2006 Nonforeign Area Cost-of-Living Allowance Survey Report: Alaska and Washington, DC, Areas, 774-809 [E7-25297]

Agencies

• Office of Personnel Management
• Personnel Management Office

[Federal Register Volume 73, Number 2 (Thursday, January 3, 2008)]
[Notices]
[Pages 774-809]
From the Federal Register Online via the Government Printing Office [www.gpo.gov]
[FR Doc No: E7-25297]



Federal Register / Vol. 73, No. 2 / Thursday, January 3, 2008 / 
Notices

[[Page 774]]


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OFFICE OF PERSONNEL MANAGEMENT


2006 Nonforeign Area Cost-of-Living Allowance Survey Report: 
Alaska and Washington, DC, Areas

AGENCY: U.S. Office of Personnel Management.

ACTION: Notice.

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

SUMMARY: This notice publishes the ``2006 Nonforeign Area Cost-of-
Living Allowance Survey Report: Alaska and Washington, DC, Areas.'' The 
Federal Government uses the results of surveys such as these to set 
cost-of-living allowance (COLA) rates for General Schedule, U.S. Postal 
Service, and certain other Federal employees in Alaska, Hawaii, Guam 
and the Northern Mariana Islands, Puerto Rico, and the U.S. Virgin 
Islands. This report contains the results of the COLA surveys conducted 
by the U.S. Office of Personnel Management in Anchorage, Fairbanks, and 
Juneau, Alaska; and in the Washington, DC, area during the spring and 
early summer of 2006.

DATES: Comments on this report must be received on or before March 3, 
2008.

ADDRESSES: Send or deliver comments to Charles D. Grimes III, Deputy 
Associate Director for Performance Management and Pay Systems, 
Strategic Human Resources Policy Division, U.S. Office of Personnel 
Management, Room 7300B, 1900 E Street, NW., Washington, DC 20415-8200; 
fax: (202) 606-4264; or e-mail: COLA@opm.gov.

FOR FURTHER INFORMATION CONTACT: J. Stanley Austin, (202) 606-2838; 
fax: (202) 606-4264; or e-mail: COLA@opm.gov.

SUPPLEMENTARY INFORMATION: Section 591.229 of title 5, Code of Federal 
Regulations, requires the U.S. Office of Personnel Management (OPM) to 
publish nonforeign area cost-of-living allowance (COLA) survey summary 
reports in the Federal Register. We are publishing the complete ``2006 
Nonforeign Area Cost-of-Living Allowance Survey Report: Alaska and 
Washington, DC, Areas'' with this notice. The report contains the 
results of the COLA surveys we conducted in Anchorage, Fairbanks, and 
Juneau, Alaska; and in the Washington, DC, area during the spring and 
early summer of 2006.

Survey Results

    Using an index scale with Washington, DC, area living costs equal 
to 100, we computed index values of relative prices in the Anchorage, 
Fairbanks, Juneau, and Rest of the State of Alaska COLA areas. Then we 
added an adjustment factor of 7.0 to the Anchorage price index and 9.0 
to the other Alaska price indexes and rounded the results to the 
nearest whole percentage point. According to the results, the COLA rate 
for the Rest of the State of Alaska COLA area should remain unchanged, 
but the COLA rates for Anchorage, Fairbanks, and Juneau should 
decrease. Section 591.228(c) limits decreases to 1 percentage point in 
a 12-month period. OPM proposes to reduce those rates no sooner than 12 
months after the effective date of the last reduction.

Office of Personnel Management
Linda M. Springer,
Director.

2006 Nonforeign Area Cost-of-Living Allowance Survey Report: Alaska and 
Washington, DC, Areas

Table of Contents

Executive Summary
1. Introduction
    1.1 Report Objectives
2. Preparing for the Survey
    2.1 COLA Advisory Committees
    2.2 Pre-Survey Meetings
    2.3 Survey Item Selection
    2.3.1 Special Considerations
    2.4 Outlet Selection
    2.5 Geographic Coverage
3. Conducting the Survey
    3.1 Pricing Period
    3.2 Non-Housing Price Data Collection
    3.2.1 Data Collection Teams
    3.2.2 Data Collection Process
    3.3 Housing (Rental) Price Data Collection
4. Analyzing the Results
    4.1 Data Review
    4.2 Special Price Computations
    4.2.1 K-12 Private Education
    4.2.2 Health Insurance
    4.2.3 Water Utilities
    4.2.4 Energy Utilities Model
    4.2.5 Tire Prices
    4.2.6 Renters' Insurance
    4.2.7 Rental Data Hedonic Models
    4.3 Averaging Prices by Item and Area
    4.4 Computing Price Indexes
    4.4.1 Geometric Means
    4.4.2 Special Private Education Computations
    4.5 Applying Consumer Expenditure Weights
    4.6 Computing the Overall Price Index for Rest of the State of 
Alaska
5. Final Results
6. Post Survey Meetings

List of Appendices

Appendix 1: Publication in the Federal Register of Prior Survey 
Results: 1990-2005
Appendix 2: Estimated DC Area Middle Income Annual Consumer 
Expenditures
Appendix 3: COLA Survey Items and Descriptions
Appendix 4: COLA Rental Survey Data Collection Elements
Appendix 5: Utility Usage and Calculations
Appendix 6: Hedonic Rental Data Equations and Results
Appendix 7: Final Living-Cost Results for Anchorage, Fairbanks, and 
Juneau, AK
Appendix 8: Final Living-Cost Results for the Rest of the State of 
Alaska

Executive Summary

    The Government pays cost-of-living allowances (COLAs) to Federal 
employees in nonforeign areas in consideration of living costs 
significantly higher than those in the Washington, DC, area. The Office 
of Personnel Management (OPM) conducts living-cost surveys to set the 
COLA rates. The methodology for conducting these surveys is prescribed 
in regulation at subpart B of part 591 of title 5 of the Code of 
Federal Regulations.
    This report provides the results of the COLA surveys OPM conducted 
in the spring and early summer of 2006 in Anchorage, Fairbanks, and 
Juneau, Alaska; and in the Washington, DC, area. The report details our 
comparison of living costs in the Alaska areas with living costs in the 
Washington, DC, area.
    For the surveys, we contacted about 930 outlets and collected 
approximately 4,300 non-rental prices on more than 320 items 
representing typical consumer purchases. We also collected about 1,800 
rental prices. We then combined the data using consumer expenditure 
information from the Bureau of Labor Statistics. The final results are 
living-cost indexes, shown in Table 1. These indexes compare living 
costs in the Alaska COLA areas to those in the Washington, DC, area. 
The index for the DC area (not shown) is 100.00 because it is, by law, 
the reference area. The living-cost indexes shown in Table 1 include 
the adjustment factor prescribed at 5 CFR 591.227.

