# Report on 1996 Surveys Used to Determine Cost-of-Living Allowances in Nonforeign Areas

> Briefs, arguments, decisions, and more.

URL: https://www.frixlaw.com/law-library/documents/fr%3A97-7362

## Record

- **Collection:** Federal Register
- **Document type:** Notice
- **Published:** March 25, 1997
- **Citation:** 62 FR 14190

## Text

SUMMARY: This notice publishes the ``Report on 1996 Surveys Used to
Determine Cost-of-Living Allowances in Nonforeign Areas.'' The surveys
were conducted by Runzheimer International under contract with the
Office of Personnel Management (OPM). The analyses and report were
prepared by OPM. The results of the surveys are used to determine cost-
of-living allowances (COLA's) paid to General Schedule, U.S. Postal
Service, and certain other Federal employees in Alaska, Hawaii, Guam
and the Commonwealth of the Northern Mariana Islands, Puerto Rico, and
the U.S. Virgin Islands. This report provides the basis for the
increases in certain COLA rates being published by OPM in the interim
rulemaking immediately preceding this notice.

DATES: Comments must be received on or before June 23, 1997.

ADDRESSES: Send or deliver comments to Donald J.Winstead, Assistant
Director for Compensation Policy, Human Resources Systems Service,
Office of Personnel Management, Room 6H31, 1900 E Street NW.,
Washington, DC 20415, or FAX to (202) 606-4264, or email comments over
the Internet to [email protected].

FOR FURTHER INFORMATION CONTACT: Donald L. Paquin, (202) 606-2838.

SUPPLEMENTARY INFORMATION: Sections 591.205(d) and 591.206(c) of title
5, Code of Federal Regulations, require that nonforeign area cost-of-
living allowance (COLA) survey summaries and calculations be published
in the Federal Register . Accordingly, the Office of Personnel
Management (OPM) is publishing the complete ``Report On 1996 Surveys
Used to Determine Cost-of-Living Allowances in Nonforeign Areas'' with
this notice. The surveys were conducted by Runzheimer International
under Government contract OPM-95-97012. OPM performed the analyses of
survey data and prepared this report, which explains in detail the
methodologies, calculations, and findings of the 1996 COLA surveys.

Survey Results

OPM computed index values of relative living costs in the allowance
areas using an index scale where the living costs in the Washington,
DC, area equal 100. (See the Executive Summary of the report.) The
results of the surveys show that the COLA rate for Kauai, HI, should be
increased from its current level of 20.0 percent to 22.5 percent and
that the COLA rate for the U.S. Virgin Islands should be increased from
17.5 percent to 20.0 percent. The survey results also show that the
COLA rates for three areas are currently at the appropriate levels, and
that the COLA rates in seven areas are above levels warranted by the
living-cost indexes. However, the Treasury, Postal Service, and General
Government Appropriations Act, 1992 (Pub. L. 102-141), as amended,
prohibits reductions in COLA rates through December 31, 1998.
Therefore, OPM is not proposing any COLA rate reductions.

Comments on Prior Surveys

OPM published the report on the Summer 1994 surveys in Hawaii,
Guam, Puerto Rico, U.S. Virgin Islands, and the Washington, DC, area in
the Federal Register (60 FR 61332) on November 29, 1995. OPM published
the report on the Winter 1995 surveys in Alaska and the Washington, DC,
area in the Federal Register (61 FR 4070) on February 2, 1996. OPM
received 6 comments on the Summer 1994 surveys and 77 comments on the
Winter 1995 surveys.
Most of the commenters believed the surveys did not fully consider
the expenses incurred in the allowance areas. Many noted
dissimilarities between the allowance areas and the Washington, DC,
area that they felt were either not accounted for in the surveys or
that affected the accuracy of the results of the surveys. These
differences included --
--Goods and services typically found in the Washington, DC, area that
are not available in the allowance areas, the cost to obtain these
goods and services in the allowance areas (e.g., shipping fees), and
the quality of the goods and services that are available;
--Goods and services typically purchased in the allowance areas that
are not typically purchased in the Washington, DC, area;
--Variations in spending patterns between the Washington, DC, area and
the allowance areas;
--Hardships encountered under adverse climate conditions;
--Climate influences on automobile purchase, maintenance, and
insurance;
--The frequency and cost of air travel in the allowance areas and the
use of Los Angeles for comparison in the measurement of air fares;
--Transportation alternatives (e.g., bus, train, subway) available in
the Washington, DC, area that are not available or are limited in the
allowance areas;
--House size, selection, necessary features, purchase price, storage
needs, and maintenance as determined by climate and availability;
--The additional need for travel, lodging, and out-of-pocket expenses
for quality medical care in the allowance areas;
--Recreational expenses in the allowance areas; and
--Out-of-area colleges and quality of local schools.
OPM has committed itself to two major initiatives that it believes
will serve as a forum for examining many of the concerns raised by the
commenters and lead to significant improvements in the COLA survey
process. These two initiatives are discussed below.

Safe Harbor Process and Report to Congress

OPM has entered into a memorandum of understanding with litigants
in the cases of Alaniz v. Office of Personnel Management and
Karamatsu v. United States that commits OPM and the plaintiffs to a
``Safe Harbor'' process for conducting studies relating to the COLA
program and the compensation of Federal employees in the allowance
areas. The purpose is to resolve issues that have long been contended
in the COLA program and to assist OPM as it prepares its report to
Congress on the COLA program, which is required by the Treasury, Postal
Service, and General Government Appropriations Act, 1992 (Public Law
102-141), as amended. That report is currently due by March 1, 1998.
OPM anticipates that the studies will examine many of the issues raised
by the comments on the Summer 1994 and Winter 1995 survey reports and
will produce a number of valuable recommendations for improving the
COLA program.

COLA Partnership

OPM has established a pilot project to involve agencies and
employee representatives directly in a partnership to help plan and
conduct COLA surveys, explore ways to improve the COLA program, and to
help everyone, including OPM, better understand issues related to the
compensation of Federal employees in the COLA areas. (Final regulations
for the pilot project were published on November 21, 1996, at 61 FR
59173.) Under the 2-year pilot project, five partnership committees are
being formed--one each in Alaska, Hawaii, Guam, Puerto Rico, and the
U.S. Virgin Islands. Regulations also allow for the formation of
subcommittees in

[[Page 14191]]

the individual allowance areas. Committee functions are expected to
include:
--Advising and assisting OPM in planning living-cost surveys;
--Observing data collection during the surveys;
--Advising and assisting OPM in the review of survey data;
--Advising OPM on the COLA program, including survey methodology and
other compensation issues relating to the allowance areas;
--Assisting OPM in the dissemination of information to affected
employees about the living-cost surveys and the COLA program.
As with the studies being conducted for OPM's report to Congress,
we anticipate that the committees may examine some of the issues raised
by the comments on the Summer 1994 and Winter 1995 survey reports and
will produce valuable recommendations for improving the COLA program.

