Connect America Fund; High-Cost Universal Service Support, 30411-30423 [2012-12539]

Agencies

• FEDERAL COMMUNICATIONS COMMISSION

[Federal Register Volume 77, Number 100 (Wednesday, May 23, 2012)]
[Rules and Regulations]
[Pages 30411-30423]
From the Federal Register Online via the Government Printing Office [www.gpo.gov]
[FR Doc No: 2012-12539]


-----------------------------------------------------------------------

FEDERAL COMMUNICATIONS COMMISSION

47 CFR Parts 36 and 54

[WC Docket Nos. 10-90, 05-337; DA 12-646]


Connect America Fund; High-Cost Universal Service Support

AGENCY: Federal Communications Commission.

ACTION: Final rule.

-----------------------------------------------------------------------

SUMMARY: In this order, the Wireline Competition Bureau (Bureau) adopts 
the methodology for establishing reasonable limits on recovery of 
capital costs and operating expenses or ``benchmarks'' for high cost 
loop support (HCLS). The methodology the Bureau adopts, builds on the 
analysis proposed in the USF/ICC Transformation FNPRM, but also 
includes several changes in response to the comments from two peer 
reviewers and interested parties and based on further analysis by the 
Bureau. These changes significantly improve the methodology while 
redistributing funding to a greater number of carriers to support 
continued broadband investment. The methodology the Bureau adopts today 
is described in detail in a technical appendix to the order.

DATES: Effective June 22, 2012.

FOR FURTHER INFORMATION CONTACT: Amy Bender, Wireline Competition 
Bureau, (202) 418-1469, Katie King, Wireline Competition Bureau, (202) 
418-7491 or TTY: (202) 418-0484.

SUPPLEMENTARY INFORMATION: This is a summary of the Commission's Order 
in WC Docket Nos. 10-90, 05-337; DA 12-646, released on April 25, 2012. 
The full text of this document is available for public inspection 
during regular business hours in the FCC Reference Center, Room CY-
A257, 445 12th Street SW., Washington, DC 20554. Or at the following 
Internet address: https://transition.fcc.gov/Daily_Releases/Daily_Business/2012/db0425/DA-12-646A1.pdf.

I. Introduction

    1. In the USF/ICC Transformation Order, 76 FR 73830, November 29, 
2011, the Commission comprehensively reformed universal service funding 
for high-cost, rural areas, adopting fiscally responsible, accountable, 
incentive-based policies to preserve and advance voice and broadband 
service while ensuring fairness for consumers who pay into the 
universal service fund (Fund). As a component of those reforms, the 
Commission adopted a benchmarking rule intended to moderate the 
expenses of those rate-of-return carriers with very high costs compared 
to their similarly situated peers, while further encouraging other 
rate-of-return carriers to advance broadband deployment. The Commission 
sought comment on a specific methodology to limit reimbursable capital 
and operating costs within HCLS and directed the Bureau to finalize a 
methodology after receiving public input in response to the proposal.
    2. The methodology the Bureau adopts today, which is described in 
more detail in the technical appendix, summarized below and available 
in its entirety at Appendix A, https://transition.fcc.gov/Daily_Releases/Daily_Business/2012/db0425/DA-12-646A1.pdf, builds on the 
analysis proposed in the USF/ICC Transformation FNPRM, 76 FR 78384, 
December 16, 2011, but also includes several changes in response to the 
comments from two peer reviewers and interested parties and based on 
further analysis by the Bureau. These changes significantly improve the 
methodology while redistributing funding to a greater number of 
carriers to support continued broadband investment. The Bureau now 
estimates that support to approximately 100 study areas with very high 
costs relative to similarly situated peers will be limited, while 
approximately 500 study areas will receive additional, redistributed 
support to fund new broadband investment.
    3. In view of the Commission's intent to ``phase in reform with 
measured but certain transitions,'' the Bureau will phase in the 
application of these limits. As directed by the Commission, the Bureau 
is providing public notice in Appendix B (https://transition.fcc.gov/Daily_Releases/Daily_Business/2012/db0425/DA-12-646A1.pdf) regarding 
the updated company-specific capped values that will be used in the 
HCLS formula. These capped values (also referred to as limits or 
benchmarks) will be used from July 1, 2012 through December 31, 2012, 
in place of an individual company's actual cost data for those rate-of-
return cost companies whose costs exceed the caps. While the HCLS 
benchmarks will be implemented beginning July 1, 2012, support amounts 
will not be reduced immediately by the full amount as calculated using 
the benchmarks. Instead, support will be reduced commencing in July 
2012 by twenty-five percent of the difference between the support 
calculated using the study area's reported cost per loop and the 
support as limited by the benchmarks, unless that reduction

[[Page 30412]]

would exceed ten percent of the study area's support as otherwise would 
be calculated based on NECA cost data, absent implementation of this 
rule. Beginning January 1, 2013, support will be reduced by fifty 
percent of the difference between the support calculated using the 
study area's reported cost per loop and the support as limited by the 
benchmarks in effect for 2013. Beginning January 1, 2014, when the 
Bureau expects to have updated wire center boundaries, as discussed 
below, the Bureau will update the regressions (the coefficients), and 
support will be limited, in full, by the benchmarks in effect for 2014. 
When fully implemented, the Bureau estimates that the roughly 100 study 
areas that are capped would see approximately $65 million in support 
reductions, while the roughly 500 study areas that are not capped would 
receive approximately $55 million in additional support for broadband 
investment.

II. Discussion

    4. In this order, the Bureau implements the Commission's rule to 
use benchmarks to impose reasonable limits on reimbursable capital and 
operating costs for rate-of-return carriers for purposes of determining 
HCLS and adopt the methodology that the Bureau will use to determine 
carrier-specific benchmarks for rate-of-return cost companies. 
Consistent with parameters set forth by the Commission, the Bureau 
compares companies' costs to those of similarly situated companies 
using statistical techniques to determine which companies shall be 
deemed similarly situated. As described in more detail in the technical 
appendix, summarized below, the Bureau uses NECA cost data and quantile 
regression analyses to generate a capital expense (capex) limit and an 
operating expense (opex) limit for each rate-of-return cost company 
study area. The regression-derived limits are set at the 90th 
percentile of costs for capex and opex compared to similarly situated 
companies. The capped values will be used in NECA's loop cost algorithm 
in place of an individual company's actual cost data for those rate-of-
return cost companies whose costs exceed the caps, which will result in 
reduced support amounts for these carriers. As directed by the 
Commission, NECA will modify the HCLS formula for average schedule 
companies to reflect the caps derived from the cost company data. 
Specifically, the Bureau directs NECA to file proposed modifications to 
the average schedule formula within 30 days of the release of this 
order. After application of the benchmark methodology, HCLS will be 
recalculated to account for the additional support available under the 
overall cap on total HCLS. Additional support will be redistributed to 
carriers whose loop cost is not limited by the benchmark methodology, 
and those carriers are required to use the additional support to 
preserve and advance the availability of modern networks capable of 
delivering broadband and voice telephony service. Beginning January 1, 
2014, carriers unaffected by the benchmark limits will receive 
additional redistributed support as calculated using a lower adjusted 
national average cost per loop (NACPL). The lower NACPL will be the 
NACPL that would be used if total reduced support, as a result of the 
application of the benchmark methodology, is redistributed to all 
carriers. Support to carriers affected by the benchmark will be 
calculated using the NACPL established pursuant to Sec.  36.622 of the 
Commission's rules. During the transition periods July 1, 2012 to 
December 31, 2012 and January 1, 2013 to December 31, 2013, the total 
amount of HCLS available to study areas not affected by the benchmark 
methodology will be the capped HCLS, as calculated pursuant to Sec.  
36.603(a) of the Commission's rules, less the total amount to be paid 
to study areas affected by the benchmark methodology during the 
transition periods. HCLS paid to the study areas not affected by the 
benchmark methodology will be calculated using an adjusted NACPL to 
produce the capped support pursuant to Sec.  36.603(a) of the 
Commission's rules. The Bureau directs NECA to provide to the Bureau a 
recalculated NACPL for redistribution and a schedule of HCLS for all 
carriers for the six-month period of July 1, 2012 to December 31, 2012 
within 30 days of the release of this order. Consistent with current 
practice, the filing NECA makes each October with the Commission shall 
include NACPL information and the schedule of HCLS for all carriers for 
the next year.
    5. The methodology that the Bureau adopts builds on the proposed 
methodology in Appendix H of the USF/ICC Transformation Order and 
FNPRM, but includes some significant improvements based on the many 
useful comments and ex parte presentations in this proceeding, the 
comments of two peer reviewers, and further analysis by the Bureau. As 
in the proposed methodology, the Bureau uses quantile regression 
analysis and NECA cost data to generate a set of limits for each rate-
of-return cost company study area and uses the regression-derived 
limits in NECA's formula for calculating loop cost. The Bureau modifies 
the proposal, however, by reducing the overall number of regressions 
from eleven to two: one for capital expenditures and one for operating 
expenditures. In addition, Commission staff examined and tested 
additional independent variables that were available from publicly 
available data sources, placed additional data sources in the record, 
and updated the methodology to reflect this further analysis. Below, 
the Bureau explains these changes to the proposed methodology and 
responds to other significant issues raised in the record.