             Table 1.--Final Living-Cost Comparison Indexes
------------------------------------------------------------------------
                       Allowance area                           Index
------------------------------------------------------------------------
Anchorage..................................................       109.81
Fairbanks..................................................       118.90
Juneau.....................................................       120.08
Rest of the State of Alaska................................       132.82
------------------------------------------------------------------------

1. Introduction

1.1 Report Objectives

    This report provides the results of the 2006 Alaska nonforeign area 
cost-of-living allowance (COLA) survey conducted by the Office of 
Personnel Management (OPM) in the spring and early summer of 2006. 
(Appendix 1 lists prior survey reports and their publication dates.) In 
addition to providing the results, the report

[[Page 775]]

describes how we prepared for and conducted the survey and analyzed the 
results. The results show comparative living-cost differences between 
the Alaska COLA areas, i.e., Anchorage, Fairbanks, Juneau, and the Rest 
of the State of Alaska, and the Washington, DC, area. By law, 
Washington, DC, is the base or ``reference'' area for the COLA program.

2. Preparing for the Survey

2.1 COLA Advisory Committees

    Before conducting the Alaska survey, OPM established COLA Advisory 
Committees (CACs) in Anchorage, Fairbanks, and Juneau. The settlement 
of Caraballo, et al. v. United States, No. 1997-0027 (D.V.I.), August 
17, 2000, provides for employee involvement in the administration of 
the COLA program. As in previous surveys, we found it valuable to 
involve employee and agency representatives in planning and conducting 
the surveys and reviewing the results.
    Each CAC is composed of approximately 12 agency and employee 
representatives from the survey area and 2 OPM representatives. The 
functions of the CACs include the following:

--Advising and assisting OPM in planning COLA surveys;
--Providing or arranging for data collection observers during COLA 
surveys;
--Advising and assisting OPM in reviewing survey data;
--Advising OPM on its COLA program administration, including survey 
methodology;
--Assisting OPM in disseminating information to affected employees 
about the surveys and the COLA program; and
--Advising OPM on special situations or conditions, such as hurricanes 
and earthquakes, as they relate to OPM's authority to conduct interim 
surveys or implement some other change in response to conditions caused 
by a natural disaster or similar emergency.

2.2 Pre-Survey Meetings

    To help us prepare for the COLA surveys, the CACs held 3-day 
meetings in Anchorage, Fairbanks, and Juneau. The CACs reviewed the 
preliminary outlet and item lists developed by OPM for the surveys. The 
committee members researched the outlets and availability and 
appropriateness of the items in each area and made recommendations to 
us concerning the survey. We incorporated these recommendations into 
the survey design.
    We found the work of the CACs to be extremely helpful and 
informative. The CACs' knowledge of the local area, the popularity of 
items and outlets, and other information about the COLA area were 
invaluable in helping us plan the survey.

2.3 Survey Item Selection

    As described in Sections 2.1 and 2.2, we consulted with the CACs as 
we selected survey items. We identified items to reflect a wide array 
of items consumers typically purchase. To determine what consumers 
purchase, OPM uses the Bureau of Labor Statistics (BLS) 2002/2003 
Consumer Expenditure Survey (CES). We aggregated CES expenditures into 
the following nine major expenditure groups (MEGs):

--Food,
--Shelter and Utilities,
--Household Furnishings and Supplies,
--Apparel,
--Transportation,
--Medical,
--Recreation,
--Education and Communication, and
--Miscellaneous.
    We further subdivided each MEG into primary expenditure groups 
(PEGs). In all, there were 45 PEGs. For example, we subdivided Food 
into the following nine PEGs:

--Cereals and Bakery Products;
--Meats, Poultry, Fish, and Eggs;
--Dairy Products;
--Fresh Fruits and Vegetables;
--Processed Foods;
--Other Food at Home;
--Nonalcoholic Beverages;
--Food Away from Home; and
--Alcoholic Beverages.
    To select survey items, we chose a sufficient number of items to 
represent each PEG and reduce overall price index variability. To do 
this, we applied the following guidelines: Each survey item should be:

--Relatively important (i.e., represent a fairly large expenditure) 
within the PEG;
--Relatively easy to find in both COLA and DC areas;
--Relatively common, i.e., what people typically buy;
--Relatively stable over time, e.g., not a fad item; and
--Subject to similar supply and demand functions.
    In all, we selected over 320 non-housing items to survey. Appendix 
2 shows how OPM organized the CES data into MEGs and PEGs, identifies 
the Detailed Expenditure Categories (DECs) for which we chose survey 
items, and shows estimated DC area middle income annual consumer 
expenditures for each DEC and higher level of aggregations.
    Appendix 3 lists the items we surveyed and their descriptions. Each 
of these items is specifically described with an exact brand, model, 
type, and size whenever practical. Thus, we priced exactly the same 
items or the same quality and quantity of items in both the COLA and DC 
areas. For example, we priced a 10.5-ounce can of Campbell's Chicken 
Noodle Soup in both the COLA and DC areas because it is typical of 
canned soups and consumers commonly purchase it.
2.3.1 Special Considerations
    Health Insurance: It is not practical to compare the prices of 
exactly the same quality and quantity of health insurance between the 
COLA and Washington, DC, areas because the same array of plans is not 
offered in each area, and a significant proportion of Federal employees 
in both the COLA and DC areas subscribe to plans not available 
nationwide. To compare the employee health benefits premiums of these 
often highly different plans, OPM would have to adjust for differences 
in benefits and coverage. Research conducted by the parties prior to 
the Caraballo settlement indicated this would not be feasible.
    Therefore, OPM uses the non-Postal Service employee's share of the 
Federal Employees Health Benefits premiums by plan for each plan 
offered in each area. OPM maintains these data in its Central Personnel 
Data File (CPDF), including the number of white-collar Federal 
employees enrolled in each plan. As described in Section 4.2.3, we used 
these data to compute the average ``price'' of health insurance for 
Federal employees in the COLA and DC areas.
    Housing: For housing items, OPM surveys rental rates for specific 
kinds or classes of housing and collects detailed information about 
each housing unit. OPM surveys the following classes of housing:

