regulated market. Id.
at 12.
With regard to the second point--the negative correlation of
different programming types between and among subscribers--Dr. Erdem
noted that Professor Crawford did not incorporate this principle into
his regression analysis. Id. Dr. Erdem acknowledged that the program
bundling that results from the negative correlation between program
types has ``important implications,'' but not implications that support
Professor Crawford's regression model. Dr. Erdem asserts that the
negative correlation between program types implies ``that subscribers
likely do not think of distant broadcasts in terms of total minutes . .
. . A more natural unit would be the availability of particular
programs, regardless of their duration or frequency.'' Id. at 13
(emphasis added). Thus, Dr. Erdem suggested that Professor Crawford's
reliance (as is the case in all Waldfogel-type regressions) on
programming minutes as the independent (explanatory) variable with
respect to program type valuation misses the real economic correlation
pertinent to a value estimate, which is the correlation between
royalties and the number of subscribers. Id.
In response to the first point, Professor Crawford noted that his
regression analysis implicitly incorporated this revenue maximization
principle because it identified, ranked, and estimated the relative
value of program categories that maximize economic value for
subscribers given the existence of retransmission costs. Written
Rebuttal Testimony of Gregory Crawford, Trial Ex. 2005, ]] 70-71
(Crawford WRT). With regard to the second point, Professor Crawford did
not expressly state that the negative correlation between programming
types applied to his results. Rather, he noted that the negative
coefficients he had estimated for duplicated network programming\39\ in
part represented the fact that, on average, a station bundle containing
duplicated network minutes would be less valuable to subscribers than
one that did not. 2/28/18 Tr. 1404, 1607-08 (Crawford) (duplicate
programming adds no value and might be blacked-out).\40\
---------------------------------------------------------------------------
\39\ He estimated no negative coefficients for the six program
categories at issue in this proceeding.
\40\ Professor Crawford also estimated a negative coefficient
for nonduplicated network minutes, but he testified that this was
solely an artifact of the regulated rate structure, in which
distantly retransmitted networks ``only pay royalties of .25 DSE.''
2/28/18 Tr. 1605 (Crawford). The Canadian Claimant Group's expert,
Professor George, understood the negative coefficients for a program
category to reflect that programs in such a category would reduce
the value of a station bundle compared with programs from other
program categories. 3/5/18 Tr. 2117-18 (George); see id. at 2031
(``the negative coefficient here is telling us that this is
effectively dragging down the value of the Canadian signals. . . .
[I]if we could replace the Program Supplier content on Canadian
signals in a sort of hypothetical world . . . with Joint Sports or
Canadian Claimant programming, the value of the signal would be
higher. And so this coefficient, the negative coefficient, isn't
really surprising to me in this context . . . .'').
---------------------------------------------------------------------------
The Judges agree with Dr. Erdem that Professor Crawford's
regression analysis does not literally demonstrate that CSOs seek to
maximize the difference between revenues and costs as they would in an
unregulated market. Because royalty costs are determined independently
from retransmission decisions (especially with regard to the first DSE,
which is retransmitted in exchange for a mandatory minimum fee, as
discussed infra), CSOs do not and cannot engage in the sort of marginal
profit maximization decisions buyers/licensees would undertake in an
unregulated market. However, that does not mean that CSOs do not engage
in maximizing behavior through marginal analyses that weigh the
relative values of adding additional programming from different program
categories, -notwithstanding the presence of the regulated royalty
rate.
The Judges give no weight, however, to Dr. Erdem's speculation as
to how subscribers value programs of varying lengths. Dr. Erdem did not
undertake any affirmative analysis and presented no original
methodology. Thus, even assuming arguendo there might be value in such
a subscriber-based value analysis, Dr. Erdem did not present one here.
c. The ``Distant Minutes'' Criticism
Dr. Erdem noted that Professor Crawford's regression, because it is
a Waldfogel-type regression, ``assigned a predominant role'' to the
number of distant minutes retransmitted by each program category. Dr.
Erdem thus characterized Dr. Crawford's regression as a ``volume
focused'' approach. Erdem WDT at 14. Dr. Erdem questioned whether
Professor Crawford's key variable--``distant minutes'' by category--
really explained a ``significant share of the variation in royalty
fees.'' Erdem WRT at 15. To answer that question, Dr. Erdem
``estimate[ed] a regression model with only total distant minutes for
each claimant group as the independent (explanatory) variable.'' Id.
Dr. Erdem found that the number of distant minutes by claimant group
explained ``very little'' of the variation in royalties as measured by
adjusted R\2\. Id. at 15-16.\41\
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\41\ R\2\ in a multiple regression model is ``the proportion of
the total sample variation in the dependent variable [royalties-by-
category here] that is explained by the independent variable here,
[the number of distant minutes by claimant group].'' Wooldridge,
supra note 34, at 868. In more practical terms, ``R\2\ provides a
measure of the overall goodness-of-fit of the multiple regression
equation [with] value ranges from 0 to 1. An R\2\ of 0 means the
explanatory variables explain none of the variation of the dependent
variable; an R\2\ of 1 means that the explanatory variables explain
all of the variation.'' ABA Econometrics, supra note 22, at 409.
``There is no clear-cut answer [as] to [w]hat level of R\2\, if any,
should lead to a conclusion that the model is satisfactory.'' Id.
---------------------------------------------------------------------------
In response, Professor Crawford noted that his regression, like all
Waldfogel-type regressions, ``does not measure the relative value of a
programming type using only the number of minutes of . . . programming
type.'' Crawford WRT ] 74. Rather, such regressions also ``measure the
average value per minute to CSOs of each programming type[,] [and then]
multiply[ ] the average value per minute by the number of minutes of
programming, giv[ing] the total value of each program type.'' Id. ] 75.
Then, the total value of each program type is converted to ``average
values per minute of each claimant's programming via Professor
Crawford's regression (and,
[[Page 3561]]
indeed, any Waldfogel-type regression). As Professor Crawford opined,
it is the ``variation in the royalties paid by CSOs'' across each
programming category that allows the regression ``to infer the average
value per minute'' of each programming category, and ``[t]hese
estimated average values per minute are the estimated coefficients'' in
the regression. Id. ] 76.
The Judges find that Dr. Erdem's analysis, although apparently
accurate, is off-point and does not diminish the value of Professor
Crawford's regression (or any similarly-constructed Waldfogel-type
regression). The Judges recognize that the two elements multiplied in
such a regression--the volume of total minutes per program category and
the value-per-minute are both functions of volume. The former, volume
of minutes per program category, is facially a volume metric. Professor
Crawford recognized that if a regression measured only volume, then it
would be properly subject to criticism. Crawford WRT ] 74. But the
latter factor in the product, the value-per-minute, is not subject to
the same criticism. The value-per-minute factor is a metric for
relative value, estimating the CSOs' relative demand for different
categories of programming. To criticize the product as related to
volume, therefore, misses the mark, because it is relative value that
the Judges must determine in this proceeding.
With regard to Dr. Erdem's rebuttal critique, in which he found the
R\2\ calculation to demonstrate little correlation between categorical
programming minutes and royalties, Professor Crawford had a persuasive
rejoinder. Professor Crawford explained that it would be as
uninformative as it would be unsurprising that the number of distant
minutes alone--as Dr. Erdem found--would better estimate the royalties
paid (via a higher R\2\). Professor Crawford explained that the purpose
of his regression is to demonstrate the ``effect'' of different
programming (by category) on the relative royalties, not simply to find
the regressor (independent variable) that best ``predicts'' the level
of royalties. Crawford WRT ]] 91-95. Thus, Professor Crawford opined,
his regression is relevant to the economic issue at hand: The relative
value of program categories.\42\
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\42\ Professor Crawford calculated an R\2\ of .247 for his
duplicate analysis and an R\2\ of.246 for his non-duplicate
analysis. Crawford CWDT Appx. B at B-2.
---------------------------------------------------------------------------
The Judges do not agree that Dr. Erdem's calculation of a higher
R\2\ alone for his alternative approach demonstrated a deficiency in
Professor Crawford's regression. As one econometric expert has
explained:
[A] low R\2\ does not necessarily imply a poor model (or vice versa)
. . . What level of R\2\, if any, should lead to a conclusion that
the model is satisfactory? Unfortunately, there is no clear cut
answer to this question, since the magnitude of R\2\ depends on the
characteristics of the data series being studied . . . . [A] high
R\2\ does not by itself mean that the variables included in the
model are the appropriate ones. . . . As a general rule, courts
should be reluctant to rely on a statistic such as R\2\ to choose
one model over another.
Rubinfeld, supra note 36, at 425, 457.
Dr. Rubinfeld's emphasis on identifying the ``appropriate''
variables leads to Professor Crawford's next response to Dr. Erdem's
critique. According to Professor Crawford, from the perspective of
economic analysis (as opposed to purely econometric analysis), Dr.
Erdem's critique failed to address the institutional and economic
concerns in this proceeding, viz., how to determine the relative value
of the different program categories in an allocation proceeding.
Crawford WRT ] 95. Professor Crawford maintained that his regression
properly identifies the relative relationships at issue in this
proceeding.
d. Alleged Failure To Focus on Impact of the ``Number of Distant
Subscribers''
Dr. Erdem asserted that a control variable in Professor Crawford's
regression--the ``number of distant subscribers''--was statistically
significant and accounted for a large share of the variability in the
royalties. Erdem WRT at 17. Accordingly, Dr. Erdem concluded that
Professor Crawford's regression inaccurately and wrongly emphasized a
correlation between program minutes (across categories) and royalty
variability, when the more significant correlation was between the
number of distant subscribers and the variability of royalties. Id.
In response, Professor Crawford explained that Dr. Erdem had failed
to use the proper measure of ``distant subscribers,'' which led Dr.
Erdem in essence to double-count the number of distant subscribers,
thus invalidating his argument. Crawford WRT ] 104.\43\ Dr. Erdem was
compelled to concede at the hearing that his manipulations in his
Models numbered 1 through 6 should all be ignored. 3/8/12 Tr. 2779-80
(Erdem).
---------------------------------------------------------------------------
\43\ In fact, as discussed infra, Dr. Erdem subsequently agreed
with Professor Crawford's criticism in this regard, and the SDC
moved for leave to correct Dr. Erdem's testimony, but the Judges
entered an order denying that motion as out of time.
---------------------------------------------------------------------------
Accordingly, the Judges do not give any weight to this
criticism.\44\
---------------------------------------------------------------------------
\44\ Dr. Erdem modeled several of his additional critiques,
discussed infra, by combining the impact of those critiques with the
impact of his admittedly erroneous measure of the number of
``distant subscriber minutes.'' The Judges separately consider those
further critiques on their own merits, not only in the interest of
completeness, but also to consider whether or not these other
criticisms have qualitative value, notwithstanding that their impact
cannot be quantified by resort to Dr. Erdem's modeling that bundled
those critiques with the admittedly tainted measure of ``distant
subscriber minutes.''
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e. The Zero Minutes Issue
Dr. Erdem pointed out that Professor Crawford's two models
contained numerous zeros (i.e., instances when there was no distant
content being retransmitted for a particular claimant category). More
particularly, Dr. Erdem noted that for the duplicated analysis, the
Canadian distant programming minutes had about 94 percent zeros,
followed by PTV with approximately 59 percent, the JSC with
approximately 10 percent, and between 5-8 percent for the remaining
categories. (These percentages remain essentially unchanged for the
nonduplicated analysis.) Erdem WRT at 17-18.
Dr. Erdem asserted that because zero represented a floor on the
number of minutes any programming category could have offered,
Professor Crawford's failure to control for the presence of a non-
trivial number of zeros has the ``potential'' to skew the coefficients
Professor Crawford estimated in his models. In an attempt to address
this issue, Dr. Erdem reworked Professor Crawford's regression approach
by including ``indicator variables'' for instances in which the distant
minute variables were zero. He then re-estimated Professor Crawford's
two models, creating what he called ``Model 3.'' Dr. Erdem's Model 3
cumulatively reworked Professor Crawford's duplicated and nonduplicated
regressions to incorporate, inter alia, the distant subscriber
instances and the zero-minutes indicator issue. Erdem WRT at 38, 40.
Dr. Erdem found that, relative to Professor Crawford's regression
model, adding the indicators for instances with zero distant minutes
increased the PS and PTV shares by approximately 6 percentage points
and 1-2 percentage points, respectively. The Devotional share increased
by approximately 1 percentage point while the CTV share decreased by
approximately 10 percentage points. The JSC share increased by
approximately 1
[[Page 3562]]
percentage point, and the Canadian share decreased by approximately
0.4-0.5 percentage points. Id.
Because these revised percentages also incorporate Dr. Erdem's
erroneous adjustment for his ``distant subscriber instances'' variable,
his ``Model 3,'' must be ignored. 3/8/18 Tr. 2779-80 (Erdem). Further,
as a separate problem with Dr. Erdem's critique, he did not opine that
Professor Crawford's treatment of the number of zeros was improper or
that it had caused a skewing of the coefficients; rather Dr. Erdem
testified only that such skewing was a ``potential'' problem--one that
Dr. Erdem would have elected to address with the use of an indicator
variable.\45\ The Judges understand this point to indicate that
although Dr. Erdem would have undertaken a different approach, he did
not opine that Professor Crawford's approach was unreasonable.
Accordingly, the Judges are unpersuaded that this criticism served to
undermine the usefulness of Professor Crawford's regression
analysis.\46\
---------------------------------------------------------------------------
\45\ An ``indicator variable,'' also known as a ``dummy
variable'' is a ``[a]variable that takes on only two values, usually
0 and 1, with one value indicating the presence of a characteristic,
attribute or effect and the other value indicating absence.''
Rubinfeld, supra note 36, at 464.
\46\ The Judges are also unconvinced that the number of zeros is
as striking as Dr. Erdem suggested. For example, the high percent of
zeros for Canadian claimants would be consistent with the inevitable
absence of any retransmissions of Canadian stations outside the
Canadian zone.
---------------------------------------------------------------------------
f. Sensitivity of Nonduplicated Minutes Model
In his nonduplicated model, Professor Crawford included as an
additional variable the total number of nonduplicated minutes. Dr.
Erdem noted that Professor Crawford explained that ``[t]his new
covariate plays the same role in the final econometric model that the
number of distant signals plays in the initial econometric model.''
Erdem WRT at 19 (quoting Crawford CWDT ] 165 n.57). However, Dr. Erdem
discovered that in this nonduplicated model the number of distant
signals was still present, together with the new variable, (i.e., the
total number of nonduplicated minutes). Dr. Erdem determined that these
two variables were almost perfectly correlated (a 0.998 correlation),
rendering ``the rationale for including that additional variable . . .
less clear.'' Erdem WRT at 19.\47\
---------------------------------------------------------------------------
\47\ When two covariates are highly or perfectly correlated with
each other, the regression can suffer from a ``multicollinearity''
problem, whereby the model does not reveal the separate effects of
each of the two variables. See Rubinfeld, supra note 36, at 465
(``Multicollinearity [a]rises in multiple regression analysis when
two or more variables are highly correlated.'').
---------------------------------------------------------------------------
To analyze this issue, Dr. Erdem performed a sensitivity analysis,
or test \48\, rerunning the nonduplicated model without the total
nonduplicated minutes variable. Dr. Erdem's ``Model 5'' presented
regression results and estimated royalty shares from this analysis. See
Erdem WRT Ex. R3. Compared to his Model 4, excluding the added variable
decreased the Program Supplier share by approximately 0.2 percentage
points, the JSC share by about 2 percentage points, the CTV share by
about 2 percentage points the PTV share by about 0.3 percentage points.
The Devotional and Canadian shares remained approximately the same. See
Erdem WRT at 19, Ex. R3.
---------------------------------------------------------------------------
\48\ A ``sensitivity analysis'' is ``[t]he process of checking
whether the estimated effects and statistical significance of key
explanatory variables are sensitive to inclusion of other
explanatory variables, functional form, dropping of potential out-
lying observations, or different modes of estimating.'' Wooldridge,
supra note 34, at 869. The issue of robustness is related to the
issue of sensitivity: ``The issue of robustness [addresses] whether
regression results are sensitive to slight modifications in
assumptions.'' Rubinfeld, supra note 36, at 43; see also Peter
Kennedy, A Guide to Econometrics at 11 (5th ed. 2003) (defining the
``robustness'' of an estimator as ``insensitivity to violations of
the assumptions under which the estimator has desirable properties .
. . .''). Importantly, because ``[e]valuating the robustness of
multiple regression results is a complex endeavor . . . there is no
agreed-on set of tests for robustness which analysts should apply.
In general, it is important to explore the reasons for unusual data
points.'' ABA Econometrics, supra note 22, at 24; accord Rubinfeld,
supra note 36, at 437.
---------------------------------------------------------------------------
The Judges find that these modest percentage point differences
would not diminish the value of Professor Crawford's nonduplicate
minute regression, in part because the regression approach is by design
an estimate rather than a precise measure.\49\ Moreover, Dr. Erdem's
modest changes are derived from his alternative models that also
incorporate his erroneous distant subscriber minutes approach, which
Dr. Erdem acknowledged to invalidate his adjustments to a number of his
models, including Models 4 and 5. See 3/8/18 Tr. 2779-80 (Erdem).
---------------------------------------------------------------------------
\49\ The Judges also do not find this to be a potential problem
with regard to the use of Professor Crawford's regression to
identify relative values, because these two covariates (the number
of nonduplicated minutes and the number of distant signals) are
control variables used to hold all other potential effects fixed
while analyzing program category minutes as the independent
variables--and the Judges do not identify in Dr. Erdem's testimony
any impact of his claimed multicollinearity on the purported
explanatory effect of program categories on royalties.
---------------------------------------------------------------------------
g. The WGNA Indicator Variable
Dr. Erdem altered Professor Crawford's approach by including a
dummy variable to indicate the presence (or absence) of WGNA. This
alteration increased the Program Supplier share by approximately 2
percentage points, increased the CTV and PTV shares by approximately 1
percentage point, respectively, and decreased the JSC shares by about 4
percentage points. The shares of the Devotional and Canadian categories
increased by 0.1 and 0.3 percentage points, respectively. Erdem WRT at
18-19.
However, Dr. Erdem did not expressly conclude that the absence of
this WGNA indicator variable in Professor Crawford's regression
analysis demonstrated that the latter's approach was inappropriate or
less relevant. Indeed, Dr. Erdem ended this particular analysis by
suggesting only that the use of an indicator variable regarding the
presence (or absence) of WGNA among the distantly retransmitted
stations could be suggestive of an outlier effect arising from the
presence of WGNA, yet Dr. Erdem conceded that ``Professor Crawford's
model does not exhibit sensitivity to outliers.'' Erdem WRT at 19
n.17.\50\ Accordingly, Dr. Erdem's criticism in this regard does not
diminish the value of Professor Crawford's regression analysis. And,
once more, Dr. Erdem's estimate of the impact of this criticism was
bundled together with, inter alia, his admittedly erroneous adjustment
for distant subscriber minutes, thereby tainting the measure of this
adjustment.
---------------------------------------------------------------------------
\50\ More particularly, Dr. Erdem acknowledged that because
Professor Crawford had utilized a ``larger sample,'' Erdem WRT at
20, n.17, Professor Crawford's regression analysis was not subject
to an outlier problem. In fact, Professor Crawford's data included
programming minutes using the population of programs carried on all
imported distant broadcast signals, rather than using estimates of
programming minutes based on sampling the programs carried on
distant broadcast signals. Crawford CWDT ] 72.
---------------------------------------------------------------------------
h. Geographical Effects
The SDC noted that a CTV economic expert witness, Dr. Christopher
Bennett, found that ``over 90% of the distant signals imported were
within 150 miles of the community served, and over 95% were within 200
miles.'' Corrected Written Direct Testimony of Christopher Bennett,
Trial Ex. 2006, ] 31 & Fig. 6 (Bennett CWDT).\51\ Accordingly, Dr.
Erdem asserted that the positive coefficients in Professor Crawford's
regression ``could'' have been driven by factors ``like'' geography,
emphasizing
[[Page 3563]]
the values and preferences of large urban areas and de-emphasizing the
values and preferences of smaller rural areas. 3/8/18 Tr. 2688-91
(Erdem).
---------------------------------------------------------------------------
\51\ Dr. Bennett, who compiled data for Professor Crawford's
regression analyses, excluded superstations such as ``WGN, WPIX,
WSBK, and WWOR, which historically were distributed nationwide by
satellite [and] were excluded in distance analyses presented in
previous copyright royalty distribution proceedings.'' Bennett CWDT
] 30, n.15.
---------------------------------------------------------------------------
In response, CTV pointed out that Professor Crawford's regression
contained variables that controlled for geographic effects. In
particular, CTV noted that the SDC had in fact acknowledged that
Professor Crawford's regression included ``system-level fixed effects
[that] introduce a form of geographic control . . . .'' \52\ SDC PFF ]
101 (citing 3/8/18 Tr. 2709-10 (Erdem)).\53\ Moreover, CTV pointed out
that Professor Crawford's regression also included as a control
variable the number of local signals at the subgroup level, which also
helped account for geographical market differences (including market
and Designated Market Area (DMA) size) across subgroups within the
systems. See Crawford CWDT App. B Fig. 22; see also Written Rebuttal
Testimony of Ceril Shagrin, Trial Ex. 2009, ] 20 & Exs. A, B (Shagrin
WRT) (number of local stations is prime indicator of market size).
---------------------------------------------------------------------------
\52\ ``Fixed effects'' variables are potential effects on the
dependent variable (here, categorical royalties) by other factors
that are unobserved by the regression. Wooldridge, supra note 34, at
461. (To put the ``fixed effects'' variables in context, they differ
from the ``error term,'' which reflects ``idiosyncratic error,''
id., and differ from a control variable in that, as noted supra, a
control variable is one that is known and expected to impact the
dependent variable (categorical royalties here), but ``is not the
object of interest in the study'' and thus held constant by the
econometrician. Stock & Watson, supra note 32, at 280.
\53\ The SDC argue that this control caused a new geographic
effect that Professor Crawford's regression ignored: ``some''
stations ``could'' be local as well as distant within some
subscriber groups. SDC PFF ] 101 (and record citations therein).
However, speculation as to the existence of this possibility and its
possible extent are insufficient to invalidate or diminish the
evidentiary value of the geographic controls used by Professor
Crawford in his regression.
---------------------------------------------------------------------------
The Judges find that Professor Crawford's regression controlled for
geographic effects. Dr. Erdem's criticism to the contrary appears to be
based on a difference of opinion as to how to account for the
geographic issue rather than any error in Professor Crawford's
regression analysis. Additionally, the Judges do not find that a
regression that weighs more heavily the value of programs retransmitted
to more people is inherently suspect. Indeed, the opposite is the case.
To use Dr. Erdem's example, population density is greater in areas
adjacent to urban areas where professional sports teams are based and
will demand more professional sports. See 3/8/18 Tr. 2689 (Erdem). This
subscriber demand causes a CSO serving their subscriber group to have a
derived demand for the retransmission of stations with more JSC
programming. More JSC programming leads to higher JSC royalties
relative to whatever other programming is more popular in areas where,
as Dr. Erdem testified, there exist ``smaller systems with smaller
number of subscribers and smaller fees . . . .'' 3/8/18 Tr. 2690
(Erdem). In short, the Judges see this phenomenon as an attribute of
Waldfogel-type regressions, including Professor Crawford's regression
analysis.\54\
---------------------------------------------------------------------------
\54\ This point regarding geographic effects also relates to
what Dr. Erdem asserted is an anomaly in a Waldfogel-type regression
such as undertaken by Professor Crawford. Dr. Erdem claims that if a
certain type of programming (Devotional, for example) were more
popular on lower fee paying cable systems, the lower fee status of
that system would cause Devotional programming to have a lower
coefficient and a lower royalty share under the regression. However,
if that cable system decided ``this category of programming isn't
doing it for us'' and thus eliminated Devotional programming, that
programming category elimination would anomalously cause the
Devotional coefficient to increase, because it would no longer be
associated with that lower fee paying cable system. 3/8/18 Tr. 2685-
86 (Erdem). The flaw in that argument is two-fold. First, although
the Devotional coefficient might increase, there would be fewer
minutes of programming to multiply by that coefficient, which would
reduce the relative share allocated to Devotional programming under
a Waldfogel-type regression. Second, a cable system would distantly
retransmit Devotional programming, even if it generated lower
royalties relative to other CSOs in other regions, because the CSO
is incentivized by increasing or retaining subscribers, not by
maximizing royalties compared with other CSOs. Again, the Judges
emphasize that the hypothetical buyer is the CSO, not the copyright
owner, and the relative value of a program category is based on its
economic contribution as part of a bundle to the CSO, not the
royalty it might generate in any other context. The royalties flow
from such carriage decisions and those decisions are made by each
CSO with varying receipts (constrained by the WTP of its subscriber
base), averaged through a Waldfogel-type regression.
---------------------------------------------------------------------------
i. Ignoring Signals That CSOs Chose Not To Carry
The SDC also criticized Professor Crawford for not taking into
account in his regression the impact on value of the stations that were
``not retransmitted.'' SDC PFF ] 81 (citing 2/28/18 Tr. 1494-5
(Crawford)) (emphasis added). The SDC noted that Professor Crawford had
written a published article that indicated that an approach accounting
for stations that were not retransmitted could have been applied to
determine program category value in the present proceeding. SDC PFF ]
82 (citing 2/28/18 Tr. 1497-98 (Crawford)). However, nothing in the
record suggested that the potential usefulness of such an alternative
regression approach called into question the validity, reasonableness,
or persuasiveness of the regression approach undertaken by Professor
Crawford in the present proceeding, which approached the relative value
analysis from a perspective that analyzed the programs and stations
that were transmitted. Indeed, the SDC do not cite any expert witness
in the present proceeding to support their conclusory assertions in
proposed findings of fact that Professor Crawford's decision not to
analyze non-transmitted stations and programs compromised his analysis
in this proceeding. See SDC RPFF ]] 81-82. Accordingly, the Judges find
that this criticism does not diminish the value of Professor Crawford's
regression analysis in this proceeding.
j. Number of Subscribers as Control Variable
The SDC noted that Professor Crawford used the log of fees paid as
his dependent variable (expressing changes in fees paid in percentage
terms), but he expressed changes in ``the number of subscribers--one of
his control variables--in level form (i.e., linear, or non-log). SDC
PFF ] 102 (citing 2/28/18 Tr. 1541, 1550 (Crawford)). The SDC's expert,
Dr. Erdem, testified that Professor Crawford's use of the linear form
for this control variable was improper, because it failed to correspond
with the actual relationship between royalty fees and subscribers,
i.e., a percentage change in the number of subscribers corresponds with
an equal change in the percentage of royalty fees). 3/8/18 Tr. 2770-71
(Erdem). As a consequence, Dr. Erdem maintained, Professor Crawford had
introduced statistical ``bias'' \55\ into his regression. Id. at 2716-
17 (Erdem).
---------------------------------------------------------------------------
\55\ ``Bias'' is ``[a]ny effect . . . tending to produce results
that depart systematically (either too high or too low) from the
true values. A biased estimator of a parameter [e.g., a regression
parameter] differs on average from the true parameter.'' Rubinfeld,
supra note 36, at 463-64. Somewhat more formally, ``bias'' reflects
``[t]he difference between the expected value of an estimator and
the population value that the estimator is supposed to be
estimating.'' Wooldridge, supra note 34, at 859.
---------------------------------------------------------------------------
To address this criticism, Dr. Erdem, undertook a sensitivity test
and transformed the control variable for the number of subscribers into
log form. 3/8/18 Tr. 2767 (Erdem). He found that this linear-to-log
transformation improved the fit of the regression, increasing the R\2\
metric from approximately .24 to .97. (A higher R\2\ indicates a
tighter fit of within the data points, see supra note 41).
In response, CTV and Professor Crawford argued that Dr. Erdem
misapplied a principle that might be valid in a ``prediction''
regression. Professor Crawford maintained though that his own
regression on behalf of CTV was an ``effects'' regression,
[[Page 3564]]
seeking to explain the issue at hand, i.e., how different program
categories correlate with the royalties paid. According to Professor
Crawford, his regression analysis was not a ``prediction'' regression
designed to identify the best predictors of royalties paid. Thus, he
argued, it was important to use control variables that keep constant
the effects on the dollar amount of royalties paid in order to
determine the relative values among program categories, which was the
purpose of the regression. 2/28/18 Tr. 1393-94, 1430, 1549-50
(Crawford).
Professor Crawford explained what he understood to be a fundamental
mistake made by Dr. Erdem:
Dr. Erdem misunderstands the purpose of an econometric analysis
in this proceeding. . . . For the goal of prediction, the focus is
on finding the explanatory variables that best predict the outcome
of interest . . . . [I]f the goal is to predict stock prices and the
price of tea in China helps, then . . . include it in the model (and
don't worry about the economic interpretation of its coefficient).
That is not the purpose in this proceeding, however. In this
proceeding, experts are using econometric analyses to help the
Judges determine . . . relative marketplace value . . . . The
dependent variable in these regressions, the royalties cable
operators pay for the carriage of the distant signals, are
informative of this relationship . . . . The key explanatory
variables in this relationship, the minutes of programming of the
various types carried on distant signals, are informative as the
impact they have on royalties reveals the relative market value of
each programming type. Other explanatory variables are included in
the model to control for other possible determinants of cable
operator royalties. This helps improve the statistical fit of the
regression (to ``reduce its noise''), providing more precise
estimates of the impact of programming minutes that are the focus of
the analysis.
. . .
The goal here is to find the econometric model that can best
reveal relative marketplace value. Doing so means crafting the
econometric model to reflect the institutional and economic features
of the environment that is generating the data being used. . . . The
econometrician determines which explanatory variables to include not
based exclusively on statistical criteria regarding the overall fit
of the model, but also on whether there are good economic and/or
institutional justifications for including that variable.
Crawford WRT ]] 91-94 (footnotes omitted) (emphasis added).
Accordingly, Professor Crawford testified that the R\2\ measure on
which Dr. Erdem relied is not relevant to the task at hand, because
that measure does not explain the relative values of the several
program categories, but rather shows ``how much of the variation in the
dependent variable can be explained by the control or explanatory
variables.'' Crawford WRT ] 93.
Applying this distinction more particularly to the present dispute,
Professor Crawford defended his use of a linear control variable for
the number of subscribers as sufficient for its intended purpose--to
avoid statistical bias and distortion. He contrasted his approach with
Dr. Erdem's claim that a log control variable would be preferable, with
Professor Crawford asserting that Dr. Erdem's proposed log
transformation did not merely control for the royalty formula, but
rather essentially replicated the formula for calculating royalties,
thereby distorting the regression results. 2/28/18 Tr. 1429-30, 1552
(Crawford). That is, Dr. Erdem's log approach might well have been
appropriate to predict a meaningful correlation between the percentage
change in royalties and the percentage change in the number of
subscribers, but that is not informative (and thus not relevant) as to
the effect, if any, of the impact of the different program categories
within the distantly retransmitted stations on the dollar amount of
royalties that were paid.
The Judges find that Professor Crawford's regression is not
compromised by his use of the linear form to express the number of
subscribers in this control variable. If the Judges' statutory task
were to identify and rank all the causes of a change in total
royalties, the change in the number of subscribers apparently might be
the chief causal element because the statutory royalty fee is a percent
of receipts. Changes in the dollar value of receipts, naturally, are
directly related, on a percentage basis, to percentage changes in the
number of subscribers. But the Judges' legal, regulatory, and economic
task in this proceeding is to determine the relative market value of
different categories of programming; thus, any correlation between the
number of subscribers and royalties is not in furtherance of that
objective. Rather, Professor Crawford's use of a linear form for the
number of subscribers served to control for the size of the system
without overriding the purpose of the regression, which was to measure
the effects (if any) of different program categories on royalties paid.
The Judges not only find Professor Crawford's assertions in this
regard persuasive, they note that his opinion has some support in the
academic literature.\56\ See G. Shmueli, To Explain or to Predict?, 25
Statistical Science 289, 290-91, 297 (2010) (``The criteria for
choosing variables differ markedly in explanatory versus predictive
contexts.''); see also F.M. Fisher, Multiple Regression in Legal
Proceedings, 80 Colum. L. Rev. 702, 720 (1980) (The R\2\ measure ``must
be approached with a fair amount of caution, since R\2\ can be affected
by otherwise trivial changes in the way in which the problem is set
up.'').
---------------------------------------------------------------------------
\56\ Professor Crawford did not support his lengthy exposition
(quoted in some detail in the text, supra), with any references to
learned treatises or other authorities, nor did Dr. Erdem support
his critique in such a manner. The experts for all parties were
guilty of this omission throughout their respective testimonies, a
problem the Judges find disturbing particularly in the present
context, causing dueling esoteric econometric positions sometimes to
devolve into ipse dixit disputes.
---------------------------------------------------------------------------
The Waldfogel-type regression is an example of modeling utilized to
explain the effects of different program categories on the relative
payment of royalties--rather than an attempt to predict the level of
royalties. Thus, as Professor Shmueli wrote, the choice of variables
can reasonably be based on the ``underlying theoretical model.'' Id.;
see also F.M. Fisher, Econometricians and Adversary Proceedings, 81 J.
Am. Stat. Ass'n 277, 279 (1986) (``There is a natural view that models
are supposed to do nothing other than predict . . .'' resulting in the
``danger'' of ignoring ``better models that do not fit or predict quite
so well but are in fact informative about the phenomena being
investigated.'') (emphasis added).\57\
---------------------------------------------------------------------------
\57\ This econometric point regarding the appropriate use of
different models is of a piece with the Judges' statement in Web IV
that no one economic model is appropriate to explain all market
activity. Determination of Royalty Rates and Terms for Ephemeral
Recording and Webcasting Digital Performance of Sound Recordings
(Web IV), 81 FR 26 316, 26 334-35 (May 2, 2016).
---------------------------------------------------------------------------
Because the Judges find in this proceeding, as in past proceedings,
that the theoretical model of a Waldfogel-type regression is reasonable
and useful in this context, Dr. Erdem's criticism regarding Professor
Crawford's use of a linear control variable for the number of
subscribers does not diminish the value of his regression analysis in
this proceeding.
k. Purportedly Incorrect Consideration of Network Programming
The SDC asserted that Professor Crawford failed to analyze
correctly the impact of the number of distant signals and the total
number of minutes in his nonduplicated minutes analysis, which caused
his coefficients to be uninterpretable and certain coefficients to turn
negative, falsely implying a negative value for such retransmitted
distant programming. However, a substantial portion of this assertion
grew out of Dr. Erdem's tardy and thus
[[Page 3565]]
rejected proposed rebuttal testimony. See 3/8/18 Tr. 2704-05 (Erdem).
Thus, Dr. Erdem's written testimony and the SDC's affirmative case at
the hearing do not support the SDC's criticisms in this regard.
However, the SDC had some success in raising this issue on cross-
examination of Professor Crawford, who appeared to acknowledge that
nonduplicated network programming had positive value that he had not
added back into his analysis. 2/28/18 Tr. 1572 (Crawford). Professor
Crawford attempted to discount the import of this factor, asserting
that adding in such values would have caused a ``common level shift''
in all the coefficients. 2/28/18 Tr. 1573 (Crawford). However, when
confronted on cross-examination with the logarithmic (percentage)
impact on the coefficients (and thus the relative values), Professor
Crawford became uncertain as to whether he should have considered the
logarithmic (percentage) impact of nonduplicated network programming.
More particularly, having considered the issue on the witness stand,
Professor Crawford was then asked by cross-examining counsel whether he
was ready to agree that he ``should have taken into account the value
of the . . . coefficient that would be implied for the nonduplicated
network programming''--to which he replied: ``So I am not sure that I
do [agree] [a]nd I am not sure that I don't.'' 2/28/18 Tr. 1581
(Crawford).
Professor Crawford and CTV further responded to this nonduplicated
network minutes argument by noting that the impact of the issue, if
any, was indeterminate, because Professor Crawford had lumped
nonduplicated network minutes with off-air programming as a single
control variable, not as an input to determine the values of the
coefficients of interest. 2/28/18 Tr. 1625-29 (Crawford). Additionally,
Professor Crawford explained that, in any event, the purpose of the
``total non-duplicate minutes'' variable was to serve the same volume
control function as the ``number of distant signals'' variable in the
initial regression.
The Judges find that Professor Crawford's admitted uncertainty as
to the impact of nonduplicated network programming minutes on the
relative values of his coefficients somewhat diminishes the probative
value of his non-duplicated model. Further, the fact that Professor
Crawford's purpose in adding these minutes was to insert a control
variable did not address whether this variable did not also affect the
calculation of coefficients for the program categories at issue.\58\
However, the absence of any hard evidence of the extent of this problem
on the measurement of the coefficients makes this deficiency difficult
to quantify. Accordingly, this criticism leads the Judges to consider
the accuracy of the estimates in Professor Crawford's nonduplicated
analysis to be less certain, and the Judges thus will look to Professor
Crawford's duplicated-minutes regression results when incorporating his
analysis and conclusions into their determination of the appropriate
allocation of shares.
---------------------------------------------------------------------------
\58\ The Judges note that although the shares are not
drastically different in the two models, the shares for CTV, who
engaged Dr. Crawford, increased more substantially under his
nonduplicated analysis, i.e., the approach as to which he expressed
uncertainty under cross-examination than any other program category.
Further, a number of categories saw either a decline or essentially
no change in their shares in the nonduplicated model compared to the
duplicated model. Compare Crawford CWDT Fig. 17 with Crawford CWDT
Fig. 20 (both reproduced supra).
---------------------------------------------------------------------------
l. Overfitting
The SDC contended that Professor Crawford's regression methodology
suffered from a problem known as ``overfitting.'' In econometrics, and
in statistics more broadly, overfitting occurs when the regression
attempts to ``estimat[e] too large a model with too many parameters.''
C. Brooks, Introductory Econometrics for Finance 690 (3d ed. 2014). See
also T. Powell & P. Lewecki, Statistics: Methods and Applications 681
(2006) (``overfitting'' is ``[w]hen [a regression] produc[es] a curve .
. . that fits the data points well, but does not model the underlying
function well [because] its shape is being distorted by the noise
inherent in the data.'').
On the other hand, when an econometrician attempts to avoid
overfitting, he or she must be mindful not to eliminate potentially
important data from the regression. Otherwise a different problem--
underfitting--can arise. To wit:
There is actually a dual problem to overfitting, which is called
underfitting. In [an] attempt to reduce overfitting, the [modeler]
might actually begin to head to the other extreme and . . . start to
ignore important features of [the] data set. This happens when [the
modeler] choose[s] a model that is not complex enough to capture
these important features . . . . [T]his incredibly important problem
is known as the bias-variance dilemma[ \59\] [and] is just as much
an art as it is a science.
\59\ The ``bias-variance dilemma'' refers to the problem that
arises when a model that tends to overfitting (too few observations
per variable) will have a low bias in the regression coefficient
(i.e., a regression line based on the data will tightly fit the data
points) but will suffer from a relatively higher variance, (i.e., a
relatively higher expected distance from the variable from its true
value. See ABA Econometrics, supra note 22, at 275-76 nn.13 & 14
(``The higher the variance, the less precise is the estimate [i.e.,]
the less the data say about the true value of the coefficient. . . .
A biased estimate differs systemically from the true value, rather
than departing from the true value only because of sampling
error.'').
---------------------------------------------------------------------------
D. Geng and S. Shih, Machine Learning Crash Course: Part 4--The Bias-
Variance Dilemma, ML@B, The Official Blog of Machine Learning @Berkeley
(July 13, 2017), available at https://ml.berkeley.edu/blog/2017/07/13/tutorial-4/(last visited May 1, 2018) (emphasis added).
In the present case, the SDC argued that Professor Crawford's
regressions suffered from overfitting for several reasons.
First, because he used ``system-accounting period fixed effects [as
distinguished from the subscriber group level], Professor Crawford's
regression employs more than 7,300 variables [and] approximately 26,000
observations . . . only about 3.55 observations per variable.'' SDC PFF
] 109 (citing Crawford CWDT at C-3; 2/28/18 Tr. 1646 (Crawford)).
According to the SDC, Professor Crawford acknowledged that ``[a]s a
rule of thumb, fewer than ten observations per variable can yield a
likelihood of overfitting.'' SDC PFF ] 111 (citing 2/28/18 Tr. 1461
(Crawford)). Because Professor Crawford had less than ten observations
per variable (3.55), the SDC argued that Professor Crawford's
regression suffered from overfitting, calling into question the
usefulness of the estimates Professor Crawford produced.
However, Professor Crawford denied that he endorsed this test, and
the Judges agree with Professor Crawford, based on the following cross-
examination colloquy:
SDC COUNSEL: [H]ave you ever heard of the One-in-Ten Rule? One-
in-Ten?
PROFESSOR CRAWFORD: Not--if you could describe it, perhaps I
have.
SDC COUNSEL: A rule of thumb--not saying it is precise--a rule
of thumb that you should have at least ten observations per . . .
per coefficient.
PROFESSOR CRAWFORD: I have not heard that specific rule, but I
understand the idea behind it. And generally the idea behind that is
if you don't have ten observations per one tends to get imprecise
parameter estimates. . . . I don't subscribe to the One-in-Ten Rule.
2/28/18 Tr. 1461, 1463 (Crawford) (emphasis added). Nowhere in this
testimony did Professor Crawford indicate a familiarity with the
supposed ``one-in-ten'' rule in counsel's question, and Professor
Crawford instead
[[Page 3566]]
attempted merely to explain his understanding of this heuristic as the
SDC's counsel had presented it.\60\ Without a more developed record
regarding the existence and applicability of this one-in-ten heuristic,
the Judges cannot find that Professor Crawford's use of ``only'' 3.55
observations per variable would have a negative impact on his
regression methodology. Moreover, because the SDC presented this
principle as a heuristic rather than a rule, the underdeveloped nature
of the record is of even greater importance. Finally, because the
problem of overfitting versus underfitting (the bias/variance dilemma
discussed supra) appears to be a judgment call for the econometric
modeler, the Judges are loath to impose this heuristic as an
invalidating principle in connection with Professor Crawford's
regression.
---------------------------------------------------------------------------
\60\ Moreover, Professor Crawford's testimony was at odds with
what the SDC's counsel actually meant by the ``one in ten'' rule as
it relates to overfitting. In the immediately subsequent testimony,
the SDC's counsel challenged Professor Crawford's opinion that ``the
idea behind that is if you don't have ten observations per
coefficient, one tends to get imprecise parameter estimates.'' Id.