Impact of COLA Changes

As with previous reports, most of the commenters were Federal
employees concerned about the impact of deep reductions in COLA rates.
They cited various financial commitments, such as home purchase, that
were made assuming COLA rates would be relatively stable. Several
commenters thought that significant reductions would have an adverse
effect on the local economy of the allowance area and that significant
reductions would cause recruitment and retention problems. As noted
earlier, Public Law 102-141, as amended, prohibits OPM from reducing
COLA rates through December 31, 1998.

General Comments

A number of commenters maintained that the salary averages used
for the surveys did not consider other sources of income besides
General Schedule salaries. They believe this resulted in an
artificially low salary average, especially for the Washington, DC,
area. OPM uses the General Schedule average salaries because it is the
predominant pay system for employees in the allowance areas and in the
Washington, DC, area. The COLA is a percentage of Federal pay, not
total family income. Therefore, OPM believes the approach used is
appropriate. However, the number of income levels used in the COLA
model and the dollar amounts assigned to those income levels are
subjects that may be researched further.
A few commenters asked whether OPM adjusted the calculations of
the percent of General Schedule workforce in each income group in each
area to reflect special rates or shift differentials. OPM included
special rates because special rates are one type of basic pay, and the
COLA is paid as a percentage of basic pay. OPM did not include premium
pay, such as shift differentials, because these are not part of basic
pay.
Several commenters felt the COLA program should take into
consideration the hardships endured in some of the allowance areas. OPM
believes the COLA model adequately measures differences in monetary
costs due to conditions in the allowance areas, although improvements
and refinements in the model may be possible. For example, OPM is
researching certain additional items, particularly those that might be
purchased more frequently in remote areas. These items include air
transportation, out-of-area college and university education, and
extraordinary medical expenses. OPM is looking at ways the tangible
cost of these items might be included in the COLA model and plans to
address this issue in its report to Congress. OPM believes, however,
that employees are compensated for nonmonetary factors such as hardship
and inconvenience under the post differential program and that such
factors should not be covered under the COLA program.
A few commenters objected to the inclusion of sales taxes in the
COLA model. The commenters argued that it would also be necessary to
compare the level of Government services available in each area. OPM
disagrees. The effect on living costs of any differences in the levels
of Government services attributable to differences in sales tax
revenues is probably not measurable. Sales tax, on the other hand, is a
recognizable consumer expense. Therefore, OPM believes it is
appropriate to include sales tax in the prices of the items it surveys.
Several commenters felt that some of the field researchers should
be Federal employees. They believe non-Federal employees have a desire
to cut Federal pay, which they view as a conflict of interest. OPM does
not believe there was such a bias, and both OPM and Runzheimer utilized
a number of quality assurance procedures, including callbacks and close
data review, to assure that the prices collected were accurate. OPM
also notes that, under the COLA partnership pilot project, data will be
collected by Federal employees from the Washington, DC, area with
observers from the COLA areas. Therefore, beginning with the 1997
surveys, non-Federal field researchers will not be involved in the
survey process.
Some commenters believe more data should be collected directly
from Federal employees. OPM notes that it has collected data directly
from Federal employees in the past and may explore this issue with the
COLA partnership committees and under the MOU Safe Harbor process. OPM
anticipates addressing this as part of its report to Congress.
Several commenters stated that OPM should publish additional
survey data (e.g., outlets surveyed, basic price data) in the report.
Publishing this volume of information is not practical and would make
the report too cumbersome and complex.
A few commenters noted that the date appearing at the top of the
Federal Register pages containing the Winter report read ``1994''
instead of ``1996.'' This was a printer's error.
Some commenters want OPM to consider higher non-Federal pay when
setting COLA rates. The law bases COLA's on living costs, not pay
levels. It also specifically bars payment of locality pay in the COLA
areas. Therefore, OPM cannot take into consideration higher non-Federal
pay in the COLA areas.
Several commenters contend that the COLA calculations should
account for locality pay received in the Washington, DC, area. OPM
recognizes that General Schedule employees in the Washington, DC, area
receive a locality pay adjustment under 5 U.S.C. 5304. Whether this
adjustment should be considered in the calculation of COLA's is an
issue OPM plans to address in its report to Congress.
Some commenters think Federal employees in the Washington, DC,
area are overgraded and that COLA's should be increased to account for
this overgrading. Grade levels vary among areas and may be higher on
average in the DC area because of the nature of the work typically
performed in this area. If it is found that overgrading is a problem in
any area, including the DC area, the solution is to properly classify
the positions--not to adjust pay or allowances.
Many commenters suggested that OPM use data published by the
American Chamber of Commerce Researchers Association (ACCRA). ACCRA
does not publish living-cost comparisons for all of the COLA areas, nor
does the ACCRA methodology conform with OPM's regulations, which were
developed subsequent to the settlement of Hector Arana, et al., v.
United States. Therefore, OPM does not use ACCRA data.
Several commenters suggested a need to survey more than once a
year. As OPM stated in an earlier Federal Register notice (60 FR
46749), OPM does not believe there is significant

[[Page 14192]]

seasonal variation in relative prices for most local consumer items in
the allowance areas compared to those in the Washington, DC, area.
There is evidence of seasonal variation in some prices, such as hotel
and motel lodging, but these are not typical local consumer items.
There is also seasonal variation in the prices of other items, such as
fresh fruits and vegetables, but that kind of variation is seen in both
the allowance areas and in the Washington, DC, area. Therefore,
relative price differences do not change significantly by season. For
this reason and because COLA surveys are costly and can be a public
burden, OPM does not believe it is appropriate to conduct COLA surveys
more frequently.
Some commenters objected to OPM's practice of making changes in
the model based on comments received without an additional comment
period to review the changes. They also requested that OPM forgo making
changes in the methodology while the joint research effort is under
way. During the Safe Harbor process and the COLA partnership pilot
project, OPM plans generally to avoid making substantive policy changes
in the COLA program and, instead, wait until after OPM has completed
its research, received public comment on it, and delivered its report
to Congress. This does not mean that OPM will make no changes, and
certainly there are administrative changes relating to survey coverage
that must be made for each survey. However, the reader will note that
OPM has made relatively few changes in this year's surveys compared
with the previous surveys.

Overall Living Cost Model

A number of commenters stated that Washington, DC, should not be
the base area for comparisons of living costs. They believe a less
expensive area should be used. OPM is required by law to use the
Washington, DC, area as the base for living-cost comparisons.
Some commenters felt that local spending patterns should be used
in pricing consumption goods and services. To compare living costs
between areas, OPM assigned a common set of weights to each item,
category, and component. These weights reflect how consumers spend
their money and were used to derive comparative indexes measuring
overall living costs. OPM used Bureau of Labor Statistics (BLS)
nationwide Consumer Expenditure Survey (CES) data for these weights. As
discussed in the report, the COLA model uses an indexing methodology.
As the report also notes, it would be preferable to use Washington, DC,
consumer expenditure data. Washington, DC, CES data, however, are not
available by income level, and OPM regulations require measurement of
living costs at multiple income levels. On the other hand, nationwide
CES data are arrayed by income level. Therefore, OPM used these data in
the COLA model. CES data are also available for Honolulu and Anchorage;
but as with the Washington, DC, data, the Honolulu and Anchorage data
are not available by income level. BLS CES data are not available for
any other nonforeign area, and OPM knows of no other source of
comprehensive consumer expenditure information by income level suitable
for use in the COLA model. Therefore, the use of local weights is not
practical.
Commenters also suggested that OPM explore the use of cross-
weighted measures, such as Fisher's ideal index. Since cross-weighted
indexes use local area weights as well as reference area weights, the
use of these approaches would face the same problems as would the use
of local weights alone (as is described above). However, the type of
measurement used and the source of CES data may also be topics for
further research.