--Four bedroom, single family unit, not to exceed 3200 square feet;
--Three bedroom, single family unit, not to exceed 2600 square feet;
--Two bedroom, single family unit, not to exceed 2200 square feet;
--Three bedroom apartment unit, not to exceed 2000 square feet;
--Two bedroom apartment unit, not to exceed 1800 square feet; and
--One bedroom apartment unit, not to exceed 1400 square feet.
    For each housing unit we surveyed, we obtained approximately 80 
characteristics about the unit. For example, we determined the number 
of bedrooms, bathrooms, square footage, whether there was a garage, air 
conditioning, security systems, and recreational activities. Appendix 4 
lists the types of detailed information we collected. We did not 
collect

[[Page 776]]

homeowner data, such as mortgage payments, maintenance expenses, or 
insurance. Under the Caraballo settlement, the parties agreed to adopt 
a rental equivalence approach similar to the one BLS uses for the 
Consumer Price Index. Rental equivalence compares the shelter value 
(rental value) of owned homes, rather than total owner costs, because 
the latter are influenced by the investment value of the home (i.e., 
influenced by what homeowners hope to realize as a profit when they 
sell their homes). As a rule, living-cost surveys do not compare how 
consumers invest their money.
    In the 2006 survey, OPM surveyed rents and used them to estimate 
homeowner rental values (i.e., rental equivalence). In late 2004 and 
2005, OPM conducted special research, the General Population Rental 
Equivalence Survey (GPRES), to obtain additional rent and rental 
equivalence information. The goal was to determine whether OPM should 
adjust the rent index before using it to estimate homeowner rental 
values. The analyses showed that no adjustments should be made. 
Therefore, OPM's use of the rents to estimate rental equivalence is 
appropriate. OPM published the GPRES results in a Federal Register 
notice on July 31, 2006, at 71 FR 43228.
    Although we surveyed rental rates for the same classes of housing 
in each area, the type, style, size, quality, and other 80-plus 
characteristics of each unit varied within each area and between the 
COLA and DC areas. As described in Section 4.2.6, we used special 
statistical analyses to hold these characteristics constant between the 
COLA and Washington, DC, areas to make rental price comparisons.

2.4 Outlet Selection

    Just as it is important to select commonly-purchased items and 
survey the same items in both the DC area and COLA areas, it is 
important to select outlets frequented by consumers and find comparable 
outlets in both the COLA and DC areas. To identify comparable outlets, 
OPM categorizes outlets by type (e.g., grocery store, convenience 
store, discount store, hardware store, auto dealer, and catalog outlet) 
and then surveys only specific items at each outlet type. For example, 
OPM surveys grocery items at supermarkets in all areas because most 
people purchase their groceries at such stores and because supermarkets 
exist in nearly all areas. Selecting comparable outlets is particularly 
important because significant price variations may occur between 
dissimilar outlets (e.g., comparing the price of milk at a supermarket 
with the price of milk at a convenience store).
    We used the above classification criteria and existing data 
sources, including previous COLA surveys, phonebooks, and various 
business listings, to develop initial outlet lists for the survey. We 
provided these lists to the CACs and consulted with them on outlet 
selection. The committees helped us refine the outlet lists and 
identify other/additional outlets where local consumers generally 
purchase the survey items.
    We also priced some items by catalog, and when we did, we priced 
the same items by catalog in the COLA areas and in the DC area. We 
priced 11 items by catalog in the Alaska areas. All catalog prices 
included any charges for shipping and handling and all applicable 
taxes, including excise taxes.
    In all, we surveyed prices from approximately 930 outlets. In the 
COLA survey areas, we attempted to survey three popular outlets of each 
type, to the extent practical. For some outlet types, such as local 
phone service, there were not three outlets. In some areas, there was 
not a sufficient number of businesses to find three outlets of each 
particular type. In the Washington, DC area, we attempted to survey 
nine popular outlets of each type, three in each of the DC survey areas 
described in Table 2.

2.5 Geographic Coverage

    Table 2 shows the Alaska COLA and DC survey area boundaries. OPM 
collected non-housing prices in outlets throughout the major cities 
described in Table 2. To collect rental data, OPM contracted with 
Delta-21 Resources, Incorporated, a research organization with 
expertise in housing and rental data collection. Delta-21 surveyed 
rental rates in locations within the same cities.
    To collect non-rental data in the DC area, OPM divides the area 
into three survey areas, as shown in Table 2. OPM collects non-rental 
prices in outlets throughout these areas. As stated in the footnote to 
Table 2, we surveyed certain items, such as golf, in areas beyond the 
counties and cities specified in Table 2. Another example is air 
travel. We surveyed the cost of air travel from Ronald Reagan 
Washington National Airport, Washington Dulles International Airport, 
and Baltimore/Washington International Airport (BWI) and surveyed the 
price of a 5-mile taxi ride originating at these airports. Both Dulles 
and BWI are outside the counties and cities shown in Table 2. 
Nevertheless, DC area residents commonly use both airports.

               Table 2.--Survey and Data Collection Areas
------------------------------------------------------------------------
   COLA areas and reference
            areas                             Survey area
------------------------------------------------------------------------
Anchorage, AK................  City of Anchorage.
Fairbanks, AK................  Fairbanks/North Pole area.
Juneau, AK...................  Juneau/Mendenhall/Douglas area.
Washington, DC-DC............  District of Columbia.
Washington, DC-MD............  Montgomery County and Prince George's
                                County.
Washington, DC-VA............  Arlington County, Fairfax County, Prince
                                William County, City of Alexandria, City
                                of Fairfax, City of Falls Church, City
                                of Manassas, and City of Manassas Park.
------------------------------------------------------------------------
Note: For selected items, such as snow skiing and air travel, these
  survey areas include additional geographic locations beyond these
  jurisdictions.

    Delta-21 surveyed rental prices as specified in the COLA areas and 
throughout the DC area. (Note: OPM does not divide the DC area into 
three separate survey areas for rental data collection but rather 
treats the area as a single survey area.) In selecting the locations 
and sample sizes within each location, OPM used 2000 census data 
showing the relative number of Federal employees and housing units by 
zip code. In doing this, we often merged several zip codes to identify 
a single location. We allocated the rental sample objectively, 
requiring Delta to attempt to obtain more rental observations in 
locations with a relatively large number of Federal employees and 
housing units and fewer observations in locations with a relatively 
small number of Federal employees and housing units. Although

[[Page 777]]

the process provided a rational way to allocate the sample, Delta was 
limited ultimately by how many units were available for rent within a 
location. Under the contract, Delta surveyed only units available for 
rent. It did not survey all renter-occupied housing.