The SDC's counsel then disagreed with the expert witness, Professor
Crawford, and asserted that ``[a]n overfitted model will be able to
estimate the parameters [a]nd you might not be able to project it to
other data, but will be able to estimate the parameters with great
precision.'' Id. As the introductory discussion of overfitting (set
forth supra) makes clear, the SDC's counsel was correct in his
presentation of the overfitting problem, but that is unrelated to
the fact that Professor Crawford's testimony demonstrated his
unfamiliarity with both the ``one-in- ten'' heuristic and its
alleged econometric importance. (The Judges are not suggesting that
a ``one-in-ten'' heuristic is not utilized by econometricians, but
rather note that the record does not establish its existence and its
applicability in this proceeding.).
---------------------------------------------------------------------------
Relatedly, Professor Crawford only acknowledged that overfitting
would be a problem if there were a one-to-one ratio of variables to
observations that would perfectly predict the variables, but with very
wide confidence intervals. Professor Crawford testified that, in his
opinion, his confidence intervals were not so wide as to diminish the
value of his regression results. See 2/28/18 Tr. 1460-62 (Crawford).
The Judges agree that Professor Crawford did not go further than
acknowledging that an absolute identity in the number of variables and
observations would create an overfitting problem.
As a more theoretical rejoinder, Professor Crawford asserted that
concerns with regard to overfitting apply to ``prediction''
regressions--not ``effects'' regressions such as Professor Crawford's
regressions and all the Waldfogel-type regressions introduced in this
proceeding. Id. at 1460, 1463.\61\ However, Professor Crawford did not
provide a sufficient explanation as to the disparate impacts of
overfitting in a ``prediction'' regression and an ``effects''
regression to allow the Judges to find that the relatively low number
of observations per variable is less important in his ``effects''
regression.
---------------------------------------------------------------------------
\61\ The Judges discussed the distinction between an ``effects''
regression and a ``prediction'' regression at length, supra, section
0.
---------------------------------------------------------------------------
Second, according to SDC, Professor Crawford's total observations
were diminished, and his regressions compromised, because he
``effectively discarded'' approximately 15% of his observations by
disregarding observations from systems with a single subscriber group,
which totaled ``approximately half of all systems in his data set'', by
virtue of his reliance on ``system-accounting period fixed effect.''
SDC PFF ] 110 (citing 2/28/18 Tr. 1458 (Crawford); Crawford CWDT at 21,
Fig. 10; 3/8/18 Tr. 2710-11 (Erdem)).
The Judges are troubled by CTV's failure to respond expressly to
this criticism.\62\ Similarly, the Judges are troubled that CTV neither
cited nor addressed the SDC's criticism that Professor Crawford did not
test his model for overfitting.
---------------------------------------------------------------------------
\62\ In its Response to the SDC's PFF, CTV helpfully cited (and
reproduced) each numbered paragraph of the SDCPFF, and conspicuously
absent from that response is any reference to ] 110.
---------------------------------------------------------------------------
The final reason the SDC criticized Professor Crawford's analysis
for overfitting was their claim that he essentially selected his
regression model out of ``more than one'' model he had previously run.
SDC PFF ] 118 (citing 3/1/18 Tr. 1888 (Bennett)). More particularly,
the SDC contended that Professor Crawford and his team disregarded at
least two regressions. First, Professor Crawford allegedly discarded a
regression without the top-six multiple-system operator (MSO)
interaction variables that were in his final model. 2/28/18 Tr. 1642-44
(Crawford). Second, the SDC asserted that Professor Crawford
disregarded ``a model run at the system level instead of the subscriber
group level,'' i.e., a model that would not have treated system-
accounting period data as a fixed effect. 3/1/18 Tr. 1888 (Bennett).
See SDC PFF ] 113 (relying on Crawford and Bennett testimony).
According to the SDC, Professor Crawford's rejection of several
models before deciding on the one he presented in evidence in this
proceeding indicated a potential likelihood of overfitting in the
regression model in evidence through his consumption of ``phantom
degrees of freedom,'' i.e., ``variables that were tried and
rejected''--rather than included in the regression model in
evidence.\63\ SDC PFF ] 113 (citing 3/8/18 Tr. 2711 (Erdem)).
---------------------------------------------------------------------------
\63\ ``Degrees of freedom'' are defined ``[i]n multiple
regression analysis, [as] the number of observations minus the
number of estimated parameters.'' Wooldridge, supra note 34, at 837.
Accordingly, statisticians understand ``degrees of freedom'' to be
measures of how much can be learned from a regression, with the
quality of knowledge improved by increasing the number of
observations, reducing the number of estimated parameters, or by
some combination of both that serves to widen the difference between
the number of observations and parameters. See What are degrees of
freedom?, https://support.minitab.com/en-us/minitab/18/help-and-how-to/statistics/basic-statistics/supporting-topics/tests-of-means/what-are-degrees-of-freedom/(last visited June 14, 2018). Dr. Erdem
does not define a ``phantom degree of freedom'' except to describe
it as an ``economic concept . . . not a statistic.'' 3/8/18 Tr. 2711
(Erdem). More particularly, a ``phantom degree of freedom'' can be
generated when the modeler reduces the number of parameters by his
or her rejection of other models that would have added a greater
number of parameters--nothing more has really been learned but the
explicit number of degrees of freedom appears larger, as an artifact
(a ``phantom'') arising from the econometrician's rejection of
models containing additional parameters. See Minitab Blog Editor,
Beware of Phantom Degrees of Freedom that Haunt Your Regression
Models!, The Minitab Blog (Oct. 29, 2015), http://blog.minitab.com/blog.
---------------------------------------------------------------------------
The SDC claimed this issue is important in the context of its
overfitting criticism because, as Professor Crawford's testimony
indicated, it is not generally good econometric practice to ``to try a
regression, to reject some variable or to reject a form, and then try
another specification and find you get a statistically improved
result.'' SDC PFF ] 115 (citing 2/28/18 Tr. 2109 (Crawford)). According
to Dr. Erdem when such an approach is taken, ``the reliability of the
coefficients at the end of that model selection process is
questionable.'' 3/8/18 Tr. 2711 (Erdem).
In response, CTV noted that it had addressed the issue of the first
supposed ``discarded'' regression without the top-six MSO interaction
variables, in its opposition to a Motion to Strike filed by SDC. In
that Opposition, CTV made particular note of Professor Crawford's
written direct testimony in which he explained why his regression
analysis did not originally treat the interaction of these top-six MSOs
as a fixed effect. See Crawford CWDT ] 166 (``Dummy variables for each
of the six largest MSOs--Comcast, Time Warner, AT&T, Verizon, Cox, and
Charter--are included as covariates to capture potential differences in
factors not included in the econometric model that could shift demand
for bundles that include imported distant broadcast signals.'').
CTV further referred to the Judges' Order Denying SDC Motion to
Strike
[[Page 3567]]
Testimony of Gregory S. Crawford (Crawford Order), which credited CTV's
position that Professor Crawford had not run such an alternative course
of action by generating a regression and then discarding it, but rather
had decided to add the top-six MSO effects as ``fixed effects'' in the
course of developing his regression approach, in order better to
isolate the correlation, if any, between the explanatory (independent)
variables at issue in this proceeding--the different programming
categories--and the dependent variable, i.e., total royalties. As the
Judges explained in the Crawford Order:
Dr. Crawford's WDT . . . explained how he first described
differences that were observed in the data among the six largest
MSOs in terms of their average receipts per subscriber. CTV Opp'n at
10-11 and Ex. 2004, Figure 6. Dr. Crawford's WDT also explained that
these differences may suggest other important differences among
these large MSOs regarding their signal carriage strategies,
pricing, and other relevant dimensions. CTV Opp'n at 11; Ex. 2004 ]
61. Dr. Crawford also described a regression without the six MSO
Interaction variables. Ex. 2004 ] 61 (unobserved differences in
average revenue per subscriber could bias estimates of relative
value of different programming).
Crawford Order at 5.
The Judges find that the SDC's criticism of Professor Crawford's
models for consuming ``phantom degrees of freedom'' is essentially a
restatement of Dr. Erdem's general claim of overfitting. Accordingly,
this argument does not add a new basis for reducing the weight the
Judges place on Professor Crawford's regression analysis.\64\
---------------------------------------------------------------------------
\64\ Although the Judges denied the SDC's Motion to Strike, they
indicated in the Crawford Order that they would consider whether the
absence of that prior work diminished the weight they might
otherwise give to the regression methodology that Professor Crawford
presented at the hearing. After considering the entire record, the
Judges do not reduce the weight they accord to Dr. Crawford's
regression analysis based on this argument.
---------------------------------------------------------------------------
On balance, the Judges find that there may be some degree of
overfitting in Professor Crawford's regression analyses that he did not
adequately explain. It further appears that this problem was the result
of a tradeoff, arising from Professor Crawford's use of a subscriber
group analysis and thus a reliance on system-accounting period fixed
effects that, as the SDC noted, reduced the number of observations in
Professor Crawford's data set. Although such potential overfitting may
exist, there is nothing in the record to demonstrate sufficiently that
this problem would support a decision to diminish the judges' reliance
on Professor Crawford's regression analysis.\65\
---------------------------------------------------------------------------
\65\ Also, Professor Crawford's use of data from the entire
population of Form 3 CSOs provided him with a wealth of data that
mitigated a potential problem with regard to potential overfitting
arising from sampling that provided too little data relative to the
number of parameters. Crawford CWDT ] 123.
---------------------------------------------------------------------------
3. Program Suppliers' Criticisms of Dr. Crawford's Analysis
a. Assumption Regarding CSO Behavior
Sue Ann Hamilton, an industry expert, testified that Professor
Crawford made a significant error (one that would apply to any
Waldfogel-type regression) when he posited that CSOs make decisions
regarding distant retransmission based on their intention to maximize
profits by selecting those stations with an optimal bundle of
programming. Corrected Written Rebuttal Testimony of Sue Ann Hamilton,
Trial Ex. 6009, at 13-14 (Hamilton CWRT). Rather, Ms. Hamilton
testified, a CSOs' selection of stations for distant retransmission is
marked by inertia, not by an affirmative analysis and weighing of
alternative stations. Id. She identified two reasons for CSO inertia.
First, distant retransmission costs represent a non-material
expenditure for CSOs compared with their other more expensive
programming and carriage decisions. Id. at 9. Second, she testified
that CSOs are more concerned with losing existing subscribers if they
drop certain stations and the associated programs than they are with
whether or not any new retransmitted station and its associated
programs might entice new subscribers.\66\ Id. In industry jargon, CSOs
are more concerned with ``legacy distant signal carriage'' than with
adjusting the roster of distantly retransmitted stations. Id. at 15.
Thus, Ms. Hamilton implied, any correlation between program categories
and royalties is spurious, because it is ``inconsistent with [her]
understanding of how CSOs actually make distant signal carriage
decisions.'' Id.\67\
---------------------------------------------------------------------------
\66\ Ms. Hamilton's assertion that CSOs are more interested in
satisfying niche signal viewers than with attracting and retaining
new subscribers is contrary to assumptions underlying much of the
survey analysis of CSO attitudes and valuations. Survey analyses are
described in Section III, infra.
\67\ Ms. Hamilton also criticized Professor Crawford for
assuming duplicated network minutes had zero value, because: (1)
Some people prefer to watch a program at times other than when aired
by a local network affiliate and (2) all programming has a value
greater than zero to a CSO. Id. at 13-14. However, Professor
Crawford explained in his oral testimony that: (1) He only dropped
duplicated network programming that was aired at the same time as
the local network programming and (2) Ms. Hamilton's conclusory
assertion that all programming has value to a CSO flies in the face
of the economic principle that consumers value only one version of
perfectly substitutable goods. 2/28/18 Tr. 1426 (Crawford).
---------------------------------------------------------------------------
The Judges find that Ms. Hamilton was a knowledgeable and credible
witness, particularly with regard to the de minimis impact of distantly
retransmitted stations on CSOs and the importance of ``legacy
carriage.'' Moreover, the Judges take note that CSO time and effort are
themselves finite resources (opportunity costs), and, as Ms. Hamilton
implied, it would behoove a rational CSO to expend more of those
resources making carriage and programming decisions with a greater
financial impact.\68\
---------------------------------------------------------------------------
\68\ Given the low value of retransmitted stations, a CSO might
rationally emphasize the value of ``legacy carriage'' as a heuristic
(without further analytical effort), assuming as Ms. Hamilton
implies, that eliminating a distantly retransmitted legacy station
and its programs is more likely to cause a loss in subscribers than
a change in station lineup is likely (without further and costly
analytical effort) to increase the number of subscribers.
---------------------------------------------------------------------------
However, the Judges do not find that the relative unimportance of
distantly retransmitted stations to a CSO deprived the regression by
Professor Crawford, or any of the regressions in evidence, of value in
this proceeding. If the reasons articulated by Ms. Hamilton caused CSOs
to emphasize legacy carriage over potential increases in value from
adding or substituting different local stations for distant
retransmission, then otherwise well-constructed regressions should
capture the relative values of those legacy-based decisions. The Judges
are mindful that regression analysis is of benefit because it looks for
a correlation between economic actors' choices (the independent
explanatory variables) and the dependent variables as potential
circumstantial evidence of a causal relationship, but it does not
purport to explain what lies behind such a potential causal relation.
Thus, Ms. Hamilton has not so much criticized regression analyses as
she has provided an answer to a different question.
Indeed, if legacy-based decision-making is prevalent, the Judges
would expect to see relatively stable shares over the royalty years
encompassed within and across the Allocation/Phase I proceedings. In
fact, the record does reflect relative stability. See, e.g., Crawford
CWDT ]] 12, 15 (in his two regressions in this proceeding, ``the
estimated parameters underlying these marginal values are stable across
years . . . .''), ] 39, Table V-3. It thus appears that past decision-
making has to an extent generally locked in (through an emphasis on
legacy carriage) decisions as to the carriage of distantly
[[Page 3568]]
retransmitted stations for the 2010-2013 period.
In sum, therefore, Ms. Hamilton's testimony, while informative and
credible, does not diminish the value of Professor Crawford's
regression or, for that matter, any other Waldfogel-type regression.
b. Minimum Fee Issue
Dr. Jeffrey Gray criticized Professor Crawford's regression because
the analysis included in the dependent variable royalties that are paid
as part of the statutorily mandated minimum fees. Gray CWRT ]] 17-18.
Any Form 3 cable system must pay a system-wide minimum fee equal to
1.064% of its gross receipts into the royalty pool for distantly
retransmitted stations, even if it does not retransmit any stations to
distant markets, up to the retransmission of one full DSE. 17 U.S.C.
111(d)(1)(B)(i) and (ii). Dr. Gray asserted that, consequently, the
data used by Professor Crawford is not informative, because the minimum
fee cost is decoupled from the marginal economic decision regarding the
retransmission of the first DSE. Gray CWRT ]] 20-22.
Dr. Gray noted that approximately 50% of CSOs did retransmit more
than one DSE, and thus voluntarily paid a royalty greater than the
minimum fee. Dr. Gray acknowledged that the data regarding this
subgroup of CSOs was informative because these CSOs had made a
discretionary choice to incur additional royalty charges in exchange
for carriage of additional distantly retransmitted stations and their
constituent programs. Accordingly, he ran what he described as
Professor Crawford's regression using only the CSOs that paid more than
the minimum fee, and his results were different from Professor
Crawford's results. However, although Dr. Gray had characterized his
work as a rerun of Professor Crawford's regression, at the hearing Dr.
Gray confirmed that he had been ``unable to replicate'' Dr. Crawford's
regression. 3/14/18 Tr. 3739 (Crawford).\69\
---------------------------------------------------------------------------
\69\ Not only was Dr. Gray unable to replicate Professor
Crawford's work, Professor Crawford also challenged Dr. Gray's
assertion that he otherwise faithfully reran Professor Crawford's
regression. 2/28/18 Tr. 1422 (Crawford) (asserting that Dr. Gray
changed a ``key element of my regression analysis . . . the
subscriber group variation [by] aggregate[ing] that subscriber group
level information up to the level of the systems, which means . . .
he cannot do fixed effects anymore . . . and he then adds additional
variables.'').
---------------------------------------------------------------------------
In any event, Dr. Gray's analysis resulted in the allocations among
program categories--presented in the table below alongside Professor
Crawford's allocations (and Dr. Gray's viewership-based allocations
discussed elsewhere in this Determination):
Table 4--Impact of Accounting for Minimum Fees Requirement on Crawford Royalty Shares, 2010-2013
----------------------------------------------------------------------------------------------------------------
Distant
Crawford Crawford- viewing
Claimant category royalty Shares modified royalty shares
royalty shares (%)
(1) (2) (3)
----------------------------------------------------------------------------------------------------------------
CCG............................................................. 3.51 5.46 3.70
CTV............................................................. 16.50 13.54 13.50
Devotionals..................................................... 0.60 0.75 1.44
Program Suppliers............................................... 23.44 61.19 45.43
PTV............................................................. 17.72 19.06 33.04
JSC............................................................. 38.23 0.00 2.89
----------------------------------------------------------------------------------------------------------------
Gray CWRT ] 24, Table 3.
In response, Professor Crawford pointed out that, contrary to Dr.
Gray's assertions, Dr. Crawford's regression did not ignore the impact
of the minimum fee, because he included an indicator variable as a
control, subsumed within his fixed effects variables, to reflect
whether the minimum fee was paid at the system level. 2/28/18 Tr. 1422
(Crawford). Thus, Professor Crawford maintained that he had already
accounted for the minimum fee effect. Accordingly, Professor Crawford
argued that Dr. Gray's analysis merely attempted to account for minimum
fee systems in a different way--by omitting those systems instead of
replicating Professor Crawford's regression that used control variables
and fixed effects to account for the minimum fee paying systems.\70\
---------------------------------------------------------------------------
\70\ Professor Crawford testified that after reviewing the
rebuttal testimony, he did a ``test'' in which he claimed to have
``dropped the minimum fee systems from the regression analysis and
re-ran the regression,'' which showed that the implied royalty
shares were ``very, very close: to his own original results. . ..''
2/28/18 Tr. 1424 (Crawford). However, Professor Crawford and CTV did
not produce this regression because, as CTV's counsel acknowledged
in response to a rebuttal, ``this is not a new analysis [and] [w]e
are not presenting any numbers here.'' 2/28/18 Tr. 18 (John Stewart,
CTV counsel).
---------------------------------------------------------------------------
Dr. Gray is correct with regard to his general principle that a
CSO's decision to distantly retransmit any particular station, when
that CSO is otherwise obligated to pay the minimum royalty fee, does
not indicate a direct correlation between the decision to retransmit
and the decision to incur a royalty obligation. By contrast, when a CSO
decides to incur an increase in its marginal royalty costs by
retransmitting more than one DSE, that decision reveals the CSO's
preference to incur the royalty cost in exchange for the perceived
value of the distantly retransmitted station and the programs in that
station's lineup.
As Dr. Gray noted, the minimum royalty fee is somewhat akin to a
``tax'' that is paid regardless of whether the CSO decided to distantly
retransmit a local station. 3/14/18 Tr. 3704 (Gray). Nonetheless, the
CSO still has several choices to make, because it will receive
something of potential value, i.e., distantly retransmitted stations,
in exchange for the ``tax.'' The first choice is binary; should it
retransmit any station or no station? As Dr. Gray noted, during the
2010-2013 period, on average 527 out of the 1,004 Form 3 CSOs analyzed
(52.5%) chose to retransmit the exact or fewer number of signals than
the regulated fees permitted; 83 paid the minimum fee yet elected not
to retransmit any local stations. Gray CWRT ] 17. Those decisions
reveal that the CSO has concluded (whether by analysis or resort to a
heuristic) that any of the marginal costs (physical or opportunity)
associated with retransmission likely exceed the value to the CSO of
such retransmission, even accounting for minimum royalties, which the
CSO must pay in any event.
[[Page 3569]]
These statistics also reveal that many CSOs decided to retransmit
stations when they were obligated to pay only the minimum royalty.
Although there is no marginal royalty cost associated with this
decision, the CSO's decision as to which stations to retransmit remains
a function of choice, preference, and ranking.\71\ Thus, the CSO in
this context would still have the incentive to select distant local
stations for retransmission that are more likely to maximize CSO
profits, through either an increase in subscribership or, as Ms.
Hamilton emphasized, by avoiding the loss of subscribers through the
preservation of ``legacy carriage'' through the non-analytical
heuristic of maintaining the status quo.\72\
---------------------------------------------------------------------------
\71\ In constructing a hypothetical market, the Judges assume
CSO rationality or bounded rationality, at the least. ``Bounded
rationality'' means that economic actors behave rationally (e.g.,
preferring potential profits to possible losses), but that
rationality is inevitably limited by their lack of full information
or the resources and ability to obtain full information necessary to
make a completely (``unbounded'') rational decision. See C.
Sunstein, Behavioral Law & Economics 14-15 (2000).
\72\ A more homespun analogy is perhaps instructive. Consider a
child who has misbehaved and is thus punished by her parents who
prohibited her from playing outside, as is her preference. Instead,
she is sent by her parents to her room for the evening, where she is
permitted to watch television (either the offense is not so great in
this example as to warrant a suspension of TV privileges or the
child has relatively permissive parents). The child has been
compelled to pay a cost (confinement to her room) and precluded from
her first choice (no confinement). If watching television is her
only (or next best) option given confinement, she will rationally
select the programs that provide her with the most utility. The fact
that she was compelled to remain in her room would not provide her
any incentive to abandon her order of preference as to the programs
she would watch, even though she would not watch any of them but for
the ``tax'' imposed by her parents (this analogy assumes that she
would not refuse to watch television, as ``cutting off her nose to
spite her face'' is assumed to be an irrational response). The CSO
that is ``confined'' to a market in which the minimum royalty fee is
imposed likewise rationally would make the best of a bad situation
and retransmit stations based on the capacity of the station to
increase CSO utility/profits, that is, assuming marginal non-royalty
costs were not prohibitive.
---------------------------------------------------------------------------
There are substantial economic bases for this finding. Because the
``tax'' of the minimum fee is paid regardless of whether distant
retransmission occurs, that ``tax'' is also in the nature of a sunk
cost. Fundamental economic analysis provides that a seller should
ignore sunk costs when making marginal decisions (although they should
try to recoup these costs if the buyers' willingness-to-pay allows it).
Nonetheless, a CSO that decides to distantly retransmit a station when
the marginal royalty cost is zero has revealed that the particular
station contains programming that would increase marginal value to that
CSO, over and above the next best alternative ``retransmittable'' local
station and above any other marginal costs (e.g., physical
retransmission costs or the opportunity cost of foregoing a different
type of cable channel in the CSO's channel lineup).
Finally, Dr. Gray's emphasis on the CSOs that retransmit more than
one DSE is misleading. Those other CSOs that pay only the minimum
royalty fee and elect to distantly retransmit one station might have
elected to pay a positive fee in the absence of the minimum fee. For
example, assuming Program Suppliers' programs were more valuable to a
CSO than the minimum fee and disproportionately more valuable than any
other program category, that CSO would have retransmitted a station
that disproportionately included Program Supplier content and willingly
paid the minimum fee (or more). Dr. Gray's criticism fails to address
this issue.
With regard to Dr. Gray's own regression, run for the first time in
rebuttal, the Judges are not surprised that his different regression
approach would yield different results. However, the Judges do not rely
on methodological approaches proffered for the first time in rebuttal,
except to the extent they appropriately demonstrate defects in another
party's approach. Because Dr. Gray acknowledged that he could not
replicate Professor Crawford's regression and because Dr. Gray
therefore utilized a different approach, the Judges do not find that
Dr. Gray's critique as it related to the minimum fee issue was
sufficient to discredit Professor Crawford's approach.\73\
---------------------------------------------------------------------------
\73\ An expert economic witness, Professor George, who otherwise
approved of Professor Crawford's analysis, notes that the treatment
of minimum fee only systems by Professor Crawford generally resulted
in a tradeoff between accuracy and bias. Specifically, Professor
George testified that lumping together CSOs paying only the minimum
fee with other CSOs (as Professor Crawford did) ``introduces some
uncertainty [and] wider confidence intervals,'' but, on the other
hand, Dr. Gray introduces ``bias'' because he has ``pull[ed] out
systems . . . where their choices are very valid.'' 3/5/18 Tr. 2045
(George). Because the Judges have found Professor Crawford's
confidence intervals to be relatively narrow, Professor George's
testimony in this regard does not affect the Judges' reliance on
Professor Crawford's analysis.
---------------------------------------------------------------------------
4. Conclusion Regarding Professor Crawford's Regression Analysis
Not only did Professor Crawford sufficiently respond to the
criticisms of his regression analysis, that analysis is based on a
number of other factors as to which no criticisms were leveled. First,
he used the universe of all programming on all distant signals, rather
than a sampling, thus avoiding any problems that may be associated by
improper sampling or inadequately sized samples. 2/28/18 Tr. 1186
(Crawford). Second, by using data and royalties at the subscriber group
level, his regression analysis related more specifically to programs
and signals actually available to subscribers and provided more
variation and observations than past regressions. 2/28/18 Tr. 1512,
1517-19, 1661 (Crawford). Third, his use of a fixed effects approach
avoided the criticism that he had omitted key variables. Crawford CWDT
] 107; 2/28/18 Tr. 1398 (Crawford). Fourth, the confidence intervals
for his proposed shares were relatively narrow at the 95% confidence
level (i.e., at a .05 significance level). Crawford CWDT ]] 117 and
176, Tables 23 & 24. Fifth, Professor Crawford acknowledged the
potential problem that his fixed effects could lead to the ``costs'' of
higher standard errors and wider confidence intervals (and, as
Professor George noted, with specific reference to the minimum fee
issue), but he was able to mitigate that effect with his rich data set,
so that his parameters remained relatively precise. Crawford CWDT ]
123. Finally, unlike the other regressions, Professor Crawford does not
estimate any negative coefficients for the coefficients of interest in
this proceeding, which makes his regression analysis (especially his
duplicated analysis that also had no negative coefficients for network
programming) more of a stand-alone estimate of relative value and less
in need of reconciliation with the survey analysis. Thus, on balance,
the Judges find Professor Crawford's regression analysis, especially
his duplicate-minutes approach, to be highly useful in estimating
relative values in this proceeding.
C. Dr. Israel's Regression Analysis
1. Introduction
On behalf of the Joint Sports Claimants, its economic expert, Dr.
Mark Israel, conducted a regression also in the general form of a
Waldfogel-type regression, but with minor modifications intended to
improve the reliability of the methodology. Written Direct Testimony of
Mark Israel, Trial Ex. 1003, ]] 23, 25 (Israel WDT). Dr. Israel's
primary purpose was to determine whether such a regression would
corroborate the results of the 2004-05 and the 2010-13 Bortz Surveys.
He concluded that the ``observable marketplace behavior'' he had
analyzed did indeed corroborate the results of both Bortz Surveys. Id.
] 8. Dr. Israel further testified that, if the Judges
[[Page 3570]]
were to find that the 2010-13 Bortz Survey did not support a finding of
relative market value, his and Professor Crawford's respective
regressions constituted the best alternative evidence of such value. 3/
12/18 Tr. 3079 (Israel).\74\
---------------------------------------------------------------------------
\74\ In addition to performing a regression analysis, Dr. Israel
also reviewed data relating to the economics of a different market--
that in which large cable networks generally, and TNT and TBS
specifically, bought sports and other programming. The Judges
discuss that analysis infra.
---------------------------------------------------------------------------
2. Dr. Israel's Regression
Dr. Israel analyzed royalties CSOs paid over a three-year period,
2010-2012, rather than the full four-year period at issue in this
proceeding, 2010-2013. Id. ] 7. Dr. Israel testified that he did not
analyze the full 2010-2013 four-year period because he had begun his
analysis when the proceeding was limited to the three-year 2010-2012
period. However, he testified that he was able to confirm the accuracy
of his regression estimates against the results from the Bortz Survey
that covered all four years. He also noted that his results
corresponded closely to the results that Professor Crawford obtained in
his regression, which spanned the full four-year period. 3/12/18 Tr.
2838-40 (Israel).
Dr. Israel, like Professor Crawford, utilized the royalty data from
the ``Form 3'' CSOs, i.e., the larger CSOs, which paid the largest
dollar amount of royalties for distantly retransmitted stations by
virtue of the large amount of ``gross receipts'' they earned from their
cable operations. Israel WDT ] 9.
Referring to the regulated nature of the cable market, Dr. Israel
noted: ``There is no market price for distant signal programming to use
in assessing relative marketplace value.'' Id. ] 16. Dr. Israel further
noted that, applying the principles laid out in prior proceedings,
``relative marketplace value'' must be estimated by consideration of
evidence as to what royalties would be paid for different categories of
programming in a ``hypothetical free market.'' Id. To ascertain that
value, and consistent with his understanding of prior determinations,
Dr. Israel focused on the relative value of program categories to the
buyers, i.e., CSOs. Id.\75\
---------------------------------------------------------------------------
\75\ Dr. Israel did not consider the relative value of program
categories from the perspective of the hypothetical sellers, which
he identified as the stations retransmitting the programs in a
bundled signal. 3/12/18 Tr. 3064 (Israel).
---------------------------------------------------------------------------
To assemble the specifications of his regression model, Dr. Israel
applied the essentials of a Waldfogel-type regression. That is, he
tested to find a correlation between: (1) Royalties paid by CSOs (the
dependent variable) and (2) minutes of programing in each category of
programming as established in this proceeding (the independent/
explanatory variable). He utilized control variables to hold constant
other potential drivers of CSO royalty payments, itemized infra. Id. ]
22.
However, he altered his approach from the Waldfogel regression
approach in the following important ways:
To reflect the fact that not all subscriber groups among a
CSO's total subscriber base received any given distant signal, Dr.
Israel prorated each signal ``based on the fraction of the number of
subscribers who received it . . . by using the variable in the CDC data
called `Prorated DSE' as a measure of the prorated distant signal
equivalents that each distant signal represents for each CSO--
Accounting Period.'' Id. ] 26.\76\
---------------------------------------------------------------------------
\76\ Thus, Dr. Israel's regression differs from Professor
Crawford's regression in that Professor Crawford analyzed the
relationship between royalties and program categories at the
subscriber group level, whereas Dr. Israel ran the regression at the
CSO level, using CDC data that prorated the DSE to reflect the
proportion of CSO subscribers who received the distant signal.
Israel WDT ] 27.
---------------------------------------------------------------------------
To account for the retransmission of non-compensable
``Network Programming'' minutes in the estimates, Dr. Israel included
those minutes to ``effectively act'' as a control variable, thus
excluding them from the calculation of shares of the royalty fund. That
is, he included these minutes in his regression because they are in
fact retransmitted and ``therefore are part of the cost-benefit
analysis that a [CSO] undertakes when deciding whether or not to carry
[a] distant signal . . . [h]ence explaining total royalty payments
[even though] they are not compensable minutes in this proceeding.''
Id. ] 27.
To improve the quality of his estimates, Dr. Israel
utilized a larger sample than employed in the Waldfogel regression.
Specifically, Dr. Israel used data from a random sample of 28 days in
each six-month accounting period in his 2010-2012 analysis, a 33%
increase in the number of sample days (21) utilized in the Waldfogel
regression. Id. ] 30.\77\
---------------------------------------------------------------------------
\77\ Dr. Israel made note of two other adjustments he made to
his regression that caused it to differ from the Waldfogel
regression. First, he eliminated a ``Mexican Stations'' category
because no such category was identified in this proceeding. Israel
WDT ] 29. Second, Dr. Israel grouped the programs from ``low power''
stations according to their appropriate program categories, rather
than carving out a miscellaneous category for ``low power''
stations, as had been done in the Waldfogel regression. Israel WDT ]
31.
---------------------------------------------------------------------------
Dr. Israel controlled for other independent variables in
essentially the same manner as in the Waldfogel regression, by
including the following control variables in his regression model:
Number of CSO subscribers from the previous accounting
period
Number of activated channels for the CSO in the previous
accounting period
Count of broadcast channels for the CSO
Indicator for whether a CSO pays the special 3.75 percent
rate royalty fee
Indicator for whether or not the CSO pays the minimum
statutory payment
Average household income for the CSO's Designated Market
Area (DMA)
Indicators for the accounting period of each observation
Id. ] 33.
Through these specifications, Dr. Israel stated that he was able to
answer what he characterized as the fundamental question: ``How much do
CSO royalty payments increase with each additional minute of each
category of programming content?'' Id. ] 34.
Applying his regression model, Dr. Israel made the following
estimations:
Table 5--Israel Regression Model Results
------------------------------------------------------------------------
Regression model
Variables all categories
------------------------------------------------------------------------
Minutes of Sports Programming........................ ** 4.836
(2.466)
Minutes of Program Suppliers Programming............. *** 0.469
(0.104)
Minutes of Commercial TV Programming................. *** 1.010
(0.355)
Minutes of Public Broadcasting Programming........... ** 0.660
(0.306)
[[Page 3571]]
Minutes of Canadian Programming...................... *** -0.973
(0.212)
Minutes of Devotional Programming.................... *** -0.701
(0.246)
Minutes of Network Programming....................... *** -0.985
(0.290)
Minutes of Other Programming......................... ** 0.916
(0.462)
Number of Subscribers (Previous Accounting Period)... *** 1.351
(0.0601)
Number of Activated Channels (Previous Accounting *** 141.8
Period)............................................. (18.73)
Median Household Income in Designated Marketing Area. *** 1.339
(0.286)
Count of Broadcast Channels.......................... -493.5
(326.5)
Indicator for Special 3.75% Royalty Rate............. *** 41,918
(4,711)
Minimum Payment Indicator............................ *** -16,501
(3,689)
Observations......................................... 5,465
R-squared............................................ 0.692
------------------------------------------------------------------------
Source: TMS/Gracenote; Cable Data Corporation; Kantar media/SRDS.
Note: Robust standard errors in parentheses.
*** p<0.01, ** p<0.05, * p<0.1.\78\
Israel WDT ] 36 Table V-I (citations omitted).
---------------------------------------------------------------------------
\78\ The ``p-value'' provides a measure of statistical
significance. It represents ``[t]he smallest significance level at
which the null hypothesis can be rejected.'' Wooldridge, supra note
34, at 867. A statistical significance level of .01, .05 and .1, as
used in the table in the accompanying text is ``often referred to
inversely as the . . . confidence level,'' equivalent to 99%, 95%
and 90%, respectively. ABA Econometrics, supra note 22, at 18.
Although ``[s]ignificance levels of five percent and one percent are
generally used by statisticians in testing hypotheses . . . this
does not mean that only results significant at the five percent
level should be presented or considered [because] [ l]ess
significant results may be suggestive, even if not probative, and
suggestive evidence is certainly worth something.'' Fisher, 80 Col.
L. Rev., supra at 717-718. Thus, ``[in] multiple regressions, one
should never eliminate a variable that there is a firm foundation
for including, just because its estimated coefficient happens not to
be significant in a particular sample.'' Id. However, care must be
taken not to confuse the ``significance level'' with the
``preponderance of the evidence'' standard, because ``the
significance level tells us only the probability of obtaining the
measured coefficient if the true value is zero,'' so one cannot
``subtract[] the significance level from one hundred percent'' to
determine whether a hypothesis is more or less likely to be correct.
Id. See also D. Rubinfeld, Econometrics in the Courtroom, 85 Col. L.
Rev. 1048, 1050 (1985) (``[I]f significance levels are to be used,
it is inappropriate to set a fixed statistical standard irrespective
of the substantive nature of the litigation.''); D. McCloskey & S.
Ziliak, The Standard Error of Regressions, 34 J. Econ. Lit. 97, 98,
101 (1996) (``statistically significant'' means neither
``economically significant'' nor ``significant [in] everyday usage
[where] `significant' means `of practical importance' . . ..'').
---------------------------------------------------------------------------
Although Dr. Israel reported the standard errors generated by his
regression (in the parentheticals in the table above, pursuant to
conventional regression notation), he did not set forth the confidence
intervals that result from these standard errors, either for his
coefficients or for the resulting shares. He acknowledged that it would
be difficult to calculate meaningful confidence intervals in this
exercise because shares of any one category are dependent on the shares
in other categories and the econometrician must ``do something more
than just a simple linear calculation.'' 3/12/18 Tr. 2975 (Israel).
Nonetheless, Dr. Israel acknowledged that confidence intervals
could be calculated from the standard errors in his regression. In
cross-examination, and by way of example, he acknowledged that the
confidence interval applicable to the JSC programming coefficient in
his regression ranged from 0.003 to 9.669. 3/12/18 Tr. 2976 (Israel).
Given this range, he agreed that the math would create a range for the
value of JSC programming, with a 95% degree of confidence, between ``a
fraction of a penny and $9.67 per minute.'' 3/12/18 Tr. 2977 (Israel).
Similarly, Dr. Israel acknowledged that, given his standard error for
CTV, he could state with 99% confidence that the value for a minute of
CTV programming ranged between 31 cents and $1.71. 3/12/18 Tr. 2978
(Israel). In similar fashion, Dr. Israel acknowledged that his
regression, and the standard errors he reported, generated the
following confidence intervals for each minute of programming: For PTV,
between $.06 and $1.26, for Canadian Programming, between -$1.39 and -
$0.56, and, for SDC programming, between -$1.18 and -$0.22.
Dr. Israel further acknowledged that the coefficients he estimated
in his regression all fell within the confidence intervals of each
other, which suggested an overlapping that could undermine the
usefulness of his results. However, he denied that such a consequence
had statistical meaning detrimental to his opinion because ``confidence
intervals tell you something about the precision of those coefficients,
but you can't step from a statement about statistical significance to a
statement about magnitude of value.'' 3/12/18 Tr. 3014 (Israel).
Nonetheless, Dr. Israel conceded that ``the confidence intervals
are . . . important if I have no other information to compare it to, so
I am testing a hypothesis based on just the regression.'' 3/12/18 Tr.
2981 (Israel). However, Dr. Israel further testified that he reached
the opinion that the regression he ran generated meaningful
coefficients because they corroborated the Bortz Survey, which was both
the primary purpose of his regression analysis and a corroborative
result that mitigated any uncertainty generated by the wide confidence
intervals arising out of his regression. 3/12/18 Tr. 2981-82 (Israel).
Dr. Israel described the coefficients derived by his regression
analysis as
[[Page 3572]]
representing the ``average value across all cable systems of an
additional minute of that category of programming.'' Israel WDT ] 37;
3/12/18 Tr. 2831 (Israel). Thus, it became a simple algebraic matter
``to determine the relative value of each type of programming.'' That
is, as with any Waldfogel-type regression, Dr. Israel simply took the
coefficient estimated by his regression for each program category and
multiplied it by the number of minutes applicable to that category, and
divided that product by the total value of all such products summed
across all categories. He expressed the ratio for any program category
X as:
[GRAPHIC] [TIFF OMITTED] TN12FE19.000
Israel WDT ] 38. Applying this ratio to each of the six categories Dr.
Israel calculated the following estimated percentage shares per
category averaged over the 2010-2012 period for which he had data:
Table 6--Israel Regression: Estimated Percentage Shares
------------------------------------------------------------------------
2010-2012
Category average share
(%)
------------------------------------------------------------------------
JSC..................................................... 37.54
Program Suppliers....................................... 26.82
CTV..................................................... 22.16
PTV..................................................... 13.48
SDC..................................................... 0.00
CCG..................................................... 0.00
------------------------------------------------------------------------
Id. Table V-2. However, Dr. Israel did not calculate share allocations
for specific years, which is how the Judges are required by statute to
make the allocations.\79\
---------------------------------------------------------------------------
\79\ Dr. Israel testified that he did run a test to determine
whether his regression results changed depending upon the time
period evaluated and that he found that his results were stable over
time. Israel WDT App. C-1. However, he did not link that result with
any sufficient assertion explaining how or why the Judges might
apply his findings for each year.
---------------------------------------------------------------------------
Dr. Israel further noted that these results were not only
consistent with the results of the Waldfogel regression for the 2004-05
years, they were consistent with the results of the regression
undertaken by Dr. Rosston, referenced supra, in an earlier proceeding
covering 1998 and 1999. Specifically, Dr. Israel's regression implied
the same rank order for the top four programming categories and a
generally similar magnitude of royalty allocations for the top three
categories as in Dr. Waldfogel's regression. Id. ] 39.
Further, with regard to his assigned task, Dr. Israel noted that
his rank order for the top four program categories was consistent
with--and thus corroborative of--the top four rank order determined by
the Bortz Survey. Dr. Israel set forth and also depicted the
consistency of his regression and the Bortz Survey as follows:
Table 7--Comparison of Bortz Survey Results to Israel Regression
--------------------------------------------------------------------------------------------------------------------------------------------------------
Bortz Survey Israel
Programming category 2010 (%) 2011 (%) 2012 (%) 2013 (%) average 2010- regression
2013 (%) 2010-2012 (%)
--------------------------------------------------------------------------------------------------------------------------------------------------------
Sports.................................................. 40.9 36.4 37.9 37.7 38.2 37.5
Program Suppliers....................................... 31.9 36.0 28.8 27.3 31.0 26.8
CTV..................................................... 18.7 18.3 22.8 22.7 20.6 22.2
PTV..................................................... 4.4 4.7 5.1 6.2 5.1 13.5
Devotional.............................................. 4.0 4.5 4.8 5.0 4.6 0.0
Canadian................................................ 0.1 0.2 0.6 1.2 0.5 0.0
--------------------------------------------------------------------------------------------------------------------------------------------------------
Id. ] 40 Table V-4.
[[Page 3573]]
[GRAPHIC] [TIFF OMITTED] TN12FE19.001
Dr. Israel acknowledged that although his ranking of the top four
categories (JSC, Program Suppliers, CTV and PTV) was consistent with
the Bortz Survey ranking, that consistency did not extend to the bottom
tier (PTV, SDC and Canadian programming). Id. ] 41. Rather, he
acknowledged that his regression estimated no value for the SDC and
Canadian programming. However, he noted that, when the three low-tier
categories are viewed collectively, his regression estimated a total
share of value (13.5%) to all three categories (actually just PTV) and
the Bortz Survey provided what he understood to be a roughly equivalent
relative value range between roughly 9% and 13% in total for Public TV,
Devotional, and Canadian programming. 3/12/18 Tr. 2880-81 (Israel).
To test the robustness of his findings, Dr. Israel conducted
several sensitivity analyses. He concluded that each of his sensitivity
analyses ``confirm[ed] the relative ranking of the various categories,
particularly of the top three categories relative to the bottom
three.'' Israel WRT ] 43. See also Id. App. C.