A. Number of Regressions

    6. The most significant change in methodology is that this analysis 
generates two caps for each company--a capex limit and an opex limit. 
The methodology proposed in the FNPRM generated eleven different caps 
for each company that would have limited the values in eleven of the 
twenty-six steps in NECA's loop cost algorithm. Based on a review of 
the record and further analysis, the Bureau concludes that a better 
approach is to divide a company's total cost in step twenty-five of the 
algorithm into its capex and opex components and use two regressions 
instead of using eleven independent regressions.
    7. Commenters took differing views on the appropriate number of 
regressions. Commenters supporting more aggregation argue that limiting 
total cost, or separately limiting capital and operating expenses, is a 
better approach and suggest the Bureau use a single regression 
equation, or at most two equations. One peer reviewer also recommended 
this approach. Conversely, some commenters argued that the proposed 
eleven limits would not have allowed the algorithm to calculate support 
as it was intended, and proposed that costs be further disaggregated to 
the underlying cost elements, i.e., ``data lines,'' that make up each 
algorithm step.
    8. The choice of how many cost limits to adopt reflects a balancing 
of considerations. Using a greater number of regressions makes it 
possible to identify outliers at a granular level, but fails to account 
for the interrelationships within the cost categories that feed into 
the twenty-six step algorithm as identified in the record and in the 
peer review. In contrast, using fewer regressions limits the 
Commission's ability to identify outliers, but enables carriers to 
account for the needs of individual networks and recognizes the fact 
that carriers may have higher costs in one category that may be offset 
by lower costs in others.

[[Page 30413]]

    9. Balancing these considerations, the Bureau concludes that it is 
appropriate to reduce the number of separate cost caps set from the 
proposed approach in Appendix H, but to retain separate limits for 
capex and opex. The Bureau is persuaded that limiting eleven separate 
cost categories could have the effect of overly limiting carriers' 
ability to optimize among spending tradeoffs. At the same time, an 
approach that only limited total cost would provide fewer safeguards 
against overspending. Capital and operating expenditures reflect 
fundamentally different measures of business performance. Using two 
regressions instead of one provides carriers flexibility to manage 
their operations, while still enabling the Commission to identify more 
instances where carriers spend markedly more in either category than 
their similarly-situated peers.
    10. The approach the Bureau adopts is also supported by other 
considerations. In particular, the methodology the Bureau adopts 
simplifies the process of fitting the benchmark computation within the 
structure of NECA's loop cost algorithm. Instead of potentially 
limiting values in eleven of the twenty-six steps, only the value for 
companies that exceed the caps in step twenty-five, total unseparated 
costs is changed. Although the components of step twenty-five are 
divided into capex and opex components for purposes of running two 
regressions and separate capex and opex limits are created, the two 
components are added together for purposes of calculating total costs, 
study area cost per loop, and ultimately HCLS.

B. Defining Capex and Opex

    11. As discussed below and in more detail in the technical 
appendix, the Bureau defines capex as the plant-related costs in step 
twenty-five, which include return on capital and depreciation, and 
defines opex as the remaining components that are added in step twenty-
five to calculate total costs. These revised definitions of capex and 
opex differ from those used in the proposed methodology in several 
important ways.
    12. The most important revision to the capex definition is the 
treatment of depreciation in relationship to capital costs. To 
determine capex limits, the proposed methodology created separate caps 
for two categories of gross plant (cable and wire facilities, and 
central office equipment), and for the depreciation and amortization 
associated with those plant categories. In the revised methodology, the 
Bureau defines capex as the return on net plant and depreciation. Many 
commenters pointed out that the proposed methodology did not properly 
account for accumulated depreciation and depreciation expense, and the 
Bureau agrees. The Bureau does not agree, however, with those who argue 
that depreciation expense should not be included in the regression 
analysis. Although depreciation is termed an ``expense'' for regulatory 
accounting purposes, as the Rural Associations and several other 
commenters point out, depreciation expense is properly considered as a 
component of capital costs because it is directly related and 
calculated as a result of capital investment. The proposed methodology 
would have limited gross plant, but did not adjust the accumulated 
depreciation or depreciation expense as would have been necessary when 
gross plant was limited by the benchmark. The method the Bureau now 
adopts includes net plant rather than gross plant, so the methodology 
appropriately accounts for accumulated depreciation.
    13. The revised opex definition includes the remaining components 
that are summed in step 25 in the NECA algorithm to determine total 
unseparated costs. The proposed methodology excluded three of these--
corporate operations expense, operating taxes, and rents--which are now 
included in determining opex. In the USF/ICC Transformation Order, the 
Commission revised the formula for limiting recovery of corporate 
operations expenses for HCLS in Sec.  36.621(a)(4) of the Commission's 
rules. Because of this separate limitation, the proposed methodology 
did not create an additional limit for corporate operations expense. 
Now that the Bureau is analyzing all operating costs as a whole, it is 
appropriate to include corporate operations expense, as well as the 
other operating expenses, taxes and rents. For purposes of this 
analysis, the methodology will use either a carrier's actual corporate 
operations expense or the amount allowable under Sec.  36.621(a)(4), 
whichever is less. Using the allowable amount, avoids restricting 
carriers affected by Sec.  36.621(a)(4) twice for their corporate 
operations expenses above that limitation.

C. Selection of Independent Variables

    14. The revised methodology also includes additional independent 
variables that were suggested by commenters and one of the peer 
reviewers, and eliminates some that had been included in the 
methodology proposed in the USF/ICC Transformation FNPRM, because the 
Bureau found the new variables to be better estimators of cost. In the 
USF/ICC Transformation FNPRM, the Commission noted that NRIC's Capital 
Expenditure Study included variables for frost index, wetlands 
percentage, soils texture, and road intersections frequency, and 
invited commenters advocating the inclusion of additional independent 
variables to identify the data source, completeness, and cost of the 
additional data, if not publicly available. The Commission specifically 
sought comment on sources of soil data other than the Soil Survey 
Geographic Database (SSURGO) used in the NRIC study and how to deal 
with areas where the SSURGO data are missing or incomplete. Many 
commenters suggest additional variables, and Bureau staff examined 
those for which data were available. The technical appendix describes 
in more detail the independent variables included in the methodology, 
those examined but excluded, and those that commenters suggested but 
that could not be included because the data were either unavailable to 
the Commission, nonpublic, or could not be generated at the study area 
level. The variables included in the revised methodology are briefly 
discussed below.
    15. The methodology uses cost-driving variables directly where 
available and proxies that are sufficiently correlated with cost 
drivers where necessary. For example, the number of loops is a direct 
measure of a study area's scale, and the number of road miles is a 
proxy for total loop length. Because most cable follows roads, it is 
reasonable to believe that the number of road miles in a study area is 
a good proxy for the cabling required to serve that area. Some 
commenters suggest that the age of plant is an important variable, and 
the Bureau agrees. Many carriers have recently replaced aging plant 
with modern communications networks capable of providing voice and 
broadband service, and those carriers are not similarly situated to 
carriers with plant that is more fully depreciated. Accordingly, while 
data on the average age of plant are not readily available, the revised 
methodology now includes a variable for the percentage of plant that 
has not yet been depreciated, which is highly correlated with plant 
age. The revised methodology also includes variables that account for 
customer dispersion: density (housing units divided by square miles); 
number of exchanges, which roughly accounts for the population centers 
in a study area; and portion of households in urbanized clusters or 
urbanized areas.

[[Page 30414]]

    16. In addition, the revised methodology includes several 
geographic independent variables that Bureau staff developed from 
various data sources. First, the Bureau agrees with the many commenters 
who argue that the proposed methodology should include soils data. 
Bureau staff used the U.S. General Soil Map (STATSGO2) soils database 
to construct two soil-based variables that are included in the revised 
methodology: depth of bedrock, and soils difficulty. Although the 
SSURGO database contains a richer set of soil variables and data at a 
more granular level than STATSGO2, it does not provide data for the 
entire country. Some commenters argue that the SSURGO data should be 
used where available and STATSGO2 for the remaining study areas, but 
the Bureau declines to use an approach that treats study areas 
differently depending on the availability of the data. In addition, 
NRIC's Capital Expenditure Study includes a frost index developed from 
the SSURGO data, but this information is not available for all areas in 
the STATSGO2 database. Several commenters discuss the need for such a 
frost index. As a proxy for this information, Bureau staff developed a 
climate variable based on the average annual minimum temperature from 
the U.S. Department of Agriculture's hardiness index.
    17. The Bureau also agrees with commenters who emphasized that 
carriers serving particular areas such as Alaska, Tribal lands, and 
national parks could face unique challenges. In particular, some 
commenters suggest that it is more costly to provide service on Tribal 
lands; the methodology now includes an additional independent variable 
for the percentage of each study area that is a federally-recognized 
Tribal land. In addition, Alaskan commenters argued that Alaska is 
unique because of its harsh climate and other factors; accordingly, the 
methodology now includes a variable indicating whether or not the study 
area is in Alaska. Some commenters also argued that it is more 
difficult to construct and maintain networks in national parks; the 
methodology also now includes an additional independent variable for 
the percentage of each study area that lies within a national park. (In 
the future, if sufficient data become available, the Bureau may 
consider including a variable that would account for all federal lands 
(i.e., that is not limited to national park lands).) NRIC's Operating 
Expenses Study found that operating expenses were correlated with 
regions, and Bureau staff tested variables for the four census-based 
regions: Western, Midwest, Northeast and South. The revised methodology 
also includes the two that were significant: the Midwest and Northeast.