3. Conducting the Survey

3.1 Pricing Period

    OPM collected data from late March through early July 2006. We 
collected non-housing price data concurrently in the Alaska areas in 
March and April and collected the bulk of the DC area data in May. 
Delta-21 collected rental data sequentially in the DC area, Juneau, 
Fairbanks, and Anchorage beginning in March and ending in July 2006.

3.2 Non-Housing Price Data Collection

3.2.1 Data Collection Teams
    In both the COLA and Washington, DC, areas, OPM central office 
staff collected non-housing price data. In the COLA areas, data 
collection observers designated by the local CAC accompanied the OPM 
data collectors. Data collection observers were extremely helpful and 
advised and assisted the data collectors in contacting outlets, 
matching items, and selecting substitutes. The observers also advised 
us on other living-cost and compensation issues relating to their 
areas.
    Because of logistical considerations, cost, and the fact OPM 
central office staff is very knowledgeable about the DC area, we did 
not use CAC data collection observers in the Washington, DC, area. 
However, we made all of the DC area data available to the CACs. This 
included both the rental and non-rental data. The non-rental data 
showed the individual prices by item, store, and survey location as 
well as averages. The rental data included a photograph and a rough 
sketch of the layout of the rental unit. We also provided the CACs with 
maps showing where each rental unit is located.
3.2.2 Data Collection Process
    The data collector/observer teams obtained most of the data by 
visiting stores, auto dealers, and other outlets. The teams also priced 
some items, such as car insurance, tax preparation fees, bank interest, 
and private education tuition, by telephone. As noted in Section 2.4, 
we surveyed some items via catalog, including all shipping costs and 
any applicable taxes in the price. We also collected other data, such 
as sales tax rates and airline fares, from Web sites on the Internet.
    For all items subject to sales and/or excise taxes, OPM added the 
appropriate amount of tax to the price for computing COLA rates. Sales 
tax rates varied by city within Alaska and in the DC area. Some sales 
tax rates also varied by item, such as restaurant meals, within a 
location.
    The data collectors collected the price of the item at the time of 
the visit to the outlet. Therefore, with certain exceptions, the data 
collectors collected the sale price if the item was on sale, and we 
used sale prices in the COLA calculations. The exceptions include 
coupon prices, going-out-of-business prices, clearance prices, mail-in 
rebates, and area-wide distress sales, which we do not use. We also do 
not collect automobile ``sale'' or negotiated prices. Instead, we 
obtain the sticker (i.e., non-negotiated) price for the model and 
specified options. The prices are the manufacturer's suggested retail 
price (including options), destination charges, additional shipping 
charges, appropriate dealer-added items or options, dealer mark-up, and 
taxes, including sales tax and licensing and title fees.

3.3 Housing (Rental) Price Data Collection

    As noted in Section 2.5, OPM contracted for the collection of 
rental data with Delta-21, which collected data in the three Alaska 
cities and in the DC area. These data included rental prices, 
comprehensive information about the size and type of dwelling, number 
and types of rooms, and other important amenities that might influence 
the rental price. Appendix 4 lists the data elements Delta-21 
collected.
    The contractor identified units for rent from various sources, 
including rental property managers, realtor brokers, listing services, 
newspaper ads, grocery store bulletin boards, and casual drive-by 
observation. The contractor then visited each rental unit, took a 
photograph of the unit, made a sketch of the floor plan based on 
exterior dimensions and shape, and noted the unit's longitude and 
latitude coordinates. We used longitude and latitude to (1) determine 
the distance of the rental unit from major commercial and Government 
centers, (2) to correlate census tract data (e.g., median income) for 
the tract in which the unit was located, and (3) to map each unit's 
location. As discussed in Section 4.2.5, we used certain census tract 
data elements along with the data Delta-21 collected to determine the 
relative price of rents.
    During the 2006 survey, OPM arranged for CAC observers to accompany 
Delta-21 rental data collectors as they collected data in the survey 
areas. This is the first year CAC observers did this, so it was a 
learning experience for the CAC members, Delta-21, and OPM. Overall, 
the observation process went well and enhanced the CAC observers' 
understanding of the rental survey process. OPM is considering allowing 
CAC members to be rental data collection observers in future COLA 
rental surveys.

4. Analyzing the Results

4.1 Data Review

    During and after the data collection process, the data collectors 
reviewed the data for errors and omissions. This involved reviewing the 
data item-by-item and comparing prices across outlets within an area to 
spot data entry errors, mismatches, and other mistakes.
    After all of the data had been collected in both the COLA areas and 
the Washington, DC, area, we again reviewed the data by item across all 
of the areas. One purpose was to spot errors not previously detected, 
but the principal reason was to look at substitute items.
    A substitute is an item similar to but not exactly the same as the 
specified survey item. For example, one of the items OPM specified was 
a man's Geoffrey Beene long sleeve dress shirt. The data collectors in 
the Alaska areas, however, discovered some stores did not carry the 
Geoffrey Beene brand. Therefore, the data collectors priced a long 
sleeve dress shirt by Arrow instead. We then priced the same shirt in 
the DC area and used the substitute price information in place of the 
prices of the originally specified item.

4.2 Special Price Computations

    After completing our data review, we made special price 
computations for five survey items: K-12 private education, Federal 
Employees Health Benefits premiums, water utilities, energy utility 
prices, automobile tire prices, renters' insurance, and rental prices. 
For each of these, we used special processes to calculate appropriate 
estimates for each survey area.
4.2.1 K-12 Private Education
    One of the items OPM surveys is the average annual tuition for 
private education, grades K-12. As in previous surveys, we found 
tuition rates varied by grade level. Therefore, we computed an overall 
average tuition ``price'' for each school surveyed by averaging the 
tuition rates grade-by-grade. Section 4.4.2 below describes the 
additional special ``use factor'' OPM applied to the

[[Page 778]]

average tuition rates in the price comparison process.
4.2.2 Health Insurance
    As noted in Section 2.3.1, OPM surveyed the non-Postal employees' 
premium for the various Federal Employees Health Benefits (FEHB) plans 
offered in each survey area. Using enrollment information from the 
CPDF, we computed two weighted average premium costs--one for self-only 
coverage and another for family coverage--for white-collar Federal 
employees in each of the COLA areas and in the Washington, DC, area. As 
shown in Table 3, we then computed an overall weighted average premium 
for each survey area by applying the number of white-collar Federal 
employees nationwide enrolled in self-only and family plans. We used 
the overall weighted average premiums as ``prices'' in the price 
averaging process described in Section 4.3.