Goods and Services Component

A number of commenters cited the lack of locally available goods
and services, and many commenters said that they had to purchase items
by catalog. OPM included catalog prices for selected items in the
surveys. Additional costs of shipping and excise taxes were added to
the catalog pricing where applicable.
A number of commenters felt that the surveys should recognize that
allowance area employees purchase goods and services that are either
not needed in the Washington, DC, area or are needed less frequently.
Generally, the COLA model compares the cost of an item in an allowance
area with the cost for the same item in the DC area. OPM believes this
is consistent with the settlement of Arana, in which the plaintiffs
asked that OPM adopt a methodology that compared specified brands,
models, and sizes whenever possible. Nevertheless, the COLA model does
reflect some differences between areas. For example, the model assumes
that cars in Alaska have certain accessories, such as engine block
heaters, that are not common in the DC area. Also, differences in home
construction (e.g., triple-pane windows and greater wall insulation
common in Alaska) are included in the model to the extent that these
differences are reflected in real estate prices. OPM anticipates
researching related issues and plans to address them in its report to
Congress.
A number of commenters felt that the surveys should recognize that
there are a limited number of restaurant choices in the allowance areas
as compared with the Washington, DC, area. The surveys measure this
indirectly to the extent that restaurant prices reflect competition.
The commenters also felt that high quality local restaurants and foods
should be surveyed. The comparison of non-chain restaurants is
difficult and would seem to be inconsistent with Arana.
Some commenters believe more brands and models of items should be
surveyed. As described in section 2.4.1 of the report, items to be
surveyed are identified according to their importance in terms of
consumer expenditures. OPM surveys nearly 200 representative items and
believes these adequately reflect typical consumer expenditures.

Housing Component

A number of commenters objected to the inclusion of historical
housing data in the surveys. The commenters objected to the use of
these data because they believe (1) the resulting allowance would
compensate employees for historical rather than current living costs,
(2) the weights used to combine the data were from a limited
demographic profile (i.e., the 1992/93 Federal Employee Housing and
Living-Patterns Survey), and (3) much of the historical home price data
were from living communities outside the area where COLA recipients
reside.
Historical housing data are based on purchase prices and interest
rates over a 10-year period. We first used these data in the summer
1994 surveys; however, we had stated our intention to do so in earlier
Federal Register notices on the COLA program. (See 55 FR 1372 and 57 FR
58559.) The reason OPM uses historical data is that relatively few
Federal employees purchase a home in any given year. By using home sale
prices and interest rates gathered over a 10-year period, the COLA
model better emulates the typical Federal employee's housing expenses
than if only the current year's purchase information were used.
OPM believes its use of the results of the 1992/93 Federal
Employee Housing and Living Patterns Survey is appropriate. OPM
received over 16,000 responses to the employee survey from the
allowance areas. Although in a universe survey such as this, there is
always the potential for a nonresponse bias, we find the results
concerning home tenure to be reasonable when compared with data from
other sources

[[Page 14193]]

such as Census data and data published by the Chicago Title and Trust
Company. Therefore, we believe it is appropriate to use the weights
derived from the employee survey to combine housing cost data.
As one commenter noted, some of the historical housing data came
from communities that are no longer surveyed. Although we made changes
in 1994 in response to employee suggestions and in light of the
employee survey results, we believe our earlier community selections
were appropriate. For example, we previously included Mililani Town in
our Honolulu surveys. Mililani Town was the most frequent place of
residence reported in the employee survey. Because of limited survey
resources, we dropped Mililani Town (and others) in order to survey
communities in and closer to Honolulu, as suggested by comments on the
results of the 1993 living-cost surveys. It would be a mistake,
however, to say that places such as Mililani Town are not
representative of where Federal employees live, and we believe it is
reasonable to use historical data from such communities.
Although it might be possible to collect historical data only for
those communities now surveyed, we do not believe this is necessary or
desirable. Community changes were made in many survey areas, including
the Washington, DC, area. Additional historical housing data would have
to be collected in each of these areas, and this would be costly and
burdensome to the public. Even so, we believe the final living-cost
comparisons for the allowance areas would remain essentially unchanged
because similar changes in community selection also were made in the
Washington, DC, metropolitan area, again using the results of the
employee survey.
Some commenters suggested that using the employee survey results
to select housing communities violated the agreement in Arana because
COLA area employees live in undesirable neighborhoods as a result of
low COLA rates. Other commenters suggested that housing communities
selected in Anchorage were inappropriate because many employees live
outside of the survey area. OPM believes it is appropriate to use both
the results of the employee survey and a methodology that compares the
costs of housing of similar sizes and in similar communities among the
various diverse areas covered by the surveys. However, we anticipate
that the housing methodology, community selection, and housing
characteristics will be subjects of study during the MOU Safe Harbor
process and among the issues considered by the COLA Partnership
Committees.
Several commenters stated that the housing costs in Anchorage were
not accurate and provided other data that showed higher median values.
OPM's contractor obtained the prices for houses that met specified
profile characteristics (e.g., size) for lower, middle, and upper
income levels. These prices were collected from real estate
professionals and various listing services. The data provided by the
commenters did not sufficiently identify the characteristics of the
sold houses for OPM to evaluate effectively these data relative to the
data that OPM's contractor reported.
Several commenters said that climate conditions (such as high
humidity, high rainfall, sunlight intensity, airborne salt, snow, and
cold weather) resulted in more frequent and higher home maintenance
costs in the allowance areas than in the Washington, DC, area. OPM has
conducted some preliminary studies of these issues, anticipates
researching them further, and plans to provide the results in its
report to Congress.
Several commenters noted that most Alaskan houses have ``Arctic
entrances'' for the removal of coats and boots, and felt that the
surveys should take this into consideration. The home purchase price
data collected reflect local home sales and include the cost of any
special features common to dwellings in each area.
Several commenters noted that military troops are provided a
housing allowance and felt that civilian employees should receive the
same. The law does not provide a separate housing allowance for
civilian Federal employees. However, as described in the report,
relative differences in housing costs between the allowance areas and
the Washington, DC, area are taken into consideration in determining
COLA rates.
A few commenters suggested that long-distance telephone
calculations be based on the local time of the call. OPM based this
calculation on the time the call was received on the assumption that
most long distance callers timed their calls for the convenience of the
receiver rather than the caller. Moreover, making the opposite
assumption could have resulted in some anomalies. For example, a long
distance call placed at 8 p.m. in Honolulu would be received in New
York at 1 or 2 a.m.