D. Use of Boundary Data

    18. All geographic independent variables were rolled up to the 
study area using Tele Atlas wire center data, which is a widely-used 
commercially available comprehensive source for this information. 
Several commenters question the accuracy of those boundaries. For 
example, the Rural Associations point to a NECA study that concluded 
many of the Tele Atlas boundaries ``differ quite significantly from 
actual boundaries.'' In addition, some companies that argue that their 
boundaries, and in particular the resulting measure of square miles in 
their service territories, were inaccurate in the proposed methodology 
have asked how they could correct errors in the data.
    19. The only comprehensive set of wire center boundaries are those 
commercially available from companies such as Tele Atlas and 
GeoResults. There is precedent for using Tele Atlas' (or a predecessor 
company's) boundaries. In particular, the Commission's hybrid cost 
proxy model uses a customer location data set that was created using an 
earlier version of the Tele Atlas boundaries.
    20. The Bureau declines to adopt NRIC's proposal that study area 
boundaries be modified before implementing the regression methodology 
based on publicly available state maps. While many states have study 
area maps available on-line, the vast majority of those maps will not 
allow Commission staff to calculate the information required for the 
analysis adopted today. Variables like road miles and those related to 
local soil conditions require having GIS-based boundaries that can be 
overlaid with other GIS-based data sets (like road networks and 
databases of soil conditions). It is not practical to derive such 
information from printed maps, images on Web sites or PDF files with 
any accuracy. In addition, it is not clear whether state maps represent 
authoritative boundaries. Therefore, the Bureau does not believe that 
the proposal by NRIC is a practical means to derive more reliable study 
area boundary information quickly.
    21. Nevertheless, the Bureau recognizes concerns remain regarding 
inaccuracies in this data set, and the Bureau adopts a two-part process 
to address these concerns. First, in the near term, the Commission will 
provide a streamlined, expedited waiver process for carriers affected 
by the benchmarks to correct any errors in their study area boundaries. 
Second, to correct any remaining inaccuracies in the Tele Atlas data 
set, the Bureau will issue a Public Notice to initiate the process of 
collecting study area boundaries directly from all rate-of-return 
carriers. The Public Notice will seek comment on data specifications 
for a data request that the Bureau would issue after receiving input 
from the public and interested parties. The Bureau expects that it will 
have updated boundary data before the Bureau reruns the regression to 
calculate capex and opex limits that will be used for calculating 
support for 2014, at which time the limits will apply in full.
    22. In light of the protections the Bureau adopts to address errors 
in the Tele Atlas data, the Bureau declines to delay implementation of 
the benchmarks beyond the 18-month phase-in described below. The 
Commission anticipated that ``HCLS benchmarks will be implemented for 
support calculations beginning July 2012.'' In many cases, more 
accurate boundaries would not change whether or not a particular 
company is capped or not by the benchmark methodology. And the 
streamlined, expedited waiver process the Bureau adopts to correct 
boundaries in the near-term will address those specific instances where 
an inaccurate boundary could result in a company losing more support 
than it would otherwise. Consistent with existing practice, if such a 
waiver request is granted and a true-up is required, a carrier' support 
amounts will be trued-up back to July 1, 2012.
    23. Specifically, any carrier whose actual boundaries are different 
from the boundaries used by the Bureau in the methodology adopted today 
may file a petition for waiver in accordance with Sec.  1.3 of the 
Commission's rules. To enable the Bureau to determine whether there are 
special circumstances (i.e., inaccurate boundaries) supporting a 
waiver, petitioners must provide accurate boundary information in a 
manner and format that Bureau staff can readily evaluate and process. 
In Appendix C (https://transition.fcc.gov/Daily_Releases/Daily_Business/2012/db0425/DA-12-646A1.pdf), the Bureau sets forth a template 
for filing study area maps to help potential petitioners file 
information efficiently, accurately, and in a manner that will permit 
the Bureau to evaluate and process the information expeditiously.
    24. While potential petitioners may choose to submit boundary 
information in other formats, the Bureau cautions

[[Page 30415]]

that information submitted in other formats may require additional 
processing, and that the processing could introduce errors and/or 
delay. For example, if petitioners file hard copy maps, those would 
need to be rectified (stretched) to have a spatial reference, and 
digitized by Bureau staff. Accordingly, petitioners that do not wish to 
use the Bureau's template may wish to consult with Bureau staff in 
advance of filing boundary information in alternate formats to ensure 
that the information submitted can be processed quickly.
    25. Regardless of how the boundary information is filed, an officer 
of the company must certify under penalty of perjury that the 
information provided is accurate. The Bureau also emphasizes that 
carriers using this waiver process solely to seek changes to their 
study area boundaries used in the benchmark methodology are not 
required to file the financial data and other information required for 
waivers as set forth in the USF/ICC Transformation Order. The financial 
data and other information set forth in the USF/ICC Transformation 
Order is relevant for petitions for waiver alleging that ``reductions 
in current support levels would threaten [a carrier's] financial 
viability, imperiling service to consumers in the areas they serve.'' 
In contrast, when considering whether there are special circumstances 
and the public interest is served by granting a waiver of the benchmark 
methodology, the Bureau will be focusing on ensuring that accurate data 
is used to perform the necessary computations, regardless of the extent 
of support reduction. In addition, carriers using this streamlined, 
expedited waiver process to make technical corrections to their study 
area boundaries need not pay the filing fee associated with requests 
for waiver of Part 36 separations rules. With the safeguard provided by 
this streamlined, expedited waiver process, the Bureau concludes it is 
appropriate to use the Tele Atlas boundaries on an interim basis.

E. Use of Quantile Regression and the 90th Percentile Cost Threshold

    26. As discussed in the technical appendix, the Bureau concludes 
that quantile regression analysis is the appropriate methodology to use 
to identify study areas that have capex and opex costs that are much 
higher than those of their similarly situated peers and to cap their 
cost recovery at amounts that are no higher than the vast majority of 
similarly situated study areas. The Bureau also concludes that it 
should set the regression-derived limits at the 90th percentile of 
costs for capex and opex compared to similarly situated companies.
    27. Some commenters criticized the use of the 90th percentile, 
arguing that it was unreasonable because approximately forty percent of 
study areas in the methodology proposed in the FNPRM would have been 
subject to limits in one or more of the eleven cost categories used in 
that analysis. On further consideration, the Bureau has concluded that 
the proposed methodology was over-inclusive because a carrier that 
exceeded the cap in only one category, but had costs well below the 
caps in the other ten, would have received reduced support. As 
discussed above, however, the Bureau is adopting a revised methodology 
that relies on aggregated capex and opex caps. Applying the revised 
methodology with a 90th percentile cap limits reimbursable costs for 
only fifteen percent of the study areas of cost companies. The net 
effect is fewer study areas will see reduced support, and more 
companies will see additional support, due to the distribution of 
support among HCLS recipients.
    28. Accordingly, the Bureau does not agree with commenters who 
argue that the methodology should limit at most those carriers with 
costs above the 95th percentile. Indeed, the Bureau notes that using 
the 90th percentile with the modifications adopted today leads to 
approximately the same number of study areas with capped costs as would 
have been the case if the 95th percentile were used with the Appendix H 
methodology. The Bureau concludes that using the 90th percentile as 
part of the revised methodology appropriately balances the Commission's 
twin goals of providing better incentives for carriers to invest 
prudently and operate more efficiently, and providing additional 
support to further advance broadband deployment. By providing 
additional, redistributed HCLS to carriers that do not have the highest 
costs among similarly situated companies, the budget for high-cost 
support should enable more broadband deployment than continued funding 
of more of the highest cost companies at current levels.
    29. In view of the fact that many carriers will receive additional, 
redistributed HCLS, the Bureau takes this opportunity to emphasize the 
obligations that attach to the additional funding. Section 254(e) of 
the Act requires that this additional funding--like all federal 
universal service support--be used ``only for the provision, 
maintenance, and upgrading of facilities and services for which the 
support is intended.'' Consistent with the USF/ICC Transformation 
Order, the overarching intent is to preserve and advance the 
availability of modern networks capable of delivering broadband and 
voice telephony service. Indeed, all rate-of-return carriers are 
required to provide broadband upon reasonable request beginning July 1, 
2012, as a condition of receiving federal high-cost universal service 
support. Carriers must use their high-cost universal service support--
including any additional funding--in compliance with these 
requirements.
    30. The Bureau further notes that all rate-of-return carriers will 
be required to file a new build-out plan, which accounts for the new 
broadband obligations, in 2013. Those plans must be updated annually to 
reflect progress on network improvements and build-out, which should 
reflect the impact of high-cost universal service support, including 
any additional funding. The Commission will be reviewing those plans 
and updates, as well as other information provided in the annual Sec.  
54.313 reports, to ensure that carriers are complying with their public 
interest obligations, including their build-out requirements. Further, 
the progress report on those plans will be part of the factual basis 
that supports the annual Sec.  54.314 certification by the states or 
carriers that support is being used for the intended purposes.