 Table 3.--2006 Average FEHB Premiums for Full-Time Permanent Employees
                      (Non-Postal Employees' Share)
------------------------------------------------------------------------
                                                              Bi-Weekly
                                       Self        Family      weighted
             Location                premium      premium      average
                                                               premium
------------------------------------------------------------------------
Anchorage........................        58.58       135.82       104.68
Fairbanks........................        55.89       132.19       101.43
Juneau...........................        56.89       131.31       101.31
DC Area..........................        52.57       121.47        93.70
Nationwide Enrollment............      620,038      918,099
Enrollment Percentage............       40.31%       59.69%
------------------------------------------------------------------------

4.2.3 Water Utilities
    OPM surveys water utility rates in each of the COLA and Washington, 
DC, survey areas. To compute the ``price'' of water utilities, we 
assumed the average monthly water consumption in each area was 7,600 
gallons. We derived this estimate from earlier COLA research, and it 
reflects the average consumption across all of the COLA areas and the 
Washington, DC, area. We used this quantity along with the rates 
charged to compute the average monthly water utility cost by survey 
area. These average monthly costs were the water utility ``prices'' we 
used in the price averaging process described in Section 4.3.
4.2.4 Energy Utilities Model
    For energy utilities (i.e., electricity, gas, and oil), OPM 
collects from local utility companies and suppliers in the COLA and DC 
survey areas the price of various energy utilities used for lighting, 
cooking, cooling, and other household needs. We use these prices in a 
heating and cooling engineering model that estimates how many kilowatt 
hours of electricity, cubic feet of gas, and/or gallons of fuel oil are 
needed to maintain a specific model home at a constant ambient 
temperature of 72 degrees in each area.
    The engineering model was developed by an economic consulting 
company under special research conducted jointly for OPM and the 
plaintiffs' representatives after the Caraballo settlement. The model 
uses local home construction information and climatic data from the 
National Oceanic and Atmospheric Administration and also includes the 
amount of electricity needed to run standard household appliances and 
lighting. For each survey area, we calculated the cost of heating and 
cooling the model home using the different heating fuels and 
electricity for lighting and appliances. Although some homes use 
additional energy sources, such as wood, coal, kerosene, and solar 
energy, we did not price or include these in the calculations because, 
based on the results of the 2000 census, relatively few homes use these 
as primary energy sources.
    For Fairbanks and Juneau, OPM surveyed the price of electricity and 
fuel oil to compute home energy costs because the 2000 Census indicated 
that these two sources were used to heat over 95 percent of the homes 
in Fairbanks and Juneau. In Anchorage, OPM surveyed gas and electricity 
prices because Census data indicated that 97 percent of the Anchorage 
homes use these energy sources for heating. In Washington, OPM surveyed 
the costs of all three fuels (gas, oil and electricity). OPM used 
percentages based on the usage of the different fuels in each survey 
area to compute a weighted average utility fuel cost for the area. 
Appendix 5 shows the energy requirements, relative usage percentages, 
and total costs by area. OPM used these total costs as the ``price'' of 
utilities in the COLA rate calculations.
4.2.5 Tire Prices
    Because of the climate, many Alaskans use snow tires on their 
automobiles during the winter months. People in the Washington, DC, 
area generally do not use snow tires. Therefore, in Alaska, we surveyed 
the price of both snow tires and all season radial tires and averaged 
the prices to compute the ``price'' for tires. We compared this with 
the average price of all season radial tires in the Washington, DC, 
area to compute a price index for tires.
4.2.6 Renters' Insurance
    One of the items OPM surveys is the price of renters' insurance. In 
the COLA areas, OPM includes catastrophic coverage for earthquakes, 
hurricanes, and/or typhoons, to the extent it is available, in the 
price. In the 2006 survey, we surveyed earthquake insurance for renters 
in Alaska. Some insurance carriers did not offer this coverage, so for 
these carriers we estimated the price of earthquake coverage. We did 
this by computing for the companies that offered earthquake coverage 
the additional cost in percent of such coverage and increasing the 
price of the policies that did not provide the coverage by that 
percentage.
4.2.7 Rental Data Hedonic Models
    As discussed in Sections 2.5 and 3.3, OPM hired a contractor to 
collect rental data, including rents and the characteristics of each 
rental unit. As described in Section 3.3, we collated these rental data 
with census tract information published by the Bureau of the Census 
using the longitude and latitude of the rental properties. We used 
census tracts, which are relatively small geographically, as surrogates 
for neighborhoods. We believe the census tract characteristics, such as 
the percentage of school age children,

[[Page 779]]

reflect the character and quality of the neighborhoods in which the 
rental units are found.
    OPM uses hedonic regression analysis, which is a type of multiple 
linear regression analysis, to compare rents in the COLA areas with 
rents in the DC area. Multiple linear regression is a type of 
statistical analysis used to determine how the dependent variable (in 
this case rent) is influenced by the independent variables (in this 
case the characteristics of the neighborhood and rental unit). In 
regression analyses, it is very important to choose the independent 
variables with great care, making certain only those meeting certain 
statistically significant thresholds are used in the analysis. To 
select the independent variables, OPM uses a special procedure 
developed jointly by OPM and the Technical Advisory Committee. (The 
Technical Advisory Committee was established under the Caraballo 
settlement and is composed of three economists with expertise in 
living-cost comparisons.) We call this procedure the Variable Selection 
Protocol (VSP).
    VSP is a multi-step procedure that uses objective criteria to 
eliminate independent variables with little statistical significance in 
the regression. It also removes variables with inexplicable signs and 
variables that negatively affect the precision of the rent indexes. An 
example of an inexplicable sign is when the landlord provides an 
amenity (e.g., a microwave), and the variable has a negative sign. In 
essence, this is the same as saying on average when the landlord did 
not provide a microwave, the property rented for more than when the 
landlord did provide a microwave. In 2006, the presence of kitchen 
range, patio, and trash pickup had negative signs and were dropped.
    How VSP drops variables that negatively affect the precision of 
rent indexes is a bit more complicated to explain. The key variable in 
the regression is the survey area, i.e., Anchorage, Fairbanks, Juneau, 
and the Washington, DC, area. As with all variables in the regression, 
these variables have parameter estimates; but the survey area parameter 
estimates are especially important because they become the rent indexes 
for each of the survey areas. Therefore, it is important that the 
survey area parameter estimates be as accurate as practicable. The 
accuracy is measured by the standard error of the survey area parameter 
estimate. In the last steps of VSP, the protocol tests each of the 
variables in the model and drops variables that if retained would raise 
the standard errors of the survey area parameter estimates.
    Using VSP, we selected variables with the greatest statistical 
significance. The variables are listed below and are shown in the 
regression output in Appendix 6.