Transportation Component

A number of commenters stated that private transportation costs
are greater in the allowance areas because of the high cost of
automobiles and increased auto maintenance resulting from poor roads,
rough terrain, salt air, and harsh weather. Many also felt that
automobile insurance premiums are higher in the allowance areas.
The COLA model takes into consideration automobile purchase price,
maintenance, insurance, and depreciation. Purchase costs and insurance
are based on price data obtained in each area. Maintenance is also
based on local price data, and the model assumes that certain types of
maintenance occur more frequently in the allowance areas than in the DC
area. For example, the model assumes that tires wear out faster in the
allowance areas than in the Washington, DC, area, and that tires have
to be purchased more frequently in the allowance areas. The model also
includes the severe driving maintenance schedule for the allowance
areas and the standard schedule for the Washington, DC, area.
Depreciation is based on the difference between the new car value and
the value of the car 4 years later, as reflected in popular guides such
as the National Automobile Dealers Association Official Used Car Guide
and the Kelly Blue Book. The model assumes that used car prices are
constant among areas, except in Fairbanks and Nome. Since new car
prices are typically higher in the allowance areas, this assumption
translates into a typically higher depreciation rate for new cars in
the allowance areas relative to the DC area. For Fairbanks and Nome,
the model uses 90 percent of the used car value to reflect an even
higher depreciation cost related to increased wear in these areas
caused by the severe climate.
A number of commenters think that OPM should have used negotiated
prices in its survey of new cars. These same commenters also believe
used car prices should be included in the surveys. As stated in the
report, it is not feasible to collect information on negotiated prices.
Negotiated prices are influenced by factors such as negotiating skills,
timing, and dealer overstock, and we expect that dealers would be
reluctant to disclose what they would accept as the final purchase
price for the vehicles surveyed. Likewise, OPM believes it highly
unlikely that OPM could price comparable used cars, in terms of make,
model and condition, in each of the allowance areas and in the
Washington, DC, area. Therefore, as stated in the report, OPM does not
survey the price of used cars.
Many commenters felt that pick-up trucks and four-wheel drive
vehicles should be priced, especially for the

[[Page 14194]]

Alaska surveys. As stated in the report, OPM surveys the cost of owning
and operating a four-wheel drive Chevy Blazer, which is a ``utility''
vehicle. OPM believes the vehicles currently surveyed are adequate for
measuring price differences for new vehicles.
Several commenters raised issues related to mass transportation
systems (e.g., bus, train, subway), which are limited or not available
in the allowance areas. As explained in the report, OPM does not survey
municipal mass transportation. The cost of bus, train, subway, or taxi
service is not part of the surveys because the service available in
many allowance areas is not comparable to the service available in the
DC area. Instead, OPM compares the cost of round-trip airfares from the
allowance areas with the cost of round-trip airfares from the
Washington, DC, area to the same destinations.
A number of commenters objected to the selection of Los Angeles as
the common destination point for comparing airfares. They stated that
the Los Angeles routes are highly competitive, which results in lower
fares compared with other destinations, and that Los Angeles is not
typical of flight destinations from the allowance areas. For the 1996
surveys, OPM included additional travel destinations. There are now six
destinations for which airfares from the allowance areas and the
Washington, DC, area are collected: Chicago, Los Angeles, Miami, New
York, Seattle, and St. Louis.
Some commenters stated that the model did not measure true air
transportation costs. The commenters stated that inter-island travel,
travel within Alaska, and travel to the contiguous 48 States requires
more frequent use of air transportation. The current model assumes that
the typical Federal employee puts 15,000 miles per year on a car, but
many Federal employees in the allowance areas may drive less than that,
particularly in some of the smaller allowance areas. On the other hand,
these employees may fly more frequently. If so, it may be appropriate
to make adjustments in the COLA model to reflect these differences. OPM
plans to study further the issue of transportation costs by mode of
transportation for its report to Congress.

Miscellaneous Component

A number of commenters felt that the medical expense portion of
the Miscellaneous Component fails to reflect the higher out-of-pocket
expenses that Federal employees in the allowance areas frequently
incur. The commenters cited several possible causes for this, including
higher costs not covered by insurance carriers, the absence of health
maintenance organizations in several allowance areas, and the need to
travel outside the area to obtain some medical services. OPM is
researching health cost issues and plans to include the results of its
research in its report to Congress.
One commenter stated that employees in the allowance areas have to
save at a higher rate to afford the down payment for a house or car or
to pay for college/university education. The commenter said that OPM
should take this into consideration and use the Goods and Services
Component index to adjust the amount of money saved relative to
Washington, DC. As noted in the report, savings made for the purpose of
future purchases of housing, durable goods, and similar items are
accounted for in the category or component weight associated with the
item.
The commenter also stated that the COLA model should take into
consideration the fact that COLA's do not count towards retirement. The
commenter believes Federal employees have to invest at a higher rate in
pensions and other savings vehicles to afford to retire in the
allowance areas. Under sections 8331(3) and 8401(4) of title 5, United
States Code, allowances (including COLA's) are explicitly excluded from
basic pay in the computation of Federal annuities under the Civil
Service Retirement System and the Federal Employees' Retirement System.
OPM believes it would be inappropriate to adjust COLA rates to take
into consideration that which the law has specifically excluded.
Therefore, OPM does not plan to adopt this recommendation at this time
but plans to address it in its report to Congress.

Office of Personnel Management.
James B. King,
Director.

Table of Contents

Executive Summary

1. Introduction
1.1 Report Objectives
1.2 Changes in This Year's Survey
1.3 Pricing Period
2. The COLA Model
2.1 Measurement of Living-Cost Differences
2.2 Step 1: Identifying the Target Population
2.2.1 Federal Salaries
2.2.2 Federal Employment Weights
2.3 Step 2: Estimating How People Spend Their Money
2.3.1 Consumer Expenditure Survey (CES)
2.3.2 Expenditure Categories and Components
2.4 Step 3: Selecting Items and Outlets
2.4.1 Item Selections--The Market Basket
2.4.2 Geographic Coverage and Outlet Selection
2.4.2.1 Geographic Areas
2.4.2.2 Similarity of Outlets
2.4.2.3 Catalog Pricing
2.5 Step 4: Surveying Prices
2.5.1 Runzheimer Data Collection
2.5.2 Data Collection Materials
2.5.3 Inclusion of Sales and Excise Taxes
2.5.4 Runzheimer's Onsite Visits
2.5.5 Surveying the Washington, DC, Area
2.6 Step 5: Analyzing Data and Computing Indexes
2.6.1 Indexes and Weights
2.6.1.1 Indexes
2.6.1.2 Item Weights
2.6.1.3 Category and Component Weights
2.6.2 Computing the Overall Index
3. Consumption Goods and Services
3.1 Categories and Category Weights
3.2 Goods and Services Survey Results
3.2.1 Exchange and Commissary Expenditure Research
4. Housing
4.1 Component Overview
4.2 Housing Model
4.2.1 Expenditure Research
4.2.2 Housing Profiles
4.2.3 Living Community Selection
4.2.4 Housing-Related Expenses
4.2.4.1 Utilities
4.2.4.2 Real Estate Taxes
4.2.4.3 Owners/Renters Insurance
4.2.4.4 Home Maintenance
4.2.4.5 Telephone Expenses
4.3 Housing Data Collection Procedures
4.3.1 Homeowner Data Collection
4.3.2 Renter Data Collection
4.4 Housing Analysis
4.4.1 Homeowner Data Analysis
4.4.2 Rental Data Analysis
4.5 Housing Survey Results
5. Transportation
5.1 Component Overview
5.2 Private Transportation Methodology
5.2.1 Vehicle Selection and Pricing
5.2.2 Vehicle Trade Cycle
5.2.3 Fuel Performance and Type
5.2.3.1 Impact of Temperature upon Fuel Performance
5.2.3.2 Impact of Road Surface upon Fuel Performance
5.2.3.3 Impact of Gradient Upon Fuel Performance
5.2.3.4 Overall Impact upon Fuel Performance
5.2.4 Vehicle Maintenance
5.2.5 Tires
5.2.6 License and Registration Fees and Miscellaneous Taxes
5.2.7 Depreciation
5.2.8 Finance Expense
5.2.9 Vehicle Insurance
5.2.10 Overall Annual Costs
5.3 Other Transportation Costs--Air Fares
5.4 Transportation Component Analyses
6. Miscellaneous Expenses
6.1 Component Overview
6.2 Component Weights
6.3 Component Categories
6.3.1 Medical Expense Category
6.3.2 Contributions Category
6.3.3 Personal Insurance and Retirement Category
6.4 Miscellaneous Expense Analyses
7. Final Results