More particularly, Dr. Israel ran three sensitivity analyses to
determine whether the following changes in his model would alter his
results in any meaningful way. These analyses examined changes that
would result from: (1) Isolating JSC minutes and comparing these
minutes ``to all other programming minutes combined . . . to test
whether the value for [JSC] minutes is sensitive to splitting out the
individual programming categories'' (as in his regression), (2)
controlling for any additional ``market-specific traits of the CSO''
(through application of a DMA ``fixed effect''), and (3) controlling
for any royalties ``that [resulted from] the 3.75% fee [rather than]
the base rate fee royalties.'' In each sensitivity analysis, Dr. Israel
found that the changes had ``no effect on any of [his] conclusions.''
Id.
3. Program Suppliers' Criticisms
Dr. Gray expressed a number of specific criticisms of Dr. Israel's
regression, in addition to Dr. Gray's criticisms of Waldfogel-type
regressions generally.
a. Alleged Sensitivity of Regression
First, Dr. Gray asserted that Dr. Israel's regression exhibits
``remarkable sensitivity'' because of the wide range of proposed
relative shares. For example, when Dr. Israel's standard errors are
converted into confidence intervals, Dr. Israel's regression indicates
a range for the JSC share ``from 0% to 63.29%'', when assumptions are
changed ``regarding the choice of explanatory variables or the assumed
functional relationship those variables have on royalty fees paid.''
Gray CWRT ] 28.
Dr. Gray testified that he replicated Dr. Israel's results exactly
and then calculated what Dr. Israel had omitted--95% confidence
intervals around the estimates of the value of an additional minute of
programming by category type. Gray WDT ] 29. Dr. Gray determined that
at the 95% confidence level, the JSC share could have been as low as
.05%, far less than the 37.5% share derived by Dr. Israel through his
point estimate, but consistent with the 0% share for the JSC estimated
by the SDC's economic expert, Dr. Erdem. Accordingly, Dr. Gray opined
that Dr. Israel's regression is both ``imprecise'' and ``unreliable.''
Gray CWRT ] 29.
Dr. Israel rejected Dr. Gray's criticisms in this regard.
Specifically, Dr. Israel maintained that it was uninformative that Dr.
Gray's sensitivity analysis diminished the statistical significance of
the former's estimates because statistical
[[Page 3574]]
significance is ``a measure . . . [of] how certain we are that the
estimate is different from zero.'' 3/12/18 Tr. 2840 (Israel). Further,
when a modeler or critic adds many additional variables, the regression
will generate lower statistical significance. Thus, according to Dr.
Israel, Dr. Gray's sensitivity analysis necessarily created the loss of
statistical significance, by introducing too many new variables that
were unrelated to the core variables (program categories) that must be
isolated and measured in this proceeding.
Dr. Israel also defended this large interval with what the Judges
see as a non sequitur--that he nonetheless still ranked the JSC first.
See id. at 3011. When confronted with the additional fact that
injecting the DMA effect into the regression resulted in a regression
with the highest R\2\ among his proffered and sensitivity regressions,
Dr. Israel testified that when ``you add a bunch of DMA fixed effects,
you're going to get a higher R-squared. The notion of choosing a
regression to maximize R-squared is given zero credit in economics.''
Id. The Judges agree with Dr. Israel on this narrow point because, as
discussed supra with regard to the Crawford regression analysis,
goodness-of-fit as measured by the R\2\ calculation is not dispositive
when evaluating a regression intended to measure specific effects
rather than to predict a result.
The Judges also agree with Dr. Israel that the replicated model
created by Dr. Gray did not necessarily discredit Dr. Israel's
analysis, given the addition of several variables in that replication.
However, the Judges agree with Dr. Gray that the large confidence
intervals around Dr. Israel's estimated coefficients--and therefore
around his shares--are troubling, especially when compared to the
narrow confidence intervals and low standard errors in Professor
Crawford's regression analysis. The Judges recognize, as in the 2004-05
Determination, that wide confidence intervals and large standard errors
call into doubt the ``precision of the results [and] caution against
assigning `too much weight' to their corroborative value.'' See also
ATA Airlines, 665 F.3d at 896 (confidence interval can be so wide that
``there can be no reasonable confidence'' sufficient for reliance by
fact finder.).\80\
---------------------------------------------------------------------------
\80\ The Judges emphasize that Dr. Israel's confidence intervals
are problematic especially because they are wide relative to those
in Professor Crawford's regression. The Judges are not finding that
wide confidence intervals, standing alone, automatically serve to
discredit a regression analysis. See generally Fisher, 80 Colum. L.
Rev., at 716 (even when the standard errors are relatively large and
the confidence intervals relatively wide, that ``does not mean that
the true coefficient is likely to be any part of that range,'' but
rather ``the estimated coefficient'' remains ``[t]he single most
probable figure . . . .'') (emphasis added).
---------------------------------------------------------------------------
b. Choice of Linear Functional Form and Inclusion of Minimum Fee CSOs
Dr. Gray took issue with Dr. Israel's use of a linear relationship
between royalties paid and minutes of programming, rather than using a
log of royalties paid. Rather, and by comparison, Dr. Gray found that
Professor Crawford's use of a log-linear relation was ``a more
realistic economic function for the functional form of the
relationship,'' particularly as ``between minutes and royalties,''
because the logarithmic calculation revealed the percentage impact that
retransmitted minutes have on royalties. Gray CWRT ] 30.\81\
---------------------------------------------------------------------------
\81\ Dr. Gray stated that he used a ``Box-Cox'' test to confirm
that a percentage-based relationship was a preferred specification
over an assumed linear relation and better fit the data. However,
Dr. Gray did not support that statement with a citation to his work
or to literature that would be supportive. Gray WRT ] 30 n. 10. When
a rebuttal expert purports to do a deeper dive into a model than the
expert whose work he or she is criticizing, support for that deeper
analysis should be provided in the written rebuttal testimony.
However, Professor Crawford also undertook (and provided a succinct
explanation of) a Box-Cox test for his regression analysis and found
the results ``strongly favoring the log-linear over the linear
model.'' Crawford CWDT ] 115.
---------------------------------------------------------------------------
In response to Dr. Gray's criticism of his use of a linear form,
Dr. Israel testified that ``taking the log is kind of a technical thing
. . . .'' 3/12/18 Tr. 2856 (Israel). Further, he did not utilize any
econometric tests to determine whether the linear form was appropriate,
particularly compared to the log-linear form.
Dr. Gray combined his log transformation of Dr. Israel's linear
approach with another of Dr. Gray's criticisms--the use of data from
CSOs that only pay the minimum fee (as he also discussed in his
criticism of Professor Crawford's regression). Adjusting for these two
purported defects, Dr. Gray found that Dr. Israel's reworked regression
produced the following radically different estimates, compared to Dr.
Israel's unadjusted regression:
Table 8--Impact of Accounting for Minimum Fees Requirement on Israel Royalty Shares, 2010-2013
----------------------------------------------------------------------------------------------------------------
Israel- Distant
Israel royalty modified viewing
Claimant category shares (%) royalty shares royalty shares
(%) (%)
(1) (2) (3)
----------------------------------------------------------------------------------------------------------------
CCG............................................................. 0.00 4.15 3.70
CTV............................................................. 22.16 27.20 13.50
Devotionals..................................................... 0.00 0.64 1.44
Program Suppliers............................................... 26.82 44.27 45.43
PTV............................................................. 13.48 19.55 33.04
JSC............................................................. 37.54 4.19 2.89
----------------------------------------------------------------------------------------------------------------
Gray CWRT ] 31 Table 4.
In response to Dr. Gray's criticism of Dr. Israel's use of data
from CSOs paying only the minimum fee, Dr. Israel stated that such data
should not simply be disregarded, because it provides useful
information regarding the carriage decisions of those CSOs. He also
noted that Dr. Waldfogel's regression, relied upon by the Judges in the
most recent Allocation/Phase I proceeding, likewise applied the data
from CSOs who paid only the minimum fee. 3/12/18 Tr. 2830 (Israel).
The Judges agree with Dr. Israel that the data regarding the
carriage decisions of CSOs who pay only the minimum fee should not be
disregarded, and adopt their findings relating to this issue in
connection with Professor Crawford's regression. See section II.B.3.b,
supra. To summarize, even when a CSO is obligated to pay the minimum
royalty fee, it still has the incentive to select stations for distant
retransmission that it believes will maximize the benefits (or, in
economic terms, utility) to the CSO.
[[Page 3575]]
However, because carriage decisions are not tied even indirectly to a
contemporaneous discretionary decision to pay royalties (beyond the
mandatory minimum 1.064% for the first DSE), they strike the Judges as
potentially less informative than discretionary decisions by CSOs to
incur an additional royalty expense in order to distantly retransmit
particular stations. Nonetheless, as explained supra in the Judges'
consideration of this issue in connection with Professor Crawford's
regressions, the Judges find no basis in the record by which they could
or should make a reasonable ``relative value'' adjustment based on
whether a CSO did or did not pay only the minimum fee.
c. Negative Coefficients
Dr. Gray further attacked the usefulness of Dr. Israel's regression
by criticizing as ``nonsensical'' the negative coefficients Dr. Israel
estimated for Canadian and Devotional programming. According to Dr.
Gray, negative coefficients are implausible because a program category
cannot have a negative market value. Gray CWRT ] 35.
In response, Dr. Israel did not dispute that the coefficients
themselves (whether positive or negative) should be understood as the
value per minute, or, equivalently, as the ``implied price'' of a
minute of programming. 3/12/18 Tr. 2832-36 (Israel). Dr. Israel
understood the negative coefficients to indicate that the inclusion of
such programming on a station lineup (i.e., a bundle) correlated with a
lower station value compared to programming that generated a ``positive
coefficient'' in the regression. 3/12/18 Tr. 2832-33 (Israel). However,
Dr. Israel conceded that even programming with negative coefficients
nonetheless have positive value when retransmitted, and he therefore
declined to assign zero value to such categories.
However, the Judges find that Dr. Israel's concession proves too
much. If programs could have positive economic value despite the
negative value of the coefficient identified by the regression, then
the coefficient does not reflect absolute market value per minute.
Rather, the coefficient must represent something else. Dr. Israel
identified that something else as the contribution of a program
category to the value of the royalty pool as compared with, that is,
relative to, the value of other program categories.\82\ Of course, this
``something else'' is something that the Judges must determine in this
proceeding--the relative value of a program from a given category to a
CSO when packaged in a station bundle, i.e., relative to the inclusion
of a program in another category.
---------------------------------------------------------------------------
\82\ For a simpler example, consider a restaurant patron offered
a three-flavor ice cream dessert. Assume for that patron chocolate
adds a utility measure (``utils'' in econo-speak) of 5, vanilla adds
a util measure of 4, strawberry adds a util measure of 3, and kiwi
adds a util measure of 2. A three-flavor combination of chocolate,
vanilla and strawberry has a total util value of 12 (5 + 4 + 3). If
kiwi is substituted for strawberry, the total util value is now only
11 (5 + 4 + 2). Thus, kiwi, relative to strawberry in this
combination, has a value in utils of -1 (reducing the value of the
dessert from 12 to 11)--even though its absolute value in utils is
+2. This negative value reflects the opportunity cost or relative
value of substituting kiwi for strawberry in the bundle, but not the
absolute market value of kiwi as an unbundled ice cream flavor.
Applying this example to a market, the coefficient represents the
value in a market populated by such bundles, not a value in a market
without bundles. Clearly, how the ``hypothetical market'' is
understood in terms of bundled programs therefore determines whether
the negative coefficients make sense and also affects the extent to
which the coefficients are of assistance in allocating the
royalties.
---------------------------------------------------------------------------
Accordingly, the Judges do not find the presence of negative
coefficients to be ``nonsensical.'' However, because of Dr. Israel's
explanation of the negative coefficients, the Judges disagree with his
decision to reset those negative coefficients to zero.\83\ And, because
negative coefficients do not mean that the programs lacked any absolute
value as contributors to the sum of royalties paid, any negative values
for program categories derived from a regression would need to be
adjusted to reflect the absolute value of such programming, given that
it indeed was retransmitted on some cable systems.\84\
---------------------------------------------------------------------------
\83\ Dr. Israel's explanation of the reason for a negative
coefficient is substantively similar to Professor George's
explanation of negative coefficients, discussed infra, as well as to
Professor Crawford's explanation of negative coefficients for
duplicative network programming, as discussed supra.
\84\ However, because the Judges find that only Dr. Crawford's
regression is sufficiently credible and because it does not contain
negative coefficients for the categories of interest, the conundrum
of negative coefficients does not affect the Judges' reliance on
regression analysis in this determination.
---------------------------------------------------------------------------
d. Criticisms by Dr. Jeffrey Stec
Dr. Jeffrey Stec, another economic expert witness for Program
Suppliers, leveled several criticisms at Dr. Israel's regression.
First, he added to the chorus of witnesses who opined that the
regulated nature of the market renders inapposite any purported
statistical relationship between royalties and program categories.
Amended Written Rebuttal Testimony of Jeffrey Stec, Trial Ex. 6016, at
15 (Stec AWRT). Nonetheless, the Judges find regression in such
circumstances to be a useful tool to ascertain relative differences in
value among program categories, notwithstanding the regulated nature of
the marketplace.
Dr. Stec also criticized Dr. Israel's regression because it
suggests that two different distantly retransmitted signals could be
associated with the same royalty level despite transmitting different
combinations of content. Stec AWRT at 25-27. The Judges do not find
this to be a valid criticism. Dr. Israel's regression identifies values
for each program category and multiplies those values by the number of
minutes transmitted for each category. These categorical values
certainly could be summed up for any given signal, as Dr. Stec's
criticism assumes. However, there is no reason why different signals
retransmitted on different cable systems to different subscriber groups
(of various sizes) could not generate the same level of royalties
notwithstanding that they contain different mixes of program
categories. This criticism misapprehends that the purpose of a section
111 allocation proceeding is not to value the signals as a whole, but
rather to value the constituent program categories across the signals.
4. The SDC's Criticisms
a. Criticisms by John Sanders
John Sanders, a media valuation expert who testified on behalf of
the SDC, criticized Dr. Israel's regression from a non-statistical
perspective. First, he opined that the concept of correlating royalty
generation with program categories is ``conceptually flawed.'' Written
Rebuttal Testimony of John Sanders, Trial Ex. 5006, at 6 (Sanders WRT).
He opined that marketplace value, or fair market value, is identified
by evaluating actual transactions that are ``modulat[ed]'' by price and
quantity. Accordingly, he asserted that a higher market value could be
associated with programming that represents a relatively small amount
of airtime. Amended Direct Testimony of John Sanders, Trial Ex. 5001,
at 21.
The Judges agree with Mr. Sanders regarding the potential for
programming to possess a relative value greater than would be suggested
by relatively low total viewership and airtime.\85\
[[Page 3576]]
However, that is not a reasonable criticism of the regression by Dr.
Israel in particular or of the Waldfogel-type regressions in general.
Such regressions, for example, have assigned a relative value to the
JSC programming that is greater than its total minutes of airtime would
suggest. See, e.g., Gray CWRT ] 31 & Table 4 (Israel regression
estimated a 37.5% JSC share whereas a viewing analysis provided only a
2.8% JSC share).
---------------------------------------------------------------------------
\85\ Royalty distribution parties have proposed fee generation
valuation methodologies in the past and the Judges and their
predecessors have generally discounted them as appropriate for
determining overall relative values. See, e.g., 2000-03 Distribution
Order, 75 FR at 26800-01. In that order, the Judges noted that the
CRT had criticized the fee generation approach, but then resorted to
fee generation reasoning in excluding PTV from a distribution from
the 3.75% Fund. Id. at 26803. The Judges later reaffirmed their
declination of fee generation valuation in the 2004-05 distribution
proceeding, noting that the fees cable systems pay are statutorily
determined and do not necessarily reflect relative value. See 2004-
05 Distribution Order, 75 FR at 57072.
---------------------------------------------------------------------------
Mr. Sanders also found fault with Dr. Israel's regression because
other evidence suggested that SDC programming had a positive value not
captured by that regression. Specifically, Mr. Sanders noted that when
WGNA removed certain programming from its retransmitted feed, it would
frequently replace that local programming with SDC programming,
suggesting that the latter has significant value. Sanders WRT at
13.\86\ While this may be indicative, anecdotally, of the value of SDC
programming as ``programming inserts on WGNA,'' it does not suggest to
the Judges any defect in Dr. Israel's regression analysis.
---------------------------------------------------------------------------
\86\ Though making a point about relative value, Mr. Sanders
acknowledged that substituted programming inserts on the WGNA
national feed are not compensable in this proceeding because they do
not constitute retransmitted local programming. Sanders WRT at 13.
---------------------------------------------------------------------------
Finally, Mr. Sanders noted that CSO program selection cannot be
viewed as a voluntary market-related decision in all instances, because
the record reflects that WGNA's parent company, Tribune Media Services
(Tribune Co. in 2010), had a practice of requiring CSOs to agree to
transmit multiple stations that it owned if a CSO wanted to transmit a
particular Tribune station. See Direct Testimony of Sue Ann R.
Hamilton, Trial Ex. 6008, at 7 (Hamilton WDT).\87\ Thus, Mr. Sanders
argued, Tribune's forced bundling diminished the assumption that a
CSO's station-by-station retransmission decision was made by
consideration of the programming categories within the station signal.
Rather, he opined that in certain instances, CSOs may well have
retransmitted WGNA and its mix of categorical programming because those
CSOs wanted to include other Tribune stations in the channel lineup.
---------------------------------------------------------------------------
\87\ Ms. Hamilton did not have direct knowledge of the existence
of this Tribune Co. policy after 2007 when she left her position
with Charter, a CSO. Rather, she opined that such tying would have
likely been a factor thereafter ``primarily due to legacy carriage
considerations.'' Hamilton WDT at 7.
---------------------------------------------------------------------------
Dr. Israel did not address this issue in his Written Rebuttal
Testimony. However, another JSC witness, Allan Singer, a Charter
Communications executive from 2011 through 2016, testified that
``during [2010-2013], an annual average of approximately 86 Charter
Form 3 systems made the decision to carry WGNA on a distant basis each
year, and on average approximately 69 of those systems did not carry
any other Tribune station in addition to WGNA [and] approximately 11
Charter Form 3 systems carried Tribune-owned stations on a local basis,
but did not carry WGNA.'' Written Rebuttal Testimony of Allan Singer,
Trial Ex. 1009, ]] 1, 5. Likewise, another JSC witness, Daniel Hartman,
a former satellite television programming executive, testified that
industry data showed ``that in 2010-13 . . . 169 Form 3 cable systems
carried a Tribune signal other than WGN (on a local or distant basis)
while not carrying WGN during the same period . . . and . . . 725 Form
3 cable systems carried WGN as a distant signal while not carrying
another Tribune signal during the same period.'' Written Rebuttal
Testimony of Daniel Hartman, Trial Ex. 1011, ] 25 (Hartman WRT).
The Judges find that the record does not support Mr. Sanders' or
Ms. Hamilton's claim that there were tying-based reasons for the
distant transmission of WGNA that would have diminished the probative
value of WGNA data as regression inputs. Additionally, to the extent
any tying-based pressures may have existed, they were not quantified
and thus this factor could not serve to alter the regression
estimates.\88\
---------------------------------------------------------------------------
\88\ Of course, Ms. Hamilton's tying-based argument would be
equally unavailing as against either the Crawford or George
regression analyses.
---------------------------------------------------------------------------
b. Criticisms by Dr. Erdem
Dr. Erdem, on behalf of the SDC, leveled several criticisms at Dr.
Israel's regression. Dr. Erdem opined that Dr. Israel's regression was
especially sensitive to: (1) The inclusion of additional variables, (2)
changes in the regression model specifications, and (3) data points
that Dr. Erdem identified as ``influential observations'' \89\ that, in
his opinion, were statistical outliers. Erdem WDT at 14-18.
---------------------------------------------------------------------------
\89\ An ``influential observation,'' also known as an
``influential data point,'' is defined as ``[a] data point whose
addition to a regression sample causes one or more estimated
regression parameters to change substantially.'' Rubinfeld, supra
note 36, at 465. An ``outlier,'' by contrast, is ``[a] data point
that is more than some appropriate distance from a regression line
that is estimated using all the other data points in the sample.''
Id. at 466 (emphasis added). Although some authorities equate all
``influential observations'' with ``outliers,'' Dr. Rubinfeld's more
careful distinction makes it clear that an ``influential''
observation or data point is not to be disregarded unless it is
outside an ``appropriate distance'' from the regression line. The
experts' dueling positions (with citations to other outside
authority) on whether the ``influential observations'' identified by
Dr. Erdem in Dr. Israel's regression are ``outliers''--and thus must
be ignored in the regression--are discussed infra.
---------------------------------------------------------------------------
i. Sensitivity to Additional Variables
Dr. Erdem testified that much of the variation within Dr. Israel's
regression could be explained by introducing the number of distant
subscribers as an independent (explanatory) variable rather than
applying it in the regression as a control variable. When Dr. Erdem
applied this subscriber count data in this manner, he claimed that
``all of the implied royalty shares'' in Dr. Israel's regression became
zero percent, and that some coefficients turned from positive to
negative. Erdem WDT at 15-16. Overall, he found that, with this one
sensitivity adjustment, the coefficients for the program categories
necessarily were no longer statistically significant. Id.
In rebuttal, Dr. Israel focused on a database issue, arguing that
Dr. Erdem had misunderstood ``the nature of the CDC data'' he used to
calculate distant subscribers, resulting in double-counted subscribers.
Israel WRT ] 24 n.22. This is the same criticism made of Dr. Erdem's
data analysis pertaining to the number of distant subscribers. As
noted, Dr. Erdem acknowledged his error, and the Judges denied the
SDC's out-of-time motion for leave to correct his testimony.
Accordingly, the Judges find that, given the acknowledged
deficiency in Dr. Erdem's application of distant subscriber data, his
criticism of Dr. Israel's regression for failure to utilize that data
as an independent (explanatory) variable rather than a control variable
cannot support Dr. Erdem's claims regarding the lack of statistical
significance in Dr. Israel's coefficients.
ii. Specification of the Functional Form of the Regression
With regard to Dr. Erdem's second criticism, he hypothesized that
``royalty payments may not have a linear relationship with several
potential variables.'' Erdem WDT at 16. Therefore, he transformed Dr.
Israel's regression from linear form to non-linear form to test for
further sensitivity. Specifically, Dr. Erdem made log transformations
to: (1) The total number of subscribers, (2) the number of distant
subscribers, (3) the number of activated channels, and (4) the number
of broadcast channels.
[[Page 3577]]
Id. These transformations indicated to him that the estimated
coefficients for the program categories changed substantially. Id. at
17.
In response, Dr. Israel asserted that he found Dr. Erdem's log
transformation/exponential versions of the former's level variables to
be something he had ``never seen . . . before.'' Israel WRT ] 24, n.22.
Rather, Dr. Israel characterized this transformation as ``simply
`fishing' for a specification that changes my result--throwing
variables into a model until the result changes.'' Id. Dr. Israel
indicated that such additions to the variables and such transformations
are ``not informative'' because they lack ``economic justification.''
Id.
At the hearing, Dr. Israel elaborated, flatly rejecting the
contention that Dr. Erdem had merely tested for non-linearities.
Rather, he testified that Dr. Erdem had ``added an extra set of
variables to the regression.'' 3/12/18 Tr. 2993 (Israel). He further
elucidated that the proper way for Dr. Erdem to have tested for another
functional form, i.e., a non-linear function, would have been to use a
log form on the right side (the explanatory variable side) of Dr.
Israel's regression, not for Dr. Erdem to pile log variables on top of
linear variables. Id. at 2994.
Finally, Dr. Israel testified that he decided to use a linear
function in order to be consistent with the previous Waldfogel
regression. Id. at 2955-56. As with the Judges' discussion regarding
Professor Crawford's regression analysis, the Judges do not find that
Dr. Israel's use of a linear relationship between royalties paid and
these additional variables diminished the value of his regression
analysis. Additionally, as discussed in connection with Professor
Crawford's regression, the Judges do not find it was necessary or
appropriate for a modeler to treat the number of subscribers, distant
or otherwise, as anything other than control variables because, in this
proceeding, the economic and regulatory purpose is to estimate the
relative values of different program categories on the level of
royalties rather than to predict or explain all of the causes or
correlations between other independent (explanatory) variables and the
level of royalties.
iii. ``Influential Observations''
Dr. Erdem identified 200 observations, out of Dr. Israel's 5,465
observations, that he labeled as ``influential observations.'' However,
Dr. Erdem did not propose that these influential observations
constituted outliers that should have been removed from Dr. Israel's
regression analysis. Quite the contrary, Dr. Erdem testified that these
influential observations ``shouldn't be excluded'' for any economic
reason, but rather demonstrate that, from an econometric perspective,
Dr. Israel's ``regression is sensitive to influential observations and
only that there ``could be subsets of data . . . that may require
additional investigation . . . .'' 3/8/18 Tr. 2708 (Erdem). Dr. Erdem
further posited that the influential observations might reflect a
``geographic effect'' that influenced Dr. Israel's coefficients, a
problem that, Dr. Erdem further opined, was not present in Professor
Crawford's regression analysis because he used ``system accounting
period fixed effects'' that have ``indirect geography implications.''
3/8/18 Tr. 2708-09 (Erdem). In fact, Dr. Erdem further contrasted
Professor Crawford's approach with Dr. Israel's approach by noting that
``Dr. Crawford's model does not exhibit sensitivity to outliers.''
Erdem WRT at 20 n.17.
In response, Dr. Israel testified that Dr. Erdem was fundamentally
wrong to suggest exclusion of what he characterized as ``influential
observations.'' More particularly, Dr. Israel asserted that ``[t]he
purpose of this regression analysis is to study the relationship
established by the full set of data, representing all Form 3 CSOs.''
(emphasis added). Moreover, Dr. Israel pointed out that ``even the
authors Dr. Erdem cited for this statistical practice, Israel WRT ] 24
n.22, themselves state that ``influential data points, of course, are
not necessarily bad data points; they may contain some of the most
interesting sample information.'' D. Belsley, D. E. Kuh, and R. E.
Welsch, Regression Diagnostics: Identifying Influential Data and
Sources of Collinearity at 3 (1980). Dr. Israel noted that the data Dr.
Erdem characterized as distorting influential observations, i.e.,
outliers, actually revealed an important influence, viz., the impact of
the relatively large size of the CSOs and Prorated DSEs that were
associated with these observations. More broadly, Dr. Israel noted that
``every regression that has ever been run is going to be sensitive to
the removal of influential observations,'' indicating that the mere
presence of such observations begs the question of whether they provide
valuable or anomalous data points. 3/12/18 Tr. at 2996 (Israel).
The Judges agree with Dr. Israel that it would be inappropriate on
this record to disregard the 200 observations that Dr. Erdem labeled as
influential observations/outliers. The Judges find that, from this
record, absent any compelling explanation as to why the data from these
200 observations are not relevant, simply ignoring those data would not
necessarily paint a more accurate picture of the population as a whole
with respect to the relationship between royalties paid and program
categories on local stations retransmitted by CSOs. The dueling
positions taken by Drs. Israel and Erdem indicate that the difference
between informative influential observations and uninformative outliers
is a matter of degree, and deciding where an observation crosses from
one type to the other is a matter of expert judgment. Dr. Erdem, who
raised this issue, did not provide a sufficient argument to support his
criticism that the impact of these data points should preclude or
diminish reliance on Dr. Israel's regression analysis. In fact, on the
present record, disregarding Dr. Israel's regression analysis because
he failed to discard ``influential'' data seems to the Judges to be
more likely to risk a cherry-picking of the data rather than an
identification of demonstrable anomalies. The Judges note, however,
that Professor Crawford's regression analysis is superior to Dr.
Israel's in that the former is not subject even to potential distortion
from influential observations.
c. Limited Impact of Dr. Erdem's Adjustments
The Judges note that, notwithstanding the merits of Dr. Erdem's
specific criticisms, there is not a wide gulf between the share values
that he identified after reworking Dr. Israel's regression to remove
the alleged influential observations, as noted by the following
comparison:
[[Page 3578]]
Table 9--Comparison of Israel Regression and Erdem's Adjusted Israel
Regression
------------------------------------------------------------------------
Erdem's
Israel adjusted
regression (%) Israel
regression (%)
------------------------------------------------------------------------
Joint Sports Claimants.................. 37.5 45.0
Program Suppliers....................... 26.8 22.6
Commercial TV........................... 22.2 21.6
Public TV............................... 13.5 7.0
Devotional.............................. 0.00 3.8
Canadian................................ 0.00 0.0
------------------------------------------------------------------------
Israel WDT ] 39 & Table V-3.; Erdem WDT at 18, Ex. 13. As for the
bottom two ranked program categories, Devotional and Canadian, Dr.
Israel was unsurprised that his regression could be less accurate in
estimating the shares for these categories. See 3/12/18 Tr. 2881, 2960
(Israel) (acknowledging ``negative coefficients for Canadian [and]
Devotional,'' explaining that ``in my experience, regressions of this
type often struggle to match at the lower end.'').
Dr. Erdem acknowledged as well that his allocations set forth in
the above table are ``very broadly comparable to the results from both
the Bortz and Horowitz surveys,'' although he hastened to opine that
``there are strong reasons to doubt that comparability of the results
is much more than a coincidence . . . .'' Id.\90\
---------------------------------------------------------------------------
\90\ The economic expert witness for the CCG, Professor Lisa
George, weighed in with a defense of Dr. Israel's regression. She
asserted that Dr. Erdem's argument that Dr. Israel's regression
technique produced ``unstable'' results reflects a fundamental
misunderstanding of the regression process. George WRT at 6-7
(``[V]ariables that do not affect royalty payments are not needed,
since they typically will just worsen precision of the estimates.
Changes to Dr. Israel's regression advocated by Settling Devotional
Claimants run counter to the goals of causal inference, tending to
increase bias and reduce precision.'').
---------------------------------------------------------------------------
5. Dr. Israel's Sensitivity Analyses
Dr. Israel is on shakier ground when it comes to defending the
results of his own sensitivity analyses of his regression.
Specifically, in his sensitivity analysis set forth in his own Model 3
(in which Dr. Israel controlled by geography by including an indicator
variable ``by DMA''), Dr. Israel estimated coefficients for Program
Suppliers and PTV that were approximately 50% higher for each category
than in the regression on which he has asked the Judges to rely. 3/12/
18 Tr. 3002-04 (Israel). When confronted on cross-examination with this
quantitative change, Dr. Israel responded by saying that he did not
view that quantitative difference ``as changing the overall rankings of
the corroboration [of the Bortz Survey].'' 3/12/18 Tr. 3004 (Israel)
The Judges are troubled by Dr. Israel's fixation on ``relative
ranks'' over the substantial ``quantitative difference'' in shares. The
present proceeding is intended, by statute, precedent, and consensus,
to allocate a dollar quantity of royalties. The rank ordering of those
allocations is not an end in itself. Moreover, the fact that one could
rank the claimant categories in that process is obvious--yet legally,
economically, and practically of no importance.
A simple example is useful. Assume three program categories, A, B
and C, seeking to split a $100 million royalty pool. A CSO survey might
estimate the following allocation of royalties:
Category A: 60%, i.e., $60 million
Category B: 30%, i.e., $30 million
Category C: 10%, i.e., $10 million
By contrast, a regression might estimate the following allocation of
this $100 million royalty pool:
Category A: 35%, i.e., $35 million
Category B: 33%, i.e., $33 million
Category C: 32%, i.e., $32 million
The rankings are identical in both the survey and the regression:
A, B, and C in descending order. However, copyright owners in
Categories C certainly would not agree that the regression results
``corroborate'' the survey result, when the regression produces $22
million more in royalties for them than the survey. Similarly,
copyright owners in Category A would be unlikely to find their $35
million payout under the regression to be ``corroborative'' of the $60
million payout they would otherwise receive pursuant to the survey.
Even copyright owners in Category B would likely chafe at the notion
that the survey results would take precedence over the regression
results--resulting in a $3 million loss--based on the strained idea
that a $33 million regression allocation corroborates a $30 million
payout.\91\
---------------------------------------------------------------------------
\91\ Alternately stated, this exercise is not analogous to
Olympic competition, where the difference in rankings--gold, silver
and bronze medals--makes all the difference. Here, copyright owners
in any claimant category would prefer more gold (royalty money) than
less. Therefore, any analysis that assumes that value attaches to
being ranked more highly would be absurd.
---------------------------------------------------------------------------
In fact, under questioning by Program Suppliers' counsel, Dr.
Israel acknowledged that an over-reliance on the rankings established
by a regression as opposed to the values estimated by the regression
could be of limited use. See 3/12/18 Tr. 3101 (Israel) (``mere
ranking'' only ``one indicator generated by his regression''). For the
foregoing reasons, the Judges do not place much weight on the relative
rankings of the program categories in Dr. Israel's regression as
evidence of relative value, or as a basis to find his sensitivity
analysis supported his regression results.
6. Conclusion Regarding Dr. Israel's Regression Analysis
The Judges give no weight to Dr. Israel's regression analysis, for
a number of reasons. First, he did not break out his proposed
allocations on an annual basis, making his average allocations
inapplicable in the present proceeding. Second, he did not perform any
analysis of data for the final year (2013) of the period at issue.
Third, his regression analysis produced large standard errors, making
his estimates less reliable than Professor Crawford's estimates and
potentially unreliable. Fourth, and relatedly, Dr. Israel failed to
produce the confidence intervals around his proposed coefficients
which, when calculated, were shown to be extremely wide. Fifth, his
regression analysis produced negative coefficients for several program
categories, which he arbitrarily reset to zero. Finally, even Dr.
Israel did not wholeheartedly advocate for the Judges' adoption of his
regression results as independent proof of reasonable royalty shares;
rather, he proposed that the Judges accept his results as corroboration
of the Bortz survey results. Perhaps no single one of these failings
would have been sufficient to justify the Judges' decision to give no
weight to Dr. Israel's
[[Page 3579]]
regression analysis. However, in combination, and in comparison to Dr.
Crawford's better constructed regression analysis, the Judges find
themselves unable to rely on Dr. Israel's regression analysis.
D. Professor George's Regression Analysis
The CCG proffered a valuation estimate based on the regression
analysis of their economic expert, Professor Lisa George. As a general
matter, Professor George testified that she believed the regression
approach was superior to other attempts to measure relative value
because it infers value from decisions actually made by market
participants. George CWDT at 2. She noted further that inferring value
from observed market decisions, known as the ``revealed preference''
method, has been an established feature of economic analysis. George
CWDT at 3 n.1. Like Drs. Crawford and Israel, she undertook a
Waldfogel-type regression. George CWDT at 6. However, she modified that
approach in a manner that she understood better focused on Canadian
programming. See id. at 5.
Professor George understood that her task was to estimate, via her
regression approach, the relative value of the several program
categories, in a hypothetical market in which no compulsory license
existed. See id. at 6. She assumed that: (1) The supply side of the
market was not relevant, because distant retransmission does not affect
local carriage decisions; (2) the cable television market is
imperfectly competitive; (3) CSOs focus on incremental revenue and
cost, in the form of royalties, transmission costs, and the opportunity
costs of transmitting (or retransmitting) any given program or signal
rather than any other program or signal; (4) distantly retransmitted
programs that are differentiated from other programs transmitted by the
CSO will have greater value; and (5) the transactions by which the
distant retransmissions would be agreed to would be between the CSO, as
buyer, and the station (or groups of stations), as sellers. Id. at 7-9.
Professor George testified that in her regression the coefficients
for the Canadian program category should be interpreted as a ``value
per unit'' or, equivalently, as an ``implicit price.'' Id. at 10,
12.\92\ With regard to the functional form, Professor George selected a
linear model because the coefficient in interest, the value of the
programming by category, is itself linear, i.e., it is measured in
dollars per minute. See id. at 11.
---------------------------------------------------------------------------
\92\ In her regression, Professor George used signal carriage
and royalty data provided by cable systems on Form 3 Statements of
Account as provided by CDC. George CWDT at 51-54; Written Direct
Testimony of Jonda Martin, Trial Ex. 4009, at 23 (Martin WDT).
Professor George obtained program categorization information that
was assembled by Danielle Boudreau from program content logs filed
with the Canadian Radio-television and Telecommunications Commission
(CRTC) by Canadian broadcasters. George CWDT at 53; Corrected
Written Direct Testimony of Danielle Boudreau, Trial Ex. 4001, at 3
(Boudreau CWDT).
---------------------------------------------------------------------------
Anticipating that past criticisms of Waldfogel-type regressions
would be repeated in this proceeding, Professor George met those points
head-on. First, she noted that the presence of price regulation not
only does not diminish the usefulness of a regression, but in fact is
the type of situation in which a regression approach to the estimation
of value is appropriate. See id. at 18. She distinguished market prices
from market decisions, noting that the latter are sufficient, standing
alone, to estimate values through regression analysis. See id. at
13.\93\ More particularly, she opined that the CSO must decide whether
the revenues to be realized from retransmission are sufficient to
warrant incurring the costs associated with retransmission (including
royalties, transmission cost, and opportunity costs). With regard to
the systems paying only the minimum fee, Professor George noted that
their decision to carry any particular signal rather than other
potential signal provides useful information regarding relative value.
See id. at 16. From a technical point of view, Professor George
explained that her regression ``accounts for minimum fee systems by
specifying a separate average (intercept) term \94\ for systems
carrying less than one distant signal equivalent and paying minimum
fees,'' which she further noted was similar to the procedure followed
by Dr. Waldfogel in his 2004-2005 regression. George CWDT at 16.
---------------------------------------------------------------------------
\93\ And, to state the obvious, if market prices were available,
no analysis of any sort would be necessary.
\94\ The ``intercept'' is defined as ``the value of the y
variable when the x variable is zero,'' and, accordingly, it is
``the parameter in a multiple linear regression model that gives the
expected value of the dependent variable when all the independent
variables equal zero.'' Wooldridge, supra note 34, at 864. The
intercept parameter ``is rarely central'' to a regression analysis.
See id. at 25.
---------------------------------------------------------------------------
Professor George explained that, although she followed the basic
specifications of the Waldfogel-type regressions, she made two
important changes. First, she estimated only the relative market value
of Canadian programming compared with the combined value of all other
program claimant categories. See id. at 23. Second, Professor George
made her estimates only for the region in which Canadian signals may be
retransmitted. See id. at 23. According to Professor George, applying
these two modifications rendered her regression both more precise and
less subject to downward bias. See id. at 25.
As in the other Waldfogel-type regressions, Professor George
included control variables in her regression, in order ``to isolate the
role of the independent variables of interest holding all else equal.''
Id. In particular, Professor George's control variables controlled for:
(1) Average income, (2) population, (3) the number of local stations,
(4) the number of subscribers, and (5) the number of active channels.
See id. The model also included ``indicator variables for binary system
attributes such as for minimum fee systems carrying less than one
distant signal equivalent.'' Id.
Her regression estimated that, within its regulatory geographic
region, Canadian programming's share of the royalties was 24.22%,
24.08%, 25.92% and 27.4% for each year, respectively, from 2010-2013.
Corrected Amended Written Direct Statement of Lisa George, Tr. Ex.
4006, at 6-7 (George CAWDT). Professor George then considered the
proportion of total U.S. royalties that were generated within this
narrow region, in order to estimate the Canadian Claimants' share of
the total royalty pool across the 2010-2013 four-year period. When
making this calculation, Professor George utilized revised data
updating compensable minutes that were contained in Professor
Crawford's regression analysis.\95\ She estimated the following shares
for Canadian programming: 6.55% for 2010, 6.61% for 2011, 7.47% for
2012 and 7.85% for 2013. George CAWDT at 4, 7.
---------------------------------------------------------------------------
\95\ Professor George had originally made her calculations for
the entire 2010-2013 period without breaking down her estimates by
year. After she reviewed data contained in Professor Crawford's
CWDT, Professor George was able to update her estimates and express
them on an annual basis. George CAWDT at 2.
---------------------------------------------------------------------------
Professor George noted that her regression produced a negative
coefficient within the Canadian region for Program Suppliers' and the
SDC's programs aired on Canadian signals. As noted supra, she explained
that a negative coefficient in this context meant that the marginal
presence of such programming ``does not allow cable systems to charge
higher prices for signal bundles, or to attract and retain
subscribers,'' relative to program categories with positive
coefficients, such as Canadian programming on the Canadian distant
signals. Id. at 32.
[[Page 3580]]
1. The JSC's Criticisms
a. Collapsing Non-Canadian Programming
The JSC's expert, Dr. Israel, took issue with Professor George's
unique decision to collapse all other claimant categories into a single
catch-all category to compare with the category of interest to her
client: Canadian programming on Canadian signals in the Canadian zone.
Israel WRT ] 12. He explained that when he altered her model to control
for the categories individually, her point estimate for Canadian
programming fell to 1.48% of the total royalty fund, which was more
consistent with the Bortz Survey share of 0.5% for Canadian
programming. See id. at A-2 to A-3.
Further, Dr. Israel opined that his alteration to control for other
program categories individually was necessary because Professor
George's collapsing of all other programming into a collective category
distorted her results by subjecting her estimation of those collapsed
minutes to ``noise'' for which she failed to account. That is, he
claimed that Professor George's Canadian share result was ``driven by
many important variables on the number of minutes by each other
category, thus subjecting her regression to omitted variable bias.''
Israel WRT ] 75 (emphasis added).\96\
---------------------------------------------------------------------------
\96\ ``Omitted variable bias'' can arise ``when a relevant
variable is omitted from the regression.'' Wooldridge, supra note
34, at 866. More particularly, omitted variable bias arises
``because a variable that is a determinant of Y [the dependent
variable] and is correlated with a regressor [independent variable]
has been omitted from the regression.'' Stock & Watson, supra note
32, at 822. The cumulative effect of any excluded variables ``shows
up as a random error term in the regression model. . . . An
important assumption in multiple regression analysis is that the
error term and each of the explanatory variables are independent of
each other.'' ABA Econometrics, supra note 22, at 10 n.21. Thus, Dr.
Israel's criticism is that the ``noise'' in Professor George's
regression reflects a bias arising from her failure to include
important data from each programming category. Id. at 160.
---------------------------------------------------------------------------
At the hearing, Professor George explained that she chose to
collapse all U.S. programming into one category because of the
``limited data'' available to her, precluding her from engaging in a
``detailed breakdown of programming on U.S. distant signals.'' 3/5/18
Tr. 2022 (George). However, she did not adequately respond to Dr.