--Age of unit (i.e., number of years since built or extensively 
remodeled);
--Age squared;
--Elevator (yes/no) (for apartments only);
--Exceptional view (yes/no);
--Fireplace (yes/no);
--Garage (yes/no) (for detached and town/row houses);
--Microwave (yes/no);
--Number of square feet combined (i.e., ``crossed'') with unit type;
--Number of bathrooms;
--Number of bedrooms;
--Percent school age children in census tract;
--Percent with BA degree or higher in census tract;
--Percent with BA degree squared;
--Square footage squared;
--Unit Type (detached house, row/townhouse, high rise apartment, garden 
apartment, and other (in-home apartments, duplex/triplex/quadplex units 
and other));
--Survey Year (2005 or 2006 DC area only); and
--Survey area (Anchorage, Fairbanks, Juneau, or the DC area).

    The Technical Advisory Committee economists recommended adding 
Washington, DC, rental observations from the previous year to the 
current year rental data. We examined the effect of including prior 
year data and found it significantly reduced the standard error of the 
survey area parameter estimate. Therefore, we included the survey year 
variable in the regression calculations.
    As is common in this type of analysis and as was done in the 
research leading to the Caraballo settlement, OPM uses semi-logarithmic 
regressions. As noted previously in this section, the regression 
produces parameter estimates for each independent variable, including 
survey area. When the regression uses the Washington, DC, area as the 
base, the regression produces parameter estimates for each of the COLA 
survey areas: Anchorage, Fairbanks, and Juneau. The exponent of the 
survey area parameter estimate (i.e., after the estimate is converted 
from natural logarithms) multiplied by 100 (following the convention 
used to express indexes) is the survey area's rent index. This index 
reflects the difference in rents in each of the COLA survey areas 
relative to the Washington, DC, area, while holding constant important 
neighborhood and rental unit characteristics captured in the survey and 
census data.
    OPM makes a technical adjustment in the above calculations to 
correct for a slight bias caused by the use of logarithms because the 
exponent of the average of the logarithms of a series of numbers is 
always less than the average of the numbers. Therefore, we added one-
half of the standard deviation of the survey area parameter estimate 
before converting from natural logarithms. (See Arthur Goldberger, 
``Best Linear Unbiased Prediction in the Generalized Linear Regression 
Model,'' Journal of the American Statistical Association, 1962.) Table 
4 shows the resulting rent indexes. We used these indexes as ``prices'' 
in the price averaging process described in Section 4.3.

                         Table 4.--Rent Indexes
------------------------------------------------------------------------
                            Area                              Rent index
------------------------------------------------------------------------
Anchorage..................................................        86.22
Fairbanks..................................................        80.55
Juneau.....................................................        94.39
Washington, DC, Area.......................................     *100.00
------------------------------------------------------------------------
* By definition, the index of the base area is always 100.00.

    Appendix 6 shows the regression equation in SAS code and the 
regression results. (SAS is a proprietary statistical analysis computer 
software package.)

4.3 Averaging Prices by Item and Area

    After OPM collects, reviews, and makes special adjustments in the 
data, OPM averages the prices of each item by COLA survey area. For 
example, we priced milk at three different grocery stores in Anchorage 
and averaged these prices to compute a single average price for milk in 
Anchorage. If we collected more than one price for a particular matched 
item within the same outlet (e.g., priced equivalent brands), we used 
the lowest price by item and outlet to compute the average. (The 
concept is that if the item and brands are equivalent, consumers will 
choose the one with the lowest price.) We repeated this item-by-item 
averaging process for each area.
    For Washington, DC, area prices, we first averaged prices within 
each of the three DC survey areas described in Section 2.5. Then we 
computed a simple average of the three DC area survey averages to 
derive a single DC area average price for each survey item.

4.4 Computing Price Indexes

    OPM computes a price index for each of the items found in both the 
COLA survey area and in the Washington, DC, area. To do this we divided 
the COLA survey area average price by the DC area

[[Page 780]]

average price and, following the convention used to express indexes, 
multiplied the result by 100. For the majority of survey items, we next 
applied consumer expenditure weights to combine price indexes. For a 
few items, however, OPM first applied special processes as described in 
Sections 4.4.1 and 4.4.2 below.
4.4.1 Geometric Means
    As described in Section 2.3, OPM selects survey items to represent 
specified detailed expenditure categories (DECs). Generally, OPM 
surveys only one item per DEC, but in some cases, it surveys multiple 
items at a single DEC. In these cases, it computes the geometric mean 
of the price indexes to derive a single price index for the DEC. (A 
geometric mean is the nth root of the product of n different numbers 
and is often used in price index computations.) For example, we 
surveyed two prescription drugs--Amoxicillin and Nexium in the 2005 
Caribbean survey. These two different prescription drugs represent a 
single DEC called ``prescription drugs.'' To derive a single price 
index for the DEC, we computed the geometric mean of the price index 
for Amoxicillin and the price index for Nexium.
4.4.2 Special Private Education Computations
    As noted in Section 4.2.1, OPM surveyed K-12 private education in 
the COLA and DC areas and computed an average tuition ``price'' that 
reflected all grade levels. Because not everyone sends children to 
private school, OPM made an additional special adjustment for K-12 
education by applying ``use factors.'' These use factors reflect the 
relative extent to which Federal employees make use of private 
education in the COLA and DC areas. For example, Table 5 shows a use 
factor of 0.7816 for Anchorage. OPM computed this by dividing 10.34 
percent (the percentage of Federal employees in Anchorage with at least 
1 child in a private school) by 13.23 percent (the percentage of DC 
area Federal employees with at least 1 child in a private school). OPM 
obtained the percentages from the results of the 1992/93 Federal 
Employee Housing and Living Patterns Survey, which is the most current 
comprehensive data available. Table 5 shows the use factors and the 
adjusted price indexes for each COLA survey area.