[[Page 14195]]

7.1 Total Comparative Cost Indexes

List of Appendices

Appendix 1:Publication in the Federal Register of Results of
Nonforeign Area Living-Cost Surveys: 1990--1996
Appendix 2: Federal Employment Weights
Appendix 3: Consumer Expenditure Survey (CES) Item Expenditures
Appendix 4: CES Category and Component Expenditures
Appendix 5: Item Descriptions
Appendix 6: Principal Pricing Changes
Appendix 7: OMB-Approved Survey Materials
Appendix 8: Consumption Goods and Services Analysis and Summary
Appendix 9: OPM Living Community List
Appendix 10: Historical Home Market Values and Interest Rates
Appendix 11: Historical Housing Data
Appendix 12: Rental Data Analyses
Appendix 13: Housing Cost Analysis
Appendix 14: Housing Summary
Appendix 15: Private Transportation Cost Analysis
Appendix 16: Auto Insurance Calculation Worksheet
Appendix 17: Air Fares Cost Analysis
Appendix 18: Transportation Analysis
Appendix 19: Transportation Summary
Appendix 20: Miscellaneous Expense Analysis--Category Index
Development
Appendix 21: Miscellaneous Expense Summary
Appendix 22: Component Expenditures
Appendix 23: Final Indexes

Executive Summary

Cost-of-living allowances (COLA's) are paid to Federal employees in
nonforeign areas in consideration of living costs higher than in the
Washington, DC, area. OPM conducts living costs surveys in order to set
the COLA rates. This report provides the results of the 1996 living-
cost surveys and compares living costs in nonforeign COLA areas to
those in the Washington, DC, area.
Survey data were collected for the Office of Personnel Management
(OPM) by Runzheimer International under contract OPM- 95-97012.
Runzheimer is a Wisconsin-based firm specializing in cost-of-living
information. The contract required Runzheimer to survey living costs in
Alaska, Hawaii, Guam, Puerto Rico, the U.S. Virgin Islands, and the
Washington, DC, area. OPM analyzed the survey data and produced this
report.
For this study, approximately 2,800 outlets were contacted and more
than 20,000 prices collected on about 200 items representing typical
consumer purchases. These data were then combined by OPM using consumer
expenditure information developed by the Bureau of Labor Statistics.
The final result of the study is a series of living-cost indexes, shown
in the table below, that compare living costs in the allowance areas to
those in the Washington, DC, area. The index for the DC area (not
shown) is 100.00 because it is, by definition, the reference area.

Table E-1.--Final Cost Comparison Indexes
------------------------------------------------------------------------
Allowance area Index
------------------------------------------------------------------------
Anchorage, Alaska............................................. 104.84
Fairbanks, Alaska............................................. 109.90
Juneau, Alaska................................................ 110.57
The rest of the State of Alaska............................... 129.24
City and County of Honolulu, Hawaii........................... 121.95
Hawaii County, Hawaii......................................... 111.89
Kauai County, Hawaii.......................................... 121.36
Maui County, Hawaii........................................... 119.53
Guam/CNMI*, Local Retail...................................... 121.88
Guam/CNMI, Commissary/Exchange................................ 116.06
Puerto Rico................................................... 102.01
U.S. Virgin Islands........................................... 119.25
------------------------------------------------------------------------
*CNMI=Commonwealth of the Northern Mariana Islands

1. Introduction

1.1 Report Objectives

This report provides the results of the February 1996 surveys. A
listing of earlier reports that provided the results of previous
surveys is shown in Appendix 1. The analyses show the comparative
living-cost differences between the Washington, DC, area and the
allowance areas listed below. By law, Washington, DC, is the base or
``reference'' area for the nonforeign area cost-of-living allowance
(COLA) program.

1. Anchorage, Alaska
2. Fairbanks, Alaska
3. Juneau, Alaska
4. The rest of the State of Alaska
5. City and County of Honolulu, Hawaii
6. Hawaii County, Hawaii
7. Kauai County, Hawaii
8. Maui County, Hawaii
9. Guam and the Commonwealth of the Northern Mariana Islands (CNMI)
10.Puerto Rico
11.U.S. Virgin Islands

1.2 Changes in This Year's Survey

This year OPM contracted with Runzheimer International to collect
price data. In previous surveys, most of the analyses of the data were
performed by the contractor. This year, OPM performed all analyses.
Appendix 6 lists the other major changes made for this survey relative
to the previous survey. Among the key changes were the following:

--Airline fares to Chicago, Los Angeles, Miami, New York, and St. Louis
were surveyed. Previously, only fares to Los Angeles were surveyed.
--Several new survey items were added, including charge card annual
fees, charge card finance charges, funeral services, motor scooters,
personal water crafts, and parcel post fees. (Also see appendix 6.)
--The living community of Mayaguez, Puerto Rico, was removed from the
survey.

1.3 Pricing Period

The prices were collected in the allowance areas and in the
Washington, DC, area in February 1996. As with the previous surveys,
the prices of some items--those dependent upon the pricing of other
items--were collected slightly later (e.g., in March 1996). In
addition, individual item prices not meeting OPM's and Runzheimer's
quality control procedures were resurveyed in April and used to verify
or replace the original prices.
As done in previous surveys, some catalog sales were included in
the survey. Only catalogs that sell merchandise in both the allowance
areas and the Washington, DC, area were used. To ensure consistent
seasonal catalog pricing, winter catalogs were used for all catalog
items surveyed.