Israel's assertions regarding the impact of this decision on the
statistical reliability of her regression. See 3/5/18 Tr. 2055 (George)
(criticizing Dr. Israel's rerunning of her model for several reasons,
but without sufficiently explaining why her collapsing of all U.S.
programming into a single category would not be problematic). The
Judges are troubled by the absence of an adequate response to this
criticism, and find insufficient her testimony as to the limited nature
of her data. Accordingly, the Judges find that this criticism serves to
diminish the weight they give to Professor George's regression results.
b. Applying Negative Coefficients
Dr. Israel also claimed error in Professor George's treatment of
the negative coefficient she estimated in her regression for Program
Suppliers and the SDC. Whereas Professor George simply used the
negative coefficient as an input for her calculation of relative values
per minute, as noted supra, when Dr. Israel's own regression estimated
negative coefficients, he reset them to zero, on the theory that a
coefficient intended to measure the value of programming could not be
negative. Thus, he opined that Professor George's application of the
negative coefficients ``distort[ed] the royalty shares for categories
with positive coefficients.'' Israel WRT ] 76.
In response, Professor George testified that her negative
coefficient is ``telling us that [Program Suppliers' programming] is
effectively dragging down the value of the Canadian signals.'' 3/5/18
Tr. 2031 (George). Alternately stated, she explained that, in her
opinion, the negative coefficient indicates that ``if we could replace
the Program Supplier content on Canadian signals in a sort of
hypothetical world . . . with Joint Sports or Canadian Claimant
programming, the value of the signal would be higher. . . . So it's not
surprising to me that more Program Supplier minutes on a Canadian
signal reduces the value of the signal.'' Id. at 2031-32 (George)
(emphasis added). Thus, she opined that the negative coefficient does
not reflect a negative monetary value for such programming, but rather
reflects the opportunity cost arising from the inclusion of programming
from such categories in the bundle of programs on the retransmitted
signal compared with programs from other categories with positive
coefficients. 3/5/18 Tr. 2117 (George).
Accordingly, because Professor George finds valuable information in
the negative coefficient, she rejected Dr. Israel's criticism that she
should have reset the negative coefficient to zero. See id. at 2043
(George) (``[My] . . . negative valuation, which is precisely
estimated, so within standard confidence intervals . . . makes sense
from theory. [I]t is completely arbitrary to replace a coefficient in a
regression model with another . . . number. It is just bad econometric
practice.'').
As discussed in connection with Dr. Israel's regression, the Judges
find (as Professor George opined) that negative coefficients are
reasonably well-explained by the fact that they reflect the relative
impact on the value of the signal \97\ of different categories of
programming rather than the absolute value of programming-by-category.
Again, though, this explanation of the negative coefficients
underscores that the coefficients represent the relative value in a
market for programs by categories as inputs to a bundle (the signal)--
economically relevant to the task at hand (allocating the royalty pool
by category) but not reflective of absolute market prices.
---------------------------------------------------------------------------
\97\ Indeed, Professor George twice referred to the value of the
program categories in the context of the ``value of the signal''
containing a bundle of programs offered to a CSO. 3/5/18 Tr. 2031-32
(George).
---------------------------------------------------------------------------
c. Weighting Results by the Number of Subscribers
Dr. Israel asserted that Professor George's regression is
inconsistent with the specifications of the Waldfogel-type regression
because she weighted her compensable minutes by the number of
subscribers of each CSO, whereas Dr. Waldfogel estimated royalty
payments per CSO, not royalty payments per subscriber. See Israel WRT ]
76. Moreover, Dr. Israel asserted that this deviation from Dr.
Waldfogel's approach was improper because it was inconsistent with the
functional form of her regression, which was otherwise of the
Waldfogel-type. See id.
In response to Dr. Israel, Professor George acknowledged that her
approach was ``quite different,'' yet she did not adequately explain
how or why her modification made her results more precise or otherwise
improved the quality of her regression. See 3/5/18 Tr. 2055 (George).
The Judges find Professor George's vague statement to be an
insufficient response to Dr. Israel's criticism.\98\
---------------------------------------------------------------------------
\98\ However, this issue was also raised by Dr. Erdem and, in
response, Professor George provided a more compelling defense, as
discussed infra.
---------------------------------------------------------------------------
2. The SDC's Criticisms
a. The Regulated Nature of the Market
Dr. Erdem criticized Professor George's regression approach
because, as she acknowledged, it did not reflect the prices that CSOs
and stations would negotiate in an unregulated market. However, Dr.
Erdem did note that her ``observed data'' revealed that distant
retransmission occurred when ``incremental benefits are higher than
incremental costs'' for the retransmitting CSOs. Erdem WRT at 20
(citing George
[[Page 3581]]
CWDT at 8-9, 20). The Judges note that this criticism is a variant of
the repeated refrain that the regulated nature of the market precluded
the use of a Waldfogel-type regression. In the context of the present
criticism as well, the Judges find that the relative preferences of
CSOs for different categories of programs are revealed through such a
regression and that Professor George's regression analysis is not
subject to appropriate criticism in this regard.
b. Compensable Minutes
Dr. Erdem also criticized Professor George's approach for using
actual compensable minutes for Canadian signals, but estimated
compensable minutes for U.S. signals in the Canadian zone. Dr. Erdem
suggested that such an approach ``is likely less precise.'' Erdem WRT
at 21. Moreover, like Dr. Israel, Dr. Erdem criticized Professor George
for using Professor Crawford's data, based on all U.S. distant signals,
as a proxy for compensable minutes in the Canadian zone. Dr. Erdem
asserted that there was no basis in the record for Professor George to
make this assumption. See id.
Professor George did not offer a sufficient response to this
criticism. Accordingly, the Judges find Dr. George's regression
analysis is compromised by this unexplained criticism. However, there
is no sufficient evidence in the record that reflects the dimensions of
this assumption or the impact it may have on Professor George's
proposed allocations. The Judges find, as noted supra, that Professor
George's lack of disaggregated data across other program categories is
insufficient to justify her less precise approach.
c. The Number of Broadcast Hours
Next, Dr. Erdem asserted that Professor George also assumed without
substantiation that ``all stations broadcast the same number of hours
throughout the day,'' which, according to Dr. Erdem, ``seems to
contradict the actual data . . . used in Professor George's analysis''.
Erdem WRT at 21-22.
Once again, Professor George did not offer a sufficient substantive
response to this criticism. Thus, the Judges find her assumption to be
unsupported by the record and her regression analysis therefore is
compromised. However, there is no sufficient evidence in the record
that reflects the dimensions of this assumption or the impact it may
have on Professor George's proposed allocations.
d. Negative Coefficients
Dr. Erdem (like Dr. Israel) is troubled by the negative coefficient
produced by Professor George's regression for Program Suppliers'
minutes. However, his concern is not aimed at Professor George's
defense of such a negative coefficient. In fact, he agreed with
Professor George regarding a ``likely'' reason for the presence of the
negative coefficient, i.e., that it ``suggests that on Canadian
signals, Program Supplier content is a close substitute for other cable
system offerings from the standpoint of viewers [and] the presence of
Program Supplier programming on Canadian distant signals does not allow
cable systems to charge higher prices for signal bundles, or to attract
or retain subscribers.'' Erdem WRT at 22 (approvingly quoting Professor
George). Rather, Dr. Erdem contended that the negative coefficient in
the context of the Canadian signal ``likely does not factor in the
complex decision making process of U.S. cable operators, who are
maximizing overall profits across all regions combined.'' Id. However,
this criticism was speculative, unsupported by a factual basis and
otherwise undeveloped, and the Judges do not find it to diminish the
value of Professor George's regression analysis.
e. Joinder of the Program Supplier and SDC Categories
Next, Dr. Erdem attempted a sensitivity analysis of Professor
George's results. In particular, he separated the Program Supplier and
SDC minutes and input this separated data into an updated model. He
found meaningful changes in the resulting coefficients, including a
``coefficient for [SDC] distant minutes [that was] positive and
statistically significant.'' Id. at 22.
In response, Professor George testified that she had combined these
two program categories because the amount of SDC programming was so low
and therefore the data would not generate enough variation. Further,
she asserted that when Dr. Erdem split apart the data for Program
Suppliers and the SDC, he created ``multicollinearity problems''
because the variables for each program category are functions of each
other. 3/5/18 Tr. 2042 (George). However, Professor George did not
point to evidence that would indicate the presence of such
multicollinearity. Moreover, she acknowledged she had combined the two
categories to obtain sufficient variation in the SDC minutes across
CSOs that would be lacking if the SDC category was analyzed separately.
That in itself was an artifact, because SDC programming is not Program
Supplier programming.
Accordingly, the Judges find that the probative value of Professor
George's regression analysis is compromised to an extent by her
artificial joinder of the Program Supplier and SDC categories.
f. Subscriber-Weighted Compensable Minutes
Dr. Erdem, like Dr. Israel, criticized Professor George's decision
to multiply the coefficients by ``the subscriber weighted compensable
distant minutes.'' Erdem WRT at 23 (``Conceptually, weighting by
subscribers may not be appropriate in Waldfogel-type regressions which
model the decisions of cable operators (i.e., decision to carry a
signal or signals with minutes of different types of content in return
for royalty payments implied by the formula.'')). Dr. Erdem replaced
Professor George's weighted compensable distant minutes with unweighted
compensable distant minutes and found that Professor George's use of
the weighted minutes approach caused ``[t]he share for the Canadian
category [to] increase[ ] significantly.'' Id.
In response, Professor George explained her reason for using
subscriber-weighted compensable minutes: ``[W]e are counting up the
subscribers who have access to this programming to give us a better
feel, because counting just systems doesn't give you really a full
picture of how many people are exposed to programming.'' 3/5/18 Tr.
2078 (George) (emphasis added).
The emphasized language above indicates that Dr. George engaged in
such weighting for the same reasons that Professor Crawford used
minutes at the subscriber group level and Dr. Israel used prorated DSE
data--to better identify which subscribers actually received the
distantly retransmitted local signal. Accordingly, the Judges find
Professor George's weighting to be an acceptable deviation from the
Waldfogel approach in the same way as Professor Crawford's subscriber
group approach and Dr. Israel's Prorated DSE approach represent
appropriate adaptions of the Waldfogel-type regression to available and
more granular data.
3. Program Suppliers' Criticisms
a. Negative Coefficients
Dr. Gray criticized Professor George for failing to reset her
negative coefficient for her combined Program Supplier/SDC minutes to
zero, as did Dr. Israel. Dr. Gray asserted that these
[[Page 3582]]
negative coefficients implied that these two program categories would
be required to pay royalties to CSOs, clearly an absurd result. See
Gray CWRT ] 35. However, as the Judges have explained, supra, these
negative coefficients do not represent negative values for programs in
the categories, but rather represent, on average, reductions in the
value of a program bundle (i.e., a station) in comparison with other
program categories.
b. The Minimum Fee Issue
Dr. Gray also criticized Professor George's regression for the same
reason he criticized all the Waldfogel-type regressions in this
proceeding--the failure to distinguish between CSOs paying only the
minimum fee and those who intentionally incurred additional incremental
costs by paying more than the minimum to distantly retransmit
additional local stations. See id. ] 37. Dr. Gray's reworking of
Professor George's regression applying only the subset of CSOs paying
greater than the statutory minimum fee found no statistically
significant relationship between CCG programming minutes and royalty
fees paid in the Canadian region, which would support an estimate of 0%
for the Canadian share (presumably because the null hypothesis \99\ was
not disproven). See Gray CWRT App. D.
---------------------------------------------------------------------------
\99\ ``An expert's expectation or contention that a particular
independent variable does not have a correlation with a particular
dependent variable is called a null hypothesis, because the expected
outcome of the analysis would show the absence of a correlation. . .
. Often, the null hypothesis is stated in terms of a particular
regression coefficient equal to zero.'' ABA Econometrics, supra note
22, at 17 (emphasis added). See also Rubinfeld, 85 Colum. L. Rev. at
1054 n.20 (``If the evidence is not sufficiently strong, the null
hypothesis is sometimes presumed to be correct, but a more accurate
description would simply say that the evidence was not sufficiently
strong to allow for its rejection.'').
---------------------------------------------------------------------------
In response, Professor George testified that even the station
retransmission choices by CSOs paying only the minimum fee provide
relevant economic information. 3/5/18 Tr. 2038-39 (George). However,
she acknowledged that incorporating the minimum-fee-paying CSOs in an
integrated analysis does add some ``uncertainty . . . to our estimates
[and] we do lose some precision from having some minimum fee systems.''
3/5/18 Tr. 2039 (George). Further, Professor George did not contest the
statistical correctness of Dr. Gray's estimate of a 0% share for
Canadian programming regarding the relative value for Canadian
programming arising from an analysis of only those CSOs paying more
than the minimum fee. 3/5/18 Tr. 2044-45 (George).
The Judges find, as noted supra, that an analysis of the CSOs
paying only the minimum fee might provide some useful information.
However, as also noted supra, the record does not provide an adequate
basis to incorporate any ``relative value'' differences based on a
distinction between CSOs that do and do not pay only the minimum fee.
4. Conclusion Regarding Professor George's Regression Analysis
In sum, the Judges find that Professor George's regression analysis
is of limited value. Her collapsing of all non-Canadian programming
into a single category was the consequence of the unavailability of
data, not a choice intended to enhance the reliability of her
estimates. Also, her negative coefficients within the Canadian zone of
compensable programming categories rendered her analysis indeterminate
and thus in need of adjustment.
III. CSO Surveys
Another analytical approach presented in this proceeding for
determining relative value of the program types retransmitted by cable
operators is analysis of data from surveys administered to CSOs, the
entities that buy the compensable programming (bundled as distant
signals). In essence, the surveys ask the CSOs to place a relative
value on the types of programming they license for retransmission to
their subscribers.
CSO survey results have long played a central role in assisting
adjudicators in assessing relative market value of cable programming.
The JSC presented the first survey report, designed by the predecessor
of Bortz Media & Sports Group, Inc. (Bortz), to establish the relative
value of the various categories of programming at issue in 1983. See
Bortz Survey,\100\ Trial Ex. 1001 at A-2. Over the years, Bortz refined
its survey design to address issues raised by the triers of fact. The
goal of the surveys was to answer the question of relative value of the
competing program categories as seen through the eyes of CSOs. Id. at
A-3--A-4. In the present proceeding, the JSC and the SDC support an
analysis based on the work of Bortz for the relevant royalty years.
Program Suppliers offer an alternative survey \101\ designed by
Horowitz Research (Horowitz Survey), which they offered as a critique
of the Bortz survey results.\102\ In addition, the CCG presented a
third survey focused on Canadian signals (Ringold Survey). Other
participants offered criticisms of the surveys.
---------------------------------------------------------------------------
\100\ The full title of the Bortz Survey is ``Cable Operator
Valuation of Distant Signal Non-Network Programming: 2010-13.''
\101\ Program Suppliers also advocated using viewing statistics
as the optimal measure of relative market value of the participating
program category groups. See infra, section IV.
\102\ Notwithstanding his survey results, Mr. Horowitz opined
that ``the Horowitz Survey is not a substitute for behavioral data
such as viewing.'' Corrected Written Direct Testimony of Howard
Horowitz, Trial Ex. 6012, at 3 (Horowitz CWDT).
---------------------------------------------------------------------------
All of the surveys the parties proffered in this proceeding were
conducted by telephone and purported to inquire of the individual at
the responding CSO who was responsible for signal carriage decisions.
Each proponent constructed its survey as a constant sum survey; that
is, respondents were asked to value each program category relative to
the other categories and as a portion of 100%.
The JSC contended that the Bortz Survey responses are a sound
measure of the relative value of programming, by category. See Bortz
Survey, Trial Ex. 1001 at 7. Program Suppliers contended that CSO
survey responses are
[d]one well, such a survey may illuminate the criterion (sic.) by
which to allocate royalties. . . . [W]hatever the reasoned judgment
of executives . . . , any cable operator survey should not be
considered a substitute for behavioral data on viewing.
Corrected Written Direct Testimony of Howard Horowitz, Trial Ex. 6012
at 21-22 (Horowitz CWDT). The Ringold Survey focuses on CCG programming
within the Canadian broadcast region. The CCG claimed the Ringold
Survey provides a better measure of the relative value of compensable
Canadian programs distantly retransmitted in the U.S.
A. Bortz Survey
As in the past, the JSC have engaged Bortz to develop and implement
a methodology to ascertain relative market value of categories of
distantly retransmitted television programming.\103\ See Bortz Survey
at A-1. Bortz made ``refinements'' to the present survey to address
concerns expressed by the CRT, CARP, and more recently, the Judges.
Specifically, Bortz refined the way in which it (1) assessed the level
of pertinent knowledge of the individual survey respondent (i.e., the
person ``most responsible for programming decisions''), (2) conformed
program category definitions to those adopted for royalty distribution
proceedings, (3) selected cable systems to participate by excluding any
that did
[[Page 3583]]
not distantly retransmit eligible non-network programming, and (4)
closed the time gap \104\ between the royalty year at issue and the
conduct of the survey relating to that year. Id. at A-5--A-12.
---------------------------------------------------------------------------
\103\ Bortz retained THA Research to conduct the 2010-13
telephone surveys. Id. at 19. Criticisms of the Bortz Survey focused
on construct and content; no party criticized the Bortz selection of
THA Research.
\104\ To avoid any criticism that there was a delay in
conducting an annual survey that could result in ``recall bias,''
Bortz conducted all but the 2010 survey beginning in the summer
following the royalty year at issue. Bortz conducted the 2010 survey
in December 2011. See Bortz Survey at A-11.
---------------------------------------------------------------------------
With regard to the survey contents, Bortz attempted to focus
respondents on the actual distant signals at issue using information
from the CSOs' Statements of Account filed with the Copyright Office.
Id. at 12. To address a criticism regarding asking respondents to
allocate ``value,'' Bortz asked them to think about relative value of
the categories and subsequently to provide estimates for each. The
interviewers then went through the list of program categories to give
respondents an opportunity to reconsider the relative values the
respondent placed on the categories. Id. at 13. Bortz also reported
other refinements responsive to criticisms of the triers of fact and
opposing parties in prior proceedings.\105\
---------------------------------------------------------------------------
\105\ Other criticisms noted by the triers of fact and opposing
parties included, e.g., breaking up the survey and completing it
through multiple callbacks, and asking for critical conclusions in a
short survey of approximately ten minutes' length.
---------------------------------------------------------------------------
The CARP determination regarding allocation of 1998-99 cable
royalties noted that the Bortz Survey focused on the demand side of a
typical market, i.e., what CSOs are willing to pay to broadcasters,
which it concluded is more likely to reflect relative values of the
programming categories. In essence, according to the CARP, in the
relevant hypothetical market the supply of programming would be fixed
and value would be determined only by the CSOs' demand as reflected in
their willingness to pay. See 1998-99 Librarian Order, 69 FR at 3613-
15. In any event, beginning with its 2009 survey, Bortz included a
question asking respondents to rank the relative cost of the
programming categories, which it alleged gave respondents a cue to
consider the supply side of the valuation. Bortz Survey at A-14--A-15.
Bortz surveyed a stratified, random sample of ``Form 3'' cable
systems,\106\ but excluded systems that did not carry distant signals
and those whose only distant signals were PTV or Canadian signals, or
both. Id. at 13-14. Bortz made five adjustments for the 2010-13 survey
questionnaires to address criticisms of their studies from earlier
proceedings. Specifically, Bortz (1) identified compensable programming
on WGNA, the most widely carried distant signal; (2) reduced the number
of signals about which they inquired; (3) did not offer ``sports'' as a
category in the constant sum question for CSOs that did not retransmit
programming within the Sports Programming category established in this
proceeding; (4) modified the ``warm-up'' questions; and (5) omitted
reference to attracting and retaining subscribers to broaden the
concept of value to CSOs. Id. at 2.
---------------------------------------------------------------------------
\106\ Form 3 cable systems are the largest systems by gross
receipts and account for over 98% of section 111 royalty deposits.
Id. at 10.
---------------------------------------------------------------------------
Initially, Bortz confirmed that the respondent self-identified as
the individual responsible for signal carriage decisions for the cable
system. Then Bortz identified the distant signals at issue and asked
each respondent to rank by ``importance'' to the system the non-network
programming on those distant signals by categories ``intended to
correspond'' to the programming categories adopted in the present
proceeding. Id. at 15-16. Bortz next asked respondents to estimate the
cost to acquire programming within the identified categories if the
cable system had been required to purchase the programming in the
marketplace. Id. at 16. Respondents were then asked to assign relative
values to the relevant programming; that is, to assign a share of 100%
of value to each category.\107\
---------------------------------------------------------------------------
\107\ The relative value question read: ``Assume you [system]
spent a fixed dollar amount in [year] to acquire all the non-network
programming actually broadcast during [year] by the stations . . .
listed. What percentage, if any, of the fixed dollar amount would
your system have spent for each category of programming?'' Id. at
18.
---------------------------------------------------------------------------
The influence of superstation WGN America (WGNA) was a major factor
in valuing compensable programming during 2010 to 2013. Bortz concedes
that survey respondents might have lacked information detailed enough
to distinguish between compensable programming and content WGN
substituted for contemporaneous broadcasts and transmitted to WGNA
subscribers.\108\ Bortz modified its prior survey questions to attempt
to address the WGNA content issue. According to Bortz, for cable
systems that only retransmit WGNA as a distant signal, survey questions
regarding WGNA programming described only compensable programming, by
agreed category as nearly as possible.\109\ In this way, Bortz sought
to address criticism that its prior survey results contained skewed
values because Bortz' survey questions failed to distinguish between
compensable and non-compensable WGNA retransmissions. Id. at 19.
---------------------------------------------------------------------------
\108\ Only programming that airs simultaneously on WGN-Chicago
(the local feed) and WGNA (the satellite feed) is compensable under
the section 111 license.
\109\ Questioners offered to send respondents a guide to
compensable WGNA programming and instructed respondents that they
could call back if the respondent needed more time to consider the
compensable program list. Bortz Survey at 30.
---------------------------------------------------------------------------
Comparing the 2004-05 survey results (which formed the basis of the
2010-13 survey) to those for the time period relevant to the present
proceeding compensable programming retransmitted by WGNA decreased by
about half, from approximately 30% of the signal to under 15%. JSC-,
CTV-, and SDC-represented programming increased in relative value from
the 2004-05 survey to the 2010-13 survey, while Program Suppliers'
content declined in relative value. Bortz attributes these changes to a
reduction in compensable retransmissions of Program Suppliers'
programming. Id. at 29.
PTV \110\ and the CCG \111\ criticized the Bortz results because
the survey excluded cable systems for which public television and/or
Canadian programming were the systems' only distantly retransmitted
signals. Bortz conceded that both PTV and CCG categories are likely
undervalued because of the survey's exclusion of PTV-only and CCG-only
systems and because of the relatively small number of Form 3 systems
that retransmit PTV and CCG signals. Bortz Survey at 46-47. Respondents
for multiple signal systems that included PTV and Canadian programming
valued public television programming on multiple signal systems at an
average of between 7.8% and 10.3% and valued Canadian signals at an
average of between 2.4% and 7.9% during the relevant period. Id. The
Bortz Survey aggregate values for PTV and CCG during the period were
substantially lower because of the exclusion of PTV-only and CCG-only
systems.\112\
---------------------------------------------------------------------------
\110\ McLaughlin and Blackburn augmented the 2004-05 Bortz
survey results by inserting stations whose only distant signal was
PTV, using the same response rates reported by Bortz. See 3/7/19 Tr.
at 2457-59 (McLaughlin). They concluded that response bias depressed
the PTV values claimed in the Bortz Survey. See Written Rebuttal
Testimony of Linda McLaughlin and David Blackburn, Trial Ex. 3002,
at 4 (McLaughlin/Blackburn WRT).
\111\ See, e.g., Corrected Written Rebuttal Testimony of
Frederick Conrad, Trial Ex. 4003, at 7-8 (Conrad CWRT) (assuming
stations with Canadian-only distant signals would assign 100%
relative value to CCG programming creates response bias).
\112\ The Bortz Survey measured all programming on Canadian
signals as one category. See Bortz Survey at 46-47. The CCG concedes
that some of the programming on Canadian signals is compensable in
other categories, such as Devotional or Program Suppliers.
---------------------------------------------------------------------------
Notwithstanding the refinements Bortz implemented in its survey for
2010-13, Mr. Trautman still professed
[[Page 3584]]
that the Judges should consider the value estimates for the Program
Suppliers and Devotional Programming categories as a ``ceiling'' or
upper bound for the allocation to those categories. Mr. Trautman
reached this conclusion largely because he was not confident that even
the modified survey accurately accounts for non-compensable programming
on WGNA, most of which he asserted falls within those two program
categories. Id. at 18.
Further, Mr. Trautman conceded that ``some adjustment'' upward of
allocations to the PTV and CCG categories is appropriate. Id. 7-8;
Trautman WRT ] 4.\113\ Professors McLaughlin and Blackburn adjusted the
2010-13 Bortz Survey results to increase the share of value allocated
to PTV and CCG programming, but Mr. Trautman argued that the
McLaughlin/Blackburn adjustments should be considered a ``ceiling'' on
the values of those two categories, because they relied in part on
Horowitz Survey results. Mr. Trautman contended the Horowitz results
were invalid because ``most'' of the respondents with PTV-only or CCG-
only distant retransmissions valued the compensable programming at less
than 100%. Trautman WRT ] 3.
---------------------------------------------------------------------------
\113\ Mr. Trautman criticized the Horowitz Survey results that
valued Program Suppliers and Devotional programming higher than the
Bortz Survey. He contended Horowitz failed to account for the amount
of non-compensable programming on WGNA, i.e., the substituted
syndicated or devotional programs WGNA adds to its lineup when it is
not simultaneously retransmitting WGN programming. Trautman WRT ] 1.
Mr. Trautman argued that Horowitz further inflated Program
Suppliers, because it attributed all programming in the allegedly
inflated ``Other Sports'' category to Program Suppliers. Id. ] 2.
---------------------------------------------------------------------------
The initial relative valuations from the 2010-13 Bortz Survey
results are:
Table 10--Initial Bortz Survey Results
----------------------------------------------------------------------------------------------------------------
Category 2010 (%) 2011 (%) 2012 (%) 2013 (%)
----------------------------------------------------------------------------------------------------------------
CCG............................................. 0.10 0.20 0.60 1.20
CTV............................................. 18.70 18.30 28.80 22.70
Devotional...................................... 4.00 4.50 4.80 5.00
PS.............................................. 31.90 36.00 28.80 27.30
PTV............................................. 4.40 4.70 5.10 6.20
Sports.......................................... 40.90 36.40 37.90 37.70
----------------------------------------------------------------------------------------------------------------
(Columns might not add to 100% because of rounding.)
See Bortz Survey at 3. Referring to the calculations performed by Ms.
McLaughlin and Dr. Blackburn, Mr. Trautman adjusted the allocations in
the Bortz Survey, to increase the relative values of PTV and CCG
programming at the expense of the relative values of the remaining
categories:
Table 11--McLaughlin/Blackburn Augmented Bortz Survey Results
----------------------------------------------------------------------------------------------------------------
Category 2010 (%) 2011 (%) 2012 (%) 2013 (%)
----------------------------------------------------------------------------------------------------------------
CCG............................................. 1.6 1.8 1.2 2.1
CTV............................................. 17.8 17.2 22.3 21.7
Devotional...................................... 3.8 4.2 4.6 4.8
PS.............................................. 30.3 33.8 28.1 26.1
PTV............................................. 7.5 8.7 6.9 9.1
Sports.......................................... 39.0 34.2 37.0 36.1
----------------------------------------------------------------------------------------------------------------
(Columns might not add to 100% because of rounding.)
See Table A-2, Trautman WRT, App. A at A-3.
After reviewing the McLaughlin/Blackburn analysis, Mr. Trautman
adjusted the Bortz Survey results in two ways. First, he adjusted the
Bortz Survey results using the McLaughlin/Blackburn augmented results,
derived by adding PTV-only and Canadian-only distant signals and
assuming CSOs would have set the relative value of the PTV and Canadian
signals at 100%. Mr. Trautman then referred to the Horowitz Survey
results, opining that it was error for McLaughlin/Blackburn to assume
CSOs would assign 100% relative value to PTV programming on PTV-only
signals.
B. Horowitz Survey
Program Suppliers retained Horowitz Research, Inc. to evaluate the
Bortz Survey and to design a proprietary survey to improve on the Bortz
Survey. Horowitz attempted to replicate and improve upon the methods
and procedures of the Bortz Survey used in the ``Phase I'' or
allocation phase of the 2004-05 cable royalty distribution
proceeding.\114\ See Horowitz WDT at 3. The Horowitz Survey sought to
measure the relative value of programming categories in attracting and
retaining subscribers. Id. In rebuttal, Horowitz evaluated the Bortz
Survey covering royalty years 2010-13. See Written Rebuttal Testimony
of Howard Horowitz, Trial Ex. 6013, at 2 (Horowitz WRT).
---------------------------------------------------------------------------
\114\ Horowitz employed Global Marketing Research Services, Inc.
to conduct the telephone surveys. Horowitz WDT at 8.
---------------------------------------------------------------------------
Horowitz also conducted its own survey, fashioned on the Bortz
Survey, but with amendments Horowitz considered necessary. The Horowitz
Survey, among other things, addressed the PTV and CCG programming the
Bortz Survey omitted. The Horowitz Survey questionnaire provided
category descriptions to assist respondents in allocating relative
value, identified examples of programming that might fit the category
description, and created a separate ``Other Sports'' category to
clarify that the definition of ``sports programming'' for purposes of
the valuation survey did not include all sports broadcasts, but only
included
[[Page 3585]]
those live college and professional team sports fitting the category
definition operative in CRB royalty distribution proceedings. Horowitz
WDT at 5-6. The 2010-13 Bortz Survey eliminated from the valuation
questions references made in prior Bortz surveys to attraction and
retention of subscribers. See Bortz Survey at 15.\115\ Horowitz opined
that omitting references to subscriber acquisition and retention
``distracted survey respondents from the purpose of allocating a fixed
budget . . . by leaving out all references to subscriber value . . .
the `primary consideration' for allocating value.'' Horowitz WRT at 2.
According to Horowitz, between 79% and 85% of CSO survey respondents
ranked programming popular with and important to current and potential
subscribers as the most important factor in their carriage decisions.
By contrast, only between 4% and 35% ranked importance to the cable
system as the primary factor influencing carriage decisions.\116\
---------------------------------------------------------------------------
\115\ In the 2004-05 Bortz Survey, the warmup questions focused
respondents on subscriber acquisition and retention by asking which
categories were most ``popular'' with subscribers. See Bortz Survey
at 39. Responding to a Judges' observation that acquisition and
retention of subscribers might be too narrow a notion of value,
Bortz replaced the popularity question with one intended to
establish distant signals' importance to the respondent's system.
\116\ See Horowitz WDT at 17. Horowitz surveyed a sample of 300
systems, inquiring about factors influencing carriage decisions. The
response categories were (1) programming popular and important to
current and potential subscribers, (2) programming important to the
cable system, and (3) other. Respondents could choose multiple
factors.
---------------------------------------------------------------------------
The Horowitz Survey results, weighted by Dr. Martin Frankel,
indicate relative market values of the programming categories at issue
\117\ in this proceeding as:
---------------------------------------------------------------------------
\117\ The numbers for Program Suppliers (PS) are derived by
adding responses for syndicated series and movies. ``Other Sports''
are left as a separately valued type of programming because the
Horowitz Survey did not and could not specify whether non-JSC sports
programming should be categorized as Program Suppliers or CTV.
Table 12--Horowitz Survey Results
----------------------------------------------------------------------------------------------------------------
Category 2010 (%) 2011 (%) 2012 (%) 2013 (%)
----------------------------------------------------------------------------------------------------------------
CCG............................................. 0.01 1.00 0.87 0.35
CTV............................................. 12.38 12.85 15.72 9.54
Devotional...................................... 3.78 5.92 5.74 3.48
PS.............................................. 37.43 28.99 28.11 28.65
PTV............................................. 7.69 13.31 15.05 15.39
Sports.......................................... 31.94 27.13 25.50 35.28
``Other Sports''................................ 6.77 10.80 9.02 7.40
----------------------------------------------------------------------------------------------------------------
See Horowitz WDT at 16; Written Direct Testimony of Martin R. Frankel,
Trial Ex. 6010 at 7 (Frankel WDT).
Mr. Horowitz's decisions to (1) rely on acquisition and retention
of subscribers and (2) create a separate ``Other Sports'' category came
under criticism, as did his methodological choice to provide examples
of shows that might fall within the categories.
C. Ringold Survey
The CCG criticized both the Bortz and the Horowitz studies and
presented its own limited survey (Ringold Survey). See Report of Gary
T. Ford and Debra J. Ringold, Trial Ex. 4010 (Ringold WDT).\118\ The
Ringold Survey attempted to establish a value for eligible programs
distantly retransmitted by cable systems in the United States,
segregating Canadian-produced programs comprising the CCG and other
programs included in the Devotional, Program Suppliers, and Sports
categories.
---------------------------------------------------------------------------
\118\ The report of results of the Canadian Survey included
Emeritus Professor Gary Ford as an author, but only Professor
Ringold signed the report; consequently, for simplicity, the Judges
refer to the report as Ringold WDT. Professors Ford and Ringold had
conducted similar surveys since 1996 and Professor Ringold presented
a longitudinal study showing the results from 1996 through 2013. See
Trial Ex. 4011. A longitudinal study analyzes data collected using
the same methodology to ask the same population of respondents the
same question(s) over time. Such studies can prove useful in
evaluating the stability and/or robustness of an estimate. Ringold
WDT at 4-5.
---------------------------------------------------------------------------
Valuation of CCG programming is complicated by the legal
prohibition on retransmission of Canadian programming outside a
geographic zone lying along the U.S. northern border. 17 U.S.C.
111(c)(4). The CCG argued that the relative value of CCG programming
inside its retransmission zone is necessarily diluted when measuring
the relative value of other claimant groups' programming over the
entirety of the United States. See Written Rebuttal Testimony of Lisa
George, Trial Ex. 4007, p. 8 (George WRT). In addition, the CCG argued
that its category is an ``unnatural'' category of programming, because
the Canadian signals include programming compensable in other
categories, viz., the JSC, Program Suppliers, and Devotional
Programming categories.
The CCG commissioned a ``double blind'' \119\ survey of cable
systems sampled from the Form 3 systems that retransmit Canadian
signals distantly. To further guard against response bias, Professors
Ringold and Ford constructed the survey to include questions regarding
the relative values of various categories of programming on
retransmitted Canadian signals as well as retransmitted superstation
and independent station signals.\120\ The Ringold Survey was conducted
by telephone and used a constant sum construct.
---------------------------------------------------------------------------
\119\ Ford and Ringold referred to their survey, conducted by
Target Research Group, as ``double blind'' in that neither the
interviewers nor the respondents were aware of the sponsor of the
survey. Written Direct Testimony of Gary Ford and Debra Ringold,
Trial Ex. 4010 at 7 (Ford/Ringold WDT).
\120\ Drs. Ringold and Ford used responses relating to
superstations and independent stations both to disguise the survey
sponsor and as comparators to substantiate their results.
---------------------------------------------------------------------------
The Ringold Survey differed from both the Bortz and Horowitz
surveys in two significant aspects. Unlike in the Bortz Survey,
interviewers in the Ringold Survey asked respondents to assign relative
values to program categories that included programming on Canadian
signals. Unlike both the Bortz Survey and the Horowitz Survey, Ringold
Survey interviewers asked each respondent to rank programming on only
one retransmitted signal at a time.
The Ringold Survey measured the average relative value of CCG
programming on retransmitted Canadian signals as:
[[Page 3586]]
Table 13--Ringold Survey Results: Relative Value of CCG Programming on Canadian Signals
----------------------------------------------------------------------------------------------------------------
Category 2010 (%) 2011 (%) 2012 (%) 2013 (%)
----------------------------------------------------------------------------------------------------------------
CCG............................................. 61.45 64.17 61.47 56.36
Program Suppliers (U.S.)........................ 11.40 21.11 12.20 21.82
Sports (JSC).................................... 26.67 14.72 24.67 20.91
``Other''....................................... 0.48 0.00 1.67 0.91
----------------------------------------------------------------------------------------------------------------
See Ringold WDT at 15, Table 1.\121\ In other words, the Ringold Survey
results indicated that Canadian-produced programming accounted for
approximately 61%, 64%, 61%, and 56%, respectively, of the value of all
programming shown on surveyed systems' Canadian signals for the years
2010-2013. Ringold WDT, at 5, 11; 15, Table 1. Ringold found that live
professional and college sports were generally valued higher on
independent and superstations than on Canadian signals. Ringold WDT at
12; 16, Table 2; 17, Table 3; see Fig. 4. Ringold also found that
movies and syndicated series were always valued higher on independent
and superstations than on Canadian signals. Ringold WDT at 12, 16,
Table 2; 17, Table 3; see Fig. 5.
---------------------------------------------------------------------------
\121\ The values for the CCG category are the aggregate of
relative values CSOs assigned to Canadian-produced news, public
affairs, religious, and documentary programs (both network and
station-produced); Canadian-produced sports programming; Canadian-
produced series, movies, arts and variety shows, and specials; and
Canadian-produced children's programming.
---------------------------------------------------------------------------
Scaling the relative value of Canadian signals within the Canadian
zone, CCG concluded Canadian signals should command the following
portions of each annual fund.
Table 14--Ringold Survey Results: Relative Value of CCG Programming
Overall
------------------------------------------------------------------------
Base rate fund
Year (%)
------------------------------------------------------------------------
2010.................................................... 5.59
2011.................................................... 5.36
2012.................................................... 5.95
2013.................................................... 6.18
------------------------------------------------------------------------
Written Direct Statement of Canadian Claimants Group at 1.\122\ CCG
does not claim any portion of the overall royalty funds for programming
on Canadian signals that is compensable in the Program Suppliers or
Joint Sports Claimants groups. Id. At the hearing, CCG did not
controvert testimony by SDC's witness, Mr. Sanders that some Canadian
programming is or should be compensable as Devotional Programming. See
3/6/18 Tr. at 2410 (Sanders).
---------------------------------------------------------------------------
\122\ The table recreated here omits the column headed ``3.75%
Fund.'' The Judges consider the 3.75% Fund separately.
---------------------------------------------------------------------------
D. Criticisms of the Survey Instruments
1. Survey Construct
The surveys the parties presented in this proceeding had some
construct similarities. Each of the surveys was directed to CSO
executives who self-identified as the person responsible for carriage
decisions for the cable systems about which the surveyor inquired. All
of the surveys were conducted by telephone \123\ by experienced survey
entities. Each survey inquired of a sample of potential respondents
drawn from the universe of Form 3 cable systems.
---------------------------------------------------------------------------
\123\ Professor Steckel criticized telephone questioning,
contending that the issues were too complex for the respondents to
weigh and analyze over the telephone. See Written Direct Testimony
of Joel Steckel, Trial Ex. 6014, at 36-37 (Steckel WDT). Telephone
surveys have been the norm for allocation proceedings.
---------------------------------------------------------------------------
a. Sampling
Professor Martin Frankel, who was retained by Program Suppliers,
criticized Bortz for including in its sampling Form 3 cable systems
that did not carry a distant signal and not correcting for the
overinclusion. See Amended Rebuttal Testimony of Martin Frankel, Trial
Ex. 6011, at 3 (Frankel AWRT). In fact, Bortz sampled from all Form 3
systems but dropped, i.e., did not interview, systems in the sample
with zero distant signals. See 2/15/18 Tr. at 247 (Trautman). In live
testimony, Professor Frankel submitted that Bortz, while not ``wrong,''
conducted its survey on a ``suboptimal'' sample frame. See 3/6/18 Tr.
at 2267, 2288 (Frankel). Professor Frankel also criticized the Bortz
Survey for disadvantaging cable systems with only PTV, CCG, or PTV and
CCG distant signals by excluding them and ``affording them no value
when producing . . . weighted results.'' Frankel AWRT at 3.
In his amended rebuttal testimony, Professor Frankel corrected for
the suboptimal sampling and for the exclusion of PTV and CCG signals in
the Bortz Survey. Even so, Professor Frankel declined to endorse even
the corrected Bortz results. Id. at 15. Professor Frankel advocated
reliance on the Horowitz Survey, which used his improved sample frame
and included distantly retransmitted PTV and CCG claimant programming.
Id. at 16.
Professor Frederick Conrad, testifying on behalf of CCG, criticized
both the Bortz Survey and the Horowitz Survey on the basis of their
sampling.\124\ See Written Rebuttal Testimony of Frederick Conrad,
Trial Ex. 4003 passim (Conrad WRT). Because so few cable systems
retransmit Canadian stations, the small sample size caused Professor
Conrad to question the validity of the results as they relate to the
CCG. Id. at 4. Further, Bortz excluded from its survey systems whose
only distantly retransmitted signal was Canadian, Public Television, or
some combination of those. Bortz then assigned a value of zero to CCG-
and PTV-only systems, without accounting for the regulatory constraints
limiting retransmission of Canadian signals to a geographic zone in the
northern tier of states. Exclusion of the CCG and PTV programming from
the Bortz Survey resulted in agreement among the parties that the Bortz
results would need an unquantified adjustment to reflect the actual
relative value of CCG and PTV programming.
---------------------------------------------------------------------------
\124\ Professor Conrad criticized the Bortz and Horowitz Surveys
on four bases: Sample size, i.e., the number of participants that
actually carry a distant Canadian signal; assigning a value of zero
to Canadian programming for systems that do not have the option to
carry Canadian signals; incompatibility of programming categories;
and flaws in either survey design or execution. See Written Rebuttal
Testimony of Frederick Conrad, Trial Ex. 4003, passim (Conrad WRT).
---------------------------------------------------------------------------
Professor Conrad recognized that the Horowitz Survey corrected for
this omission by Bortz. Id. at 6. Inclusion of the ``missing'' stations
did not, however, address all of the issues troubling Professor Conrad.
Notably, when Horowitz asked CSOs whose only distantly retransmitted
signal was Canadian, for example, the CSO nevertheless stated the
relative value of the Canadian programming at less than 100%. Id. at 7.
According to Professor Conrad, this purported anomaly suggests a
problem with the construct of the survey or a problem of communicating
the task to either the
[[Page 3587]]
interviewers or the respondents.\125\ Given that Canadian signals
include less than 100% Canadian content, the Judges reject this
particular criticism.