F. Other Issues

    31. Retroactivity. The Bureau disagrees with commenters who assert 
that applying the benchmarks to limit HCLS payments constitutes 
retroactive rulemaking. A rule does not operate retroactively merely 
because it is ``applied in a case arising from conduct antedating [its] 
enactment'' or ``upsets expectations based on prior law.'' Rather, a 
rule operates retroactively if it ``takes away or impairs vested rights 
acquired under existing law, or creates a new obligation, imposes a new 
duty, or attaches a new disability in respect to transactions or 
considerations already past.''
    32. Here, it cannot fairly be said that the application of these 
benchmarks will take away or impair a vested right, create a new 
obligation, impose a new duty, or attach a new disability in respect to 
the carriers' previous expenditures. There is no statutory provision or 
Commission rule that provides companies with a vested right to continue 
to receive support at particular levels or through the use of a 
particular methodology. Although application of the benchmarks may 
affect the amount of support a carrier receives for expenditures made 
in 2010

[[Page 30416]]

(or before), it does not change the legal landscape in which those 
expenditures were made. Rather, as the Commission observed in the USF/
ICC Transformation Order, ``section 254 directs the Commission to 
provide support that is sufficient to achieve universal service goals, 
[but] that obligation does not create any entitlement or expectation 
that ETCs will receive any particular level of support or even any 
support at all.''
    33. Indeed, consistent with the Commission's focus on service to 
consumers, the Commission declined to provide any group of companies 
with a blanket exception from universal service reforms for past 
investments, recognizing that the current rules were not efficiently 
serving universal service goals. Providing such exceptions would have 
made it impossible to reform the system over any reasonable time 
period. Instead, the Commission established an avenue for companies to 
demonstrate a need for temporary and/or partial relief from the new 
rules to ensure its customers do not lose service. Moreover, the 
decision to phase in the application of the limits over 18 months 
provides a greater opportunity for carriers to make any necessary 
adjustments.
    34. Critically, the revised methodology now includes an independent 
variable that captures age of plant, further addressing 
``retroactivity'' concerns with respect to capex. Adding this variable 
raises the cost limits for carriers that have invested recently, by 
allowing their costs to be judged relative to a peer group of other 
carriers that have also invested recently. The Bureau also notes that 
application of the limits to operating expenses clearly presents no 
``retroactivity'' concerns.
    35. Predictability and Sufficiency. The Bureau also rejects the 
argument that implementing these benchmarks will undermine the 
predictability or sufficiency of support. At the outset, the Bureau 
notes that this general argument effectively seeks reconsideration of 
the Commission's policy judgment to adopt a rule imposing limits on 
capex and opex in the first instance, which is beyond the scope of this 
order to implement a methodology as directed by the Commission. As the 
Commission explained in the USF/ICC Transformation Order, the HCLS 
mechanism operates in fundamentally the same way with or without the 
benchmarks. In both cases, a certain amount of unpredictability exists 
because a carrier's support depends in part on a national average that 
changes from year to year, and companies ``can only estimate whether 
their expenditures will be reimbursed through HCLS.'' Moreover, as the 
Commission has suggested, if anything, support will now be more 
predictable for most carriers because the new rule discourages 
companies from exhausting the fund by over-spending relative to their 
peers. The addition of several new independent variables that capture 
attributes that do not change over time (e.g., depth of bedrock, soils 
difficulty, the percentage of study area that is a federally-recognized 
Tribal land, the percentage of each study area that lies within a 
national park, whether the study area is in the Midwest, Northeast, or 
Alaska) also improves the predictability of support. In addition, as 
described below, the same regression coefficients will be used for 
capex and opex in 2013 as those calculated for 2012, which will provide 
more certainty as the application of the limits is phased in. 
Accordingly, commenters' concerns that support amounts will fluctuate 
radically from year to year are speculative and unpersuasive.
    36. As for sufficiency, the very purpose of the benchmarks is to 
ensure that carriers as a whole receive a sufficient (but not 
excessive) amount of HCLS, which is one component of high-cost support. 
As discussed above, the methodology compares carriers' costs to those 
of similarly situated carriers and reduces HCLS only to the extent that 
a carrier over-spends relative to its peers. Moreover, excess support 
is redistributed to carriers that otherwise may be at risk of losing 
HCLS altogether, and may not otherwise be well-positioned to further 
advance broadband deployment. Thus, the application of benchmarks is 
not only consistent with the Commission's interpretation of 
``sufficient'' as requiring that the fund remain ``sustainable,'' which 
the DC Circuit found to be a reasonable interpretation in Rural 
Cellular Association v. FCC, but it also complies with the stated 
intent of section 254 that the Commission's universal service 
mechanisms should preserve and advance universal service.

G. Implementation

    37. The limits on costs eligible for reimbursement though HCLS will 
be implemented beginning July 1, 2012, but support amounts will not be 
reduced immediately by the full amount as calculated using the 
benchmarks. Instead, support will be reduced beginning July 1, 2012 and 
until December 31, 2012 by twenty-five percent of the difference 
between the support calculated using the study area's cost per loop as 
reported by NECA and the support as limited by the benchmarks, however, 
the reduction shall not be greater than ten percent of the study area's 
HCLS support based on the cost data filed with NECA. Beginning January 
1, 2013 and until December 31, 2013, support will be reduced by fifty 
percent of the difference between the support calculated using the 
study area's cost per loop as reported by NECA in October 2012 and the 
support as limited by the benchmarks in effect for 2013. Beginning 
January 1, 2014, when the Bureau expects to have updated wire center 
boundaries, as discussed above, the regression coefficients will be 
updated and the cost data submitted by NECA in October 2013 will be 
incorporated, and support will be limited, in full, by the benchmarks 
in effect for 2014.
    38. By delaying the full impact of the reductions until 2014, 
companies who would be adversely affected are provided adequate time to 
make adjustments and, if necessary, demonstrate that a waiver is 
warranted either to correct inaccurate boundary information and/or ``to 
ensure that consumers in the area continue to receive voice service.'' 
For many companies affected by the benchmarks, the initial twenty-five 
percent phase-in reduction is a small percentage of their total HCLS. 
For those whose reduction would be more than ten percent of their HCLS 
based on NECA cost data, the reduction is limited to ten percent for 
the remainder of 2012. Moreover, continuing to limit the impact of 
support reductions in 2013 provides an additional opportunity for 
carriers to make further adjustments. On balance, the Bureau finds that 
this measured transition strikes a reasonable balance between the goals 
of promptly making available additional support to those carriers who, 
under the new rule, will receive redistributed HCLS to further advance 
broadband deployment and providing an adequate amount of time for 
carriers that will experience reductions in support to make 
adjustments.
    39. The Bureau also take steps to provide more certainty regarding 
the operation of the limits on capex and opex. In particular, to 
provide carriers with more certainty regarding the impact of the fifty 
percent phase-in in 2013, the same regression coefficients for capex 
and opex will be used in 2013 as those calculated for 2012, which 
enables carriers to estimate their 2013 support now. That is, the 
regressions will not be updated, but individual study area caps will be 
recalculated

[[Page 30417]]

based on the 2011 cost data filed with NECA, which will be submitted to 
the Commission in NECA's annual filing in October 2012. This will allow 
higher caps for those study areas with significant network investment 
in 2011. By taking into account the 2011 cost data filed with NECA, 
study areas that may not have qualified for HCLS based on their costs 
in prior years may be eligible to qualify for HCLS in 2013, thereby 
providing those study areas with additional support for broadband 
investment. In addition, study areas whose costs drop below their 
computed benchmark for 2013 no longer will be considered capped, and 
therefore will receive support based on their own actual costs and will 
be eligible to receive redistributed support like other uncapped study 
areas.

III. Procedural Matters

A. Paperwork Reduction Act

    40. This document does not contain new or modified information 
collection requirements subject to the Paperwork Reduction Act of 1995 
(PRA), Public Law 104-13. In addition, therefore, it does not contain 
any new or modified information collection burden for small business 
concerns with fewer than 25 employees, pursuant to the Small Business 
Paperwork Relief Act of 2002, Public Law 107-198, see 44 U.S.C. 
3506(c)(4).

B. Final Regulatory Flexibility Act Certification

    41. Final Regulatory Flexibility Certification. The Regulatory 
Flexibility Act of 1980, as amended (RFA) requires that a regulatory 
flexibility analysis be prepared for rulemaking proceedings, unless the 
agency certifies that ``the rule will not have a significant economic 
impact on a substantial number of small entities.'' The RFA generally 
defines ``small entity'' as having the same meaning as the terms 
``small business,'' ``small organization,'' and ``small governmental 
jurisdiction.'' In addition, the term ``small business'' has the same 
meaning as the term ``small business concern'' under the Small Business 
Act. A small business concern is one which: (1) Is independently owned 
and operated; (2) is not dominant in its field of operation; and (3) 
satisfies any additional criteria established by the Small Business 
Administration (SBA).
    42. This Order implements, but does not otherwise modify, the rule 
adopted by the Commission in USF/ICC Transformation Order. These 
clarifications do not create any burdens, benefits, or requirements 
that were not addressed by the Final Regulatory Flexibility Analysis 
attached to USF/ICC Transformation Order. Therefore, the Commission 
certifies that the requirements of this order will not have a 
significant economic impact on a substantial number of small entities. 
The Commission will send a copy of the order including a copy of this 
final certification, in a report to Congress pursuant to the Small 
Business Regulatory Enforcement Fairness Act of 1996, see 5 U.S.C. 
801(a)(1)(A). In addition, the order and this certification will be 
sent to the Chief Counsel for Advocacy of the Small Business 
Administration, and will be published in the Federal Register. See 5 
U.S.C. 605(b).

C. Congressional Review Act

    43. The Commission will send a copy of this order to Congress and 
the Government Accountability Office pursuant to the Congressional 
Review Act.