2. The COLA Model

2.1 Measurement of Living-Cost Differences

The COLA model measures living-cost differences between the
allowance areas and the Washington, DC, area by selecting
representative items that people purchase in these locations,
calculating their respective cost differences, and combining them
according to their importance to each other (as measured by relative
percentage of expenditures). This involves the following major steps:
Step 1: Identify the segment of the population for which the
analysis is targeted (i.e., typical Federal white-collar employees).
Step 2: Estimate how these people spend their money.
Step 3: Select items to represent the types of expenditures people
usually make and outlets at which people typically make purchases for
each selected item.
Step 4: Conduct pricing surveys of the selected items in each area.
Step 5: Compute price ratios for the surveyed items and aggregate
them according to the relative importance of each item.

2.2 Step 1: Identifying the Target Population

The study estimates living-cost differences for typical Federal
white-

[[Page 14196]]

collar employees who have annual base salaries between approximately
$12,000 and $88,000, the range of the General Schedule. Because living
costs may vary depending on an employee's income level, living costs
are analyzed at three income levels.
2.2.1 Federal Salaries
To determine the appropriate income levels, OPM analyzed the 1995
distribution of salaries for General Schedule employees in all of the
allowance areas combined. OPM divided this distribution into three
income groups of equal size and identi fied the minimum, maximum, and
median salary in each group. The median values were then rounded to the
nearest $100 to produce the three representative income levels of
$21,600, $32,900, and $50,300. OPM compared living costs at each of
these three income levels to produce three sets of estimated
expenditures for each allowance area and for the Washington, DC, area.
OPM combined these estimated expenditures into a single overall index
for each allowance area using the employment weights described below.
2.2.2 Federal Employment Weights
OPM used the minimum and maximum values of each income group and
the 1995 distribution of General Schedule employees by salary in each
allowance area to derive employment weights. These were combined with
similar data from 1993 and 1994 to produce a relatively stable moving
average. (OPM introduced moving averages last year to lessen the impact
of new data.) From these averages, OPM calculated the percentage of the
General Schedule workforce in each income group in each area. These
percentages were the weights used to combine estimated expenditures to
compute the final index. Appendix 2 shows the General Schedule
employment distributions and how the percentage weights were derived.
Appendix 23 shows how the weights were used in the final calculations.

2.3 Step 2: Estimating How People Spend Their Money

2.3.1 Consumer Expenditure Survey
Expenditure patterns used in the calculations are based on national
data from the Consumer Expenditure Survey (CES). OPM obtained from the
Bureau of Labor Statistics ``prepublished'' CES results for 1991, 1992,
and 1994. The Bureau of Labor Statistics has advised OPM that
``prepublished'' CES data may not be statistically significant. To
OPM's knowledge, however, it is the only source of comprehensive
consumer expenditure information by income level. Therefore, it is used
in the model.
CES data are used in two ways: to identify appropriate items for
the survey and to derive item, category, and component weights. The
item weights are not income-sensitive. Aggregated CES data are analyzed
by income level to derive category and component weights. These weights
are income-sensitive. The CES data used in this study are shown in
Appendices 3 and 4. As with the Federal employment weights, the 3 years
of CES data were combined to produce a relatively stable moving
average.
2.3.2 Expenditure Categories and Components
The CES is grouped into small, logical families of items. For
example, pre-published data for beef are grouped into four
subcategories: ground beef, roast, steak and other. The steak and roast
groupings were further separated into smaller clusters of items (e.g.,
sirloin and round steak, chuck and round roast). OPM separated the CES
items into the four main cost components specified in OPM's
regulations: Consumption Goods and Services, Transportation, Housing,
and Miscellaneous Expenses. To develop weighting patterns for the three
income levels, OPM performed linear regression analyses on the CES data
shown in Appendix 3.\1\ These analyses produced estimated expenditures
at the three income levels identified in section 2.2.1 above. OPM
converted these expenditures to percentages of total expenditures for
the four components to produce the values shown in the table below.
These were the weights used to combine the expenditures for each of the
components into an overall value for each income level in each
allowance area and the Washington, DC, area.
---------------------------------------------------------------------------

\1\The midpoint of the moving average of CES data was 1992.
Therefore, for the purposes of these regressions, OPM adjusted
Federal salaries to reflect 1992 pay rates. OPM used the pay
increases for 1993 (3.7%), 1994 (0.0%) and 1995 (2.0%), to deflate
the 1995 salaries. This produced adjusted Federal salaries of
$20,400, $31,100, and $47,550 for use in the regression equations.

Table 2-1.--Component Expenses Expressed as a Percentage of Total Expenses
----------------------------------------------------------------------------------------------------------------
1992
adjusted Goods and Housing Transportation Misc. Total
1995 income level income services (percent) (percent) (percent) (percent)
level* (percent)
----------------------------------------------------------------------------------------------------------------
$21,600........................ $20,400 39.62 25.72 18.48 16.18 100.00
32,900......................... 31,100 38.97 24.46 18.22 18.35 100.00
50,300......................... 47,550 38.37 23.28 17.98 20.37 100.00
----------------------------------------------------------------------------------------------------------------
*Income levels are adjusted as described in footnote 1.
(Values may not total because of rounding.)

Goods and Services Component items were further separated into ten
categories, and linear regression techniques were used to estimate
expenditures on these ten categories by income level. The weights for
these categories are shown in section 3.1. The same technique was also
used to compute category weights for the Transportation and
Miscellaneous Components and to produce ratios of renters to homeowners
at each income level.

2.4 Step 3: Selecting Items and Outlets

2.4.1 Item Selections--The Market Basket
As noted above, CES items were grouped into ``clusters'' of
expenses to determine which items to survey. These clusters were chosen
so that no market basket item would have an overwhelmingly large or an
insignificantly small item weight.
For each of these clusters, a set of items to price was identified.
Collectively, these items are called a ``market basket.'' Because it
would have been impractical to survey all of the thousands of items
consumers might buy, the market basket contains representative items,
such as cheddar cheese, that represents itself and the many other
related items that

[[Page 14197]]

consumers purchase (e.g., edam, gouda, jack, swiss, etc). The market
basket that OPM and Runzheimer used had approximately 200 items ranging
from table salt to new cars to home purchases.
Whenever practical, the item description included the exact brand,
model, type, and size, so that exactly the same items could be priced
in all areas if possible. For example, a 10.5-ounce can of Campbell's
vegetable soup was selected for the survey because it is representative
of canned and packaged soups, is a commonly-purchased brand, and is
found in all areas. Appendix 5 provides a list of the items surveyed
and their descriptions.
Changes in the item list and descriptions are an important aspect
of the COLA survey. These changes are necessary to improve the survey
and keep the item descriptions current. For this survey, several of the
items or descriptions were changed. The major changes and the reasons
for each are listed in Appendix 6.
2.4.2 Geographic Coverage and Outlet Selection
Just as it is important to select commonly-purchased items and
survey the same items in all areas, it is important to select outlets
frequented by consumers and find equivalent outlets in all areas. This
involves deciding which geographic areas to survey and which outlets to
survey within these geographic areas.
2.4.2.1 Geographic Areas
For some areas, the choice of which area(s) to survey was obvious.
In Nome, for example, the whole city is surveyed because Nome is a
small city, and Federal employees live throug hout the city. For other
areas, specific communities had to be identified. To do this, OPM used
the results of the 1992 Federal Employee Housing and Living Patterns
Survey. Among other things, that survey obtained information on where
Federal employees lived. OPM used this information to select the living
communities in which housing costs were priced. Runzheimer then
identified outlets within a normal shopping radius of these housing
communities. Outlets within a living community or within an adjoining
living community were generally considered to be within a normal
shopping radius.
2.4.2.2 Similarity of Outlets
Whenever possible, Runzheimer selected popular outlets that were
comparable to outlets in other areas. For example, Runzheimer surveyed
the price of grocery items at supermarkets in all areas because most
people purchase their groceries at such stores and because supermarkets
are found in nearly all areas.\2\ The selection of comparable outlets
is particularly important because comparing the prices of items
purchased at dissimilar outlets would be inappropriate (e.g., comparing
the price of a box of cereal at a supermarket with one sold at a
convenience store).
---------------------------------------------------------------------------