---------------------------------------------------------------------------
\125\ Professor Conrad criticized both surveys for lacking
independent pre-testing to detect confusion or anomalies. 3/5/18 Tr.
at 1969-70 (Conrad).
---------------------------------------------------------------------------
b. Respondents
All three surveys sought to elicit responses from the individual at
each cable system that had primary responsibility for signal carriage
decisions. In the Bortz Survey, the questioners asked several questions
at the outset to establish that they were speaking with the appropriate
individual. See, e.g., Trautman WDT at 14-15.
Testimony at the hearing was in conflict regarding carriage
decision-makers. Horowitz Research, Inc. employed a cable system
executive to screen respondents to assure that they were the
appropriate respondents, viz., the respondents responsible for making
carriage decisions at the system level. See Horowitz WDT at 8. Fact
witnesses disagreed about the level at which carriage decisions are
made. Compare 2/21/18 Tr. at 930 (Burdick) (carriage decisions at
Schurz Communications decentralized to local CSOs) with 2/22/18 Tr.
(Singer) at 1082-84 (carriage decisions made at system level, not at
corporate headquarters), 1144-45 (respondents intimately familiar with
categories and signals they carry). Ms. Sue Ann Hamilton testified that
cable programming decisions \126\ are generally centralized at the
corporate level in an increasingly consolidated cable industry. 3/19/18
Tr. at 4295 (Hamilton). She opined that respondents to the Bortz Survey
were insufficiently ``sophisticated . . . , programming-focused and
experienced'' to understand the categories at issue in this proceeding.
Id. at 4311.
---------------------------------------------------------------------------
\126\ Ms. Hamilton also testified that distant signal
programming was an insignificant consideration in cable systems'
programming decisions. 3/19/18 Tr. at 4306.
---------------------------------------------------------------------------
c. Constant Sum Methodology
All three surveys were structured as ``constant sum'' surveys; that
is, respondents were asked to allocate value among the programming
categories at issue, with the sum of those values to equal 100%. An
increase in valuation of one category must result in a decrease in
value in one or more other categories.
Among the many criticisms of the three surveys,\127\ Professor Joel
Steckel, a witness for Program Suppliers, criticized in general the use
of the constant sum survey structure. See Written Direct Testimony of
Joel Steckel, Trial Ex. 6014, at 34-35 (Steckel WDT). Professor Steckel
criticized Professor Mathiowetz's touting of the suitability of a
constant sum construct in this context. He noted that she cited prior
testimony that relied on academic literature from the 1960s and 1970s.
See Written Rebuttal Testimony of Joel Steckel, Trial Ex. 6015, at 21
(Steckel WRT). Countering the perceived endorsement of constant sum
survey methodology by the CARP,\128\ Professor Steckel cited recent
academic studies that conclude that a measurement based on paired
comparisons, i.e., comparisons across only two categories, out-predict
constant sum surveys by 22 percentage points. Id. at 36 (citations
omitted).
---------------------------------------------------------------------------
\127\ Professor Steckel asserted two standards to which a survey
must conform: Reliability, i.e., the ability to replicate the
survey's results, and validity, i.e., the conclusion that the survey
measures what it purports to measure. See 3/13/18 Tr. at 3269
(Steckel). He opined that neither the Bortz Survey nor Horowitz
Survey measures what it purports to measure nor what the statute
requires the Judges to determine. He concluded that both, therefore,
lack construct validity. See Steckel WRT at 21.
\128\ Professor Mathiowetz did cite multiple royalty allocation
decisions that relied on Bortz surveys. See Written Rebuttal
Testimony of Nancy Mathiowetz, Trial Ex. 1007, at 5-6 (Mathiowetz
WRT). She did not contend those decisions were an endorsement of the
constant sum methodology; rather she cited those decisions as
support for the conclusion that the Bortz Survey addresses the
relevant question of interest in these proceedings. Id.
---------------------------------------------------------------------------
On rebuttal, Professor Steckel reviewed the changes in the Bortz
Survey between the 2004-05 proceeding and the present proceedings.
While he conceded some improvement, he concluded that the changes were
insufficient to bestow construct validity on the Bortz Survey. See
Steckel WRT at 26. Viewing the Horowitz Survey as an augmented Bortz
Survey, Professor Steckel also noted some improvements, but concluded
that those improvements in form were insufficient to reorient the
Horowitz Survey to the question of interest in this proceeding, viz.,
relative value of program categories.\129\
---------------------------------------------------------------------------
\129\ Given the task to choose the lesser of the two evils,
Professor Steckel concluded that the Horowitz Survey was a slightly
better instrument because, inter alia, it included PTV and CCG
stations and programming, it broke out ``other sports'' categories
from those represented by the JSC, and its interviewers did a better
job of reminding respondents of program categories, stations at
issue. Steckel WDT at 38.
---------------------------------------------------------------------------
Professor Mathiowetz endorsed the constant sum survey method used
by Bortz in the present proceeding. Professor Mathiowetz concluded,
however, that the Horowitz Survey did not employ a valid constant sum
construct because of the differences Horowitz introduced as alleged
improvements to the Bortz Survey. See Mathiowetz WRT at 16. Professor
Mathiowetz opined that the Horowitz changes in fact rendered the
Horowitz Survey both unreliable and invalid. Id. at 26. For example,
Professor Mathiowetz opined that Horowitz's inclusion of program
examples and ``such as'' descriptions rendered the questions
misleading. Id. Similarly, incorrect information in program category
descriptions resulted in invalid valuations for the various program
categories. Id. at 17-18. Professor Mathiowetz criticized Horowitz's
creation of an ``Other Sports'' category when no such category is a
part of this proceeding. She faulted Horowitz's failure clearly to
identify noncompensable programming on WGNA. Id. at 19.
In the Bortz Survey, interviewers asked respondents about a maximum
of eight distant signals even if their systems carried more. See Bortz
Survey at 31. Professor Mathiowetz criticized the Horowitz decision to
ask a single respondent to answer on behalf of all distantly
retransmitted signals for the surveyed system, rather than limiting
those to a manageable number. Respondents to the Horowitz Survey were
asked to evaluate from one to ``over fifty'' discrete signals. See
Mathiowetz WRT ] 48. According to Professor Mathiowetz, this inclusion
of so many signals for valuation rendered the survey burdensome and
invalid, as respondents would not or could not make fine distinctions
between the distantly retransmitted program lineups at multiple
systems. Id.
Dr. Jeffery Stec, an economic expert called by Program Suppliers,
performed reliability analyses of the Bortz Survey results by comparing
responses of CSOs for consistency over time. He concluded that the
Bortz Survey responses were not reliable as they were not consistent
over time, notwithstanding Mr. Trautman's assertions that the Bortz
results were consistent over time. See Amended Written Rebuttal
Testimony of Jeffery Stec, Trial Ex. 6016, at 30-34 (Stec AWRT).
2. Survey Content
a. Programming Categories
Surveyors inquired about programming on retransmitted distant
signals using the category designations adopted in the present
proceeding. CSOs, however, do not acquire categories of programs for
retransmission; by law they must acquire entire signals which often
[[Page 3588]]
bundle together multiple categories of programming.\130\
---------------------------------------------------------------------------
\130\ PTV and, to a lesser extent, CCG signals are exceptions to
this bundling phenomenon.
---------------------------------------------------------------------------
Professor Steckel criticized the Bortz and Horowitz surveys for
requiring CSOs, unaided and in the course of a brief telephone survey,
to disaggregate signals and reconfigure the programming from each into
compensable categories. See Steckel WDT at 29-30. Professor Steckel
opined that, because of the perceived complexity of the survey
construct, respondents were compelled to satisfice \131\ with shortcuts
and heuristics to create a defensible answer to the overly complicated
questions. Id. at 31-32; 3/13/18 Tr. at 3298 (Steckel).
---------------------------------------------------------------------------
\131\ Satisfice means ``to choose or adopt the first
satisfactory option that one comes across.'' See www.dictionary.com,
last visited 07/19/2018.
---------------------------------------------------------------------------
More than one witness downplayed Professor Steckel's complexity
criticism, asserting that the survey respondents are experienced
professionals thoroughly familiar with the programming categories
copyright owners utilize in CRB distribution proceedings. See, e.g., 3/
13/18 Tr. at 3176 (Hartman) (CSOs negotiate for linear channels, but
channels fall into categories. ``It's our day-to-day job to . . . know
those, that type of programming.''); 2/22/18 Tr. at 1144-45 (Singer).
Participants proffering survey results as a measure of relative value
also asserted that cable system executives could accurately allocate
program category values by reference to the ``dominant impression'' of
each signal's content or the ``signature programming'' of a given
signal. See 2/15/18 Tr. at 281, 334 (Trautman); 2/22/18 Tr. at 1001
(Singer).
Ms. Sue Ann Hamilton testified that the programming categories
adopted in royalty distribution proceedings are unique and ``quite
different from the industry understanding of what programming typically
falls in a particular programing genre.'' Id. at 10; see 3/19/18 Tr. at
4309, 4312 (Hamilton); Hamilton WRT at 17-18. For example, she
testified that ``most cable operators'' would not recognize that pre-
and post-game interviews and highlight compilation telecasts would fall
into the Program Suppliers category, or that locally produced high
school team sports would fall into the Commercial Television category.
Id. at 11. Other industry witnesses disagreed. See 2/22/18 Tr. at 1046-
47 (Singer) (categories ``straightforward''). Ms. Hamilton further
opined that cable operators were not likely to differentiate between
network and non-network sports telecasts and that migration of live
team sports programming to regional cable networks further complicates
the equation. See Hamilton WRT at 17-18; 3/19/18 Tr. at 4315
(Hamilton).
Dr. Stec gave weight to Ms. Hamilton's testimony. See Stec AWRT at
23-25. According to Dr. Stec, the Horowitz Survey results, gained after
the surveyors provided category descriptions and program examples,
demonstrate the fallacies of the Bortz Survey and its reliance on CSO
executives' familiarity with the program categories. Id. at 27. The
Horowitz category descriptions and examples were also roundly
criticized, however.\132\ Nothing in Dr. Stec's analysis supports his
contention that there is a causal relationship between changes in an
interviewer's category or program descriptions in the two major
surveys, from which Dr. Stec concludes that the Horowitz results are
more valid than the Bortz results.
---------------------------------------------------------------------------
\132\ See discussion at section Sec. III.D.2.b.
---------------------------------------------------------------------------
A related criticism from Professor Conrad was that the categories
about which respondents were questioned were not comparable. Id. at 10-
11. In other words, all programming categories other than CCG and PTV
are characterized by homogeneity in types of program content. The CCG
and PTV categories, on the other hand, are based on program origin and
include programs that span the categories making them, in this context,
``unnatural categories.'' See 3/5/18 Tr. at 1965 (Conrad). Even though
cable systems might retransmit PTV signals, all of which are
compensable entirely from the PTV category, PTV stations might
broadcast children's programming, nationally produced specials or
series, or locally-produced programming. On the other hand, some of the
CCG programs might be allocable to another category but some might
not.\133\
---------------------------------------------------------------------------
\133\ For example, Mr. Trautman acknowledged that the Bortz
Survey did not differentiate by category programming transmitted on
Canadian signals even though some of the programs should be
compensated not in the CCG group, but in other categories. 2/20/18
Tr. at 629 (Trautman).
---------------------------------------------------------------------------
b. Augmentation of Categories
Professor Mathiowetz criticized aspects that distinguish the
Horowitz Survey from the Bortz Survey. Her two most significant
criticisms related to Mr. Horowitz's use of program examples and the
creation of an ``Other Sports'' category.\134\
---------------------------------------------------------------------------
\134\ Professor Mathiowetz also opined that the Horowitz Survey
was not a valid constant sum survey because some of the Horowitz
respondents, the PTV-only and CCG-only systems, could be asked about
only one category of programming, and thus not requiring a sum of
percentages at all. 2/20/18 Tr. at 511 (Mathiowetz). While correct
as to PTV-only systems, this opinion disregards the fact that
Canadian stations transmit both CCG-compensable programs and, for
example, Devotional programs compensable from the SDC royalty funds.
---------------------------------------------------------------------------
Professor Mathiowetz asserted that a questioner's volunteering of
examples tends to bias survey results. See 2/20/18 Tr. at 699
(Mathiowetz); but see 3/5/18 Tr. at 1967-68 (Conrad) (examples can hurt
or help or have no effect on responses). According to Professor
Mathiowetz, Respondents assume a questioner has valid information or
knows something that is important to the survey outcome. See 2/20/18
Tr. at 699 (Mathiowetz). Thus, even a knowledgeable respondent might be
influenced by a questioner's prompting. As she noted, in a relative
valuation, a shift in one category affects potentially the value of
every other category. Id. at 727.
Furthermore, according to Professor Mathiowetz, some of the
examples used in the Horowitz Survey were simply erroneous. 2/20/18 Tr.
700 (Mathiowetz). Use of erroneous examples illustrated Professor
Mathiowetz's criticism of Mr. Horowitz's creation of an ``Other
Sports'' category. In an effort to differentiate live team college and
professional sports, i.e., the programs to be compensated from JSC's
share of the royalty funds, interviewers introduced ``other sports
programming.'' For WGNA-only systems, the category description ended
with ``Examples include horse racing.'' Id. at 27. According to
Professor Mathiowetz, in 2013, WGNA carried only a single horse race.
Accord Trautman WRT 20-21.\135\ For WGNA and PTV systems, the
interviewers prompted, ``Examples include NASCAR auto races,
professional wrestling, and figure skating broadcasts.'' Horowitz WDT
(App. A) at 26. WGNA retransmitted no programming fitting the
description of the examples. 2/20/18 Tr. at 703 (Mathiowetz). Professor
Mathiowetz also expressed doubt that non-JSC sports broadcasts
accounted for sufficient distantly retransmitted airtime to warrant a
separate category, even for survey inquiry purposes. Id. at 702. As she
noted in another context, in a constant sum survey, variation in one
[[Page 3589]]
category necessarily effects the relative value of other categories.
See 2/20/18 Tr. at 727 (Mathiowetz).
---------------------------------------------------------------------------
\135\ Mr. Trautman further argued that cable systems retransmit
a ``substantial amount'' of other sports programming, most of which
is non-compensable under the section 111 license. Trautman WRT at
16. He contended that, notwithstanding the examples of rare
compensable sports broadcasts, CSO respondents likely confused the
volume of non-compensable sports programs as belonging in the
unfamiliar Other Sports category inserted by Mr. Horowitz. Id.
---------------------------------------------------------------------------
Professor Conrad agreed with the criticism of enumerating examples
of ``other sports'' or any program category. 3/5/18 Tr. at
1967(Conrad). According to Professor Conrad, citing examples might cut
either way. If the example is typical of the category, then citing it
will have no effect. An atypical example might help a respondent
``think outside the box'' and trigger a broader, more accurate
response. For other respondents, however, an atypical example might
narrow focus to incidents closely related to the particular example and
therefore confine the respondent's thinking too narrowly. Id. at 1968.
Professor Conrad cautioned that a ``rare example'' will bias downward
the counts for more typical choices. Id.
Mr. Horowitz assigned all ``Other Sports'' points to Program
Suppliers. See Horowitz WDT at 3, 5. This allocation ignores the
possibility that a portion of ``other sports'' might be attributable to
CTV. Without evidence to support the assignment of all ``other sports''
value to Program Suppliers, the category becomes even more problematic.
c. Value Measurement
Dr. Jeffery Stec, criticized the Bortz Survey on several grounds.
See Stec AWRT at 11-12. His primary criticism is that the Bortz Survey
measures, at best, only a CSO's willingness to pay. Id. at 17. Dr. Stec
disputes the assertion by Mr. Trautman and Bortz that CSO respondents
are familiar with the rates charged for programming and that their
responses are, therefore, a reflection of the ``supply side.'' Id. at
18; see 3/13/18 Tr. at 3432-50 (Stec). Dr. Stec contends that a CSO's
willingness to pay is also influenced by its own market factors, e.g.,
local market demand or competition from other CSOs. Id. at 19-20.
According to Dr. Stec, relative willingness to pay is not the same as
relative market value. Id. at 22.
An underlying assumption in each survey is that cost is the
equivalent of value. Economists do not measure such a subjective trait
as value. According to Professor Steckel, value, in an economic sense,
can only be surmised by reference to external indicators of value.
Steckel WDT at 36-40; but see Mathiowetz WRT ]] 4, 11-12 (Steckel
incorrect; CARP precedent accepted Bortz as measure of relative market
value). Professor Steckel opined that resource allocation does not
equate to value and that marketplace value is measured by a CSO's
return on investment. Steckel WDT at 21. Because of the cable
television market structure, i.e., program acquisition in a bundle,
CSOs are unable to assess market returns by program category. Id.
Professor Steckel proposed--as a possible alternative to surveying CSO
executives' best guesses about supply-side relative values--a survey of
demand-side program consumers. Steckel WDT at 40-41 (``customers are
the best judges of what customers want, value, and will do.'').
Alternatively, Professor Steckel recommended relying on viewership to
establish relative values. See Steckel WRT at 4.
Mr. Horowitz also criticized Bortz for asking a cost question,
opining that cost is not the equivalent of value. Horowitz WDT at 7. He
testified that the Bortz Survey erroneously mixed the concepts of value
and cost. 3/16/18 Tr. at 4146-47 (Horowitz). Mr. Horowitz contended
that by asking about expense in a warmup question, Bortz conflated the
concepts of cost and value.\136\ Mr. Horowitz noted that the Bortz
Survey did not define ``relative value'' and made no mention of
subscriber attraction and retention.\137\ Id. Further, Mr. Horowitz
criticized the form of the budget allocation (constant sum) question as
ambiguous. The question asked how much the respondent's system ``would
have spent'' during the relevant year. See, e.g., Bortz Survey at B-5
(Question 4a.). Mr. Horowitz maintains this sentence structure is open
to interpretation. Id. Treatment of PTV, CCG, and WGNA.
---------------------------------------------------------------------------
\136\ Question 3 of the Bortz Survey asked respondents as a
warmup question to rank how ``expensive'' it would be to acquire the
programming in each category if the system had to acquire the
programming ``in the marketplace.'' See, e.g., Bortz Survey at B-4.
\137\ See supra note 110 and accompanying text.
---------------------------------------------------------------------------
d. PTV and Canadian Measures
Various parties criticized the treatment of PTV and CCG claimant
groups in almost every relative value measure, including the surveys.
As noted, Ms. McLaughlin and Dr. Blackburn criticized both the survey
and regression methodologies, but applied their ``changed
circumstances'' \138\ analysis to estimate the relative value of PTV
programming and PTV's relative claim to royalties deposited in the
Basic Fund.\139\ Professor Conrad opined that it was a ``strange
practice'' to assign a value of zero to Canadian programming for
respondents who did not retransmit any Canadian signals. See 3/5/18 Tr.
at 1964-65 (Conrad). He testified that the better practice would have
been to characterize Canadian programming for non-CCG signals as
``missing data'' and to impute values from data actually collected. Id.
at 1965.
---------------------------------------------------------------------------
\138\ See infra section 200E;VI. McLaughlin and Blackburn used
the Judges' 2004-05 distribution determination as their starting
point. See Testimony of Linda McLaughlin & David Blackburn, Trial
Ex. 3012 at 9 (McLaughlin/Blackburn WDT).
\139\ PTV does not participate in the 3.75% Fund or the Syndex
Fund. McLaughlin and Blackburn were careful, therefore, to relate
their valuations to the Basic Fund. See McLaughlin/Blackburn WDT,
passim.
---------------------------------------------------------------------------
Mr. Trautman acknowledged a slight participation bias in the Bortz
Survey, but testified that the number of PTV-only and CCG-only cable
systems (approximately 60 systems in the aggregate) was insignificant
and that including them would have made little difference in his
results. See 2/15/18 Tr. at 507 (Trautman). The triers of fact for
these royalty allocation proceedings have long recognized that the
results of the survey methodology employed by Bortz exhibited a bias
against PTV and Canadian claimants. The Judges in the 2004-04
proceeding acknowledged that the participation bias affecting results
for both PTV and CCG was troubling, but that
[i]t would be inappropriate to overstate the impact of this problem.
No one in this proceeding maintains that it substantially affects
more than a small portion of the total royalty pool . . . . Nor has
it been shown that the Bortz survey's remaining non-PTV-Canadian
estimates were thrown outside the parameters of their respective
confidence intervals solely because of this problem. That is, the
PTV-Canadian problem does not substantially affect any of the
remaining categories in some disproportionate way.
2004-05 Distribution Order, 75 FR at 57067. Nonetheless, on rebuttal,
Mr. Trautman adjusted the Bortz Survey results based on the McLaughlin/
Blackburn testimony that supported a greater valuation of the PTV and
CCG claimant groups and by referring to the Horowitz Survey responses
to further adjust the augmentation proposed by McLaughlin/Blackburn.
See Trautman WRT at 47-48; 2/20/18 Tr. at 523-24 (Trautman).\140\
---------------------------------------------------------------------------
\140\ Mr. Trautman made the further adjustment by reference to
the Horowitz Survey actual responses from PTV-only cable systems.
See 2/2/0/18 Tr. at 525-26 (Trautman).
---------------------------------------------------------------------------
Further, in the present proceeding, the Judges have the advantage
of competing surveys such as the Ringold Survey commissioned by the CCG
that dealt with PTV and Canadian programming, and other methodologies
that did not suffer from the participation bias that discounts the
Bortz Survey results.
[[Page 3590]]
e. Impact of WGNA
Participants in the present proceeding wrangled with valuation of
WGN programming distantly retransmitted on the WGN ``Superstation,''
WGN America (WGNA).\141\ WGNA did not offer for retransmission, a
program lineup identical to the one broadcast locally on WGN. Only
those programs carried simultaneously on WGN and WGNA are compensable
under the section 111 license. WGNA substituted syndicated or
devotional programming for elements of the WGN signal. In the 2004-05
proceeding, the Judges criticized the Bortz Survey for failing to
measure and value accurately the compensable programs retransmitted on
WGNA. In fact, Bortz acknowledged this failure to differentiate
compensable from noncompensable programs on WGNA and conceded that the
survey results for Program Suppliers (the category most frequently
retransmitted on WGNA) and Devotional Programming should be considered
the ceiling for those categories. See 75 FR at 57067. In the 2004-05
determination, the Judges cited repeatedly the lack of record evidence
regarding the quantitative adjustment for over-valuing noncompensable
programming retransmitted on WGNA. See, e.g., id.
---------------------------------------------------------------------------
\141\ According to the Bortz Survey, approximately three-fourths
of cable systems retransmitting distant signals retransmitted WGNA.
Bortz Survey at 25.
---------------------------------------------------------------------------
In the present proceeding, Bortz employed a separate questionnaire
form to survey cable systems that retransmitted only the WGNA signal.
Bortz created a WGNA programming list that identified compensable
programming and provided the list to survey respondents before
continuing with the questions. See Bortz Survey at 30. Bortz continued
to use its standard questionnaire for cable systems that carried WGNA
along with other distant signals. See Bortz Survey at B-2 (``This
Appendix provides examples of the survey instruments used to interview
respondents at systems that carried distant signals in addition to or
other than WGN during the relevant survey year.'') (emphasis added).
The Horowitz Survey's questions relating to WGNA directed
respondents not to assign any value to noncompensable programming,
describing noncompensable programs as ``substituted for WGN's blacked
out programming.'' Mr. Trautman opined that the ``blacked out''
instruction in the Horowitz Survey was meaningless because respondents
would ``have no reason to be aware of which [programming is
substituted].'' See 2/20/18 Tr. at 535 (Horowitz).
WGNA was the most widely-retransmitted station in the U.S. during
the period at issue in this proceeding.\142\ In the 2010-2013 timeframe
WGNA was retransmitted by approximately three-fourths of the cable
systems retransmitting distant signals and reached over 41 million
distant subscribers. See Wecker Report, ] 23; Bortz Survey at 25. Bortz
attempted to improve on the measure of WGNA retransmissions criticized
in the 2004-05 proceeding. Horowitz also addressed the issue from the
2004-05 Bortz survey, but with less specificity than Bortz achieved in
its 2010-13 survey for WGNA-only cable systems.
---------------------------------------------------------------------------
\142\ For purposes of the royalty years at issue in this
proceeding, WGNA as a superstation cast a long shadow on valuation
methodologies. Following the period at issue in the present
proceeding, WGNA began the process of converting to a cable network,
which would, in time, remove it from consideration in royalty
allocation proceedings.
---------------------------------------------------------------------------
E. Conclusions Regarding Surveys
Surveys of cable system programming executives provide insight into
the value those executives assign to the categories of programs
eligible to receive a portion of the retransmission royalties cable
systems deposit with the Copyright Office. No participant in any
television royalty proceeding has developed a method to measure the
actual market value of a content creator's product as bundled into a
broadcast signal. Indeed, the value of a content creator's product will
vary depending on the nature of the bundle and the buyer of that
bundle; every creator and every viewer is likely to place a different
value on every product. As buyers of the broadcast signals, CSO
executives' valuations reflect their conclusions regarding the extent
to which the category of programming contributes to the return on that
investment; i.e., helps the cable system attract and retain
subscribers.\143\
---------------------------------------------------------------------------
\143\ Subscribers are a major source of revenue for cable
systems; consequently, CSOs focus on retention of subscribers. In
some instances, a CSO might relicense a signal with less viewed,
niche programming to avoid losing a subscriber to a competing
system. See 3/19/18 Tr. at 4297-99 (Hamilton).
---------------------------------------------------------------------------
Surveys of CSO executives admittedly measure only the demand side
of a value calculation. Several witnesses in the present proceeding
criticized the focus only on a demand-side valuation. See, e.g., 3/13/
18 Tr. at 3433 (Stec) As noted in the discussion of relative value in
allocation proceedings, the Judges accept that there are valid reasons
for focusing on the demand side in this proceeding. See 1998-99
Librarian Order, 69 FR at 3615 (in relevant hypothetical marketplace,
supply of broadcast programming is fixed and does not determine value).
Indeed, in the present proceeding, both the regression and viewership
methodologies also attempt to measure value from a demand-side
perspective: Regressions by measuring various demand variables, such as
subscribers, and the viewership study by measuring consumption of
programming by viewers. In the current regulated market structure,
CSOs' purchase of broadcast signals as bundles reflects a derived
demand, one step removed from the supply and demand measured at the
station acquisition level. CSOs deposit royalties based on distant
signal equivalents (or a minimum fee) that is divorced from the
individual program content copyright owner. In this context, the
buyers' demand, as measured primarily by revealed preferences, is the
only equitable measure of compensation to copyright owners.
Bortz, Horowitz, and Ringold used a constant sum construct, asking
respondents to value program categories by percentages and requiring
that their allocations totaled 100%. The Bortz Survey muddled the
concepts of cost and value by means of its warm-up question that asked
survey respondents to rank program categories by how expensive it would
have been for the CSO to acquire them. This may have injected some
confusion into the respondent's estimation of relative value. The
question of interest in this proceeding is not cost; rather, it is
relative value. It is unclear how, if at all, the injection of a cost
question furthers that inquiry.
Further, as in past surveys Bortz did not survey cable systems that
carried only PTV and/or CCG signals; those systems thus had no
opportunity to allocate any of their hypothetical budgets to PTV or CCG
programming. See id. The Horowitz Survey included PTV- and CCG-only
systems, but threw a curve ball by including an ``Other Sports''
category when there may have been little to no ``other sports''
content, and assigning the entire value of that category to Program
Suppliers. Horowitz also may have introduced bias by providing program
examples for some of the program categories. The examples, at best,
would have had no effect on the results; but at worst, could have
skewed results unnecessarily.
For all of the reasons highlighted by critics of the survey
valuation method, the Judges agree that surveys are not a perfect
measure. Nonetheless, survey results have been cited in prior royalty
distribution proceedings as a generally acceptable starting point to
measure
[[Page 3591]]
relative program category value. Previous allocation determinations
have relied heavily and almost exclusively on Bortz surveys. That
reliance serves as precedent for the current Judges.\144\ Adoption of a
methodological precedent does not, however, preclude the Judges'
consideration of current evidence.\145\ In the present proceeding, the
Judges have three CSO surveys to consider. The methodological precedent
thus gives rise to additional evidence to guide the Judges' treatment
of the survey methodology. Notwithstanding the differences in approach,
the results derived from the Bortz Survey and the Horowitz Survey are
compatible. Further, the relative valuations of CSO executives do not
vary wildly from the valuations derived from participants' regression
analyses.
---------------------------------------------------------------------------
\144\ In the 1998-99 CARP determination, the Panel concluded
that the Bortz Survey was the most ``robust'' and ``powerfully and
reliably predictive'' model for determining relative value . . .''
for all categories except PTV, Canadian Programming, and Music
Claimants. Report of the Copyright Arbitration Royalty Panel to the
Librarian of Congress, Docket No. 2001-8 CARP CD 98-99, at 31 (Oct.
21, 2003) (1998-99 CARP Report); see also 1998-99 Librarian Order,
69 FR at 3609. For PTV, the Panel acknowledged the inherent bias
against PTV in the Bortz Survey, but found the changed circumstances
and fee-generation evidence proffered by PTV to be unpersuasive and
declined to increase the PTV allocation percentage from the 1990-92
determination. Id. at 3616.
\145\ For Canadian Claimants, the CARP had no Bortz results so
it used a fee-generation methodology. Id. at 3618. In the 2000-03
determination involving only the Canadian Claimants, the Judges
distinguished the precedential mandate of a fee-generation
methodology and applicable changed circumstances evidence. See 2000-
03 Distribution Order, 75 FR at 26807.
---------------------------------------------------------------------------
The Judges conclude that the allocation measures resulting from the
Horowitz Survey, with adjustments, are the survey results that most
closely reflect the relative value of the agreed categories of
programming in the hypothetical, unregulated market. Regardless of
proffered evidence to the contrary, the Judges find that the surveyed
cable system executives were sufficiently familiar with the compensable
content on the signals their respective systems retransmit.\146\
---------------------------------------------------------------------------
\146\ Further, the categories endorsed by the Judges in the
present proceeding have not changed for decades, giving CSOs time to
acquaint themselves fully with the programming comprising each
agreed category, whether or not they routinely agree with the
programming characterizations at issue in these proceedings. The
Judges do not gainsay that there have been changes in CSO personnel
over the years, but it is nonetheless not unreasonable to think that
even with changes in personnel, the CSOs have maintained an
institutional memory of the requirements of these proceedings.
---------------------------------------------------------------------------
The doubly regulated nature of compensable Canadian programming
complicates assignment of a value to that category. The clarity of the
Ringold Survey, with its comparisons to superstations and independent
stations, establishes the relative value of Canadian and non-Canadian
programming on Canadian signals to cable systems retransmitting within
the Canadian zone of the U.S. The Ringold Survey takes the relative
values of Canadian programming on Canadian signals to cable operators
that retransmit them within the Canadian zone. The CCG did not provide
any means of converting those results into a royalty share for the CCG
category (or any other program category). The Ringold survey is thus of
minimal assistance to the Judges.
Horowitz did not exclude from its sample systems that distantly
carried only PTV and/or Canadian signals. The Judges conclude that
Horowitz's use of examples to ``aid'' respondents, while flawed, was
not likely to skew significantly results in any of the established
categories. Horowitz. Horowitz's inclusion of Other Sports created a
value where none, or next to none, existed and allocated all Other
Sports value to Program Suppliers.
For all the reasons described above, particularly the acknowledged
systematic bias against PTV and CCG programming, the Judges accord
relatively less weight to the ``Augmented'' Bortz Survey. On balance,
the Judges find the Horowitz Survey results to be more reflective of
CSOs actual valuations of the program categories defined by agreement
and adopted in this proceeding. However, the Judges cannot accept
allocation of 100% of the Other Sports relative value to Program
Suppliers. For that reason, the Judges conclude that the most
appropriate treatment of the Other Sports ``points'' is to reallocate
them in proportion to the relative values established outside the Other
Sports category. The Judges' calculations are illustrated in Table
15.\147\
---------------------------------------------------------------------------
\147\ For example, for 2010, eliminating the relative value of
Other Sports from the 100% constant sum leaves an allocation of
93.23% of the total assessed value. Recasting that 93.23% as the
whole, the 3.78% relative value assigned to Devotional programming
in 2010 would translate to 3.52% (3.78% of 3.78 x 93.23 = 100x; x =
3.52).
Table 15--Horowitz Survey Results After Reallocating ``Other Sports'' to Remaining Categories
----------------------------------------------------------------------------------------------------------------
2010 (%) 2011 (%) 2012 (%) 2013 (%)
----------------------------------------------------------------------------------------------------------------
CTV............................................. 13.28 14.41 17.28 10.30
Program Suppliers............................... 40.15 32.50 30.90 30.94
JSC............................................. 34.26 30.41 28.03 38.10
SDC............................................. 4.05 6.64 6.31 3.76
PTV............................................. 8.25 14.92 16.54 16.62
CCG............................................. 0.01 1.12 0.96 0.38
----------------------------------------------------------------------------------------------------------------
With regard to the ultimate question of interest in the present
proceeding, the Judges conclude that survey results offer one
acceptable measure of relative value, particularly for Sports, Program
Suppliers, Commercial TV, and Devotional programming. With regard to
PTV and Canadian programming, adjustments resulting from the
McLaughlin/Blackburn evidence and the Ringold Survey assure a
reasonable relative value of PTV and Canadian signals, respectively.
Considering all of the evidence presented in this proceeding, the
Judges conclude that the constant sum survey methodology, with
adjustments, provides relevant information relating to the relative
value for each of the six categories remaining at issue. Considering
the more persuasive regression analyses, however, the Judges afford
less evidentiary power to the values derived from these adjusted survey
results. The Judges conclude that Dr. Crawford's first (duplicate
minutes) regression analysis is a stronger base on which to make the
category allocation determination.
IV. Viewership Measurement
Program Suppliers, unique among all participants in this
proceeding, proposed an allocation methodology based on the relative
amount of aggregate viewing of the programs in each of the agreed
program categories.
[[Page 3592]]
They presented this methodology through the report and testimony of
economist Dr. Jeffrey Gray.\148\
---------------------------------------------------------------------------
\148\ Dr. Gray also performed an analysis of the relative
``volume'' (i.e., total number of minutes) of the different
categories of programming, which he described as ``useful'' but not
``sufficient'' information concerning the relative value of
programming. See Corrected Amended Direct Testimony of Jeffrey S.
Gray, Ph.D., Trial Ex. 6036, ]] 17-18, 32-34 (Gray CAWDT); 3/14/18
Tr. at 3696-97 (Gray); 3/15/18 Tr. at 3834-36 (Gray). As Dr. Gray
himself conceded that his volume analysis was an insufficient basis
for determining relative value of programming, the Judges will not
rely on it. See also Written Rebuttal Testimony of Dr. Mark A.
Israel, Trial Ex. 1087, ] 38 (Israel WRT) (``measures of volume do
not translate directly into value''). The Judges need not consider,
therefore, criticisms concerning the accuracy of Dr. Gray's volume
analysis. See Analysis of Written Direct Testimony of Jeffrey S.
Gray, Ph.D., Trial Ex. 1089, at ]] 11-17 (Wecker Report); 2/22/18
Tr. at 1169 (Harvey); Written Rebuttal Testimony of Christopher J.
Bennett, Trial Ex. 2007, ]] 36-43 (Bennett WRT); 3/1/18 Tr. at 1861-
64 (Bennett).
---------------------------------------------------------------------------
A. Viewership as a Measure of Value
Dr. Gray posited a hypothetical market structure divided into a
primary market and a secondary market. In the primary market
broadcasters would purchase from copyright owners the right to
broadcast programs in their local market (as is currently the case) and
would at the same time obtain the right to retransmit the programs into
distant markets. In the secondary market the broadcasters would sell
their entire signal to cable operators, most likely as part of
retransmission consent negotiations. In the hypothetical primary market
the broadcaster would pay the copyright owner both a royalty to
broadcast the program in the local market and a surcharge for the right
to retransmit each program into distant markets. The broadcaster would
recoup that surcharge as part of its transaction with the cable
operator in the secondary market. See 3/14/18 Tr. at 3682-84, 3779-81
(Gray); Hamilton WDT at 14.
Dr. Gray stated that ``[i]t is axiomatic that consumers subscribe
to a CSO to watch the programming made available via their
subscriptions'' and that ``[t]he more programming a subscriber watches,
the happier the subscriber is, and the more likely she will continue to
subscribe, all else equal.'' Gray CAWDT ] 13. He concluded, therefore,
that ``a measure of the happiness, or `utility,' an individual
subscriber gets from a specific program is the number of minutes that
subscriber spent viewing the program offered to him or her by the CSO''
and ``[a] measure of the utility all subscribers get, in total, from a
specific program is the total level of subscriber viewing of the
program.'' Id.
Applying this economic principle to the hypothetical market, Dr.
Gray opined that expected viewing in the distant market would determine
the value of the programming in the distant market. See 3/14/18 Tr. at
3684-85, 3873-74. Program Suppliers assert that actual and projected
subscriber viewing information would be critical to negotiations
between cable operators and broadcasters for the right to retransmit
broadcast signals in an unregulated market. See PS PFF ] 17; Hamilton
WDT at 14; 3/19/18 Tr. at 4317-19 (Hamilton). Consequently, Program
Suppliers argue that subscriber viewing information is the most
reasonable metric for determining relative market value. See PS PFF ]
18; Hamilton WDT at 14-15; 3/19/18 Tr. at 4317-19 (Hamilton); 3/14/18
Tr. at 3822-23, 3873-74 (Gray).
B. Implementation of the Viewing Study
In the broadest sense, Dr. Gray's methodology for determining the
relative value of programming in the various program categories was to
assign all compensable distantly retransmitted programs on a sample of
stations to appropriate program categories, aggregate the quarter hours
of expected viewing for every program in each category, and divide the
total number of expected quarter hours of viewing for each program
category by the sum of expected quarter hours of viewing for all
categories. See Gray CAWDT ] 22; 3/14/18 Tr. at 3684-85, 3689-90
(Gray).
To accomplish this, Program Suppliers obtained, at Dr. Gray's
direction, data on cable systems and retransmitted television signals
from Cable Data Corporation (CDC),\149\ television programming data
from Gracenote,\150\ program logs for Canadian television stations from
the Canadian Radio-television and Telecommunications Commission
(CRTC),\151\ and viewing data from Nielsen's National People Meter
(NPM) database.\152\ See 3/14/18 Tr. at 3685-88 (Gray). Due to cost
considerations, Dr. Gray created a sample of approximately 150
distantly retransmitted stations for each year and instructed Program
Suppliers to obtain program and viewership data only for those stations
included in his sample. See Gray CAWDT at 24 App. B; 3/14/18 Tr. at
3686-89 (Gray).
---------------------------------------------------------------------------
\149\ CDC data is a compilation of information provided by cable
systems to the Copyright Office on their semi-annual statements of
account (SOAs). It includes information about the number of distant
signals that each cable system carries, the number of subscribers
receiving each distant signal, and the amount of royalties paid. See
Gray CAWDT ] 28; Martin WDT at 5. From this information, CDC
provided, inter alia, an analysis of which counties fall within a
television station's local service area. See Martin WDT at 5-6.
\150\ Gracenote (formerly Tribune) provides a compilation of
information about each television program airing throughout each
day, including the station on which the program aired; whether the
program was local, network or syndicated; the program and episode
titles; and the type of program. See Gray CAWDT ] 27; 3/14/18 Tr. at
3686-87 (Gray).
\151\ The CRTC program logs include station call signs, program
title, actual starting and ending time, and country of origin for
each program broadcast on Canadian television stations. Dr. Gray
used them to determine the country of origin of programs broadcast
on Canadian stations, since U.S.-origin programs are excluded from
the Canadian Claimant category. See Gray CAWDT ] 29.
\152\ A ``people meter'' is a device attached to a television
set that passively detects the channel to which the television is
tuned, and includes a means for each household member to identify
him- or herself as the person watching the TV. The NPM database is
derived from a national sample of households equipped with people
meters and is used for measuring national broadcast and cable
networks. See Direct Testimony of Paul B. Lindstrom, Trial Ex. 6017,
at 4 (Lindstrom WDT); 3/14/18 Tr. at 3496-97, 3505-07 (Lindstrom).
---------------------------------------------------------------------------
Dr. Gray did not calculate viewing shares directly from the Nielsen
viewing data. Instead, he used the Nielsen data as inputs to a
regression algorithm that permitted him to calculate expected distant
viewing for each program in each quarter-hour throughout each year
based on a number of independent variables including what Dr. Gray
described as ``a measure of local ratings.'' See Gray CAWDT ]] 36-38;
3/14/18 Tr. at 3692 (Gray).\153\ Dr. Gray stated that he employed
regression to compensate for the high incidence of non-recorded viewing
in the Nielsen data, as well as instances where viewing data were
missing. Id. at 3690-91. Regression analysis allowed Dr. Gray to
estimate positive viewing even in instances where there was zero
observed viewing in the Nielsen data, by increasing low estimates and
decreasing high estimates. Dr. Gray described this as ``data
smoothing,'' and opined that ``[i]t's a desirable outcome in general
when estimating based upon other estimates, in particular.'' Id. at
3691. In addition, regression allowed Dr. Gray to ``fill in the
blanks'' where Nielsen data was missing. Id.
---------------------------------------------------------------------------
\153\ The other independent variables include the time of day
that the program aired and the program type. See 3/14/18 Tr. at 3692
(Gray).
---------------------------------------------------------------------------
Based on his regression analysis Dr. Gray derived the following
viewing shares:
[[Page 3593]]
Table 16--Gray Viewing Shares
----------------------------------------------------------------------------------------------------------------
Royalty share
Claimant ---------------------------------------------------------------
2010 (%) 2011 (%) 2012 (%) 2013 (%)
----------------------------------------------------------------------------------------------------------------
Canadian Claimants.............................. 1.96 3.93 3.58 5.16
Commercial Television........................... 15.83 12.06 15.48 10.61
Devotionals..................................... 1.18 2.44 1.07 1.10
Program Suppliers............................... 50.94 49.92 36.17 45.09
Public Television............................... 27.96 29.09 41.64 33.29
JSC............................................. 2.13 2.57 2.06 4.76
---------------------------------------------------------------
Total....................................... 100 100 100 100
----------------------------------------------------------------------------------------------------------------
Gray CAWDT ] 38, Table 2.
Program suppliers propose that Dr. Gray's viewing shares serve as
one end of a range of reasonable royalty allocations (the other end
being determined by the Horowitz survey). PS PFF ] 355.