D. Data Quality Act

    44. The Commission certifies that it has complied with the Office 
of Management and Budget Final Information Quality Bulletin for Peer 
Review, 70 FR 2664 (2005), and the Data Quality Act, Public Law 106-554 
(2001), codified at 44 U.S.C. 3516 note, with regard to its reliance on 
influential scientific information in the Report and Order in GN Docket 
No. 09-191 and WC Docket No. 07-52.

IV. Modeling Limits on Reimbursable Operating and Capital Costs

    45. Overview. This appendix describes a methodology for determining 
carrier-specific limits on High Cost Loop Support (HCLS) payments to 
rate-of-return cost carriers with very high capital expenses (capex) 
and operating expenses (opex) relative to their similarly situated 
peers. Building on the record received in response to the USF/ICC 
Transformation FNPRM, and the comments of two peer reviewers, the 
methodology adopted today refines the HCLS calculation algorithm 
proposed in the FNPRM. This appendix describes both the econometric 
process used to establish carrier-specific limits to HCLS payments for 
rate-of-return cost companies and the implementation process.
    46. The methodology described herein provides a detailed and 
implementable mechanism for examining all rural rate-of-return cost 
study areas and limiting HCLS payments in those study areas that have 
costs higher than the vast majority of their similarly-situated peers. 
The Bureau uses data from all the rural rate-of-return cost carriers. 
The Bureau uses quantile regression for parameter estimation rather 
than ordinary least squares for reasons set forth below. The most 
significant change in methodology from that described in the FNPRM is 
that this analysis creates two caps, one each on capex and opex, rather 
than capping eleven different NECA algorithm steps. Because this 
methodology builds upon NECA's existing algorithm for calculating 
average loop costs, the revised methodology can be implemented quickly 
and simply.
    47. Background. Today, cost companies eligible for HCLS file with 
NECA annual detailed cost data, pursuant to Part 36, at the study area 
level reporting their costs in many different cost categories. The cost 
categories are then fed into NECA's 26-step Cost Company Loop Cost 
Algorithm. The early algorithm steps calculate intermediate values 
(based on the reported cost categories) and feed into the later 
algorithm steps. Algorithm step 25, which calculates the carrier's 
total unseparated cost for that study area, sums several of the 
preceding algorithm steps and then feeds into algorithm step 26, which 
computes the carrier's total unseparated cost per-loop for that study 
area by dividing the value for algorithm step 25 by the number loops in 
the study area. HCLS for each study area is then calculated by the 
Expense Adjustment Algorithm. This algorithm ultimately determines HCLS 
payments based on a study area's cost per-loop compared to the 
nationwide average cost per-loop.
    48. Methodology for Imposing Limits. Appendix H of the FNPRM 
proposed to create 11 caps (four capex caps and seven opex caps). 
Several commenters argued that the Bureau should reduce the number of 
caps because efficient carriers might limit their total expenditures by 
spending a large amount in one cost category to avoid spending even 
more money in other categories. Additionally, some commenters and one 
of the peer reviewers suggested the use of a single cap, that is, a 
single dependent variable in the cost regressions, noting that the 90th 
percentile of total cost is not the sum of the 90th percentiles of cost 
components.
    49. For the reasons described in the HCLS Benchmarks Implementation 
Order, the Bureau concludes that using two caps, one for capex and one 
for opex, provides the appropriate balance between identifying 
unusually high costs and providing carriers operational flexibility.

[[Page 30418]]

    50. To implement this revised framework, the updated methodology 
separates algorithm step 25 (Total Unseparated Costs) into total capex 
and total opex cost components. The current algorithm step 25 sums 
algorithm steps 13 through 24. As a result of the updated methodology, 
capex components are now summed into algorithm step 25A and opex 
components are summed into algorithm step 25B. Consistent with the 
methodology proposed in Appendix H, a company whose actual costs for 
algorithm step 25A or algorithm step 25B are above the 90th percentile 
for that cost, compared to similarly situated companies, would be 
limited to recovering amounts that correspond to the 90th percentile of 
capex or opex costs, i.e. the costs that ninety percent of similarly 
situated companies would be estimated to have by the regression 
equation. Algorithm step 25C becomes the new Total Unseparated Costs by 
summing algorithm steps 25A and 25B. It then feeds into algorithm step 
26 (Study Area Cost per Loop) and the subsequent Expense Adjustment 
Algorithm as before. The Bureau identifies the capex and opex 
components below.
    51. Use of Quantile Regression. As proposed in the FNPRM, the 
Bureau uses quantile regression to estimate the caps for the capex and 
opex cost components. The goal of the regression methodology is to 
identify study areas that have capex and opex costs that are much 
higher than their similarly-situated peers and to cap their cost 
recovery at amounts that are no higher than the vast majority of 
similarly-situated study areas. Quantile regression allows us to 
directly estimate the 90th percentile costs for study areas with given 
characteristics. The critical values become the capex and opex caps.
    52. The Bureau concludes that quantile regression is preferable to 
ordinary least squares for this application. Ordinary least squares 
regression cannot be used to identify the proper critical values in the 
tail of the cost distribution without making strong assumptions about 
the nature of the cost distribution, in particular, that error terms 
are Gaussian (normally distributed) and homoscedastic. In contrast, 
quantile regression requires no assumptions about the error terms. This 
is important because the error terms of the ordinary least squares 
regressions for capex and opex are both heteroscedastic and non-normal. 
While methods exist to estimate corrections for heteroscedasticity and 
non-normal error terms in ordinary least squares regression, these 
would require additional computational steps without improving the 
precision of the quantile estimate.
    53. Quantile regression is also more resistant to the presence of 
outliers than ordinary least squares, which can produce biased 
parameter estimates when outliers are present. Thus, quantile 
regression parameter estimates are more stable than ordinary least 
squares parameter estimates if the data include outliers. And although 
ordinary least squares has methods available for dealing with outliers, 
such as excluding them from the analysis or using dummy variables, 
these methods generally require an exercise of judgment to identify 
outliers. Quantile regression largely avoids the need to make such 
determinations.
    54. Another significant advantage of quantile regression is that it 
allows the independent variables to have different effects on the 
dependent variable in the different quantiles. Thus, for example, as 
the percentage of a study area that is national parkland increases 
(holding everything else constant), the size of the study area's cost 
increase could differ based on where it falls in the cost distribution 
of similarly-situated study areas (which quantile it is in). This is 
not allowed in ordinary least squares, which assumes that the marginal 
effect is the same on all study areas. Given that the Bureau is 
examining study areas with high costs relative to other study areas 
conditioned on the independent variables used in the design, this is a 
helpful property.
    55. Use of the Log-Log Specification. As proposed in the FNPRM, the 
Bureau uses the log-log specification, and therefore take the natural 
log of the variables most sensitive to scale effects. For the dependent 
variables, the capex regression uses the natural log of capex, and the 
opex regression uses the natural log of opex. The Bureau also uses the 
natural logs of all independent variables used in the methodology 
except those that are dummy variables, a pure index, or a percentage 
(namely, Climate, Difficulty, PctTribalLand, PctPark, Alaska, MW, and 
NE).
    56. Some commenters and a peer reviewer argued that the Commission 
failed to demonstrate the need for taking the natural logs for both the 
dependent and independent variables. Additionally, a commenter argued 
that doing so was appropriate when the dependent variable is known to 
have a multiplicative relationship, and therefore the regressions 
should use the variables in levels (i.e., that the Bureau should not 
take the natural log of the variables) or that the Bureau should 
examine cost per loop. Another commenter, as well as both peer 
reviewers, noted that the manner in which zeros are dealt with, even 
when using quantile regression, can affect the results.
    57. Because the Bureau's econometric specification is a reduced 
form, taking the logs of both the dependent and independent variables 
is acceptable so long as the resulting relationship is linear. The 
Bureau disagrees with commenters who suggested that the variables 
should be left in levels. Figure 1 shows that the scatter plot of (the 
level of) opex versus (the level of) the number of loops is not 
obviously linear. In contrast, Figure 2 displays the scatter plot of 
the natural log of opex versus the natural log of loops, and shows that 
the relationship is linear. Further, in a simple ordinary least squares 
regression of opex on the number of loops and the natural log of the 
number of loops, both variables are significant. This indicates that 
the relationship between opex and loops is nonlinear.
    58. Further, some commenters argued that the Bureau should predict 
costs per loop and that if this were taken approach, density would 
become an important independent variable. Figure 3 shows that opex per 
loop as a function of density is nonlinear. In contrast, Figure 4 shows 
that the relationship between the natural log of opex and density is 
linear. Similarly, the graph of capex versus road miles does not appear 
to be linear, but natural log of capex versus the natural log of road 
miles does. The Bureau thus concludes that the log transformation of 
the dependent and independent variables that are scale sensitive is the 
appropriate specification.
    59. Finally, the reduction in the number of regressions in the 
final methodology eliminates the problem of taking the natural log of 
zero in the dependent variable. Because the final methodology uses two 
regressions rather than eleven, the values of the dependent variables 
are never less than or equal to zero, as was the case for many of the 
values in the algorithm step 8 regression as originally proposed in the 
FNPRM. Further, none of the independent variables that the Bureau uses 
have zero values.
    60. Fit of the Regression Model. Some commenters argued that the 
regressions in the proposed methodology suffered from low pseudo R-
square values, and therefore the proposed methodology should be 
abandoned. Another commenter asserted that alternative models (i.e., 
those that were based on levels or on cost per loop) were superior to 
the proposed model because the