                         Table 5.--Summary of Private Education Use Factors and Indexes
----------------------------------------------------------------------------------------------------------------
                                                  Employees w/children in
                                                      private schools                                Price index
                COLA survey area                --------------------------  Use factor  Price index     w/use
                                                  Local area    DC area                                 factor
----------------------------------------------------------------------------------------------------------------
Anchorage......................................        10.34        13.23       0.7816        37.97        29.67
Fairbanks......................................         8.56        13.23       0.6470        21.39        13.84
Juneau.........................................        12.43        13.23       0.9395        23.95        22.50
----------------------------------------------------------------------------------------------------------------

4.5 Applying Consumer Expenditure Weights

    Next, OPM applies consumer expenditure weights to aggregate price 
indexes by expenditure group. As noted in Section 2.3, OPM uses the 
results of the BLS 2003/2004 Consumer Expenditure Survey to estimate 
the amounts middle income level consumers in the DC area spend on 
various items. Using expenditure weights, OPM combines the price 
indexes according to their relative importance. For example, shelter is 
the most important expenditure in terms of the COLA survey and 
represents about 30 percent of total consumer expenditures. On the 
other hand, the purchase of newspapers at newsstands represents less 
than 1/10th of 1 percent of total expenditures.
    Beginning at the lowest level of expenditure aggregation (e.g., 
sub-PEG), we computed the relative importance of each survey item 
within the level of aggregation, multiplied the price index times its 
expenditure percentage, and summed the cross products for all of the 
items within the level of aggregation to compute a weighted price index 
for the level. We repeated this process at each higher level of 
aggregation (e.g., PEG and MEG). Appendix 7 shows these calculations 
for each COLA survey area at the PEG and MEG level. The process 
resulted in an overall price index for Anchorage, Fairbanks, and Juneau 
but not for the Rest of the State of Alaska.

4.6 Computing the Overall Price Index for Rest of the State of Alaska

    Pursuant to the Caraballo settlement agreement, OPM did not conduct 
a living-cost survey in the Rest of the State of Alaska COLA area. 
Instead, OPM obtained information published by the University of Alaska 
and the Alaska Department of Labor and Workforce Development that 
compared prices in Anchorage with various other locations in Alaska. 
OPM used these data to compare prices in Kodiak, Alaska, with prices in 
Anchorage to compute, to the extent practical, Kodiak price indexes at 
the PEG and MEG level using Anchorage as the base. OPM then multiplied 
the MEG price indexes by the Anchorage indexes shown in Appendix 7 to 
estimate price differences in Kodiak compared with the DC area. OPM 
used the expenditure weights and the process described above to 
aggregate these indexes and produce an overall price index for the Rest 
of the State of Alaska, as shown in Appendix 8.

5. Final Results

    To compute the overall living-cost index, OPM adds to the price 
index a non-price adjustment factor. The parties in Caraballo 
negotiated these factors to reflect differences in living costs not 
captured by the surveys, and OPM adopted these factors in regulation as 
part of the new methodology. The factor for Anchorage is seven index 
points, and the factor for Fairbanks, Juneau, and the Rest of the State 
of Alaska is nine index points. The resulting living-cost indexes are 
shown in Table 6.

             Table 6.--Final Living-Cost Comparison Indexes
------------------------------------------------------------------------
                       Allowance area                           Index
------------------------------------------------------------------------
Anchorage..................................................       109.81
Fairbanks..................................................       118.90
Juneau.....................................................       120.08
Rest of the State of Alaska................................       132.82
------------------------------------------------------------------------

6. Post Survey Meetings

    In September 2006, the Anchorage, Fairbanks, and Juneau CACs held 
1-day meetings to review the survey results. We provided the committee 
members with various reports showing the data we collected, examples of 
how we reviewed these data, the data we used in our analyses, and the 
results at the PEG and MEG level, as shown in Appendix 7. We explained 
how we analyzed the rental data and used

[[Page 781]]

expenditure weights to combine price indexes to reflect overall living 
costs.

    Appendix 1.--Publication in the Federal Register of Prior Survey
                           Results: 1990-2004
------------------------------------------------------------------------
             Citation                             Contents
------------------------------------------------------------------------
71 FR 63179.......................  Report on 2005 living-cost surveys
                                     conducted in Puerto Rico and the
                                     U.S. Virgin Islands.
70 FR 44989.......................  Report on 2004 living-cost surveys
                                     conducted in Hawaii and Guam.
69 FR 12002.......................  Report on 2003 living-cost surveys
                                     conducted in Alaska.
69 FR 6020........................  Report on 2002 living-cost surveys
                                     conducted in Puerto Rico and the
                                     U.S. Virgin Islands.
65 FR 44103.......................  Report on 1998 living-cost surveys
                                     conducted in Alaska, Hawaii, Guam,
                                     Puerto Rico, and the U.S. Virgin
                                     Islands.
63 FR 56432.......................  Report on 1997 living-cost surveys
                                     conducted in Alaska, Hawaii, Guam,
                                     Puerto Rico, and the U.S. Virgin
                                     Islands.
62 FR 14190.......................  Report on 1996 living-cost surveys
                                     conducted in Alaska, Hawaii, Guam,
                                     Puerto Rico, and the U.S. Virgin
                                     Islands.
61 FR 4070........................  Report on winter 1995 living-cost
                                     surveys conducted in Alaska.
60 FR 61332.......................  Report on summer 1994 living-cost
                                     surveys conducted in Hawaii, Guam,
                                     Puerto Rico, and the U.S. Virgin
                                     Islands.
59 FR 45066.......................  Report on winter 1994 living-cost
                                     surveys conducted in Alaska.
58 FR 45558.......................  Report on summer 1992 and winter
                                     1993 living-cost surveys conducted
                                     in Alaska, Hawaii, Guam, Puerto
                                     Rico, and the U.S. Virgin Islands.
58 FR 27316.......................  Report on summer 1993 living-cost
                                     surveys conducted in Hawaii, Guam,
                                     Puerto Rico, and the U.S. Virgin
                                     Islands.
57 FR 58556.......................  Report on summer 1991 and winter
                                     1992 living-cost surveys conducted
                                     in Alaska, Hawaii, Guam, Puerto
                                     Rico, and the U.S. Virgin Islands.
56 FR 7902........................  Report on summer 1990 living-cost
                                     surveys conducted in Alaska,
                                     Hawaii, Guam, Puerto Rico, and the
                                     U.S. Virgin Islands.
------------------------------------------------------------------------