C. Criticism of Dr. Gray's Viewing Study
Program suppliers' proposed use of Dr. Gray's viewing analysis as a
basis for allocating royalty shares was roundly criticized by nearly
all other participants through their respective experts. The criticism
ranged from general disagreement with the underlying premise that
viewership is an appropriate measure of relative value, to specific
critiques of how Dr. Gray executed his study.
1. Viewership Not an Appropriate Measure
Several economists testified that viewership is not an appropriate
measure of relative value, at least when apportioning value among
different program types.\154\ See, e.g., Written Direct Testimony of
Michelle Connolly, Trial Ex. 1005, ] 33, and citations to designated
prior testimony therein (Connolly WDT); Israel WRT ] 42; see also 3/7/
18 Tr. at 2474 (McLaughlin) (``We can look at viewing, which I don't
see as a measure of value itself . . . .''). For example, Dr. Mark
Israel, an economist testifying for the JSC, opined that Dr. Gray's
viewing analysis ``provides no reliable basis for determining the
relative valuation'' of the agreed categories of programs, primarily
because ``it treats all viewing minutes as the same and thus does not
account for the fact that minutes of different types of programming
have different values.'' Israel WRT ] 42. Dr. Israel argues that it is
not valid to treat all minutes of viewing equally without considering
the number of minutes of each type of content that is available. ``If
the same number of minutes of all types of content were available, then
the total amount of each that viewers choose to consume could indicate
their relative value. But given the smaller number of available minutes
of Sports programming, one cannot support such a conclusion.'' Id.
---------------------------------------------------------------------------
\154\ Dr. Erdem, an economist testifying on behalf of the SDC,
conceded that, in past proceedings, he had found viewership to be a
reasonable basis for apportioning royalties among claimants within
the same program category. See 3/8/18 Tr. at 2791-93 (Erdem); accord
Amended Written Direct Testimony of John S. Sanders, Trial Ex. 5001,
at 22.
---------------------------------------------------------------------------
Professor Crawford, an expert witness for CTV, sought to
demonstrate the lack of a one-to-one correlation between viewing
minutes and relative value by examining the affiliate fees cable
operators pay in an unregulated market to carry cable channels with
different types of content. His analysis showed that cable systems pay
far more for sports content than non-sports content with the same level
of viewership. See Written Rebuttal Testimony of Gregory S. Crawford,
Ph.D., Trial Ex. 2005, ] 36 & Fig. 1 (Crawford WRT).
Dr. Israel posited that many viewers may choose to view a given
category of programming only as a second choice because their first
choice is not available. See Israel WRT ] 42. Stated differently, a raw
viewing measurement conveys no information about the intensity of the
viewers' preferences for particular types of programming. See Connolly
WDT ] 29. In its pursuit of greater subscription revenues, ``the
perceived intensity of subscriber preferences'' would be a key
consideration for cable operators. Id. ]] 29-30.
Several economists found Dr. Gray's focus on subscribers' viewing
patterns to be misplaced because it is cable operators, not
subscribers, who pay for programming to fill their channel lineups.
See, e.g., Israel WRT ] 43; Written Rebuttal Testimony of Matthew Shum,
Trial Ex. 4004, ] 7 (Shum WRT). ``Naturally, the value of distant
signals to CSOs derive [sic] in part from the value that existing and
potential subscribers place on them. . . . Nevertheless, as a
principle, the relative market values for distant signal programming
depend on the CSOs' valuations of the programming, and not on
subscribers' valuations. Shum WRT ] 7. According to CCG expert
Professor Shum, viewing is, at best, ``a measure of subscribers'
valuations'' rather than CSOs'. Id. ] 8.
Dr. Gray's critics assert that viewership is not a primary
consideration for cable operators. A cable operator's goal in selecting
distant signals is to grow subscriber revenue by attracting new
subscribers, retaining existing subscribers, and increasing
subscription fees. See Connolly WDT ]] 29, 31-32. Cable operators seek
to increase profits by offering bundles of channels that will appeal to
subscribers with varying tastes, including tastes for niche
programming. See Shum WRT ]] 10-11; Connolly WDT ]] 31-32. According to
JSC expert Professor Connolly, ``the economics of bundling suggests
that the most profitable addition to a cable system's programming is
for content that is negatively correlated with content already offered
by the cable system[,]'' thus, ``in the context of the economic value
of individual programming within a bundle to a CSO, neither simple
viewership data nor volume of programming is an appropriate metric for
the relative market value of programming on distant signals.'' Connolly
WDT ]] 32, 31; accord Crawford CWDT ] 7 (``channels that appeal to
niche tastes are more likely to increase cable operator profitability
due to the likelihood that household tastes for such programming are
negatively correlated with tastes for other components of cable
bundles''). As Professor Shum explained:
[N]iche programming, which may have small viewership numbers, may
actually have higher incremental value for CSOs relative to mass
appeal programs with larger viewerships. . . . While this may seem
paradoxical, the reason is that many mass appeal programs (e.g.,
gameshows or sitcom
[[Page 3594]]
reruns) are close substitutes for each other, and hence if many
viewers watch a mass appeal program on a distant signal, that merely
subtracts from, or ``displaces,'' the viewership of similar programs
on non-distant signals. Thus adding a distant signal station with
mass appeal programming merely shuffles existing viewers between the
added stations and other stations already carried by the CSO and
does not attract new viewers to the CSO's offerings. The rational
CSO would have no value for such a distant signal. In contrast, the
viewership of niche programs, no matter how small, represent ``new
eyeballs'' for the CSOs, as those viewers would not find similar
programs on other channels in the CSO's bundles. These viewers would
be among the ``new subscribers'' who may otherwise not initiate
service with the CSO if distant signal programming were not
available.
Shum WRT ] 12 (footnotes omitted).
Parties critical of using viewing as a measure of value point to
empirical evidence to corroborate arguments based on economic theory.
Dr. Wecker and Mr. Harvey demonstrate (based on Dr. Gray's analysis)
that paid programming (i.e., infomercials) had a higher viewing share
than JSC programming in three of the four years covered by this
proceeding. See Wecker Report ] 44 & Table 7. The JSC point out that,
according to Dr. Gray's theory equating viewership with value, cable
operators would place a higher value on paid programming than live
sports broadcasts, even though Mr. Allan Singer, a former cable
industry executive and JSC witness, testified that content such as
infomercials actually detracts from the value of a signal. Singer WRT ]
7. Mr. Singer also testified that there is ``clearly not'' a ``one-to-
one correlation between audience viewing levels and value,'' though it
is a ``component'' of value. 2/22/18 Tr. at 1047-48 (Singer). Mr.
Daniel Hartman, a media consultant and former DirectTV executive
testifying for the JSC, stated that ratings were ``definitely not a
determinative factor'' in a multi-channel video program distributor's
(MVPD's) negotiations with suppliers of programming. 3/12/18 Tr. at
3155-56 (Hartman). Nor do ratings figure into the rates that MVPD's pay
or the contractual terms and conditions they agree to when they
negotiate with suppliers of programming. Id. at 3156-57. CTV argues
that, while Program Suppliers' witness Sue Ann Hamilton testified to
the importance to cable operators of prospective viewing by
subscribers, she also stated that she did not obtain Nielsen data on
viewing of distant signals. CTV PFF ]] 147-148 (citing Hamilton WDT at
5-6; 3/19/18 Tr. at 4326 (Hamilton)).
Program Suppliers responded by holding to the position that
viewership is the most direct measurement of relative value of
programming for the reasons articulated supra,\155\ relying primarily
on Dr. Gray's and Ms. Hamilton's testimony in support of Dr. Gray's
viewing study. See, e.g., PS Reply PFF ] 129.
---------------------------------------------------------------------------
\155\ See supra, section IV.A.
---------------------------------------------------------------------------
2. Reliance on Incomplete Nielsen Data
On January 22, 2018, two weeks before the scheduled commencement of
the allocation hearing in this proceeding,\156\ Program Suppliers filed
a ``Third Errata'' to Dr. Gray's written direct testimony. See Third
Errata to Amended and Corrected Written Direct Statement and Second
Errata to Written Rebuttal Statement Regarding Allocation Methodologies
of Program Suppliers (Jan. 22, 2018) (Third Errata). The stated reason
for this Third Errata was that Dr. Gray had discovered that the Nielsen
viewing data he had been provided for his analysis did not include any
data for distant viewing of WGNA. Id. at 1; see also 3/14/18 Tr. at
3518 (Lindstrom). WGNA, the national satellite feed for WGN-Chicago,
was the most widely retransmitted distant signal in the U.S. during the
years covered by this proceeding.
---------------------------------------------------------------------------
\156\ The hearing had been scheduled to begin on February 5. The
Judges granted Program Suppliers' motion to delay the start of the
hearing until February 14 for reasons unrelated to Dr. Gray's Third
Errata. See Order Continuing Hearing and Permitting Amended Written
Rebuttal Statements, Denying Other Motions, and Reserving Ruling on
Other Requests (Jan. 26, 2018).
---------------------------------------------------------------------------
The SDC moved to exclude the Third Errata from evidence, arguing
that Program Suppliers were seeking to introduce ``substantial
revisions to its proposed allocation methodology'' and not ``mere
corrections of errors.'' Settling Devotional Claimants' . . . Motion to
Strike MPAA's Purported ``Errata'' to the Testimony of Dr. Jeffrey Gray
at 9 (Jan. 25, 2018). The SDC argued that, in addition to using a
Nielsen dataset that included WGNA viewing data, Dr. Gray proposed ``an
all-new regression in addition to the regression [he] previously
proposed, and a new sample weighting methodology underlying all of its
computations.'' Id. The Judges granted the SDC's motion and excluded
the Third Errata, reasoning that it was too late to introduce a new
analysis. See 2/15/18 Tr. at 232 (Barnett, C.J.); accord Order Granting
MPAA and SDC Motions to Strike IPG Amended Written Direct Statement and
Denying SDC Motion for Entry of Distribution Order, Docket Nos. 2012-6
CRB CD 2004-09 (Phase II), 2012-7 CRB SD 1999-2009 (Phase 2), at 5
(Oct. 7, 2016) (striking Amended Written Direct Statement that was
filed without leave and that introduced a substantially modified
regression specification).
As a result of the Judges' exclusion of the Third Errata, the
version of Dr. Gray's viewing analysis in the record is based on a
Nielsen dataset that does not include viewing data for WGNA. While it
is undisputed that the use of this incomplete dataset almost certainly
affected Dr. Gray's computations, the record does not reveal the
magnitude of the effect on each participant's viewing share.
Dr. Gray testified that, in spite of the missing WGNA data, his
viewing analysis produced viewing shares that were within a ``zone of
reasonable consideration.'' 3/14/18 Tr. at 3764 (Gray). He based his
opinion on ``a dramatic decline in compensable programming carried on
WGNA and a dramatic decline in viewing of WGNA programming, such that
it had become increasingly less important over time.'' Id. at 3763; see
also 3/14/Tr. at 3522 (Lindstrom) (``I haven't quantified it, but based
on past experience, I would say that . . . there wasn't much that was,
in fact, compensable programming that was on.''). In addition, Program
Suppliers argue that Dr. Gray's computed viewing shares were based on
accurate Nielsen data as to viewing on the remainder of the
approximately 150 stations in his sample for each year and were
reliable as to those stations. See PS PFF ] 109; 3/14/18 Tr. at 3525,
3537-38 (Lindstrom). Moreover, Dr. Gray testified that the Crawford and
Israel fee-based regression analyses, as modified by Dr. Gray, support
his estimated viewing shares as being within a zone of reasonableness.
See 3/14/18 Tr. at 3744-45 (Gray).
Other participants dispute this. The JSC point to evidence that,
while compensable Program Suppliers' programming declined in the 2010
to 2013 time frame (and as between that period and the 2004-05 period),
the amount of compensable JSC programming remained stable. See Cable
Operator Valuation of Distant Signal Non-Network Programming 2010-13,
Trial Ex. 1001, at 28 Table III-2 (Bortz Report); see also Hartman WRT
] 14, Table III-1 (telecasts of JSC programming on WGNA remain
relatively constant during 2010-13 and between 2010-13 and 2004-05).
The JSC argue that the omission of the WGNA data thus
disproportionately affected the JSC, as compared to Program Suppliers.
JSC PFF ] 162.
[[Page 3595]]
The SDC, through the testimony of their economist Dr. Erdem,
similarly argue that the absence of WGNA data is likely to
disproportionately bias the results against claimant categories with
smaller distant viewership. See Erdem WRT at 32.
Several experts testified that the imputed zero distant viewing
values that Dr. Gray input into his regression for the missing WGNA
data necessarily affected the predicted viewing that the regression
produced. See Wecker Report ] 33 (``choosing to code zero distant
viewing for large stations such as WGNA . . . created counterintuitive
associations within the data where stations with extremely large
distant subscribers are predicted to have low numbers of viewers''); 2/
22/18 Tr. at 1299-1300 (Harvey). Dr. Gray appears to have conceded this
point. See 3/15/18 Tr. at 4054-55 (Gray).
3. Reliance on Unweighted Nielsen NPM Data
The Nielsen data on which Dr. Gray relied was an extract from
Nielsen's NPM database. See 3/14/18 Tr. at 3685-88 (Gray). The NPM data
are derived from a geographically stratified sample of about 22,000
television households that is ``designed in such a way so that every
household in the United States has a probability of being selected''
and represents approximately 110 million U.S. television households.
Id. at 3507, 3539-40 (Lindstrom); 2/22/18 Tr. at 1179 (Harvey);
National Reference Supplement 2010-2011, Trial Ex. 2021, at 1-1
(Nielsen Supplement). A subset of the NPM data, known as Local People
Meter (LPM) data, is used for measuring viewership in the top 25 local
markets. 3/14/18 Tr. at 3556 (Lindstrom); Sanders WRT ] 6.viii. Nielsen
disproportionately oversamples the (mostly urban) LPM markets, with 600
to 1000 metered households in each. See Nielsen Supplement at 1-1;
Erdem WRT at 27.
a. Use of Nielsen NPM Data
Several witnesses opined that the NPM database is the wrong tool
for measuring local and distant viewing to individual television
stations because the NPM data are not designed to measure viewership in
local or regional markets. See Corrected Written Rebuttal Testimony of
Susan Nathan, Trial Ex. 1090, at 3, 5-6 (Nathan CWRT); 2/22/18 Tr. at
1180-81, 1213 (Harvey); Written Rebuttal Testimony of Ceril Shagrin,
Trial Ex. 2009, ] 24 (Shagrin WRT). Ms. Shagrin contended that an
appropriate sample to measure distant viewing would need to oversample
small markets, and the NPM does not oversample small markets.
Consequently, the NPM data could not produce a proper measure of
distant signal viewing. Shagrin WRT at ]] 18, 22, 24; 3/1/18 Tr. at
1778 (Shagrin).
The CCG and SDC both argued that their program categories are
underrepresented in the NPM sample design. See CCG PFF ] 200; SDC PFF
]] 130-131. By statute, Canadian television stations may only be
carried by cable systems within 150 miles of the U.S.-Canada border or
north of the forty-second parallel. 17 U.S.C. 111(c)(4). Many
communities within that ``Canadian Zone'' are not included in the NPM
sample. 3/15/18 Tr. at 4071-73 (Gray); Sanders WRT, App. E; Boudreau
CWDT at 87. Similarly, the SDC claim that many portions of the ``Bible
Belt'' are not included in the NPM sample. See Sanders WRT, ] 6.xi,
Apps. E-F.
More generally, some experts argued that Dr. Gray's use of the NPM
data resulted in a high number of instances of zero recorded viewing in
the data he fed into his regression algorithm. Viewing of distantly-
retransmitted signals is a relatively small phenomenon, and in many
regions the NPM had an insufficient number of metered households to
measure that viewing. See Nathan CWRT at 5-6, 8; Wecker Report ]] 21-22
& Table 4; 2/22/18 Tr. at 1180-81, 1183-84, 1252-54 (Harvey); Gray
CAWDT ] 35. Ninety-four percent of the quarter hour observations in Dr.
Gray's dataset showed zero recorded viewing, and only 0.96% of the
observations reported two or more distant viewing households. See
Wecker Report ]] 18, 21-22 & Table 4; Shum WRT ] 17; see also Bennett
WRT ] 49 & Fig. 16. Approximately 20% of the distantly-retransmitted
stations in Dr. Gray's sample have no recorded local or distant viewing
in the Nielsen data. See Shum WRT ] 18.
Dr. Gray, and Mr. Lindstrom of Nielsen,\157\ defended the use of
NPM data for measuring viewership of programs on distant signals. Dr.
Gray testified that he consulted with Nielsen concerning his selection
of data and the uses to which he intended to put it, and Nielsen found
his approach to be reasonable. See 3/14/18 Tr. at 3932-33 (Gray); 3/15/
18 Tr. at 3846 (Gray). He relied on his regression analysis to project
distant viewership values to quarter hours on stations in his sample,
including those stations in portions of the country that were not
included in the Nielsen NPM sample. See id. at 4073. Mr. Lindstrom
testified that Nielsen recommended the NPM database because ``it is
recognized that the meter is by far the best technology and best method
for being able to measure television usage.'' 3/14/Tr. at 3506
(Lindstrom). Mr. Lindstrom also testified that, while the NPM is a
measurement of nationwide viewing, ``all national viewing is inherently
aggregations of local usage. . . . It's all based on viewing built up
from a very localized level. . . . [I]f you believe in sampling--and
I'm a big believer in sampling--and the core methodology behind it,
that you are getting a very good measure of the viewing going on in
those homes and that when looked at in aggregate, it is a very solid
number.'' Id. at 3508-10.
---------------------------------------------------------------------------
\157\ Mr. Lindstrom retired in June 2017 after nearly 40 years
at Nielsen. See 3/14/18 Tr. at 3495-96 (Lindstrom). Prior to his
retirement, Mr. Lindstrom was a Senior Vice President in charge of
custom research and custom analysis for Nielsen's media business.
See id. at 3496. He testified in this proceeding with Nielsen's
``full cooperation and support.'' Id. at 3495.
---------------------------------------------------------------------------
Regarding the ``zero viewing'' criticisms, Dr. Gray testified that
instances of no recorded viewing are to be expected, and constitute
``information regarding the level of viewing for the Nielsen sample.''
3/15/18 Tr. at 3973 (Gray); see Gray CAWDT ] 35; 3/14/18 Tr. at 3717
(Gray). Similarly, Mr. Lindstrom explained that, given Nielsen's
sampling rates and the levels of distant viewing, one would expect a
large number of individual quarter-hour observations to show no
recorded viewing. He emphasized that it is necessary to aggregate and
average the observations to get an accurate picture of viewing. See 3/
14/18 Tr. at 3527-28 (Lindstrom). ``[I]f you believe in sampling, then
the aggregation is, in fact, going to give you solid results . . . .
[I]f you're going to look at the individual pieces, then the individual
pieces are highly subject to criticism because you're not supposed to
look at individual pieces.'' Id. at 3529.\158\
---------------------------------------------------------------------------
\158\ Program Suppliers also sought to cast doubt on the
experience and expertise of the witnesses who criticized Dr. Gray's
use of the NPM database for his viewing study. See, e.g., PS Reply
PFF ] 66 (``Ms. Shagrin testified that she had never worked on
custom analysis projects while at Nielsen, and that she did not
understand how Dr. Gray used Nielsen's custom analysis in his
methodology.'').
---------------------------------------------------------------------------
b. Application of Improper Sample Weights to the Nielsen Data
In order to project viewing data from sample households to the
broader television audience, Nielsen employs sophisticated weighting
schemes. ``The weights measure the number of people in the population
that are represented by each member of the sample. For example, if [a]
sample member has a weight of 20,000 for a selected day, this
[[Page 3596]]
means that on that day the sample member represents 20,000 in the
population.'' Nathan CWRT at 5 (quoting Nielsen online tutorial on
weighting (internal quotations and footnote omitted)). Dr. Gray was
supplied with Nielsen's national weights, but not with weights that
would permit accurate projection to local or regional markets. See 3/
14/18 Tr. at 3711, 3715-16 (Gray). He chose to use the unweighted
Nielsen data, rather than weights that would project to a national
audience. Dr. Gray testified that he was concerned that using the
national weights would produce anomalous results, where numbers of
projected viewers for a distant signal would, in some cases, exceed the
number of cable households that receive the signal on a distant basis.
See id. at 3715-16.
Ms. Susan Nathan, a media research consultant, agreed that it would
have been inappropriate for Dr. Gray to apply the NPM national weights
to data concerning distant viewing. See Nathan CWRT, at 9. However, Ms.
Nathan also found Dr. Gray's use of unweighted Nielsen data
inappropriate:
In arriving at his distant viewing estimates, Dr. Gray treats
each NPM sample household as equal--even though each NPM sample
household is not equal in Nielsen's sample design. Rather, each
household is representative of a different number of potential
viewers. Simply estimating the number of sample participants that
might view a given program is not an accurate means of estimating
viewership. By ignoring the weighting and assuming one people meter
household is the same as another, Gray also applies the unweighted
data in a manner for which it was not intended.
Id. Mr. Gary Harvey, a statistician and applied mathematician,
similarly criticized Dr. Gray's use of unweighted data: ``[B]ecause Dr.
Gray doesn't take into account any weighting . . . you don't know how
important that household is . . . for your particular area.'' 2/22/18
Tr. at 1182 (Harvey); see id. at 1201-02.
Dr. Gray responded that his decision to use the unweighted Nielsen
data was the best of three options available to him. He could have used
the sample weights in the NPM database, which project each quarter-hour
observation out to the number of households in the NPM survey that
particular Nielsen household represented on that particular day. Dr.
Gray was concerned that this would produce anomalous results, where the
predicted number of viewing households could exceed the number of
distant subscribers with access to that distant signal. See 3/14/18 Tr.
at 3714-15 (Gray). He could have used sample weights that project each
observation to the particular distant viewing market, but those weights
were not available from Nielsen, and would have been impracticable for
him to develop. Id. at 3715-16. Or he could have taken the approach
that he ultimately settled on and used the unweighted Nielsen data. See
id. at 3716. Dr. Gray pointed out that Nielsen used unweighted data in
a similar fashion in a previous proceeding and noted that, in any
event, he was not interested in the absolute number of viewer quarter
hours, but the relative level of viewing among the parties. See id. He
concluded that performing a regression on the unweighted Nielsen
viewing numbers was ``a reliable methodology to do so.'' Id.
4. Sample of Stations Biased Results
Dr. Gray selected his sample of stations using a statistical
technique called stratified random sampling. He ranked the universe of
distantly-retransmitted stations by numbers of distant subscribers,
divided the stations into strata proportionate to the number of distant
subscribers reached by the signal, and randomly selected stations from
each stratum. 3/14/18 Tr. at 3686 (Gray). He selected stations from the
stratum containing the stations with the most distant subscribers with
100% probability (i.e., he selected all of them). The probability of
selecting any given station declined with each succeeding stratum, with
the probability of selecting a given station in the final stratum
ranging from approximately 2.4% (i.e., 19 in 792) to approximately 3.5%
(i.e., 22 in 632). See Bennett WRT ] 28, Figs. 6-9. In order to account
for the differing probabilities of selection between the different
strata, Dr. Gray had to weight the viewing data. Data pertaining to the
largest stations, which were selected with 100% probability received a
weight of 1. Data pertaining to stations with a lower probability of
selection received a higher sample weight (the reciprocal of the
probability of selection). See 3/15/18 Tr. at 3964-65 (Gray). The
stations with the fewest number of distant subscribers, which had the
lowest probability of being selected, received the highest sample
weight, ranging from 28.73 to 41.68. See Bennett WRT ] 28, Figs. 6-9.
Use of a stratified random sample (with appropriate weighting) can
allow oversampling of elements with a given characteristic (in this
case stations with larger numbers of distant subscribers), while still
being able to make statistical inferences about the universe of
elements as a whole. However, Dr. Bennett, an economist and
econometrician who testified for CTV, criticized this approach, arguing
that Dr. Gray's sampling design is prone to error and bias and that Dr.
Gray made a number of errors implementing his sample. See generally
Bennett WRT.
a. Sample Design Led to a Biased Sample
Dr. Bennett describes Dr. Gray's sample design as an example of
``cluster sampling'' because Dr. Gray sampled stations (which air
multiple programs) rather than sampling programs directly. See Bennett
WRT ]] 15-16. Cluster sampling, according to Dr. Bennett, is ``more
prone to bias than simple random samples of equal size'' because
``individual clusters often contain a non-random and relatively
homogenous set of units.'' Id. ] 17, 18 & Fig.1. In the context of
television programming, Dr. Bennett observed that programs assigned to
particular claimant categories are often concentrated by station type
(i.e., Canadian, educational, network, independent, or low power).
Over- or under-sampling of stations of a particular type could thus
have a substantial impact on the volume and viewership share of the
categories of programming that are disproportionately carried on those
stations. Id. ] 18. If the sample of stations is not proportionately
representative of the station types in the population, the program
types will not be representative of the population of television
programs.
Dr. Bennett argues that Dr. Gray's samples of stations were, in
fact, not representative of the station types in the population. See
id. ] 29. Dr. Bennett offers as evidence of unrepresentativeness the
proportion of educational stations in Dr. Gray's samples in each year
as compared to the proportion of educational stations in the
population. He notes that Dr. Gray consistently under-sampled
educational stations in 2010, 2011, and 2013, and oversampled
educational stations in 2012. See id. ] 32 & Fig. 10. Conversely, he
finds that Dr. Gray over-sampled independent stations in 2010, 2011,
and 2013, and under-sampled them in 2012. See id. ] 34 & Fig. 11. Since
independent stations carry a greater proportion of Program Suppliers'
programs than other station categories, Dr. Bennett concludes that Dr.
Gray's computations of volume and viewership overstate those values for
Program Suppliers' programming. See id. ]] 39-42. Dr. Bennett opines
that Dr. Gray should have included station type as a stratification
variable to avoid potential bias. See id. ] 19.
[[Page 3597]]
Dr. Gray acknowledged that it would have been possible, as Dr.
Bennett suggested, to stratify with respect to program type. See 3/14/
18 Tr. at 3771 (Gray). However, he argued that not performing that
stratification did not render his sample biased. ``I'm appealing to
randomness. I think bias is a strong word.'' Id. He also acknowledged
that he could have done some ``post-sampling weighting, which would
have changed [the] estimate slightly,'' but did not do so. Id.
b. Sampling Frame and Sampling Weights Were Incorrect
Dr. Bennett points out (and Dr. Gray confirms) that some duplicate
stations were included in Dr. Gray's samples. See id. ]] 21-25 & Fig.
3; 3/15/18 Tr. at 3859-63 (Gray). This occurred, for example, when the
CDC data Dr. Gray received listed certain stations twice--once with a
``DT'' suffix after the call sign and once without (e.g., CBUT and
CBUT-DT). See Bennett WRT ] 24 & Fig. 4.
As a result of these duplicates, Dr. Bennett found that Dr. Gray's
sampling frame included more stations than were in his target
population.\159\ Bennett WRT ] 22. Dr. Bennett argues that the mismatch
of Dr. Gray's sampling frame and the population of distantly-
retransmitted stations rendered the sampling frame unsuitable to
represent the target population. Id. ] 21. Dr. Bennett argues that
``Dr. Gray's failure to remove duplicate stations . . . distorts his
count of unique stations, his assignment of stations to individual
strata, and the sampling weights that he calculates based on his
incorrect station count,'' which could affect Dr. Gray's analysis in
several ways:
---------------------------------------------------------------------------
\159\ ``A sampling frame is an enumeration of the items from
which a sample is selected. Ideally, the sampling frame will be
identical to--and therefore representative of--the target population
that one seeks to study.'' Bennett WRT at ] 21.
a. Double-counting some stations in the sampling frame, which
changed the likelihood of selection for all stations outside the top
stratum; and
b. Where both versions of the duplicative station were selected,
such as for CBUT . . . 2010, overrepresentation of the duplicate
station in the sample, and the exclusion of a non-duplicate station
from the sample; and
c. Incorrect sampling weights being applied to sampled stations
in strata with one or more of the duplicative stations.
Id. ] 25.
Dr. Bennett argued that ``the errors in Dr. Gray's sampling weights
are further compounded by the fact that Dr. Gray has dropped sampled
stations that did not have coverage in the Gracenote Data.'' Id. ] 26.
Over the four years at issue in this proceeding, Dr. Gray had to drop
between five and eight sampled stations per year (for a total of 24 of
his 609 sampled stations) because Gracenote could not provide
programming information for them. See id. ] 27. The omitted stations
were distributed unevenly across the sample strata and subject to
different sample weights. Dr. Bennett opines that Dr. Gray should have
adjusted his weighting to account for the number of missing stations
across the strata for each year. See id. ] 28. In addition, Dr. Bennett
testified that Dr. Gray failed to apply his sample weights in
performing his regression analysis, leading to biased results. See id.
]] 58-59.
Dr. Gray acknowledged the existence of duplicate stations in his
sample. See 3/15/18 Tr. at 3859 (Gray). He explained that at the time
that he drew the sample there were a number of stations that had the
same call signs with different suffixes, and, after consultation with
CDC and Nielsen, he was unable to determine whether or not they were
the same or different signals. See 3/14/18 Tr. at 3719-20. He opted to
treat them as different stations because, if he had treated them as the
same station and they proved to be different stations he would have had
to discard the sample and start over. Id. Having duplicate stations in
the sample effectively resulted in a smaller sample and a higher margin
of error. See id. at 3721; 3/15/18 Tr. at 3853-56 (Gray). Dr. Gray
testified, however, that the existence of duplicate stations did not
render his viewing estimates biased or incorrect. See 3/15/18 Tr. at
3859 (Gray).
Dr. Gray also acknowledged that the existence of duplicate stations
resulted in the application of different sample weights to different
subscriber groups that received the same signal. See id. at 3861-62. He
maintained, however, that applying differing sample weights did not
``make the make the estimated viewing biased or wrong.'' Id. at 3861.
Regarding his sampling weights, Dr. Gray acknowledged that he
should have recalculated them to reflect the removal of certain
stations from the sample for which data were unavailable. See id. at
3867. He opined that the difference would be de minimis, ``given the
types of stations that did not have programming data.'' Id. ``[E]very .
. . sensitivity analysis I ever did with respect to viewing had . . .
almost de minimis impacts. . . . I would not expect it to impact the
overall calculated shares.'' Id. at 3867-68.
Contrary to Dr. Bennett's assertion, Dr. Gray testified that he
applied his sample weights to the Nielsen data and maintained that
``it's an unbiased measure of viewing.'' Id. at 3861-62.
c. Erroneous Application of Random Sample to Geographic Stratified
Sample
Dr. Erdem criticized Dr. Gray's sampling technique because it
superimposed a random selection on a geographically-stratified
sample.\160\ He argued that the two sampling schemes are incompatible,
because ``[t]here is no guarantee that the stations in Dr. Gray's
sample were broadcast or retransmitted in the . . . geographic areas
sampled by Nielsen.'' Erdem WRT at 26. As a result, ``[l]ocal or
distant viewership would be underreported or completely missing if
geographies where a particular station is retransmitted are not sampled
by Nielsen.'' Id. Consequently, Dr. Erdem considered Dr. Gray's data
source to be ``practically unusable,'' and concluded that ``no reliable
conclusions can be drawn on the basis of the sample that Dr. Gray
uses.'' Id. at 25.
---------------------------------------------------------------------------
\160\ Nielsen's sample is a tiered sample of geographic areas,
see Erdem WRT at 25; see also 3/14/18 Tr. at 3507, 3539-40
(Lindstrom), unlike Dr. Gray's sample, which was stratified by the
number of distant subscribers. See 3/14/18 Tr. at 3686 (Gray).
---------------------------------------------------------------------------
Dr. Gray responded that Dr. Erdem's criticism ``would have been a
concern, had [he] not used regression analysis.'' 3/14/18 Tr. at 3718
(Gray). He conceded that ``Dr. Erdem has a legitimate point'' and that
it is not ``ideal'' to superimpose a random sample on top of a
geographic sample. Id. He testified, however, that he had overcome that
criticism by using regression analysis to predict viewing ``even in
those areas of underrepresentation by Nielsen.'' Id. at 3718-19. As a
consequence, he was not concerned about Dr. Erdem's criticism. Id. at
3719.
5. Other Methodological Errors
Experts for the other parties lodged a barrage of criticisms of a
variety of methodological choices that Dr. Gray made in performing his
analysis.
a. Imputation of Zeroes for Missing Nielsen Data
The NPM data that Nielsen provided to Dr. Gray included only
observations of positive viewing. See 3/14/18 Tr. at 3712 (Gray). For
several million station/quarter-hour pairings during the relevant
period there was no record of positive viewing in the NPM data. See
Wecker Report ] 21. Dr. Gray added zero-viewing records for these
station/quarter-hour pairings and used these zero values as input in
his regression analysis. See id.; Bennett WRT ] 53 & Fig. 17.
[[Page 3598]]
Dr. Bennett and Mr. Harvey both criticized this practice. Dr.
Bennett argued that ``Dr. Gray's practice of equating missing records
with zero viewing 1acks foundation and undermines the reliability of
his regression analysis. . . . Dr. Gray offers no logical explanation
for why zero might be the correct value to use in place of a missing
record.'' Bennett WRT ] 54. Dr. Bennett posited the existence of an
apparent contradiction: ``[E]ither the missing values truly correspond
to zero viewing and the regressions serve no purpose--why estimate a
known quantity--or the true values of the missing records potentially
differ from zero, in which case Dr. Gray has imposed an incorrect
assumption that biases the estimated relationship between distant and
local viewing.'' Id.
Mr. Harvey argued that Dr. Gray failed to demonstrate that a
sufficient number of NPM households received a given distantly
transmitted signal to conclude that the absence of viewership data
indicated zero viewing. 2/22/18 Tr. at 1203-07 (Harvey). ``[Y]ou might
have zero people meters, in which case [a zero viewing observation] is
useless data. . . .'' Id. at 1335. In Mr. Harvey's view, ``there is no
possible way to come up with some metric . . . for these smaller
samples without knowing the number of people meters. . . .'' Id.
Dr. Gray explained that ``[t]here was [sic] never any zeros in the
Nielsen data. They only have recorded viewing and non-recorded
viewing.'' 3/24/18 Tr. at 3712 (Gray). The data that Nielsen provided
to Dr. Gray were ``all recorded viewing values.'' Id. He testified that
the absence of an entry for recorded viewing for a given quarter hour
meant that ``there was no Nielsen household in the sample viewing''
that channel at that particular time. Id. In those cases he added an
entry with a zero-household count. See id. at 3712-13. Dr. Gray
distinguished between instances zero local viewing and data that was
``missing'' because local viewing for that channel was not measured by
Nielsen. See id. at 3895-97; 3/14/18 Tr. at 3717-18. In the latter
instance, he imputed a local rating based on the average local rating
for programs of the same type during that particular quarter hour. See
id.; 3/15/18 Tr. at 3897-3900 (Gray).
b. Incorrect Measure of Local Ratings
As an input for his regression analysis, Dr. Gray used a ``measure
of local ratings'' that he constructed by dividing local viewing (as
measured by Nielsen) by the size of the market--i.e., ``the number of
subscribers reached by the particular signal.'' See 3/14/18 Tr. at 3693
(Gray). Dr. Bennett clarifies that, by number of subscribers, Dr. Gray
refers to the total number of local and distant subscribers who receive
the signal. See Bennett WRT ] 56.
Dr. Bennett faults Dr. Gray's inclusion of the number of distant
subscribers in the denominator when calculating his measure of local
ratings. ``Dr. Gray's inclusion of distant subscribers in his `measure'
of local viewing means that, all else equal, he will assign higher
local viewing to a station with the fewest distant subscribers, and
vice versa.'' Id.
Dr. Gray maintained that, after consultation with Nielsen, he found
his measure of local ratings to be reasonable. See id. at 3932-33.
c. Regression-Based Estimates in Lieu of Nielsen Observations of
Positive Viewing
Dr. Gray computed his viewing shares based solely on the estimates
he computed using his regression analysis. He used the observations of
positive viewing in the Nielsen NPM data solely as an input into the
regression analysis, not in the final computation of viewing shares.
Dr. Bennett described this procedure as being ``without . . . support''
and argued that Dr. Gray's reliance on estimated viewing ``further
undermines the reliability of his viewing analysis.'' Id. ]] 50-51.
Specifically, Dr. Bennett argued that, as compared with the
observations of positive viewing in the Nielsen NPM data, Dr. Gray's
estimates are biased in favor of Program Suppliers and PTV programming,
and biased against CTV and CCG programming. See id. ] 64 & Figs. 21-22;
3/1/18 Tr. at 1874-75 (Bennett). Professor Shum reiterates the same
point with respect to CCG programming, arguing that Dr. Gray's analysis
systematically lowered estimates of distant viewing of Canadian signals
because (a) the regression undercounted local viewing by excluding
local viewing in Canada; (b) Canadian stations were underrepresented in
Dr. Gray's 2010 sample; and (c) Canadian signals cannot be carried
outside the Canadian Zone. See Shum WRT ]] 25-38. Professor Shum
proposes adjustments to Dr. Gray's viewing shares to account for the
first two purported defects, but he was unable to propose an adjustment
to account for the third. See id. ]] 29-30, 33-35, 38.
Dr. Gray maintained that basing his viewing shares on the predicted
viewing he computed through his regression analysis was both reasonable
and superior to using Nielsen's viewing estimates for that purpose. See
3/15/18 Tr. at 3940-41, 3943, 3948 (Gray). In particular, he argued
that, while Nielsen's measurements were of ``geographically-focused
areas,'' his regression analysis produces estimates of relative viewing
``throughout the United States.'' Id. at 3949. He acknowledged that his
regression would not produce particularly good estimates of the level
of distant viewing, but opined that his estimates were ``more accurate
on a relative basis for the United States.'' Id.; see id. at 3946,
3948.
d. Miscategorized Programs
Dr. Bennett asserts that Dr. Gray incorrectly assigned thousands of
programs to the wrong claimant categories. For example, he states that
Dr. Gray's algorithm failed to consider Gracenote's title and program
type fields when assigning programs to the CCG category and, as a
result, incorrectly assigned JSC programming on Canadian signals to the
CCG category. Bennett WRT ]] 44-45; see also Wecker Report ] 12 (Dr.
Gray included nearly all MLB, NHL, NBA, and NFL broadcasts on Canadian
signals in the CCG category); 2/22/18 Tr. at 1169-70 (Harvey) (``Dr.
Gray was very clear in his testimony that he intended to code Canadian
broadcasts of Major League Baseball games and football games into the
JSC Category, but he did not do that.''); Bennett WRT ] 18, n.11
(``obvious program categorization errors'' in table showing 20 CTV
programs on Canadian stations and 5 Devotional programs on Educational
stations). In addition, Dr. Bennett states that Dr. Gray didn't
consider whether a program coded as ``religious'' was syndicated before
he assigned it to the Devotional category. Dr. Bennett asserts that
nonsyndicated religious programming belongs in the CTV category. Id. ]
46.
Dr. Gray compared the category classification that he performed to
Dr. Bennett's. He found that their respective algorithms assigned
programs to the same category 93.5% of the time. See Gray CWRT ] 50. As
to the programs where Dr. Gray's categorization differed from Dr.
Harvey's, Dr. Gray was unable to determine which categorization was
correct with undertaking a program-by-program review.\161\ See id.
Instead, Dr. Gray performed a sensitivity analysis to determine whether
using Dr. Bennett's categorizations would have an impact on his (Dr.
Gray's) share calculations. See id. ] 51. Using Dr. Bennett's program
categorizations resulted in a modest increase in Program Suppliers'
[[Page 3599]]
viewership share in each royalty year, ``consistent with no bias in
intent on the part of Dr. Bennett or me.'' Id. ] 52.
---------------------------------------------------------------------------
\161\ Dr. Gray testified about a number of specific instances in
which his categorization differed from Dr. Bennett's, and, on
further review, he stood by his categorization. However, he did not
perform a comprehensive review. See 3/14/18 Tr. at 3721-23 (Gray).
---------------------------------------------------------------------------
D. Analysis
1. Relevance and Impact of Prior Decisions
Program Suppliers' use of viewing data to propose allocations of
cable royalties among program categories has a long, if not illustrious
history. MPAA (to use the Program Suppliers' contemporaneous
designation) first offered a Nielsen study in the Copyright Royalty
Tribunal's (CRT) adjudication of 1979 cable royalties. See 1979 Cable
Royalty Distribution Determination, 47 FR 9879, 9880 (Mar. 8, 1982). At
that time the CRT found Nielsen's viewership study to be the ``single
most important piece of evidence in [the] record.'' Id. at 9892. Over
time, however, decision makers' (first the CRT, then the CARPs)
reliance on Nielsen studies waned. See 1998-99 CARP Report, supra note
144, at 33 (recounting history of use of Nielsen studies by CRT and
CARPs). In 2003 a CARP, with the approval of the Librarian of Congress
(Librarian) declined to use the Nielsen study as a direct measure of
relative value of programming to cable operators:
[T]he Nielsen study does not directly address the criterion of
relevance to the Panel. The value of distant signals to CSOs is in
attracting and retaining subscribers, and not contributing to
supplemental advertising revenue. Because the Nielsen study ``fails
to measure the value of the retransmitted programming in terms of
its ability to attract and retain subscribers,'' it can not be used
to measure directly relative value to CSOs. The Nielsen study
reveals what viewers actually watched but nothing about whether
those programs motivated them to subscribe or remain subscribed to
cable.
Id. at 38 (citations omitted). Or, as the Librarian summarized
pithily, ``[t]he Nielsen study was not useful because it measured the
wrong thing.'' 1998-99 Librarian Order, 69 FR at 3613.
More recently the Judges have relied upon evidence of viewership in
a pair of ``Phase II'' distribution cases.\162\ In the 2000-03 cable
Phase II distribution case, the Judges concluded that ``viewership, as
measured after the airing of the retransmitted programs is a
reasonable, though imperfect proxy for the viewership-based value of
those programs.'' Distribution of 2000, 2001, 2002 and 2003 Cable
Royalty Funds, 78 FR 64984, 64995 (Oct. 30, 2013) (2000-03 Cable Phase
II Decision) (footnote omitted). The Judges agreed with Program
Suppliers' expert in that case \163\ that ``viewership can be a
reasonable and directly measurable metric for calculating relative
market value . . . . Indeed, the Judges conclude that viewership is the
initial and predominant heuristic that a hypothetical CSO would
consider in determining whether to acquire a bundle of programs for
distant retransmission . . . .'' Id. at 64996. Similarly, in the 1998-
99 Phase II proceeding, the Judges found a viewership analysis to be an
``acceptable `second-best' measure of value'' for distributing funds
allocated to the devotional programming category among claimants in
that category. See Distribution of 1998 and 1999 Cable Royalty Funds,
80 FR 13423, 13432-33 (Mar. 13, 2015) (1998-99 Cable Phase II
Decision).