[[Page 30419]]

R-square values were higher when using levels or cost per loop.
    61. The Bureau concludes that the revised methodology offers 
sufficient predictive power. Although the pseudo R-square values in the 
proposed methodology ranged from 0.2745 to 0.5863, the pseudo R-square 
values in the revised methodology are .6684 for capex and 0.6234 for 
opex. The Bureau concludes that the final specification has sufficient 
predictive power to provide a reliable method for setting reasonable 
limits on carriers' costs. The Bureau also notes that because the 
dependent variables are different, and because the Bureau is performing 
quantile regression rather than ordinary least squares regression--the 
method proposed by NRIC--the Bureau cannot directly compare the pseudo 
R-square values from the methodology the Bureau uses to the R-square 
values from commenters' alternative specifications.
    62. Elimination of Independent Variables From Specification. If a 
variable is significant in either the capex or opex regression, the 
variable is included it in both regressions. The Bureau is cognizant of 
Dr. Koenker's comments that in quantile regression (as in ordinary 
least squares regression), the inclusion of non-significant variables 
can inflate the variance of the prediction (yet leave the prediction 
unbiased). Nevertheless, the Bureau keeps variables that are 
significant in either regression in both regressions because they can 
have offsetting effects in the regressions. For example, a carrier 
facing close-to-the-surface bedrock (which would make trenching more 
difficult than usual) may find it efficient to use an aerial solution 
rather than to trench through bedrock. The presence of close-to-the-
surface bedrock could then lower the carrier's capex cost but raise its 
opex cost because cables on poles may be more costly to maintain. Thus, 
bedrock could raise that carrier's opex costs but could plausibly lower 
that carrier's capex expenditures. If the Bureau omitted bedrock from 
the capex regression, the Bureau could be biasing the coefficient 
values in the regression and therefore biasing the predicted 90th 
percentile values for capex.
    63. Further, the Bureau notes that unlike the regressions in the 
proposed methodology, the vast majority of the variables in the updated 
methodology's regressions are significant in both regressions. The 
Bureau also notes that adding statistically insignificant variables to 
the regressions do not bias the Bureau's predictions. In light of all 
these considerations, the Bureau therefore believes it is better to 
include variables that are significant in either of the regressions in 
both.
    64. In its Updated Opex Study, NRIC suggests creating a cap that 
uses not just the regression coefficients, but also adds a standard 
deviation to each regression coefficient. The Bureau declines to do so 
here. Adding the estimated standard error to the parameter estimates is 
a non-standard way of creating a confidence interval in the context of 
quantile regression. In contrast, using the regression quantiles 
methodology gives a direct unbiased estimate of the 90th percentile 
predictions for capex and opex.
    65. Use of Census Block Centroids. Consistent with the methodology 
set forth in the FNPRM, the Bureau determines which census blocks are 
in each study area by using the census blocks' centroids. This enables 
us to generate certain demographic variables for each study area, such 
as the number of housing units in a study area. Because study area 
boundaries do not always coincide with census block boundaries, some 
census blocks will fall into two different study areas. Where a census 
block's centroid falls inside the study area boundary, the Bureau 
associates that block with that study area, and if a census block's 
centroid falls outside of the study area boundary, the Bureau does not.
    66. Some commenters suggested that associating census blocks with 
study areas based on the census block's centroid can distort population 
and/or housing unit counts. While NRIC argues that such errors do not 
necessarily cancel each other out, they did not have a material impact 
on the cost caps in the case of Nebraska. The Bureau concludes that its 
approach is reasonable. The Bureau could split census blocks that cross 
study area boundaries into pieces and then assume that end-user 
locations are spread evenly within census blocks so that housing units 
are proportionately attributed to study areas. This would increase 
computational complexity but not necessarily accuracy because end-user 
locations are not uniformly distributed within census blocks. The 
Bureau further notes that the vast majority of study areas have many 
blocks and therefore such errors would tend to cancel each other out. 
Of the 726 study areas covered by the updated methodology have 1.1 
million census blocks in them, so on average, each study area has about 
1,567 census blocks. The smallest number of census blocks in a study 
area is 26, the 5th percentile is 132, and the 10th percentile is 187. 
Therefore, the vast majority of study areas would not be affected by 
this issue. Also, there is only one variable that uses the number of 
housing units (which is derived from the census blocks in the 
analysis), the natural log of density (see LnDensity below), so the 
effect of any error should be small.
    67. Dependent Variables. As described above, the dependent 
variables in the regressions are the natural log of the capex 
components and the natural log of opex components of algorithm step 25. 
Below the Bureau defines capex and opex, but in short, the Bureau 
assigns all the constituent parts of algorithm step 25, which 
calculates the carrier's total unseparated cost for that study area, to 
either capex or opex. Because the Bureau is now aggregating capex costs 
into a single capex variable, and operational costs into an opex 
variable, variations in individual capex and opex components are 
smoothed. This allows us to include data on all elements of capex and 
opex while still achieving good regression fits.
    68. For the purpose of the updated methodology that adopted today, 
the Bureau defines capex to be the plant-related costs in the current 
algorithm step 25. The Bureau thus includes the return to capital 
components, which are algorithm step 23 and algorithm step 24. The 
Bureau also includes depreciation in capex (algorithm step 17 and 
algorithm step 18). Although accounting textbooks typically define 
depreciation as an operating expense, they do so because firms need to 
recognize a periodic charge against earnings to expense the declining 
value of assets over the estimated life of the assets. Because 
depreciation is inherently tied to the carriers' asset investment 
decisions, the Bureau assigns it to capex. Note that in its Opex Study, 
NRIC considered depreciation to be sufficiently non-operations-based 
that NRIC took depreciation out of opex. Although some commenters urged 
that depreciation be excluded from the methodology altogether, the 
Bureau disagrees for two reasons. First, depreciation is a valid 
measure of plant that goes beyond the measure of net plant that goes 
into algorithm steps 23 and 24. Depreciation is a function of not just 
the amount of gross plant, but also the useful life of the plant that 
is used, a meaningful measure. Second, by including depreciation, the 
Bureau includes all the portions of the existing algorithm step 25.
    69. For the purpose of the updated methodology, the Bureau defines 
opex to be the remaining components of the current algorithm step 25. 
The Bureau includes algorithm steps 13 and 14 in opex because they are 
maintenance

[[Page 30420]]

expenses. The Bureau also includes algorithm steps 15 and 16 in opex 
because they are network expenses. Algorithm step 21 in included in 
opex because it is corporate benefits. Discussed below in more detail 
are the other algorithm steps included in opex.
    70. Algorithm step 19 is corporate operations expense, which is 
limited in accordance with Sec.  36.621(a)(4) of the Commission's 
recently revised rules. Although this step is already limited by the 
updated formula limiting recovery of corporate operations expenses, and 
was excluded in the methodology as proposed in the FNPRM, the Bureau 
now includes it in opex because the goal of the updated methodology is 
to examine opex in its entirety. Algorithm step 19 uses DL535 and 
DL550, which are the lesser of the allowable or actual corporate 
operations expenses, not the unadjusted corporate operations expenses, 
so a study area that is affected by Sec.  36.621(a)(4) is not being 
affected twice by the higher-than-allowable amount.
    71. The Bureau similarly includes algorithm step 20 (operating 
taxes) in opex in the revised methodology. Although the methodology 
proposed in Appendix H excluded step 20, after further consideration, 
the Bureau concluded that taxes are an expense that must be paid, just 
like other operational expenses.
    72. Finally, the Bureau includes algorithm step 22 (rents) in opex. 
This step was excluded from the proposed methodology in Appendix H 
because the regression fit was poor. Because rents can now be included 
as a part of opex as a whole as opposed to in its own separate 
category, the Bureau includes it in the updated methodology.
    73. Independent Variable Specification. The Bureau's reduced-form 
regression specification uses as independent variables exogenous 
factors that the Bureau believes affect a study area's capex and opex. 
These variables fall into the following categories: scale, age of 
plant, customer dispersion, and geography. Additionally, the 
independent variables the Bureau examined and include in this updated 
methodology are those that are currently available to the Commission 
and exist for all study areas in the regression analysis.
    74. To the extent that the Bureau had the requisite data, staff 
also tested other variables that commenters suggested be included. 
First the variables the Bureau included in the methodology are 
described below, then the variables that the Bureau examined and 
ultimately excluded, and finally, the variables that commenters 
suggested but that the Bureau could not include in the methodology due 
to data issues. All geographic independent variables were rolled up to 
the study area using Tele Atlas study area boundary data. The Bureau 
did not include inputs to the production process (such as employees) in 
the regressions because carriers can choose the amount of these inputs. 
In other words, carriers with markedly higher costs than their 
similarly situated peers may be using substantially more of these 
inputs.
    75. Table 1 and Table 2 respectively show descriptive statistics 
for and correlations between the variables included in the updated 
methodology. The regression results are included in Table 3.
    76. Scale. The Bureau uses several variables to measure scale: The 
number of loops, road miles, road crossings, and the number of study 
areas held under common control in the state. All the scale measures 
the Bureau includes in the updated methodology are significant in the 
opex regression and all but LnRoadMiles are significant in the capex 
regression.
    77. Because the number of loops is a direct measure for the scale 
of the study area, the Bureau includes the natural log of the number of 
loops (LnLoops) in the updated methodology. The Bureau expects that the 
amount of plant a carrier must install will be positively correlated 
with capex and opex costs because more loops require more investment 
and operations cost. LnLoops is statistically significant.
    78. The Bureau also includes the natural log of the number of road 
miles (LnRoadMiles), which is a proxy for total loop length. Several 
commenters argued that some measure of loop length was an important 
variable. Although some (but not all) cost carriers may report such 
data to the Department of Agriculture's Rural Utilities Service (RUS), 
such data are both incomplete and unavailable to the Bureau. The Bureau 
agrees with NRIC that cable generally follows roads, so the number of 
road miles in a study area should correlate with the cabling required 
to serve that area.
    79. In its Capital Expenditure Study, NRIC predicted that road 
intersections would slow fiber construction and impose other costs and 
found that the number of intersections was a significant predictor of 
predicted construction costs. The Bureau agrees that the number of such 
crossings is another good proxy for scale and therefore included the 
natural log of road crossings (LnRoadCrossings).
    80. The scale variables (LnRoadMiles) and road crossings 
(LnRoadCrossings) are significant in the opex regression, but have the 
opposite sign from each other. Only road crossings are significant in 
the capex regression.
    81. The last scale variable is the number of study areas in the 
state that are owned by the same holding company or have common control 
in the state (LnStateSACs). The Bureau anticipated that this variable 
would be a good predictor of capex and opex costs because some expenses 
could be shared among study areas. For capex, study areas that are part 
of a larger organization (i.e., the study area has more commonly-owned 
study areas in the state) may allow installation crews to be deployed 
more efficiently. For opex, study areas that are part of a larger 
organization can share various expenses, especially headquarters-
related expenses, which would allow for some specialization among 
management employees. The Bureau found LnStateSACs to be significant 
for both capex and opex.
    82. Age of Plant. Commenters stated that age of plant was an 
important variable for two reasons: First, because the cost of recent 
capital investments is higher due to inflation and second, because the 
return component of capital expenses is calculated on net plant, and 
recent investment will be depreciated less fully than old plant. While 
the Bureau cannot readily determine the average age of carriers' plant, 
the percentage of the plant that has not yet been depreciated 
(PctUndepPlant) should be highly correlated with plant age: More 
recently installed plant will be less depreciated. Holding all else 
constant, the less of a carrier's plant is depreciated (which yields a 
higher PctUndepPlant), the higher its capex should be. The intuition 
for the effect of PctUndepPlant on opex is ambiguous. The Bureau finds 
that this variable is a strong cost predictor for both capex and opex.
    83. Customer Dispersion. The Bureau includes three variables that 
account for customer dispersion. Many commenters asserted that density 
was an important cost predictor, and that their costs are high in part 
because of the rural areas they serve. The Bureau therefore expects 
that density is negatively correlated with both capex and opex costs. 
Density (LnDensity) is the natural log of the following quotient: 
number of housing units in the study area divided by the size of the 
study area in square miles as reported by the Tele Atlas boundaries. 
The Bureau finds that it is significant in both regressions.
    84. The Bureau also includes the natural log of the number of 
exchanges in the study area as a proxy for customer