                                        Appendix 2.--Estimated DC Area Middle Income Annual Consumer Expenditures
                                  [Asterisks show Detailed Expenditure Categories (DECs) for which OPM surveyed items.]
--------------------------------------------------------------------------------------------------------------------------------------------------------
          Level                           Code                              Group                             Category name                Expenditures
--------------------------------------------------------------------------------------------------------------------------------------------------------
1........................  TOTALEXP.........................  .................................  Total Expenditure......................      $55,614.27
2........................  FOODTOTL.........................  MEG..............................  Food...................................        6,922.88
3........................  CERBAKRY.........................  PEG..............................  Cereals and bakery products............          454.23
4........................  CEREAL...........................  .................................  Cereals and cereal products............          150.11
5........................  010110...........................  .................................  Flour..................................            8.77
5........................  010120...........................  .................................  Prepared flour mixes...................           13.22
5........................  010210...........................  .................................  Ready-to-eat and cooked cereals *......           85.68
5........................  010310...........................  .................................  Rice *.................................           17.63
5........................  010320...........................  .................................  Pasta, cornmeal and other cereal                  24.81
                                                                                                  products *.
4........................  BAKERY...........................  .................................  Bakery products........................          304.11
5........................  BREAD............................  .................................  Bread..................................           88.56
6........................  020110...........................  .................................  White bread *..........................           35.96
6........................  020210...........................  .................................  Bread, other than white *..............           52.60
5........................  CRAKCOOK.........................  .................................  Crackers and cookies...................           68.85
6........................  020510...........................  .................................  Cookies *..............................           45.26
6........................  020610...........................  .................................  Crackers...............................           23.58
5........................  020810...........................  .................................  Frozen and refrigerated bakery products           25.43
                                                                                                  *.
5........................  OTHBAKRY.........................  .................................  Other bakery products..................          121.28
6........................  020310...........................  .................................  Biscuits and rolls *...................           40.04
6........................  020410...........................  .................................  Cakes and cupcakes *...................           40.09
6........................  020620...........................  .................................  Bread and cracker products.............            3.20
6........................  020710...........................  .................................  Sweetrolls, coffee cakes, doughnuts....           23.95
6........................  020820...........................  .................................  Pies, tarts, turnovers.................           14.00
3........................  ANIMAL...........................  PEG..............................  Meats, poultry, fish, and eggs.........          914.88
4........................  BEEF.............................  .................................  Beef...................................          258.77
5........................  030110...........................  .................................  Ground beef *..........................           99.60
5........................  ROAST............................  .................................  Roast..................................           41.70
6........................  030210...........................  .................................  Chuck roast *..........................           10.63
6........................  030310...........................  .................................  Round roast *..........................            9.00
6........................  030410...........................  .................................  Other roast............................           22.07
5........................  STEAK............................  .................................  Steak..................................           99.04
6........................  030510...........................  .................................  Round steak *..........................           17.29
6........................  030610...........................  .................................  Sirloin steak *........................           30.40
6........................  030710...........................  .................................  Other steak............................           51.36
5........................  030810...........................  .................................  Other beef.............................           18.43
4........................  PORK.............................  .................................  Pork...................................          133.75
5........................  040110...........................  .................................  Bacon *................................           24.19
5........................  040210...........................  .................................  Pork chops *...........................           29.82
5........................  HAM..............................  .................................  Ham....................................           29.05
6........................  040310...........................  .................................  Ham, not canned *......................           28.45
6........................  040610...........................  .................................  Canned ham *...........................            0.60
5........................  040510...........................  .................................  Sausage................................           21.16
5........................  040410...........................  .................................  Other pork.............................           29.54
4........................  OTHRMEAT.........................  .................................  Other meats............................          104.10
5........................  050110...........................  .................................  Frankfurters *.........................           21.48
5........................  LNCHMEAT.........................  .................................  Lunch meats (cold cuts)................           71.11

[[Page 782]]

 
6........................  050210...........................  .................................  Bologna, liverwurst, salami *..........           21.27
6........................  050310...........................  .................................  Other lunchmeats.......................           49.83
5........................  LAMBOTHR.........................  .................................  Lamb, organ meats and others...........           11.52
6........................  050410...........................  .................................  Lamb and organ meats...................            8.99
6........................  050900...........................  .................................  Mutton, goat and game..................            2.52
4........................  POULTRY..........................  .................................  Poultry................................          162.22
5........................  CHICKEN..........................  .................................  Fresh and frozen chickens..............          129.26
6........................  060110...........................  .................................  Fresh and frozen whole chicken *.......           39.10
6........................  060210...........................  .................................  Fresh and frozen chicken parts *.......           90.16
5........................  060310...........................  .................................  Other poultry..........................           32.96
4........................  FISHSEA..........................  .................................  Fish and seafood.......................          217.94
5........................  070110...........................  .................................  Canned fish and seafood *..............           26.72
5........................  070230...........................  .................................  Fresh fish and shellfish *.............          121.55
5........................  070240...........................  .................................  Frozen fish and shellfish *............           69.66
4........................  080110...........................  .................................  Eggs *.................................           38.09
3........................  DAIRY............................  PEG..............................  Dairy products.........................          324.73
4........................  MILKCRM..........................  .................................  Fresh milk and cream...................          121.80
5........................  090110...........................  .................................  Fresh milk, all types *................          109.79
5........................  090210...........................  .................................  Cream..................................           12.00
4........................  OTHDAIRY.........................  .................................  Other dairy products...................          202.94
5........................  100110...........................  .................................  Butter.................................           20.89
5........................  100210...........................  .................................  Cheese *...............................           98.59
5........................  100410...........................  .................................  Ice cream and related products *.......           55.36
5........................  100510...........................  .................................  Miscellaneous dairy products...........           28.10
3........................  FRUITVEG.........................  PEG..............................  Fruits and vegetables..................          446.86
4........................  FRSHFRUT.........................  .................................  Fresh fruits...........................          228.66
5........................  110110.......