---------------------------------------------------------------------------
\162\ Prior to the cases to determine allocation and
distribution of 2010-13 cable and satellite royalties the Judges and
their predecessors referred to the process of dividing royalties
among program categories as ``Phase I,'' and the process of dividing
royalties allocated to a program category among the claimants within
that category as ``Phase II.'' When the Judges decided to conduct
both processes simultaneously for 2010-13 cable and satellite
royalties they decided to refer to them as the ``allocation phase''
and ``distribution phase,'' respectively, to avoid any expectation
that the processes would be carried out sequentially.
\163\ Then, as now, the Program Suppliers' principal witness
regarding the analysis of Nielsen viewership data was Dr. Gray.
---------------------------------------------------------------------------
The Copyright Act mandates that the Judges act
on the basis of a written record, prior determinations and
interpretations of the Copyright Royalty Tribunal, Librarian of
Congress, the Register of Copyrights, copyright arbitration royalty
panels (to the extent those determinations are not inconsistent with
a decision of the Librarian of Congress or the Register of
Copyrights), and the Copyright Royalty Judges (to the extent those
determinations are not inconsistent with a decision of the Register
of Copyrights that was timely delivered to the Copyright Royalty
Judges pursuant to section 802(f)(1)(A) or (B), or with a decision
of the Register of Copyrights pursuant to section 802(f)(1)(D)),
under this chapter, and decisions of the court of appeals. . . .
17 U.S.C. 803(a)(1). In interpreting a nearly identical provision under
the CARP system,\164\ the Librarian stated that ``[w]hile the CARP must
take account of Tribunal precedent, the Panel may deviate from it if
the Panel provides a reasoned explanation of its decision to vary from
precedent.'' Distribution of 1990, 1991 and 1992 Cable Royalties, 61 FR
55653, 55659 (Oct. 28, 1996) (1990-92 Librarian Order) (citation
omitted). In a subsequent decision, the Librarian observed that ``prior
decisions are not cast in stone and can be varied from when there are
(1) changed circumstances from a prior proceeding or; (2) evidence on
the record before it that requires prior conclusions to be modified
regardless of whether there are changed circumstances.'' 1998-99
Librarian Order, 69 FR at 3613-14.
---------------------------------------------------------------------------
\164\ The earlier provision, former section 802(c) of the
Copyright Act, stated that CARPs ``shall act on the basis of . . .
prior decisions of the Copyright Royalty Tribunal, prior copyright
arbitration panel determinations, and rulings of the Librarian . . .
.''
---------------------------------------------------------------------------
As an initial matter, the Judges find that the 1998-99 CARP Report
and the 1998-99 Librarian Order are relevant ``precedent'' \165\ that
the Judges must consider in connection with Dr. Gray's analysis of
Nielsen viewing data; the 1998-99 Cable Phase II Decision and the 2000-
03 Cable Phase II Decision are not. The task of distributing royalties
among a reasonably homogeneous group of programs differs from that of
allocating royalties among heterogeneous categories, and different
considerations apply to each. See Indep. Producers Grp. v. Librarian of
Congress, 792 F.3d 132, 142 (DC Cir. 2015) (IPG v. Librarian);
Distribution of 1993, 1994, 1995, 1996 and 1997 Cable Royalty Funds, 66
FR 66433, 66453 (Dec. 6, 2001).
---------------------------------------------------------------------------
\165\ The decision whether or not to accept a methodology for
determining relative market value is factually-dependent, so it is a
misnomer to describe a previous decision declining to rely on
viewership as ``precedent''--i.e., controlling under the principle
of stare decisis. Nevertheless, it is a ``prior determination'' ``on
the basis of '' which Congress has directed the Judges to act (along
with the written record and other items enumerated in the statute).
See 17 U.S.C. 803(a)(1).
---------------------------------------------------------------------------
In considering Dr. Gray's viewing study, therefore, the Judges are
mindful of the earlier decisions that found viewership studies
unhelpful in allocating royalties among program categories. In
particular, the Judges examine whether there is record evidence that
would compel a different conclusion in the present case.\166\
---------------------------------------------------------------------------
\166\ No party has alleged changed circumstances that would bear
on the Judges' reliance, vel non, on viewing data.
---------------------------------------------------------------------------
2. Rejection of Viewership as a Measure of Relative Value
Although the record supports a conclusion that viewership is a
measure of value, the weight of the evidence demonstrates that it is an
incomplete measure of value.
The Judges agree in principle with Dr. Gray that the focus of the
relative market value inquiry is on the hypothetical market in which
copyright owners license programs to broadcasters for retransmission by
cable operators. See 3/14/18 Tr. at 3683-84 (Gray). Experts from
multiple parties agreed that, in the hypothetical market, cable
operators would continue to acquire
[[Page 3600]]
entire signals, rather than individual programs. See id. at 3683; 2/28/
18 Tr. at 1377-78 (Crawford); 3/5/18 Tr. at 2157-58 (George). In this
market structure copyright owners' compensation (the object of this
proceeding) would flow from broadcasters to copyright owners, and would
be recouped through the retransmission fee charged by the broadcaster
to the cable operator. See 3/14/18 Tr. at 3682-84, 3779-81 (Gray).
That market does not exist in a world with a compulsory license, so
there is no evidence of the surcharge that broadcasters would pay to
copyright owners for the right to license distant retransmissions. Most
parties have used the transaction in which a cable operator acquires
the right to retransmit programming as a proxy. Program Suppliers, by
contrast, focus on the consumer demand for programs as measured by
viewership.
At bottom, Dr. Gray's study is premised on the truism that,
ultimately, programming is acquired to be viewed. See Gray CAWDT ] 13.
Consumers subscribe to cable in order to watch the programming carried
on the various channels provided by the cable operator. Cable operators
acquire broadcast and cable channels that carry programming their
subscribers want to view. Broadcasters acquire programs that will
attract viewers.\167\ Viewing is the engine that drives the entire
industry. It is an example of the economic concept of derived demand.
The demand for programming at each step in the chain is derived from
demand further along the chain, all the way to the television viewer.
Program Suppliers corroborated Dr. Gray's economic insight with
evidence that at least some MVPDs consider viewership metrics in making
program acquisitions.
---------------------------------------------------------------------------
\167\ Broadcasters' reasons to attract viewers are driven by
advertising-revenue considerations rather than subscriber attraction
and retention considerations.
---------------------------------------------------------------------------
Consequently, based on the evidence presented in this proceeding,
the Judges disagree with the Librarian's statement that viewership
studies are not useful because they ``measure [ ] the wrong thing.''
1998-99 Librarian Order, 69 FR at 3613. Viewership is no less relevant
to the question of how a copyright owner would be compensated by a
broadcaster in the hypothetical market than to the question of what a
cable operator would be willing to pay to a broadcaster. Both are
relevant because the copyright owner's compensation would be a function
of downstream demand in the hypothetical market.
However, even accepting that viewership is relevant to the question
of value doesn't end the inquiry. There is record evidence supporting
the contention that, in the analogous market for cable channels, cable
operators will pay substantially more for certain types of programming
than for other programming with equal or higher viewership. See
Crawford WRT ] 36 & Fig. 1.\168\ These empirical data support economic
arguments about the role of bundling and ``niche'' programming in cable
operators' decision making. See Shum WRT ]] 10-12; Connolly WDT ]] 31-
32; Crawford CWDT ] 7. It is clear to the Judges that relative levels
of viewership do not adequately explain the premium that certain types
of programming can demand in the marketplace. In short, viewing doesn't
provide the whole picture.
---------------------------------------------------------------------------
\168\ See also discussion of Dr. Israel's ``cable content
analysis,'' supra, section V.
---------------------------------------------------------------------------
The Judges conclude, therefore, that viewership, without any
additional evidence to account for the premium that certain categories
of programming fetch in an open market, is not an adequate basis for
apportioning relative value among disparate program categories.
3. Rejection of Dr. Gray's Study due to Incomplete Data
The Judges also must reject Dr. Gray's study because he computed
his predicted distant viewing on the basis of incomplete data.
Specifically, the use of erroneous zero viewing observations for
compensable WGNA programming rendered Dr. Gray's results unreliable.
WGNA was, by far, the most widely retransmitted signal in the U.S.
during the period covered by this proceeding, reaching over 40 million
distant subscribers. See Wecker Report, ] 23. That provided an
opportunity for any compensable program retransmitted on WGNA to be
viewed by a substantial number of households. Yet nearly none of those
compensable programs were credited with any positive distant viewing on
WGNA in Dr. Gray's regression. The Wecker Report, moreover,
demonstrates that there were significant amounts of positive distant
viewing in Nielsen's NPM database for programs carried on WGNA. See id.
] 26 & App. G. As Dr. Wecker and Mr. Harvey demonstrated, the numerous
zeros for distant viewing on WGNA that were input into Dr. Gray's
regression, combined with the use of the number of distant subscribers
as a variable in the regression specification, created an erroneous
negative correlation between distant subscribership and distant
viewing. See id. ]] 33; 2/22/18 Tr. at 1299-1300 (Harvey); see also 3/
15/18 Tr. at 4054-55 (Gray) (appearing to concede point).
The aggregate effect of the missing WGNA data on Dr. Gray's
predictions of distant viewing, and on the viewing shares he computed
therefrom, cannot be determined with any certainty from the record. It
was incumbent on Program Suppliers to demonstrate that the effect of
the missing WGNA data did not have a substantial influence on Dr.
Gray's results. They failed to do so. Program Supplier's efforts to
argue, essentially, that the omission of the WGNA data was harmless
error are unavailing. The JSC rebutted Dr. Gray's assertion that
compensable programming on WGNA had declined significantly, showing
that JSC programming on WGNA remained stable during the 2010-2013
period. See Bortz Report, at 28 Table III-2. The Wecker Report rebutted
Dr. Gray's assertion that his computed viewing shares were accurate as
to the non-WGNA stations in his sample. See Wecker Report, ]] 33. As
for Dr. Gray's assertion that his viewing analysis produced viewing
shares that were within a ``zone of reasonable consideration,'' 3/14/18
Tr. at 3764 (Gray), the ``zone of reasonableness'' is a legal construct
that is solely within the purview of the Judges. Dr. Gray's views on
what lies within or without a zone of reasonableness are immaterial.
4. Other Asserted Methodological Defects
As recounted above,\169\ several experts identified what they found
to be methodological errors in Dr. Gray's analysis, including his
decision to use Nielsen NPM data and not to apply Nielsen's weighting
to that data; his sample design and application of sampling weights;
his program categorization; his imputation of zero viewing values to
quarter hours not represented in the Nielsen data; and his substitution
of regression-based predicted distant viewing values for the observed
distant viewing in the Nielsen data. Because the Judges have found an
adequate basis for rejecting Dr. Gray's viewing study based on its
failure to provide a complete measurement of value, and its reliance on
incomplete data, the Judges do not need to evaluate the remaining
critiques.
---------------------------------------------------------------------------
\169\ See sections 0-0.
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E. Conclusion Concerning Viewing Study
Dr. Gray's viewing study provides an incomplete and therefore
inadequate measure of relative market value of disparate categories of
distantly-
[[Page 3601]]
retransmitted programming. While viewing is relevant to value, it does
not adequately measure the premium that cable operators are willing to
pay for certain types of programming in the analogous market for cable
channels.
Even if viewing were an adequate basis for apportioning value among
program categories, Dr. Gray's study is fatally flawed by its reliance
on Nielsen data that omitted distant viewing on WGNA--the most widely
retransmitted station in the United States.
For the foregoing reasons, the Judges will not rely on Dr. Gray's
viewing study for apportioning royalties among the program categories
represented in this proceeding.
V. Cable Content Analysis
Dr. Israel also undertook an analysis that he characterized as a
``Cable Content Analysis''--focusing on the dollar amount paid by CSOs
to carry sports and other programming during the years 2010-13. More
particularly, for the years 2010-13 he considered the amounts that
cable networks spent per hour of programming televised in relation to
total household viewing hours (HHVH). Israel WDT ] 45. As explained in
more detail, infra, Dr. Israel concluded that CSOs place a high value
per hour on live sports programming compared with other program
categories. He further opined that his Cable Content Analysis presented
results that were consistent with the share estimates determined by the
Bortz Survey. Israel WDT ] 46.
More particularly, according to Dr. Israel, his Cable Content
Analysis demonstrated that in each year of the 2010-13 period, CSOs
networks paid significantly more per hour for JSC programming than for
any other category of programming. Making this point in an alternative
manner, Dr. Israel testified that the JSC's programming share of CSO
expenditures was larger than the JSC programming share of CSO broadcast
minutes or HHVH. Israel WDT ] 46.
Table V-5 of Dr. Israel's WDT, set forth below, compares total
program hours, total HHVH, and total CSO expenditures for JSC
programming with all other categories of programming on the top twenty-
five cable networks:
Table 17--Cable Content Analysis 2010-2013, Summary of Top 25 Networks
----------------------------------------------------------------------------------------------------------------
Total Expenditures Expenditures
Category programming Total HHVH Expenditures per hour of per hour of
hours % (000) % ($M) % programming viewing
[A] [B] [C] [D] = [E] =
[C] / [A] [C] / [B]
----------------------------------------------------------------------------------------------------------------
JSC............................. 9,274.0 15,164,368.9 $12,524.7 $1,350,517.6 $0.826
Non-JSC......................... 866,726.0 496,492,970.2 42,702.0 49,268.2 0.086
JSC / Non-JSC................... 0.01 0.03 0.29 27.41 9.60
JSC % of Total.................. 1.06 2.96 22.68 .............. ..............
----------------------------------------------------------------------------------------------------------------
Israel WDT ] 47 Table V-5.
As this table shows, for the top twenty-five cable networks, JSC
programming represents approximately 1% of all programming in terms of
hours transmitted and less than 3% of total HHVH. Nonetheless, these
top twenty-five cable networks applied more than 22% of their
programming budgets to acquire the rights to transmit JSC programming.
Dr. Israel further highlighted the importance of JSC programming to
these cable networks, relative to other categories, by expressing the
data on a per hour basis. Dividing total expenditures by total hours of
programming per category, he showed that expenditures per hour of JSC
programming are worth more than 27 times other programming categories.
Dr. Israel also calculated these expenditures per hour of household
viewing and found that JSC programming was worth almost 10 times more
per hour of viewing than all other programming categories on the top
twenty-five cable networks. Israel WDT ] 47; Table 17, supra.
Dr. Israel also looked more granularly at two cable networks, TBS
and TNT, which he noted (without opposition) carried a mix of JSC and
other program categories. His analysis showed patterns that were
similar to what he had found with regard to the top twenty-five cable
networks, viz., that JSC programming was far more valuable than all
other program categories. Specifically, during the years 2010-13, JSC
programming accounted for approximately 2% of the total programming
hours transmitted by TBS, and about 3% of the total programming hours
transmitted by TNT. In terms of viewership, the JSC generated roughly
5.5% of total HHVH on TBS during the four-year period and about 7.9% on
TNT. In contrast to these relatively small percentages of programming
and viewing hours, TBS spent 44.4% of its 2010-13 programming budget on
JSC programming, and TNT quite similarly spent 45.5%. Once again,
expressing these choices on an hourly basis, expenditures per hour of
JSC programming were more than 40 times greater than expenditures per
hour of all other programming on TBS, and expenditures per hour of JSC
programming were almost 30 times greater than expenditures per hour of
all other kinds of programming on TNT. In terms of expenditures per
HHVH, TBS spent more than 13 times as much on JSC programming than on
other program categories, and TNT spent almost 10 times as much
compared with its spending on other program categories. Israel WDT ] 48
& Table V-6.
According to Dr. Israel, these absolute and relative differences
are reflected in ``the significantly higher license fees that cable
systems and other MVPDs [Multichannel Video Programming Distributors]
pay to carry these networks.'' Israel WDT ] 51. Dr. Israel presented
data to support this point, analyzing the 97 nationally and regionally
distributed cable networks with a minimum of 50 million subscribers in
2013. Of these 97 networks, he found that 14 offered telecasts of JSC
events and 83 did not. Over the full 2010-13 period, Dr. Israel found
that the average license fee for the 14 cable networks that offered JSC
programming (along with other programming) was $0.753 per subscriber
per month, whereas for the 83 cable networks that did not offer JSC
programming, the average license fee over the four year period was much
lower, $0.174 per subscriber per month. Israel WDT ] 51.
[[Page 3602]]
In opposition, Program Suppliers asserted that this analysis ``is
irrelevant to this proceeding.'' PSPFF ] 354. In support of this
argument they rely on Dr. Gray's assertion that ``consistent with
Professor Crawford's economic arguments, after negotiating programming
deals with cable networks carrying live team sports programming, CSOs
may then have a sufficient quantity of that type of programming to
bundle for its current and potential subscribers [such that] live team
sports programming would be less valuable to CSOs than other types of
programming.'' Gray CWRT ] 60.
In response to this opposition, the JSC asserted that Dr. Gray had
misapplied Professor Crawford's explanation that CSOs have an incentive
to add differentiated distant signal programming to their bundles
``because it can help to attract and retain subscribers.'' JSC RPFF ]
46 & n.174 (and record citations therein). More particularly, the JSC
argued that Program Suppliers' argument regarding program-type
saturation would not apply only to JSC programming. As they asserted:
``[T]hat argument would apply equally to [Program Suppliers] (and
others), whose content likewise is on cable networks in addition to
local and distant signals; it provides no basis to ascribe a lower
relative value to JSC.'' JSC PFF ] 50 (and record citations therein).
The Judges understand Dr. Israel's Cable Content Analysis to be in
the nature of an assertion that a similar market provides relevant and
meaningful information regarding the relative values of distantly
retransmitted local programs in a hypothetical market in which the
statutory royalty structure did not exist. As such, Dr. Israel's
approach is similar to the ``benchmark'' approach that is a hallmark of
the sound recording and musical works rate proceedings within the
Judges' jurisdiction. That is, parties in those proceeding regularly
present economic evidence regarding royalty rates in other markets,
urging the Judges to find sufficient comparability between the
``benchmark'' market and the hypothetical market at issue. When Judges
decide whether and how to weigh such benchmark evidence, they begin
with the following foundational analysis that is equally applicable
here:
In choosing a benchmark and determining how it should be
adjusted, a rate court must determine [1] the degree of
comparability of the negotiating parties to the parties contending
in the rate proceeding, [2] the comparability of the rights in
question, and [3] the similarity of the economic circumstances
affecting the earlier negotiators and the current litigants, as well
as [4] the degree to which the assertedly analogous market under
examination reflects an adequate degree of competition to justify
reliance on agreements that it has spawned.
In re Pandora Media, 6 F. Supp. 3d 317, 354 (S.D.N.Y. 2014), aff'd sub
nom., Pandora Media, Inc. v. ASCAP, 785 F.3d 73 (2d Cir. 2015).
In the present case, Dr. Israel has not attempted to make such a
structured analysis. Rather, the Judges understand his argument to be
based on the assumption that the rights at issue are comparable (i.e.,
the programs can be categorized in a similar manner) and the buyers/
licensees (the CSOs) are identical in both markets. However, in all
other respects--regarding economic circumstances, competitive
positions, and the nature of the seller/licensor--the relative
similarities or differences are unexplored.
Accordingly, the Judges are reluctant to put much weight on Dr.
Israel's Cable Content Analysis. At most, the Judges rely on his Cable
Content Analysis as demonstrating that JSC programming enjoys a level
of demand out of proportion to its broadcast minutes, not inconsistent
with the results of his regression analysis and Dr. Crawford's
regression analysis.
VI. Changed Circumstances
The Judges and their predecessors have looked at a ``changed
circumstances'' analysis in prior proceedings. In the 1998-99 cable
distribution proceeding, the CARP recommended allocation to the four
largest categories strictly based on the Bortz survey results.\170\
Because PTV and CCG were undervalued by the Bortz survey, the CARP
recommended adjustment of allocations to those categories, giving
``some weight'' to the remarkable increases in relative fee generation
and in ``changed circumstances'' as measured by an increase in
subscriber instances.\171\ See Final Order, Distribution of 1998 and
1999 Cable Royalty Funds, 69 FR 3606, 3617 (Jan. 26, 2004). In the
2000-03 distribution proceeding, the Judges salvaged consideration of
changed circumstances by differentiating a fee generation methodology
from a changed circumstances evidentiary consideration. See
Distribution Order, \172\ 75 FR 26798, 26805-07 (May 12, 2010) (2000-03
Distribution Order). Ultimately, the CARP concluded that changed
circumstances, as measured by changes in subscriber instances alone,
revealed a change in programming volume, which did not necessarily
translate to a change in programming value. 1998-99 Librarian Order, 69
FR at 3616.
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\170\ SDC did not challenge the relative share indicated by the
Bortz results. 1998-99 Librarian Order, 69 FR at 3609 n.15.
\171\ A ``subscriber instance'' as used in these proceedings
relating to distant signal retransmission means one subscriber
having access to one distant signal.
\172\ The 2000-03 Distribution Order was a ``Phase I'' or
category allocation determination.
---------------------------------------------------------------------------
In the present proceeding, PTV retained Ms. Linda McLaughlin and
Dr. David Blackburn, who filed joint written testimony. See Trial Ex.
3012. The McLaughlin/Blackburn report focused on the share of royalties
that would reflect the relative value of PTV programming only. See 3/7/
18 Tr. at 2446 (McLaughlin). McLaughlin and Blackburn began with the
PTV share from the 2004-05 distribution proceeding, which was based
largely on Bortz survey results. See Amended Testimony of McLaughlin
and Blackburn, Trial Ex. 3007 at 7 (McLaughlin/Blackburn AWDT). Using
primarily data from the Cable Data Corporation (CDC), they analyzed not
just changes in subscriber instances, but external changes in various
unit measures from 2005 to the relevant period, 2010-13, viz., distant
subscriber instances, distant signal transmissions, and the balance of
programming types distantly retransmitted. See id. at 7-8. Each of
their unit measures indicated an increase in the PTV relative share,
and all of their unit measures indicated a basis for an increase in
PTV's relative share for the period at issue in this proceeding. As Ms.
McLaughlin testified, however, an increase in unit measures does not
compel a conclusion that value also increased. 3/7/18 Tr. at 2648
(McLaughlin).
For valuation, McLaughlin and Blackburn analyzed survey results,
regression analyses, and viewership studies. For survey analysis, they
used the 2004-05 Bortz survey as a starting point. The Bortz Survey
omitted respondents whose distantly retransmitted signal carried only
PTV or only CCG or only PTV and CCG together.\173\ McLaughlin and
Blackburn added those omitted stations to the Bortz Survey results,
using the overall Bortz response rates by stratum, and by assuming, for
example, that the PTV-only systems would assign a relative value to PTV
of 100%.\174\ They then
[[Page 3603]]
recalculated the Bortz Survey relative value for PTV, by stratum, using
the relative values she determined. McLaughlin and Blackburn noted that
the increase resulting from their augmentation of the Bortz Survey
yielded a smaller PTV relative value (9.9%) than did the Horowitz
Survey (15.8%), which included PTV- and CCG-only systems from the
outset. They attributed this discrepancy to the participation bias
evident in the Bortz data, i.e., that fewer eligible systems carrying
PTV responded to the Bortz Survey than the Horowitz Survey. See
Rebuttal Testimony of McLaughlin and Blackburn, Trial Ex. 3002, at 4
(McLaughlin/Blackburn WRT).
---------------------------------------------------------------------------
\173\ Ms. McLaughlin estimated that the average number of
omitted stations over the period 2010-13 was 16 per year. See 3/5/18
Tr. at 2457 (McLaughlin).
\174\ Ms. McLaughlin also assumed that CCG-only systems would
assign a relative value of CCG at 100%. 2/20/18 Tr. at 719-20
(Mathiowetz); 3/6/18 Tr. at 2291 (Frankel). In fact, not all
Canadian programming falls within the CCG category for royalty
purposes. CCG conceded that, for example, some programming broadcast
on Canadian stations should rightfully be attributed to the SDC. 3/
7/18 Tr. at 2675 (Erdem); Boudreau CWDT at 3-4, 10. The volume of
mischaracterized programming is not great, but, as Professor
Mathiowetz pointed out, a change in the relative allocation to any
one category necessarily changes the allocation to other categories.
2/20/18 Tr. at 701 (Mathiowetz).
---------------------------------------------------------------------------
On rebuttal, McLaughlin and Blackburn noted that their own
calculations augmenting the Bortz survey probably also underestimated
the relative value of PTV, because they originated with the 2004-05
Bortz survey, which was tainted with participation bias. See id. at 4.
McLaughlin and Blackburn asserted that participation bias also
discounted the value of the 2010-13 Bortz Survey as an accurate measure
of the relative value of PTV programming. Id. at 5.
McLaughlin and Blackburn looked at Professor Crawford's econometric
study to confirm that marginal value per minute of distantly
retransmitted programs changed in a like manner to her unit
measurements. She noted increases in relative value from Dr.
Waldfogel's 2004-05 regression analysis, on the one hand, and Professor
Crawford's and Dr. Israel's regression analyses on the other: 20.8%
under Professor Crawford's analysis and 15% using Dr. Israel's
analysis. 3/7/18 Tr. at 2472-73 (McLaughlin). As Ms. McLaughlin
testified, the Crawford study establishes a price, from which value may
be ascertained: ``value is . . . a quantity times a price. . . . '' 3/
7/18 Tr. at 2653 (McLaughlin).
Ms. McLaughlin opined that viewership is just another unit measure,
not a valuation. Nonetheless, she contended that the results of Dr.
Gray's viewership analysis were consistent with the survey and
regression analyses, indicating a PTV relative market value of 12.6%.
See McLaughlin/Blackburn WDT at 23.
The Judges find that quantifying changes in various unit measures,
while not without corroborative value, is not a definitive approach to
relative valuation, especially in comparison to other more probative
approaches, such as regression analyses. Apparently, PTV ultimately
made the same assessment. See PTV PFF ] 11 (``[Professor] Crawford's
econometric framework is the best suited methodology to determine the
claimants' shares in this proceeding for the years 2010 through
2013.''). Accordingly, the Judges consider PTV to have adopted
Professor Crawford's regression analysis as the methodology on which it
has relied in this proceeding.
VII. Nonparticipation Adjustment for PTV
In its proposed findings of fact and conclusions of law, PTV raised
the issue of Basic Fund allocation adjustment to account for PTV not
being a participant in the 3.75% Fund. See PTV PFF/PCL at ]] 43-45.
Although there was mention of the 3.75% Fund in the record of the
proceeding, no party addressed the issue comprehensively. The Judges
issued an order seeking additional briefing, including an inquiry about
both the 3.75% Fund and the Syndex Fund. See Order Soliciting Further
Briefing (Jun. 29, 2018) (June 29 Order). Specifically, the Judges
asked
[w]hether the interrelationship between and among the Basic Fund,
the 3.75% Fund, and the Syndex Fund affects the allocations within
the Basic Fund, if at all, and, if so, how that affect should be
calculated and quantified.
June 29 Order at 1. The Judges expressly asked for legal analysis of
the issue. The Judges refused to allow introduction of any new evidence
but agreed to accept affidavits, if appropriate, to clarify the record
evidence of any witness. Id. at 2.
In their responses, the parties agreed that only Program Suppliers
were entitled to any royalties in the Syndex Fund and that the size of
the fund was so insignificant in context that the Judges should not
make any adjustment to allocations in the Basic Fund to compensate for
any party's exclusion from the Syndex Fund. See, e.g., SDC Brief at 1
n.1; SDC Responsive Brief at 5 (``given the minuscule amount of money
in the Syndex Fund, any calculation to compensate for that fund would
constitute nothing more than a rounding error to a second or third
decimal place. . . .''). The parties offered analysis and argument
regarding the 3.75% Fund.
The essence of the Judges' question is whether the record evidence
was intended to propose an allocation of all royalty funds in all three
funds, which might imply an adjustment to the Basic Fund allocations
for parties that did not participate in the other two funds. Program
Suppliers submitted affidavits from their witnesses asserting that
their analysis focused on the Basic Fund only. Accordingly, according
to the Program Suppliers' argument, the Judges should simply scale the
Basic Fund allocation by eliminating PTV from the calculation of
allocation percentages for the 3.75% Fund. See Program Suppliers'
Responsive Brief at 6. PTV and the SDC both argued contrariwise that
the Judges should scale the Basic Fund up for PTV. PTV/SDC derived
their argument from prior allocation determinations. See PTV Brief at
5-7; SDC Brief at 1-5.
All parties agree that the PTV category is ineligible for an
allocation of royalties assigned to the 3.75% Fund.\175\ The Judges
found, however, that the parties did not agree whether PTV's
nonparticipation in the 3.75% Fund affects the allocations within the
Basic Fund. Moreover, the Judges found that the arguments and evidence
presented by the parties was insufficient for the Judges to resolve the
issue. That problem was compounded by the fact that prior
determinations, regarding how the 3.75% Fund allocations might affect
the Basic Fund allocation, were themselves contradictory and did not
address all the issues the Judges have concluded are relevant.
Consequently, on June 29, 2018, the Judges entered an Order soliciting
further briefing regarding:
---------------------------------------------------------------------------
\175\ The five parties eligible to share the royalties allocated
to the 3.75% Fund (CCG, CTV, JSC, Program Suppliers, and the SDC)
agree that, to reflect PTV's nonparticipation in the 3.75% Fund, the
Judges must adjust each eligible group's share of that fund in
proportion to its respective share of the Basic Fund. See 2004-05
Distribution Order, 75 FR at 57071; Declaration of Howard Horowitz ]
4 (Jul. 13, 2018); Declaration of Jeffrey S. Gray ] 8 (Jul. 16,
2018); see also JSC Initial Brief at 3-4. The Judges apply this
approach in allocating shares in the 3.75% Fund in the present
proceeding.
Whether the interrelationship between and among the Basic Fund,
the 3.75% Fund, and the Syndex Fund affects the allocations within
the Basic Fund, if at all, and, if so, how that affect should be
---------------------------------------------------------------------------
calculated and quantified.
Order Soliciting Further Briefing (Jun. 29, 2018) (3.75% Fund
Order).\176\ In
[[Page 3604]]
accordance with the 3.75% Fund Order, the parties filed briefs and
responding briefs on these issues, The Judges weighed the parties'
arguments and based on their analysis, the Judges do not adjust PTV's
share of the Basic Fund to reflect its nonparticipation in the 3.75%
Fund or to reflect any alleged inconsistencies between the record
evidence, on the one hand, and the separate allocations to the Basic
Fund and the 3.75% Fund, on the other.
---------------------------------------------------------------------------
\176\ The parties agreed that Program Suppliers are entitled to
receive 100% of the remaining royalties from the Syndex Fund.
Further, the amount in that Fund, less than $10,000 per six-month
accounting period, see JSC Initial Brief at 2 n.1, is so low that,
even assuming arguendo allocations to the Syndex Fund would require
an adjustment to the Basic Fund, such an adjustment would be
``inconsequential.'' CTV Initial Brief at 11 n.20; see also SDC
Initial Brief at 1 n.1 (the Syndex Fund comprises ``only about 0.01%
of total royalties paid in 2010-2013.''). Accordingly, the
discussion in this section is limited to the impact, if any, of the
allocations to the 3.75% Fund on the allocations in the Basic Fund.
---------------------------------------------------------------------------
A. Arguments of the Parties
The parties disagree as to how, if at all, the scaling of the 3.75%
Fund allocations might affect allocations in the Basic Fund. PTV argues
that it is entitled to an ``Evidentiary Adjustment,'' \177\ whereby its
share of the Basic Fund is ``bumped up'' \178\ to offset its
nonparticipation in the 3.75% Fund. PTV Initial Brief at 1-2. PTV
alleges that this increase is necessary because ``[t]he surveys and
econometric estimates of value to CSOs determine shares of the Combined
Royalty Funds for each of the programming claimants'' and that ``[a]s a
result, in order for PTV to receive the share of total value to CSOs
estimated by the . . . experts, it must receive a larger share of the
Basic Fund, since it will receive no share from the [3.75% Fund].'' Id.
at 7 (quoting McLaughlin/Blackburn WDT at 24-25). In addition, PTV
maintains that it is entitled to this Evidentiary Adjustment regardless
of whether the Judges allocate the Basic Fund shares based on survey
evidence, regression evidence, or viewing evidence. PTV Responding
Brief at 12-21. PTV also argues that this result is supported by
precedent and by the record in this proceeding. PTV Initial Brief at
10-16.\179\
---------------------------------------------------------------------------
\177\ PTV broadly defines the phrase ``Evidentiary Adjustment''
as the process by which ``the Judges must . . . convert the
[evidentiary] studies' estimated shares based on the `Combined
Royalty Funds' [i.e., estimated without explicit regard to an
itemization among the three specific funds] to shares tailored to
the particular funds from which the parties are entitled to
recover.'' Id. at 1. For the sake of clarity, the Judges utilize the
phrase ``Evidentiary Adjustment'' more narrowly in this
Determination, to mean only the potential bump up of PTV's share of
the Basic Fund to account for its nonparticipation in the 3.75%
Fund.
\178\ Of course, because the Basic Fund is finite, any bump up
in PTV's share would necessitate a decrease in the percentage
allocations to the other five claimant groups proportionate to their
relative shares (inter se) of the Basic Fund.
\179\ The Judges discuss the relevant prior rulings, infra,
section 0.
---------------------------------------------------------------------------
JSC, CTV, and the SDC agree that prior rulings support PTV's
assertion that it is entitled to a bump up in its Basic Fund share, but
only to the extent the Judges tie the Basic Fund allocations to the
Bortz Survey results and no other allocation methodology.\180\ Those
parties maintain that the language in prior rulings supports such an
adjustment only to that limited extent. See JSC Initial Brief at 7-8;
CTV Initial Brief at 10; SDC Initial Brief at 9-10.
---------------------------------------------------------------------------
\180\ In prior rulings by the Judges and the Librarian (in the
CARP era), the Bortz survey was the only survey of CSO
representatives given any credence. In the present case, the
Horowitz Survey also surveyed CSO representatives. The Judges find
no basis to treat these two surveys differently in connection with
the issue of whether PTV should receive an increase in its Basic
Fund share to account for its nonparticipation in the 3.75% Fund.
---------------------------------------------------------------------------
By contrast, CCG argues that, in light of the evidence presented,
PTV's Basic Fund shares should be adjusted upward, regardless of the
allocation methodology employed by the Judges, to account for PTV's
non-participation in the 3.75% Fund. See CCG Initial Brief at 6.
At the other extreme, Program Suppliers oppose any increase in
PTV's Basic Fund share, arguing that such an increase ``effectively,
albeit indirectly, compensates PTV for royalties to which it is not
entitled.'' Program Suppliers Initial Brief at 2. Further, Program
Suppliers argue that relevant prior rulings that may have suggested PTV
was entitled to this upward adjustment were based on incorrect
reasoning and that none of them ``rises to the level of controlling
precedent.'' Id. at 7; see Program Suppliers Responding Brief at 2.
Finally, arguing in the alternative, Program Suppliers assert that,
even under PTV's view of the relevant prior rulings, PTV would not be
entitled to the Evidentiary Adjustment it seeks unless ``PTV's Basic
Fund share was derived solely from the Bortz Survey.'' Program
Suppliers Initial Brief at 7.
B. Analysis
1. Statutory Law and Regulations
Any upward adjustment of PTV's share of the Basic Fund to account
for its non- participation in the 3.75% Fund would be inconsistent with
the regulations that established the 3.75% Fund because CSOs are
expressly exempted from paying into the 3.75% Fund for the distant
retransmission of noncommercial educational stations. See 37 CFR
387.2(c)(2).\181\
---------------------------------------------------------------------------
\181\ The original regulatory text was located in 37 CFR, part
308. See 37 CFR 308.2(c)(2). In 2016, the Judges recodified this
provision in Part 387, without changing the relevant language. See
Adjustment of Cable Statutory License Royalty Rates, 81 FR 24523
(April 26, 2016); Adjustment of Cable Statutory License Royalty
Rates 62812 (Sept. 13, 2016) (Note that the CFR version of Part 387
erroneously lists the second Federal Register page cite as page
62813.).
---------------------------------------------------------------------------
More particularly, the CRT established the 3.75% Fund in 1982 to
offset the negative economic effects on owners of copyrights on
commercial programming arising from the FCC's elimination of its rule
setting a ceiling on the number of distant commercial stations a CSO
could retransmit. See Final Rule, Adj. of the Royalty Rate for Cable
Sys., 47 FR 52146 (Nov. 19, 1982). The regulation implements
Congressional policy as expressed in 17 U.S.C. 801(b)((2)(B), which
provides that ``[i]n the event that the . . . [FCC] . . . permit[s] the
carriage by cable systems of additional television broadcast signals
beyond the local service area . . . the royalty rates established by
section 111(d)(1)(B) may be adjusted to ensure that the rates for the
additional [DSEs] resulting from such carriage are reasonable in light
of the changes effected by the [FCC] . . . . ''). See also Malrite T.V.
of New York, Inc. v. FCC, 652 F.2d 1140, 1148 (2d Cir. 1981) (``The
plain import of Sec. 801 is that the FCC, in its development of
communications policy, may increase the number of distant signals that
cable systems can carry and may eliminate the syndicated exclusivity
rules, in which event the [CRT] is free to respond with rate
increases.'').\182\
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\182\ In economic terms, the new 3.75% Fund royalties substitute
a tariff for a quota, in order to maintain some form of protection
of the value of copyrights on local commercial programs in markets
into which CSOs would now be able to retransmit an unlimited number
of commercial stations from distant locales.
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Thus, any upward adjustment in the Basic Fund by the Judges to
``compensate'' PTV--i.e., non-commercial stations--would constitute an
unlawful back-door attempt to modify this regulation and would be
inconsistent with the statutory provision on which it is based. See
generally 5 U.S.C. 706(2)(A) and (C) (agency action unlawful if ``not
in accordance with law'' or ``in excess of statutory jurisdiction,
authority, or limitations, or short of statutory right.'').
2. Administrative Process
Even assuming arguendo that applicable statutory law permits the
adjustment PTV seeks, any such adjustment would amount to an
adjudicatory change to an economic policy that was created through a
separate administrative rulemaking proceeding initiated for the express
purpose of protecting only those copyright owners who, as a result of
FCC action, lost the protection afforded by the ceiling on the number
of a CSO's distant retransmissions of commercial broadcasts. See 47 FR
52146. The Judges
[[Page 3605]]
will not shoehorn a de facto change in the regulations in this
adjudicatory proceeding by permitting PTV to share in the royalty
revenue collected by the levy of the ``penalty rate'' \183\ of 3.75% of
gross receipts.
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\183\ See, e.g., PTV Initial Brief at 4 (3.75% rate ``sometimes
called the `Penalty Rate' '' because it applies higher royalty rate
``to the retransmission of additional distant signals beyond the
limited number that cable systems could carry under the [f]ormer FCC
Rules.'').
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3. Unauthorized Redistribution of Wealth and Income
Any adjustment upward to PTV's Basic Fund allocation to account for
its nonparticipation in the 3.75% Fund would amount to a redistribution
of wealth and income by the Judges that is not authorized by law or
regulation. That is, any reduction in the Basic Fund royalties paid to
owners of copyrights on programs distantly retransmitted on commercial
stations to ``compensate'' PTV for its nonparticipation in the 3.75%
Fund would constitute the imposition of an economic loss on the former
and an economic windfall on the latter, in terms of the value of the
program copyrights (a redistribution of wealth) and the flow of
royalties realized from such ownership (a redistribution of income).
The Judge find no basis in law to support such a transfer of wealth or
income.
PTV argues though that ``[n]othing could be further from the
truth'' than the characterization of its position as seeking to share
in the 3.75% Fund. PTV Responding Brief at 5. In point of fact, PTV's
argument is tantamount to an attempt to appropriate value from the
3.75% Fund. Although PTV does not seek a ruling that it is legally
entitled to share in the 3.75% Fund, it seeks a ruling that it is
economically entitled to appropriate value from the Basic Fund, as
measured by its non-participation in the 3.75% Fund. The Judges are as
concerned with the economic incidence of the application of the so-
called Evidentiary Adjustment as they are with the legal incidence of
PTV's attempt to appropriate wealth and income from a fund that, by
law, belongs to other claimants.\184\
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\184\ The distinction between economic incidence and legal
incidence is typically exemplified in the analysis of sales taxes.
The seller bears the legal incidence by writing a check to the
governmental unit assessing the tax, but the seller and the consumer
share the economic incidence of the sales tax, the latter paying a
portion of the tax in the form of a higher prices for the taxed
item, with the allocation of the economic incidence between merchant
and consumer determined by the elasticity of demand for the taxed
item. See R. Posner, Economic Analysis of Law at 491-495 (6th ed.
2003). Analogously, the economic incidence of PTV's argument is
transparent; although the legal incidence of its argument--bumping
up its Basic Fund share--is not expressly prohibited, 100% of the
economic incidence of its argument is a shift to itself wealth and
income from the lawful participants in the 3.75% Fund.
---------------------------------------------------------------------------
In the face of the foregoing points, PTV and all the other parties
except Program Suppliers nonetheless argue that two factors--evidence
and precedent--support the subsidy sought by PTV. The two arguments are
considered below.
4. The Evidence-Based Argument
As an initial matter, the Judges note that the evidence-based
argument asserted by PTV and other parties in support of the
Evidentiary Adjustment cannot overcome the legal points, discussed
above, that make it legally impermissible to bump up PTV's share of the
Basic Fund.
Additionally, the Judges find the evidence-based argument made by
and on behalf of PTV, standing alone, to be insufficient. Broadly, PTV
and other parties assert that the Evidentiary Adjustment is
necessitated by the purported nature of the survey evidence and the
regression evidence.\185\ The Judges reject this argument.
---------------------------------------------------------------------------
\185\ Again, PTV makes the same argument with regard to the
viewing evidence. However, that issue is moot, because, as explained
supra, the Judges do not apply the viewing evidence in making
allocations.