[[Page 30421]]

dispersion (LnExchanges). Although the straightforward measure of 
density calculates the average customer density within the study area, 
the number of exchanges roughly accounts for the number of population 
centers within the study area because most population centers will have 
their own exchanges. The more population centers (holding other factors 
constant), the higher capex and opex costs will be because more cabling 
will be required to connect the customers within the study area to each 
other, and the farther the employees will need to drive to fix any 
troubles. The variable LnExchanges is significant in both regressions.
    85. The final customer dispersion variable accounts for the portion 
of households in urban clusters or urbanized areas (PctUrban). To the 
extent that rural carriers also serve urbanized pockets, the Bureau 
would expect their costs to be higher, holding all other variables 
(including road miles) constant, because wage rates may be higher near 
urbanized areas. The Bureau thus expects PctUrban to be positively 
correlated to opex, and it is. PctUrban's effect on capex is less 
clear: The labor costs associated with trenching are capitalized, so to 
the extent that labor near urban areas is more expensive, the higher 
capital costs should be. But capitalized labor is only one of many 
costs in capex, so the effect may not be strong. PctUrban is positive 
but not significant in the capex regression.
    86. Geography. Commenters suggested the inclusion of several 
geographically-based variables such as soil type. The Bureau agrees. 
When creating many of the indexes for geographic variables, the Bureau 
took into account the location of roads within the study area because 
cabling generally follows roads. For these variables the Bureau 
overlaid road data in the study area with the sources of geographic 
information and calculated variables that were either percentages, or 
where appropriate, averages.
    87. For example, commenters stated that soil type is an important 
cost predictor. The Bureau therefore constructed a soil difficulty 
index (Difficulty). This index is similar to the index in the NRIC 
capex study in which soil types were matched with construction 
difficulty values established for the Commission's High Cost Proxy 
Model (HCPM), which the Commission used to calculate high-cost support 
for non-rural carriers. The STATSGO2 database the Bureau uses lists 
more soil types than the original STATSGO database, however, so there 
are many soil types in the STATSGO2 database for which there are no 
construction difficulty values from the HCPM. NRIC tried several 
options, but settled on assuming the soil difficulty level to be 1 (the 
lowest level of difficulty) for those soil types not found in the 
table. The Bureau's soil difficulty index builds on the NRIC 
methodology. When faced with soil types that do not appear on the 
original HCPM list, the Bureau interpolates the difficulty rating based 
on similar soil types in the HCPM list. The Bureau manually associates 
unmatched soil types from the STATSGO2 data with similar soil texture 
in the original HCPM table, and used the difficulty rating of the 
similar soil types in the HCPM list for the new soil type in the 
STATSGO2 database. The new extended table associates a difficulty 
rating for all soil types in the STATSGO2 database. The Bureau then 
calculated the average soil construction value along the roads in each 
study area.
    88. The Bureau finds soil difficulty to be a statistically 
significant predictor in opex. Although NRIC found that soil difficulty 
was a significant predictor of construction costs, Difficulty is 
positive in capex, but not significant. Although the Bureau also 
expected soil difficulty to be positive in the capex regression, an 
alternative hypothesis is that in locations where trenching is 
unusually expensive, an efficient carrier may install aerial plant (use 
poles rather than trench). This would involve lower capital costs than 
trenching, but higher future operations costs. Thus, it is plausible 
that in the presence of difficult-to-trench soils, carriers experience 
no obvious change in capex or, in some circumstances possibly even 
reduced capex costs.
    89. Because NRIC suggested that the methodology account for close-
to-the-surface bedrock, the Bureau calculated the percentage of road 
miles within each study area where bedrock was within 36 inches of the 
surface (PctBedrock36). The NRIC capex study found that predicted 
construction costs were positively associated with close-to-the-surface 
bedrock, so the Bureau might expect that the coefficient on 
PctBedrock36 should be positive in the capex regression.
    90. The Bureau finds that close-to-the-surface bedrock is 
significant in the opex regression, but that it is not significant in 
the capex regression. This result could occur for the same reasons as 
for soil construction difficulty above or because the construction 
difficulty of bedrock has already been captured by the soil difficulty 
variable.
    91. Pointing to the NRIC Capex study, which suggested that 
construction costs are higher in areas where the ground is frozen more 
often, several commenters argued that the regressions should include a 
frost index. The frost index in the NRIC capex study uses of the number 
of frost-free days from the SSURGO data. Unfortunately, this 
information is not available for all areas in the STATSGO2 database. 
The Bureau believes that the USDA's hardiness index is a useful proxy 
for this information, and the Bureau uses it to create a simple index 
called Climate that is based on the average annual minimum temperature. 
The lower the minimum temperature, the more days the ground is likely 
to be frozen. The higher the index, the more frost-free days the study 
area would have. Based on the comments in the record, the Bureau 
expected this variable to be negatively correlated with capex (the 
higher the index, the more frost-free days the area should have, so 
construction costs should be lower).
    92. The Climate variable (Climate) is positive and has low p-values 
in the regressions, which means that it is unlikely to be a spurious 
result. However, it is positively correlated with capex and opex.
    93. Commenters also stated that it is more difficult to construct 
and maintain networks on tribal lands and in national parks because of 
permitting and similar issues, so the Bureau includes two additional 
variables: (1) The percentage of each study area that is a federally-
recognized Tribal land (PctTribalLand), and (2) the percentage of each 
study area that lies within a national park (PctParkLand).
    94. The coefficient for the percentage of the study area that is 
tribal land (PctTribalLand) is positive for both capex and opex 
regressions, but is significant in only the opex regression. The 
percentage of the study area that is national park land (PctParkLand) 
is positive and significant in both regressions. As can be seen in 
Table 1, most of the study areas do not contain either tribal or 
national park land, and it may be a simple lack of data that causes a 
lack of significance for PctTribalLand in the capex regression. 
Nonetheless, the Bureau agrees that both capex and opex costs could be 
higher in the presence of these factors, so the Bureau includes them in 
the model.
    95. Finally, based on comments in the record that certain areas of 
the country face unique circumstances, the Bureau tested several 
regional variables. Alaskan commenters suggested that Alaska was unique 
because of its harsh climate and other factors. The Bureau therefore 
added the dummy variable Alaska to the regressions, which equals

[[Page 30422]]