\2\In the Washington, DC, area, Runzheimer surveyed groceries at
two kinds of supermarkets (i.e., full-service supermarkets and
``warehouse-type'' supermarkets) because both types of supermarkets
are common in this area. Runzheimer did not survey ``warehouse-
type'' supermarkets in any other area because they are relatively
uncommon and probably not well frequented by Federal employees.
---------------------------------------------------------------------------

Although major supermarkets, department stores, and discount stores
represented a sizable portion of the survey, outlets were also selected
to represent the diversity of consumer shopping options. For example,
department stores could have been used for pricing all clothing items
surveyed. However, this would not have reflected the range of consumer
choices. Therefore, some clothing items were priced in men's and
women's clothing stores, other clothing items in department stores,
others in shoe stores, and still others in discount stores. For each
item, the same type of outlet (e.g., clothing store, discount store,
department store) was selected in each area whenever possible.
2.4.2.3 Catalog Pricing
A limited amount of catalog pricing was included in the survey to
reflect this common purchasing option. Eleven item prices were surveyed
by catalog. Catalog pricing allowed the comparison of comparable items
that would have been difficult to price otherwise. All catalog prices
included any charges for shipping and handling and all applicable
taxes.

2.5 Step 4: Surveying Prices

As noted earlier, Runzheimer obtained over 20,000 prices on about
200 items from approximately 2,800 outlets. In each survey area,
Runzheimer was required to get at least three price quotes for each
item, if practical. There were certain exception items. For example,
essentially all of the available home sales and rental data meeting the
survey specifications were obtained. For other items, such as utilities
and real estate tax rates, only one quote was obtained in each area
because these items have uniform rates within an area. Because the
Washington, DC, area has six survey communities, Runzheimer was
required to get at least 18 price quotes for most items in this area,
if practical.
2.5.1 Runzheimer Data Collection
Most of the price data were collected onsite by Runzheimer's
Research Associates (RA's). The RA's were independent contractors hired
by Runzheimer to visit retail outlets in each area and collect prices.
All of these RA's were residents of the area. To avoid any real or
perceived conflicts of interest, Runzheimer refrained from hiring
research associates who were either employees of the Federal Government
or who had immediate family members who were employees of the Federal
Government. Runzheimer also collected price data by telephone and
through on- line computer services. In addition, Runzheimer performed
numerous quality control checks, often verifying survey data through
telephone calls and comparing current data-gathering results with those
from earlier surveys.
2.5.2 Data Collection Materials
The living-cost surveys conform with the provisions of the
Paperwork Reduction Act and are approved by the Office of Management
and Budget (OMB). The OMB-approved survey collection materials are
found in Appendix 7. All Runzheimer-developed worksheets or other
survey materials conformed with those approved by OMB.
2.5.3 Inclusion of Sales and Excise Taxes
For all items subject to sales and/or excise taxes, the appropriate
amount of tax was added prior to analysis. Runzheimer gathered
applicable information on taxes by contacting appropriate sources of
information in the allowance areas and the Washington, DC, area.
2.5.4 Runzheimer's Onsite Visits
Full-time Runzheimer research professionals traveled to each
allowance area to supervise data collection activities and perform
various quality control checks as necessary. These visits all occurred
during the pricing period so that these professionals could answer any
of the RA's data collection questions or provide additional training
and instruction if necessary.
The researchers visited living communities within the allow ance
areas to look at housing and to talk with local real estate
professionals. They also visited numerous retail outlets to verify that
comparable items were being priced at comparable outlets. In addition,
they

[[Page 14198]]

obtained general information about the local economy.
2.5.5 Surveying the Washington, DC, Area
As noted earlier, Runzheimer was required to get more price quotes
in the DC area than in the allowance areas because of the size and
diversity of the DC metropolitan area and because DC is the basis for
all comparisons. For the purposes of the COLA surveys, the DC area was
divided into six survey areas: two in the District of Columbia, two in
Maryland, and two in Virginia. The outlets surveyed were within a
normal shopping radius of the housing communities identified in
Appendix 9. Survey data from each of the six DC survey areas were
combined using equal weights.

2.6 Step 5: Analyzing Data and Computing Indexes

2.6.1 Indexes and Weights
2.6.1.1 Indexes
Nonforeign area COLA's are derived from the living-cost indexes.
These indexes are mathematical comparisons of living costs in the
allowance areas compared with living costs in the Washington, DC, area.
An index is a way to state the difference between two prices (or sets
of prices). For example, if a can of corn costs $1.00 in the allowance
area and 80 cents in the DC area, canned corn is 25 percent more
expensive in the allowance area than in DC. That difference can also be
stated as a price index of 125.
2.6.1.2 Item Weights
OPM computed indexes for hundreds of items. As briefly described in
section 2.3, OPM used weights derived from the CES to combine these
indexes. These weights reflected the relative amount consumers normally
spend on different items. For example, the price of a can of corn has a
lower weight than the price of a pound of apples because, according to
the CES, people generally spend less on canned corn than on apples.
The COLA model uses a fixed-weight indexing methodology. The
weights used are based on the expenditure patterns of consumers
nationwide as reported by the CES. This is the only source of which OPM
is aware that provides expenditure information by income level.
2.6.1.3 Category and Component Weights
As described in section 2.3.2, OPM also computed income sensitive
category and component weights. This allowed the combination of
comparative price data in a manner that reflected the spending patterns
of people at each income level. The way data were combined varied among
the components.
For the Goods and Services and Miscellaneous Expense compo nents,
OPM combined indexes within each category using the CES weights to
derive an overall index for the category. The category indexes were
then combined into an overall component index using the income-
sensitive category weights described above. For the Transportation and
Housing Components, OPM used the same approach in combination with a
cost-build-up approach. For example, the annual cost of owning and
operating an automobile was computed by taking individual prices (e.g.,
automobile financing, insurance, gas and oil, and maintenance) and
computing an overall dollar cost for each area. These costs were
compared with those in the DC area to compute the Private
Transportation Category index. This index was then combined with the
Other Transportation Category index using income sensitive category
weights to compute an overall Transportation Component index for each
area.
2.6.2 Computing the Overall Index
The item, category, and component indexes were combined using the
process prescribed in section 591.205(c) of title 5, Code of Federal
Regulations. That is a five-step process that involves converting the
indexes to dollar values and weighting these, combining them, and
comparing them to compute a final weighted- average index. The process
is described below.
First, OPM used the CES data and the income ranges described in
section 2.2.1 to determine how much money consumers typically spend on
each component at each income level. These amounts appear in the table
below and in Appendix 22. They were derived by taking the component
weights shown in Table 2-1 times the representative income levels
described in section 2.2.1.