---------------------------------------------------------------------------
a. The Survey Evidence
With regard to the survey evidence, PTV notes that the survey
questions did not explicitly ask the respondents to ``differentiat[e]
between the Basic, 3.75% and Syndex Rates,'' and ``their responses
presumably were based on their past payments at all rates into the
Combined Royalty Funds.'' PTV Initial Brief at 10-11 (emphasis added);
see also CTV Initial Brief at 6 (survey responses measure relative
value of distant signals ``without regard to the royalty rate paid for
any particular signal''). According to this argument, the survey
responses could not reflect the effects, if any, of the higher royalty
rate of 3.75% of gross receipts paid by CSOs into the eponymous 3.75%
Fund. Rather, according to this argument, the survey responses
reflected relative value in the combined royalty funds. Therefore, PTV
asserts that it is entitled to the Evidentiary Adjustment, bumping up
its Basic Fund allocation to offset the economic effect of its
nonparticipation in the 3.75% Fund.
The Judges find this argument to lack sufficient merit. The two
surveys were designed to allow for the selection of respondents to the
surveys who were the individuals most responsible for programming
carriage decisions at the CSO. See Bortz Survey at 14-15 & App. B;
Horowitz WDT at 9, 24; see also 2/15/18 Tr. 254 (Trautman); 3/16/18 Tr.
4109 (Horowitz). Neither survey was designed to question whether the
individuals who self-reported in fact possessed this knowledge, or to
test the extent or specific aspects of respondents' knowledge.
The Judges decline to presume, in the context of this 3.75% Fund
dispute, that the survey respondents lacked knowledge as to the
variable royalties paid for distantly retransmitted stations, when the
accepted survey evidence upon which the Judges rely (the same type of
survey evidence on which their predecessors have consistently relied)
presumes the opposite, i.e., that the respondents are indeed
knowledgeable regarding this sector of the cable industry.\186\ Indeed,
the argument that the Judges should presume that the survey respondents
were ignorant of the impact on royalty costs of retransmitting a given
number of distant local stations \187\ also proves too much, because it
would call into question any reliance on the survey evidence.
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\186\ The Judges part company with the CARP determination
(adopted by the Librarian), allocating royalties for 1998 and 1999,
in which the CARP stated that the adjustment is warranted because
``the Bortz respondents . . . presumably did not know that PTV would
not be eligible to receive part of their budget allocation . . . .
'' Distribution of 1998-1999 Cable Royalties, at 26 n.10 (Oct. 21,
2003), adopted by the Librarian 69 FR 3606 (Jan. 26, 2004). When the
Judges have qualified and relied upon expert survey witnesses, the
Judges cannot, without contrary evidence, inject a presumption
inconsistent with their qualifications. The Judges consider that and
other prior rulings infra.
\187\ The Judges find no reason to presume that survey
respondents who were otherwise deemed by the survey experts, based
on answers to introductory questions, to be knowledgeable about
their programming and carriage decisions, would not also be aware
that they could add an educational station without incurring the
higher 3.75% royalty, whereas the addition of a commercial station
in certain instances did trigger the 3.75% royalty. All parties
accepted, and the Judges agreed, that the individuals responsible
for making distant retransmission decisions for the cable systems
understood that the CSO paid the minimum fee of 1.064%, regardless
of whether they distantly retransmitted any local stations. It would
be inconsistent to presume, on the one hand, that CSO executives
were cognizant of a 1.064% minimum fee, but were ignorant of the
3.75% rate--more than 300% greater than that minimum fee--when the
responsible executives answered the surveys.
---------------------------------------------------------------------------
Moreover, the Bortz Survey includes a question--Question #3--in
which the respondents are directed to consider the costs associated
with the retransmission of categories of programs. Although the
question is linked to the cost of program categories rather than the
cost of retransmitting entire stations, the question was designed as a
``warm-up'' question that would encourage
[[Page 3606]]
respondents to be cognizant of the costs associated with their
decisions to distantly retransmit stations containing the categories
represented in this proceeding. See Bortz Survey, App. at 15. Thus, the
Bortz Survey evidence tends further to support the assumption that the
respondents were cognizant of the costs, including the royalty costs,
associated with retransmitting distant local stations.\188\
---------------------------------------------------------------------------
\188\ Although Question #3 referred to program categories, it is
still relevant to the 3.75% Fund issue, because only the five other
claimant categories (i.e., other than PTV) could have triggered the
higher royalty cost. Thus, a knowledgeable survey respondent could
not be presumed to lack knowledge of the different impact on value
from adding an educational station rather than a commercial station.
---------------------------------------------------------------------------
For these reasons, the Judges cannot adopt a presumption that the
survey respondents, deemed knowledgeable in all other pertinent
respects regarding distant retransmissions of local stations, were
ignorant of the royalty costs associated with the number and type of
local stations they carried. Thus, there is not a sufficient
evidentiary predicate for the application of the Evidentiary
Adjustment.\189\
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\189\ In response to the Judges' 3.75% Fund Order, Program
Suppliers submitted a Declaration by Howard Horowitz, who designed
the Horowitz Survey, in which he stated that it is ``appropriate''
to apply the allocation of the Horowitz Survey shares ``to any fund
in which all parties participate.'' Declaration of Howard Horowitz ]
4 (July 16, 2013). This statement would support the Judges'
decision, but the Judges give no weight this declaration, for two
reasons. First, Mr. Horowitz did not offer any such testimony during
the proceeding; therefore his declaration is impermissible new
testimony (not clarifying testimony). Second, in the absence of
persuasive hearing testimony, Mr. Horowitz cannot opine as to what
would be the ``appropriate'' allocation of the Horowitz Survey
shares. What is an appropriate allocation in this context is a
question of law reserved to the Judges.
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b. The Regression Evidence
Turning to the Crawford and Israel regressions, PTV's arguments
fare no better. As the SDC explained in its briefing: ``Each regression
includes an indicator for retransmission of a 3.75% signal [with]
statistically significant coefficients for the indicator variables
suggest[ing] that there is a systematic difference in the amount of
royalties paid by systems and subscriber groups that retransmit 3.75%
signals and those that do not.'' SDC Initial Brief at 4. Thus, the
Crawford and Israel regression analyses demonstrated a correlation
between the amount of royalties paid by a CSO and its participation in
the 3.75% Fund. This correlation is essentially tautological. CSOs who
pay the higher 3.75% royalty rate for the distant retransmission of one
or more additional commercial local stations (previously ``non-
permitted'' under the since-repealed FCC ``ceiling'' regulation) will
pay higher royalties than CSOs that pay no more than 1.064% to
retransmit such stations. See id. (correlation is ``not surprising,
considering that retransmission of a 3.75% signal by definition carries
a higher rate''). Moreover, Dr. Crawford confirmed that the coefficient
for the 3.75 control variable in his regression analysis was both large
and statistically significant. Crawford WDT at App. B Fig. 22.\190\
---------------------------------------------------------------------------
\190\ CTV, on whose behalf Dr. Crawford undertook his regression
analysis, argues in its briefing that Dr. Crawford's 3.75% Fund
coefficient ``may already be accounted for to some degree'' in his
overall regression analysis. CTV Responding Brief at 7 (emphasis
added). Not only is this statement highly conditional (as noted by
the italicized language, CTV also did not submit a supporting
declaration from Dr. Crawford properly clarifying how his hearing
testimony supported this assertion, despite the Judges' invitation
in the 3.75% Fund Order to submit witness statements. Instead, CTV
referred to Dr. Crawford's hearing testimony on an unrelated issue
in which he stated, with regard to a different control variable,
that its coefficient estimate should be included in a regression
analysis when there are ``good'' economic and statistical reasons to
do so. See 2/28/18 Tr. 1643 (Crawford). The Judges do not dispute
this point, but it is not relevant to the task at hand. As an
indicator (dummy) variable in a regression designed to generate
estimates for relative value results among program categories, the
3.75% Fund variable was designed to control for the influence of the
3.75% Fund impact on those relative values. Dr. Crawford further
testified that any control variable that would correlate
significantly with the dependent variable should be included in the
regression model so that it does not bias the coefficients of
interest (the program categories' coefficients in the present case),
Id. at 1644 (Crawford). Thus, the excerpt from Dr. Crawford's
testimony, when considered in context, does not demonstrate that the
impact of participation in the 3.75% Fund is already ``accounted
for'' in his overall regression analysis in a manner relevant to the
present issue.
---------------------------------------------------------------------------
Likewise, Dr. Israel ``[s]imilar to Dr. Waldfogel,'' included an
indicator variable ``for whether a CSO pays the special 3.75 percent
fee,'' and he held this factor ``constant'' in order to determine the
extent of any correlation between royalty payments and additional
minutes of programming category content. Israel WDT ]] 33-34. In his
regression model, Dr. Israel estimated a coefficient of 41,918 for his
``Indicator for Special 3.75% Royalty Rate,'' multiple times the
coefficients he estimated for any other variable. Id. ] 36, Table V-1.
Thus, the regression evidence in the hearing records provides
independent support for distinguishing the allocations in the 3.75%
Fund from the allocations in the Basic Fund. Accordingly, the
regression evidence provides substantial support for rejecting PTV's
proposed bump-up in its Basic Fund allocation to offset its non-
participation in the 3.75% Fund.\191\
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\191\ The Judges emphasize a distinction between their
consideration of the 3.75% Fund regression coefficients and their
evaluation of the various coefficients relied on by Dr. Erdem to
predict the level of royalty payments. The Judges discounted Dr.
Erdem's emphasis on coefficients relating, for example, to the
number of CSO subscribers, because such coefficients, as Dr.
Crawford testified, simply re-created the royalty formula. However,
now the Judges are called upon to distinguish and apply a separate
royalty formula--the formula for the 3.75% Fund--from the formula
for the Basic Fund. In this latter context, the coefficients related
to the 3.75% Fund are indeed relevant. Accordingly, what constituted
vice in the critique of the Crawford regressions with regard to
allocations among the program categories is virtue in distinguishing
between two different categories of rate formulas.
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5. The Effect of Prior Decisions
The second argument raised by PTV and supported by several other
parties, is that the Judges are bound by prior decisions of CARP
panels, the Librarian, and the Judges, in which the Evidentiary
Adjustment was either applied or found to be generally valid. PTV
Initial Brief at 10-12; PTV Responding Brief at 9-12; JSC Initial Brief
at 4-6; CTV Brief at 1-6; SDC Initial Brief at 1-7. That is, they argue
that prior rulings, by the force of their reasoning or as controlling
law, require the Judges to bump up PTV's share of the Basic Fund to
account for its non-participation in the 3.75% Fund.
More particularly, PTV and other parties make this argument in
several alternative forms, from broad to narrow. PTV and CCG argue that
prior rulings support increasing PTV's share of the Basic Fund to
reflect not only the survey-based allocations but also the regression-
based allocations, whereas JSC, CTV, and the SDC assert that PTV's
survey-based allocations should be bumped-up, only to the extent the
Judges apply the survey share percentages in making their overall
allocations.
The Judges conclude that there is neither controlling law nor any
prior determination or other ruling that binds them on this issue.
Further, the Judges do not agree with the explanations in two prior
rulings that applied or legitimized the application of the Evidentiary
Adjustment. To the extent those prior rulings might, arguendo,
constitute controlling law or might, arguendo, have properly applied or
legitimized the Evidentiary Adjustment on the record in those cases,
the Judges find those rulings distinguishable, based on the particular
facts of the present case.
a. The 1986 CRT Determination
In a 1986 determination regarding the distribution of 1983
royalties, the CRT ruled that public television (represented by PBS in
that proceeding) was not entitled to participate in the 3.75%
[[Page 3607]]
Fund because ``non-commercial educational stations could be carried on
an unlimited basis prior to FCC deregulation, and . . . no cable
operator paid the 3.75% rate to carry any noncommercial stations.''
1983 Cable Royalty Distribution Proceeding, 51 FR 12792, 12813 (Apr.
15, 1986), aff'd sub nom. Nat'l Ass'n of Broadcasters v. CRT, 809 F.2d
172, 179 n.7 (2d Cir. 1986) (``because cable carriage of noncommercial
educational stations was not limited by the old distant signal rules,
PBS is not eligible for royalties at the new 3.75% rate''). Further,
there was no argument by the parties, and no discussion in the 1986
determination, with regard to the issue at hand, viz. whether PTV
should receive an upward adjustment to its Basic Fund allocation to
account for its non-participation in the 3.75% Fund. See 51 FR 12792 et
seq.
Accordingly, the Judges find no aspect of the 1986 determination to
be on point with regard to whether PTV is entitled to an upward
adjustment in its Basic Fund share to offset its non-participation in
the 3.75% Fund. Indeed, the 1986 determination would be consistent with
the rejection of such an adjustment.
b. The 1992 CRT Determination
The next CRT determination concerned distribution of cable
television royalties for the 1989 year. 1989 Cable Royalty Distribution
Proceeding, 57 FR 15286 (Apr. 27, 1992). PBS was again denied any share
of the 3.75% Fund ``because PBS stations are not paid for at the 3.75%
rate . . . . '' 57 FR at 15303.
In this 1992 case, public television claimants, through PBS,
requested the bump up in their adjustment to the Basic Fund that is at
issue in the present proceeding, i.e., ``to back out the 3.75%
portion'' from the Basic Fund. See 57 FR at 15300. The CRT rejected
this proposed adjustment, relying on the testimony of Paul Bortz
(president of the entity that administered the Bortz Survey), who
stated that ``there was nothing in his survey to suggest that
respondents were considering their 1989 copyright payment as the fixed
budget they were allocating.'' Id.
The Judges find this rationale to be cryptic at best, because there
is no obvious logical link between Mr. Bortz's description of the
mindset of the CSO survey respondents and its impact on whether PBS's
share of the Basic Fund should have been adjusted upward to reflect the
survey evidence. In fact, Mr. Bortz's testimony could be construed as
supportive of the upward adjustment in the public television claimants'
share of the Basic Fund. Accordingly, the Judges do not find any
controlling or persuasive authority in the 1992 determination that can
serve as guidance in the present proceeding.
c. The 1990-92 CARP Report and the Librarian's Order
In the proceeding to allocate royalties for the 1990-1992 period,
PTV argued on behalf of public television claimants for an Evidentiary
Adjustment to its share of the Basic Fund, as that share was estimated
by the CARP's reliance on the Bortz Survey.\192\ The CARP ruled, with
regard to the question of whether to adjust PTV's share of the Basic
Fund:
---------------------------------------------------------------------------
\192\ While this proceeding was pending, Congress abolished the
CRT. The proceeding continued under the auspices of the CARP
appointed to distribute the royalties.
PTV also contends that a further adjustment should be made in
its award because its total share of the adjusted Bortz Survey must
come entirely from the Basic Fund and the Bortz survey does not
differentiate between the Basic fund and the 3.75 fund in which PTV
does not participate.
. . .
PTV's proposed further adjustment to allow for its non-
participation in the 3.75 fund is rejected for the same reason given
by the [CRT] in the 1989 proceeding. Mr. Bortz specifically
disavowed any intention or implication in his survey to have
respondents answer based on their royalty payments.
1990-92 CARP Phase I Distribution Report 120, 124 (Jun. 3, 1996) (1990-
92 CARP Report). The Judges find that the CARP's reliance on the prior
reasoning of the CRT only serves to repeat the cryptic nature of that
prior ruling, and does not offer any basis on which the Judges may rely
to resolve the issue in this proceeding.
When Congress instituted the CARP process, it also charged the
Librarian with the duty to accept or reject, in whole or in part, the
decision of a CARP, and charged the Register with the duty to provide
recommendations to the Librarian. 17 U.S.C. 802(f) (2003) (superseded).
Discharging her duty in that 1990-92 proceeding, the Register made
specific recommendations to the Librarian regarding the issues
pertaining to the 3.75% Fund, all of which the Librarian adopted. The
Register described, and the Librarian agreed, that the CARP's reasoning
supporting its distribution of the 3.75% Fund was ``at best, terse.''
Distribution of 1990, 1991 and 1992 Cable Royalties, 61 FR 55653, 55662
(Oct. 28, 1996) (Librarian's Order).
In her recommendations, the Register more specifically addressed
the issue at hand, rejecting PTV's request for the Evidentiary
Adjustment.
The Panel did not act arbitrarily in rejecting PBS's \193\ Bortz
adjustment for the same reasons articulated by the [CRT] in 1989. .
. . [T]he approach used in the Bortz survey itself remained
unchanged. As in the 1989 proceeding, Bortz did not ask cable
operators to base their program share allocation according to the
royalties they actually paid. Thus, in awarding PBS programming a
specific share, a [CSO] did not take into account that its stated
share only applied to the Basic Fund and not the 3.75% fund. . . .
The Bortz survey numbers therefore do not necessarily require the
adjustment demanded by PBS. Thus, the Panel was reasonable in
adopting the [CRT's] 1989 rationale because PBS's argument, and the
design parameters of the Bortz survey, were fundamentally the same.
\193\ The Librarian identified the public television claimants
as the PBS claimants, rather than the PTV claimants as had the CARP.
---------------------------------------------------------------------------
Id. at 55668. However, for the first time in a distribution proceeding,
the door was opened to an argument that this Evidentiary Adjustment
might be appropriate in certain contexts, as the Register further
recommended:
The Panel did not state that it was using PBS's Bortz numbers as
the sole means of determining its award. In fact, the Panel awarded
PBS a share that is less than the unadjusted Bortz survey numbers.
Had the Panel stated that it was attempting to award PBS its Bortz
share, then PBS's argument might have some validity. However, since
the Panel did not, it did not act arbitrarily in denying PBS's
requested adjustment.
Id. (emphasis added).
d. The 2003 CARP Determination and the Librarian's Order
In 2003, for the first time, public television claimants, through
PTV, were successful in obtaining a ruling that supported the
application of the Evidentiary Adjustment. Specifically, a CARP adopted
PTV's argument that it was entitled to the Evidentiary Adjustment,
whereby its share of the Basic Fund was increased to offset the impact
of its non-participation in the 3.75% Fund. The CARP Report was adopted
by the Librarian, upon the recommendation of the Register. 1998-99 CARP
Report, supra note 144, at 26, n.10, adopted by the Librarian, 69 FR
3606.
The 1998-99 CARP found that, based on the evidence, PTV's ``raw
Bortz figure'' was 2.9% for both 1998 and 1999, prior to the
application of the Evidentiary Adjustment. 1998-99 CARP Report at 26
n.10. The CARP then, over JSC's opposition, bumped up this ``raw''
percentage ``to account for PTV's non-participation in the 3.75% . . .
fund[ ].'' Id. The CARP explained its rationale:
[[Page 3608]]
The Adjustment makes sense in the context of a CSO Survey where
the respondents are allocating a fixed budget among the various
claimant groups--unless JSC can demonstrate that the respondents
already understood that PTV does not participate in the 3.75% Fund.
JSC has made no such showing.
Id.
The CARP also sought to distinguish the prior rejections of this
Evidentiary Adjustment by the CRT and the 1990-92 CARP panel.
The Panel is aware that the 1989 CRT rejected this Adjustment to
Bortz and the 1990-1992 CARP adopted that rejection . . . . The
Panel believes the 1989 CRT and 1990-92 CARP did not fully
appreciate the logic supporting this Adjustment. It is precisely
because the Bortz respondents did not answer based on their actual
royalty payments and presumably did not know that PTV would not be
eligible to receive part of their budget allocation that the
Adjustment is warranted.
Id. (citation omitted) (boldface added). However, the 1998-99 CARP
Report did not make an upward adjustment to PTV's overall Basic Fund
allocation or to any measure of its relative share of the Basic Fund
other than the Bortz Survey percentage, concluding:
[W]e disagree with PTV's assertion that it is entitled to such an
Adjustment no matter which methodology is employed. . . . We view
PTV's position that the adjustment should be made for any
methodology merely as an attempt to circumvent mathematically the
legal precedents established by the CRT, and PTV has presented no
legal justification for reversing these precedents.
Id. Consistent with this limitation, the 1998-99 CARP did not apply the
Evidentiary Adjustment to the regression approach utilized by Dr.
Gregory Rosston, an economic expert who presented a regression analysis
on behalf of another party. See 1998-99 CARP Report, supra note 144, at
45-51 (discussing Rosston regression approach). However, although the
CARP did not apply the Evidentiary Adjustment, it did not explicitly
state its reasoning, nor did the CARP provide any specific rationale
for not applying the Evidentiary Adjustment to the Rosston regression
approach, other than to refer to the general discussion in that same
report.. See id. at 48 n.21 & 59 n.29 (citing p. 26 n.10).
In the end, the CARP applied the Evidentiary Adjustment by
increasing PTV's Basic Fund minimum allocation, or ``floor,'' as
derived from the Bortz Survey, from 2.9% to 3.2%. 1998-99 CARP Report,
supra note 144, at 25-26, & n.10. The final allocation to PTV though
was based on additional evidence, which led the CARP to establish PTV's
share above this floor, at 5.49125%, the same level as in the prior
proceeding. Id. at 69; see 69 FR 3606, 3610, 3616 & n.32.
The Librarian, upon the recommendation of the Register, accepted
the CARP Report in its entirety. 69 FR at 3606. However, neither the
Register nor the Librarian made any specific recommendations or
findings regarding the Evidentiary Adjustment applied by the CARP to
increase PTV's allocation floor from 2.9% to 3.2%. See 69 FR at 3616-
17[.
In the present proceeding, Program Suppliers assert that, because
the CARP set PTV's Basic Fund share above the 3.2% floor, it had not
actually applied the Evidentiary Adjustment to the Bortz Survey
results. Therefore, Program Suppliers argue that the CARP's analysis
regarding the Evidentiary Adjustment was mere dicta, rather than a
controlling endorsement of the Evidentiary Adjustment. Program
Supplier's Responding Brief at 3-4. The Judges disagree with Program
Suppliers' characterization of that ruling. The fact that PTV's
ultimate Basic Fund Share exceeded the floor does not call into
question the ruling by the CARP or the Librarian that the Evidentiary
Adjustment, in their opinion, should be applied.\194\
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\194\ However, as discussed infra, for other reasons, the Judges
do not conclude that the decisions by the CARP and the Librarian to
apply the Evidentiary Adjustment are dispositive in the present
proceeding.
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e. The Judges' 2010 Determination
In 2010, the Judges determined the allocation of royalties for the
2004 and 2005 distribution years.\195\ See 2004-05 Distribution Order.
There, the Judges applied the Evidentiary Adjustment on behalf of PTV,
as proposed by the ``Settling Parties.'' \196\ Id. at 57070. However,
the Judges did not engage in any analysis of the Evidentiary Adjustment
(and indeed did not even describe that adjustment or identify it by
name). Rather, they simply adopted as a ``starting point'' the
augmented Bortz Survey ``which includes appropriate adjustments to the
PTV share'' and then referred to paragraph 317 of the ``Settling
Parties'' Proposed Findings of Fact. That paragraph stated: ``Because
PTV receives payments from only the Basic fund, an adjustment to the
augmented survey results is needed to produce PTV's share of the Basic
fund, as recognized by the CARP in the 1998-99 Proceeding.'' Id.
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\195\ Congress replaced the CARP system with the Judges in 2004
(effective 2005). Copyright Royalty and Distribution Reform Act of
2004, Public Law 108-419, 118 Stat. 2341 (Nov. 30, 2004).
\196\ The ``Settling Parties'' were comprised of: JSC, CTV, PTV,
and Music Claimants. Id. at 57064.
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In the present proceeding, PTV further notes that, in that 2010
proceeding, Professor Waldfogel asserted that his regression approach,
like the Bortz survey approach, had not differentiated between the
Basic Fund and the 3.75% Fund, thus purportedly supporting an
application of the Evidentiary Adjustment to the regression
allocations. PTV Initial Brief at 14-15. PTV further asserts that
Professor Waldfogel's testimony was consistent with Dr. Rosston's
testimony in the prior proceeding, supporting the application of the
Evidentiary Adjustment to Basic Fund allocations based on regression
analyses. Id. at 13-14. Notwithstanding that testimony, in neither of
those cases did the CARP, the Librarian, or the Judges find that the
Evidentiary Adjustment should be applied to the regression results. See
JSC Responding Brief at 7, 9.
6. The Prior Decisions Are Not Binding
The Judges do not find the foregoing findings and conclusions
sufficient to overcome the analysis they undertake in this proceeding.
First, none of the prior cases considered the dispositive statutory or
regulatory issues discussed herein. Second, the prior cases are
factually distinguishable, because neither the survey evidence nor the
regression evidence support the application of the Evidentiary
Adjustment to PTV's share of the Basic Fund. Third, as explained below,
as a matter of law, the Judges are not duty bound to apply the
Evidentiary Adjustment on behalf of PTV as it relates to the survey
evidence, notwithstanding the conclusions in the two most recent
distribution cases.
The Copyright Act does not equate relevant prior rulings with
binding legal precedent. Rather, the Act provides only that the Judges
shall ``act on the basis . . . of prior determinations and
interpretations . . . .'' 17 U.S.C. 803(a)(1) (emphasis added). As the
D.C. Circuit has explained, this provision does not mandate that the
Judges abide by specific findings in prior rulings, provided the Judges
set forth a ``reasoned explanation'' for a departure from those
findings. See Program Suppliers v. Librarian of Congress, 409 F.3d 395,
402 (D.C. Cir. 2005). In the present determination, the Judges have
explained the legal, administrative, policy, economic, and factual
reasons why an application of the Evidentiary Adjustment on behalf of
PTV is unwarranted. The two prior rulings that applied the Evidentiary
Adjustment did not address these multiple factors, and
[[Page 3609]]
certainly did not consider the issue at the depth warranted by the
supplemental briefing required in this proceeding.
Further, the prior decisions reveal that the relevant tribunals
went through an evolution, from prohibiting the application of the
Evidentiary Adjustment, to acknowledging its potential application and,
then, to supporting its application. Thus, the ``controlling'' aspect
of those prior decisions, if any, appears to be the proposition that
this thorny issue needs to be considered in detail, and that no prior
decision should be extended if the successor tribunal, through reasoned
explanation, finds good cause to render a decision different from the
one that immediately preceded it.
7. The Waiver Argument
In its Responding Brief, PTV asserts, for the first time, that
Program Suppliers, the SDC, and JSC, each ``waived'' its right to
contest the application of the Evidentiary Adjustment. PTV Responding
Brief at 21-26.\197\ PTV makes two basic arguments in support of its
theory of waiver. First, it argues that Program Suppliers, the SDC, and
JSC ``knowingly and intentionally'' did not ``submit evidence or
advance arguments'' regarding the Evidentiary Adjustment, seeking to
depart from or to distinguish the prior determinations that adopted
PTV's construction of the Evidentiary Adjustment. Id. at 21. Second,
PTV notes that none of these parties raised the issue of the
application of the Evidentiary Adjustment in closing arguments. Id. at
22. PTV acknowledges that Program Suppliers did address the issue
previously, but only in response to PTV's PCL addressing the
Evidentiary Adjustment issue. See PTV Initial Brief at 9 (citing
Program Suppliers' RPCL ] 12. Accordingly, PTV, relying on four
decisions,\198\ asserts that Program Suppliers, the SDC, and JSC waived
their arguments against the Evidentiary Adjustment.
---------------------------------------------------------------------------
\197\ There is an element of irony in PTV's assertion of waiver
for the first time in its Responding Brief. By not making this legal
argument of waiver in its July 16, 2018 Initial Brief, PTV prevented
adverse parties from addressing the issue of waiver. See, e.g., U.S.
v. Layeni, 90 F.3d 514, 522 (D.C. Cir. 1996); In re Brand Name
Prescription Drugs Antitrust Litig. 186 F.3d 781, 790 (7th Cir.
1999) Although PTV might claim that it could not have been certain
it had the right to assert the waiver argument until it had reviewed
these parties' Initial Briefs, such a position would be belied by
the fact that PTV's waiver argument is based on the alleged absence
from the hearing record of adverse facts relating to facts or
arguments concerning the impact, if any, of the 3.75% Fund
allocations on the allocations of the Basic Fund. Thus, PTV appears
to have waived its waiver argument. Nonetheless, the Judges consider
and reject PTV's waiver argument on the merits.
\198\ The cases are cited at PTV's Responding Brief at 22 n.85
and discussed below.
---------------------------------------------------------------------------
The Judges find PTV's waiver argument to be inapposite, given the
procedural posture of the proceeding. The Judges found the hearing
record and legal arguments to be incomplete with regard to the impact,
if any, of allocations in the 3.75% Fund on the allocations in the
Basic Fund. That deficiency extended to PTV's briefing as well as to
the briefing of the other parties. In an attempt to cure the
incompleteness, the Judges, sua sponte, entered the 3.75% Fund Order,
which specifically noted the insufficiency of the facts (``exhibits
[and] witness testimonies'') and the law (``legal arguments''), which
could be remedied by supplemental ``memoranda of law,'' as well as new
affidavits that ``clarif[ied]'' the extant record. Id. at 1. In sum,
the deficiencies in the factual presentations and legal briefings of
the parties were the bases for the Judges' ordering of supplemental
briefing.\199\ It would be anomalous for the Judges to now reverse
course and find that the arguments relevant to this issue had been
waived prior to the submission of supplemental filings, when those
deficiencies had themselves engendered the 3.75% Fund Order.
---------------------------------------------------------------------------
\199\ The Judges regularly exercise discretion to seek
supplemental briefing in order to address an issue that had not been
sufficiently addressed during the hearing. A judicial order
directing the filing of supplemental papers is the preferred method
by which judges should address issues they find to have been
insufficiently considered. See United States Nat'l Bank of Oregon v.
Ind. Agents of America, 508 U.S. 439 (1991) (affirming D.C.
Circuit's sua sponte raising of unaddressed issue and ordering
supplemental briefing). Moreover, supplemental briefing provides the
parties a full and fair opportunity to address relevant issues that
were insufficiently developed and argued. Trest v. Cain, 522 U.S.
87, 92 (1997) (``We do not say that a court must always ask for
further briefing when it disposes of a case on a basis not
previously argued . . . [but] often . . . that somewhat longer (and
often fairer) way `round is the shortest way home.'') (dicta); see
also R. Offenkrantz & A. Lichter, Sua Sponte Actions in the
Appellate Courts: The ``Gorilla Rule'' Revisited, 17 J. App. Prac.
113, 120 (Spring 2016) (noting the Supreme Court's ``preference for
ordering supplemental briefing when a new issue is raised sua sponte
. . . . ''); B. Miller, Sua Sponte Appellate Rulings: When Courts
Deprive Litigants of an Opportunity to be Heard, 39 San Diego L.
Rev. 1253, 1281-82, 1297-1300 (2002) (courts more likely to raise,
sua sponte, ``questions of law,'' and ``routinely ask the parties
for supplemental briefs when deciding a new issue.''); R. Ginsburg,
The Obligation to Reason Why, U. Fla. L. Rev. 205. 214-15 (1985) (in
D.C. Circuit, if judges identify a potentially dispositive point not
raised by the parties, they generally invite supplemental briefs).
In the present case, the Judges also have wide statutory
discretion to cure deficiencies in the legal or factual record to
mitigate the harm that might otherwise necessitate a finding of
waiver. See 17 U.S.C. 801(c) (``The . . . Judges may make any
necessary procedural . . . rulings in any proceeding under this
chapter. . . . ''). The ordering of supplemental briefing is one
example of the exercise of that discretion, and its invocation
renders moot a claim that legal arguments had been waived.
The parties' supplemental briefing ultimately did not address
all of the legal reasons in the full detail that the Judges now rely
upon to conclude that they cannot bump-up PTV's share of the Basic
Fund to offset its non-participation in the 3.75% Fund. However, as
Nat'l Bank of Oregon further holds, a court can rule sua sponte even
if the parties fail to address in their supplemental briefing the
issue on which the court sought such briefing. Id. at 447. Moreover,
in that decision, the Supreme Court held that lower courts may
reframe the legal issues posed by the parties, in order to ensure
that the law is correctly applied, lest the parties force the court
to misstate the law. Nat'l Bank of Oregon at 446-47. In the same
vein, ``[a] court should apply the right body of law even if the
parties fail to cite their best cases.'' Palmer v. Bd. Of Educ., 46
F.3d 682, 684 (7th Cir. 1995 (Easterbrook, J.). Here, a fortiori,
because PTV did not make its legal waiver argument until it filed
its Responding Brief (the very tactic of which it accuses Program
Suppliers regarding the substantive Evidentiary Adjustment issue),
the adverse parties had no opportunity to cite any cases.
---------------------------------------------------------------------------
The four cases PTV string cites in its responding brief,\200\ are
not on point, and do not alter the Judges' analysis. U.S. v.
Laslie,\201\ American Wildlands v. Kempthorne,\202\ and U.S. v. L.A.
Tucker Truck Lines, Inc.,\203\ all involved litigants who raised issues
for the first time during judicial review of action by a trial court or
administrative agency, and thus had engaged in an ``intentional
relinquishment of a known right,'' which is the essence of an act of
waiver. Laslie, 716 F.3d at 614. These cases are clearly
distinguishable because: (1) The arguments raised with regard to the
impact, if any, the 3.75% Fund has on allocation of the Basic Fund
relate to an issue still before the tribunal hearing the matter; (2)
the Judges have called for supplemental briefing on the very issue; and
(3) the Judges' have concluded that the issue can and should be decided
as a matter of law.
---------------------------------------------------------------------------
\200\ See PTV Responding Brief at 22 n.85.
\201\ 716 F.3d 612 (D.C. Cir. 2013).
\202\ 530 F.3d 991 (D.C. Cir. 2008).
\203\ 344 U.S. 33 (1952).
---------------------------------------------------------------------------
The final case cited by PTV is Intercollegiate Broadcast. Sys.,
Inc., v. Copyright Royalty Bd., 574 F.3d 748 (D.C. Cir. 2009). There,
the D.C. Circuit declined to consider an argument, raised by an
appellant for the first time ``[n]early a year after appealing the
Judges' order, and almost three months after filing its opening brief.
. . . '' Id. at 755. Although the D.C. Circuit accepted the
supplemental briefing and permitted responsive briefing, the court
expressly noted that it was allowing that briefing ``without
prejudice'' as to whether it would consider the delinquent issue on
appeal. Id. The D.C. Circuit ultimately ruled that it would not
consider the
[[Page 3610]]
issue, noting that, notwithstanding its discretionary ``power'' to
consider the delinquently briefed issue, it chose not to exercise that
discretion, in part because of the incomplete nature of the briefing
and the far-reaching consequences of the delinquently raised issue. Id.
at 755-56.
Intercollegiate is clearly not on point. To the extent the D.C.
Circuit's procedure for weighing whether to consider a delinquently
raised issue is analogous to the present case, the D.C. Circuit
emphasized that it was a matter of discretion. Likewise, the Judges
have the discretion, pursuant to 17 U.S.C. 801(c), to make procedural
rulings in furtherance of their statutory duties. The fact that the
D.C. Circuit chose in Intercollegiate to allow supplemental briefing--
without prejudice to its ultimate ruling that the delinquently asserted
issue would not be heard--in no way suggests that the Judges in this
proceeding are barred (by an assertion of waiver, or otherwise) from
exercising their statutory discretion by deciding the issue at hand,
after ordering supplemental briefing.
C. Conclusion Regarding Nonparticipation Adjustment
For the foregoing reasons, the Judges do not apply an Evidentiary
Adjustment to or otherwise adjust PTV's share of the Basic Fund to
reflect PTV's nonparticipation in the 3.75% Fund.
VIII. Conclusions and Award
As many witnesses testified in this proceeding, no one methodology
can be a perfect measure of relative market value of categories of
television programs distantly retransmitted by cable television
systems. That is inevitable, because the market value of distantly
retransmitted programs cannot be measured directly: Cable systems do
not buy retransmission rights from the program copyright owners and
cable systems do not acquire retransmission rights to broadcast
stations in marketplace transactions. In the applicable scheme, prices
are set by statute. Neither the copyright owners' valuations nor the
general laws of supply and demand apply in all their particulars in
setting prices as they would in an unregulated market. Use of different
methodologies can assist the Judges by illuminating different aspects
of the buyers' valuation.
In this proceeding, the participants, through their respective
expert witnesses, took a variety of approaches to estimate how cable
systems value programming on distant signals. Some witnesses looked to
survey evidence in which CSOs estimated relative value of programming
by category. Cable system fact witnesses also considered whether the
value of the distantly retransmitted programs is generated more by
acquisition of new subscribers or by retention of niche viewers.
A broadcast station's valuation of programming is driven by each
show's popularity among viewers: Viewership translates to advertising
income for the broadcast station. Program Suppliers advocated looking
at that viewership to determine relative value. While viewership is
important for broadcasters, the Judges conclude, based on the evidence
and arguments presented, that viewership, without more, is an
inadequate measure of relative value of different categories of
programming distantly retransmitted by cable systems. The Judges,
consistent with the past several allocation decisions, give no weight
to viewership evidence in allocating royalties among the various
program categories.
Several participants' econometricians who testified in this
proceeding analyzed value from the perspective of what CSOs actually
had done in terms of deciding which distant signals to retransmit on
their systems. The essence of their regression approaches was the same
as the fundamental correlation in the Waldfogel regression analysis in
the 2004-05 proceeding--the correlation between royalties paid and
minutes of programming in each program category on each distant signal.
As discussed, the Judges place primary reliance on Professor Crawford's
regression analysis, and rely on his duplicated minutes approach, as to
which he expressed no methodological reservations during his testimony.
After considering all the methodologies and supporting evidence
presented by the copyright owner groups, the Judges are struck by the
relative consistency of the results across the accepted
methodologies.\204\ In this proceeding, the Judges conclude that the
Horowitz Survey responses and Professor Crawford's duplicate minutes
regression analysis, adjusted to account for methodological limitations
in these approaches, are the best available measures of relative value
of the program categories.
---------------------------------------------------------------------------
\204\ As noted, Dr. Israel's Cable Content Analysis, although
not a methodology that the Judges adopted, provided information on
JSC-related expenditures in a related market sufficient to lend some
support for the award of a significant share to JSC (as indicated by
the methodologies that the Judges have adopted), even though the
shares are disproportionate to the number of programming hours
retransmitted. Similarly, the McLaughlin/Blackburn ``changed
circumstances'' adjustments bolster the results of methodologies
valuing PTV programming above the lower bound set by regression
analyses.
---------------------------------------------------------------------------
The Bortz and Horowitz Surveys, together with the McLaughlin
``Augmented Bortz'' results and the Crawford and George regressions,
taking into account the confidence intervals (when available)
surrounding the point estimates, define the following ranges of
reasonable allocations for each program category in each year:
Table 18--Ranges of Reasonable Allocations
--------------------------------------------------------------------------------------------------------------------------------------------------------
2010 2011 2012 2013
-------------------------------------------------------------------------------------------------------
Min. (%) Max. (%) Min. (%) Max. (%) Min. (%) Max. (%) Min. (%) Max. (%)
--------------------------------------------------------------------------------------------------------------------------------------------------------
JSC............................................. 26.73 41.85 24.82 39.42 28.03 43.81 30.12 45.88
CTV............................................. 13.28 20.48 14.41 23.91 14.25 23.30 10.30 22.60
Program Suppliers............................... 23.88 40.15 22.10 35.70 19.56 30.90 17.27 30.94
PTV............................................. 6.70 17.46 7.90 21.21 6.10 21.61 8.30 29.39
SDC............................................. 0.48 4.20 0.33 6.64 0.25 6.31 0.23 5.20
CCG............................................. 0.01 6.55 1.12 6.61 0.70 7.47 0.38 7.85
--------------------------------------------------------------------------------------------------------------------------------------------------------
Within these ranges, the Judges use Professor Crawford's point
estimates as the starting point for most categories because the Judges
find the Crawford (duplicate minutes) analysis to be the most
persuasive methodology overall on this record. For two specific
categories, however, the Judges deviate from the Crawford analysis
based on other record
[[Page 3611]]
evidence. Specifically, the Judges make a modest upward adjustment to
Professor Crawford's allocation for the SDC category based on the
Horowitz survey results and the Augmented Bortz survey results,
together with testimony concerning the ``niche'' value of devotional
programming. Similarly, the Judges make a modest upward adjustment to
the CCG category based on Professor George's analysis and testimony
that Professor Crawford's analysis (as well as the survey evidence)
undervalues Canadian programming to a degree. The Judges adjust the
Crawford-based allocations for the remaining categories to account for
the increased allocations to the SDC and CCG categories, and to ensure
that the percentages total 100% after rounding. The resulting
allocations are:
Table 19--Basic Fund Allocations
----------------------------------------------------------------------------------------------------------------
2010 (%) 2011 (%) 2012 (%) 2013 (%)
----------------------------------------------------------------------------------------------------------------
JSC............................................. 32.9 30.2 33.9 36.1
CTV............................................. 16.8 16.8 16.2 15.3
Program Suppliers............................... 26.5 23.9 21.5 19.3
PTV............................................. 14.8 18.6 17.9 19.5
SDC............................................. 4.0 5.5 5.5 4.3
CCG............................................. 5.0 5.0 5.0 5.5
---------------------------------------------------------------
Total....................................... 100.0 100.0 100.0 100.0
----------------------------------------------------------------------------------------------------------------
As discussed in section VII, the Judges considered and rejected
PTV's arguments that the allocations of Basic Fund royalties must be
adjusted to account for PTV's non-participation in the 3.75% Fund.
Consequently, the allocations for the Basic Fund set forth in Table 1
are identical to the allocations set forth in Table 19. To arrive at
the allocations for the 3.75% Fund set forth in Table 1, the Judges
have reallocated the PTV share from Table 19 proportionally among the
categories that participate in that fund. In accordance with the
consensus view of the parties, the Judges have allocated 100% of the
funds remaining in the Syndex Fund (after distribution of the Music
Claimants' share) to Program Suppliers.
The allocations described in Table 1 at the outset of this
Determination reflect the Judges' weighing of the evidence and their
findings regarding allocation to each category of programming within
the respective ranges of reasonable allocations.
The Register of Copyrights may review the Judges' Determination for
legal error in resolving a material issue of substantive copyright law.
The Librarian shall cause the Judges' Determination, and any correction
thereto by the Register, to be published in the Federal Register no
later than the conclusion of the 60-day review period.
October 18, 2018.
So ordered.
Suzanne M. Barnett,
Chief United States Copyright Royalty Judge.
David R. Strickler,
United States Copyright Royalty Judge.
Jesse M. Feder,
United States Copyright Royalty Judge.
The Register of Copyrights closed her review of this Determination
on January 28, 2019, with no finding of legal error.
Dated: January 29, 2019.
Suzanne M. Barnett,
Chief United States Copyright Royalty Judge.
Approved by:
Carla B. Hayden,
Librarian of Congress.
[FR Doc. 2019-01544 Filed 2-11-19; 8:45 am]
BILLING CODE 1410-72-P