1 for the 17 study areas in Alaska and zero elsewhere.
    96. The Bureau also includes regional dummies because in its 
Original Opex study NRIC found that opex costs were correlated with 
regions. Although NRIC did not include region dummy variables in the 
regression, instead opting to use 2005 median home value, which it also 
used in its Updated Opex Study, the Bureau includes region in the 
updated methodology. The Bureau tested the four census-based regions: 
Western (West), Midwest (Midwest), Northeast (Northeast) and South 
(South). The Bureau found that Midwest and Northeast were each 
significant in at least one regression, so the updated methodology 
includes them.
    97. Use of Soil Database Information. The Bureau's source for soil 
data is the U.S. General Soil Map (STATSGO2) soils database. The Bureau 
selected STATSGO2 as a data source because it provides data for the 
entire country. The Soil Survey Geographic Database (SSURGO) soils data 
from the Natural Resource Conservation Service (NRCS) that the Nebraska 
Rural Independent Companies capex study used to generate soil, frost 
and wetland variables is an attractive database because it contains a 
richer set of soil variables and contains data at a smaller granular 
area than the STATSGO2 database. Unfortunately, as can be seen from the 
graph on page 23 of the NRIC comments, not only do the SSURGO data not 
cover Guam or American Samoa, and much of Alaska, but there are also 
numerous other holes in the data in many states. Thus, there are many 
study areas in Alaska where there is no SSURGO data and even some 
conterminous United States study areas such as the West Kentucky Rural 
Telephone Coop (Study Area Code 260421) where there is virtually no 
SSURGO spatial data. The Bureau therefore could not apply the results 
of a SSURGO-based model to these companies because the needed data 
would be missing. The Bureau concludes, therefore, that it is not 
practical to use the SSURGO data at this time.
    98. Two commenters argue that the Bureau should use the SSURGO data 
for study areas covered by it and use STATSGO2 for the remaining study 
areas. The Bureau has concerns about this approach for several reasons, 
and ultimately declines to do so. In particular, the commenters' 
proposed approach would mean that those study areas for which the 
SSURGO data are not universally available would be treated 
inconsistently with those for which the SSURGO are universally 
available. In addition, it would be challenging to combine the two data 
sets for those study areas where the Bureau has only some SSURGO data. 
Given these problems, the Bureau concludes that the implementation and 
fairness benefits of a nationally uniform approach based on STATS2GO 
outweigh the benefits of using SSURGO data for a subset of areas. 
Discussed below are the elements of the STATSGO2 data the Bureau uses.
    99. Independent Variables Tested But Not Used in the Model. Based 
on commenters' suggestions and the analysis proposed in Appendix H, the 
Bureau tested several additional variables that were ultimately 
excluded from the final model because they were not significant for 
either capex or opex.
    100. In its Capex Study, NRIC found that rain frequency increased 
construction cost per household. Following NRIC's model, the Bureau 
used the Samson weather station data, and for each study area, 
calculated the average number of days per year with greater than 0.5 
inches of rainfall (DaysAbvPt5). The Bureau found DaysAbvPt5 was not 
significant in either regression.
    101. The Bureau also tested the average slope in study areas 
(slope) using data in the STATSGO2 database. The Bureau's hypothesis 
was that the steeper the slope, the more difficult it would be to build 
and maintain cabling. The coefficient on slope was insignificant (i.e., 
statistically indistinguishable from zero) in both regressions and 
therefore dropped from the model.
    102. The Bureau similarly tested the percentage of the study area 
that was water (PctWater), but did not include it in the updated model 
because it was insignificant in both regressions. This is unsurprising. 
The proposed model included PctWater to account for the fact that 
cabling may have to be run around bodies of water, but the updated 
model accounts for the number of road miles (as a proxy for loop 
length), so the additional cabling associated with routing around water 
has already been accounted for.
    103. The Bureau tested the percentage of road miles where the water 
table was within 36 inches of the surface (PctWaterTable36). The Bureau 
found the variable PctWaterTable36 to be weakly significant in opex, 
but it had an implausible negative sign in both the capex and opex 
regressions. Because of the sign issue and because inclusion of the 
variable does not markedly improve the fit, the Bureau excludes it from 
the model so as not to lower the cap for study areas with high water 
tables.
    104. Accipiter suggested adding the percentage change in loops 
(PctLoopChange) to account for study areas that are growing, because 
growing carriers ``are prone to have unique cost structures.'' The 
Bureau believes the PctUndepPlant proxies for this, but out of an 
abundance of caution, the Bureau tested PctLoopChange, but found that 
it was insignificant, suggesting that PctUndepPlant is proxying for the 
unique cost structures that Accipiter is concerned about.
    105. Based on NRIC's updated opex regression, the Bureau tested 
statewide median house values, but found them to be insignificant. This 
is unsurprising because statewide values include mostly urban houses. 
The Bureau's regional independent variables, however, helped capture 
the intended effect.
    106. The Bureau also tested the natural log of the number of stream 
crossings (LnStreamCross), which could increase construction costs in 
the same way that road crossings do. The Bureau found LnStreamCross to 
be significant and negative in opex, but insignificant in capex. 
Because the coefficient was an implausible sign in the opex regression 
without an offsetting plausible coefficient in the other regressions, 
the Bureau omitted LnStreamCross from both regressions.
    107. The proposed model also included the number of census blocks 
in the study area. Although the natural log of the total number of 
census blocks (LnBlocks) was weakly significant in the opex regression, 
it was not significant in the capex regression. Although the Bureau 
generally included variables that were significant in at least one 
regression in both regressions, the Bureau omitted census blocks from 
the updated model regressions for two reasons. First, commenters did 
not think that the number of blocks was a good proxy for density. Also, 
the Bureau is now accounting for customer dispersion and density 
directly through independent variables LnRoadMiles, LnRoadCrossings and 
LnDensity.
    108. Unavailable Independent Variables. Several carriers suggested 
additional variables to the regression analysis, but the Bureau was 
unable to include them because the data were either unavailable to the 
Commission, nonpublic, or data could not be generated at the study area 
level. The Bureau recognizes that some of the unavailable variables 
could be significant if they could be included, but given the other 
enhancements made to the regressions described herein, the Bureau 
concludes that the methodology is adequate to identify cost outliers 
among similarly situated companies.

[[Page 30423]]

    109. The NRIC capex study postulated that the presence of wetlands 
would increase construction costs because of need for additional 
``approvals and specialized techniques.'' It found that wetlands were 
positively correlated with increased predicted construction costs. As 
NRIC points out, however, wetlands data are not available for Colorado, 
Wisconsin and Montana. Since the Bureau's objective is to develop a 
methodology that applies equally to all cost carriers, the Bureau could 
not include wetlands data in the updated methodology.
    110. Similarly, commenters suggested the following additional 
variables that, if not already proxied in the model, could not be used 
because they were unavailable to the Commission, nonpublic, or data 
could not be generated at the study area level: Age of investment; 
broadband speed capability; cable route miles or cable sheath miles; 
status as carrier of last resort; copper versus fiber networks; cost of 
living and labor costs; environmental; legal and regulatory costs; loop 
length/average loop length; right of way costs and vacant lots; and 
weather patterns.
    111. One commenter argues that the Bureau's methodology should 
include variables that are not universally available and that it is 
better to comprehensively study a representative sample of study areas 
and apply the results to the wider population of study areas. The 
commenter does not specify, however, how the Bureau could apply that 
knowledge to study areas for which the information is unavailable.
    112. Implementation. For each study area, the regressions will be 
used to generate the 90th percentile predicted values for both the 
natural log of capex and the natural log of opex. These values will 
then be converted back to ``levels'' by using the inverse of the 
natural log function.
    113. The lower of the study area's original algorithm step 25A and 
the level of the predicted 90th percentile capex value will be retained 
in algorithm step 25A. Similarly, the lower of the study area's 
original algorithm step 25B and level of the predicted 90th percentile 
opex value will be retained in algorithm step 25B. These values will 
then be summed in algorithm step 25C, which will feed into algorithm 
step 26.

V. Ordering Clauses

    114. Accordingly, it is ordered, that pursuant to the authority 
contained in sections 1, 2, 4(i), 201-206, 214, 218-220, 251, 254, and 
303(r), and of the Communications Act of 1934, as amended, and section 
706 of the Telecommunications Act of 1996, 47 U.S.C. 151, 152, 154(i), 
201-206, 214, 218-220, 251, 254, 303(r), 1302, and pursuant to 
Sec. Sec.  0.91, 0.131, 0.201(d), 0.291, 0.331, 1.3, and 1.427 of the 
Commission's rules, 47 CFR 0.91, 0.131, 0.201(d), 0.291, 0.331, 1.3, 
1.427 and pursuant to the delegations of authority in paragraphs 210, 
217, 226 and 1404 of USF/ICC Transformation Order, 26 FCC Rcd 17663 
(2011), 76 FR 73830, November 29, 2011, that this Order is adopted, 
effective June 22, 2012.
    115. It is further ordered, that the Commission shall send a copy 
of this Order to Congress and the Government Accountability Office 
pursuant to the Congressional Review Act, see 5 U.S.C. 801(a)(1)(A).
    116. It is further ordered, that the Commission's Consumer and 
Governmental Affairs Bureau, Reference Information Center, shall send a 
copy of this Order, including the Final Regulatory Flexibility 
Certification, to the Chief Counsel for Advocacy of the Small Business 
Administration.

Federal Communications Commission.
Sharon E. Gillett,
Chief, Wireline Competition Bureau.
[FR Doc. 2012-12539 Filed 5-22-12; 8:45 am]
BILLING CODE 6712-01-P