Table 2-2.--Typical Consumer Expenditures by Income Level and Component
----------------------------------------------------------------------------------------------------------------
Goods and
Income level services Own/rent Transportation Misc. Total
----------------------------------------------------------------------------------------------------------------
Lower....................................... $8,558 $5,556 $3,992 $3,495 $21,600
Middle...................................... 12,821 8,047 5,994 6,037 32,900
Upper....................................... 19,300 11,710 9,044 10,246 50,300
----------------------------------------------------------------------------------------------------------------
(Note: Values may not total because of rounding.)

Second, for each allowance area, OPM multiplied the dollar values
above by the component indexes for the allowance area. Because the
housing component consisted of two indexes (one for owners and another
for renters), two sets of total relative costs were produced--one for
owners and another for renters.
Third, for each allowance area and income level, OPM combined the
total relative costs for owners and renters using as weights the
proportion of owners and renters as identified in the CES. (See section
4.2.1.) This produced an overall expenditure dollar amount for each
income level in each allowance area.
Fourth, OPM computed a single overall average expenditure for each
allowance area by combining the income level expenditures using the
allowance area General Schedule employment distribution as weights.
This produced a single overall dollar expenditure value for the
allowance area. Using the same General Schedule employment weights, OPM
also computed a single overall dollar expenditure value for the DC
area.
The final step was to divide the overall dollar expenditure for the
allowance area by the overall dollar expenditure for the DC area to
compute a final index. These indexes are shown in the last section of
this report and in Appendix 23.

3. Consumption Goods and Services

3.1 Categories and Category Weights

Based on the CES data, OPM identified ten categories of expenses
within the Goods and Services Component. Using linear regression
analyses and the CES data, OPM identified the portion of total Goods
and Services expenditures that the typical

[[Page 14199]]

consumer spends in each category at various income levels. The
categories and the relative expenditures are shown in the table below:

Table 3-1.--Category Weights Expressed as a Percentage of Goods and
Services Expenditures by Income Level
------------------------------------------------------------------------
Income levels
Category --------------------------------------
Lower Middle Upper
------------------------------------------------------------------------
Food at Home..................... 27.04 24.04 21.15
Food Away from Home.............. 13.60 14.16 14.71
Tobacco.......................... 3.09 2.55 2.02
Alcohol.......................... 2.66 2.64 2.62
Furnishings and Household
Operations...................... 14.98 15.99 16.97
Clothing......................... 13.54 14.22 14.87
Domestic Service................. 1.73 1.94 2.14
Professional Services............ 6.95 7.01 7.07
Personal Care.................... 3.62 3.52 3.43
Recreation....................... 12.80 13.93 15.02
--------------
Totals..................... 100.00 100.00 100.00
------------------------------------------------------------------------
(Note: Values may not total because of rounding.)

3.2 Goods and Services Survey Results

Section 2.6 of this report provides a detailed explanation of the
economic model used to analyze the price data. As it applies to Goods
and Services, the approach involved comparing the average prices of
market basket items in each allowance area with those in the
Washington, DC, area. The resulting price ratios were aggregated into
subcategory and then category indexes using the moving-average
expenditure weights derived from the CES data.
Appendix 8 shows for each allowance area ten category indexes, the
weights used at each of the three income levels, and the overall Goods
and Services Component indexes. The Washington, DC, area is not shown
because it is, by definition, the reference area. Therefore, the DC
indexes are 100.
3.2.1 Exchange and Commissary Expenditure Research
Executive Order 10000, as amended, requires OPM to adjust COLA
rates when employees have special purchasing privileges, such as
unlimited access to commissaries and exchanges. In Guam, employees have
such access, so OPM directed Runzheimer to price the same marketbasket
of Goods and Services items at the commissaries and exchanges in Guam
as it used for the local retail pricing. One price quote was obtained
for each marketbasket item found in these facilities.
It was not assumed that people with access to military facilities
made all purchases in these facilities. Instead, the results of an OPM
survey of Federal employees was used to determine the percentage of
purchases that families typically make in military facilities versus
local outlets. For example, as the following table shows, it is
estimated that employees with commissary/exchange access in Guam
purchase approximately 70% of their Food at Home items at a commissary
and purchase the remaining 30% of such items in local retail outlets.

Table 3-2.--Percentages of Purchases Made at the Commissaries and
Exchanges in Guam
------------------------------------------------------------------------
Category Percentage
------------------------------------------------------------------------
Food at Home................................................ 70.0
Food Away................................................... 0.0
Tobacco..................................................... 64.0
Alcohol..................................................... 76.0
Furnishings. & Hsld. Op.................................... 64.5
Clothing.................................................... 43.7
Domestic Service............................................ 0.0
Professional Services....................................... 0.0
Personal Care............................................... 49.3
Recreation.................................................. 49.7
------------------------------------------------------------------------

These percentages were used to aggregate the local retail and
commissary/exchange prices into one set of appropriate, blended prices,
hereinafter referred to as the Commissary/PX prices. The blended prices
were compared to the local retail prices in the Washington, DC, area to
compute Commissary/PX Goods and Services Category indexes, which were
then combined using CES weights to derive an overall Commissary/PX
Goods and Services Component index. Just as with the Guam Local Retail
Goods and Services Component index, the Guam Commissary/PX Goods and
Services Component index was combined with the indexes for the Housing,
Transportation and Miscellaneous Expense Components to derive a single,
overall Commissary/PX index for the Guam allowance area.

4. Housing

4.1 Component Overview

The Housing Component consists of the following expenses related to
owning or renting a dwelling:

--mortgage or rent payments,
--utilities,
--real estate taxes,
--homeowner's or renter's insurance,
--home maintenance, and
--telephone expenses.

At each of the three income levels, the annual housing costs for
homeowners and renters were measured separately. The results were then
combined using as weights the percentages of owners and renters
reported by the CES.

4.2 Housing Model

4.2.1 Expenditure Research
The CES was used to determine the national average ratio of
families who own, as opposed to rent, their residences at each income
level. Using the tenure data by income range as input into a linear
regression analysis, OPM calculated the owner and rent weights shown
below and in Appendix 23. OPM excluded data for home owning families
without a mortgage because they were not typical of Federal homeowners
in the base area or in the allowance areas.

[[Page 14200]]

Table 4-1.--Owner/Renter Weights
------------------------------------------------------------------------
Income levels
--------------------------------------
Category Lower Middle Upper
(percent) (percent) (percent)
------------------------------------------------------------------------
Homeowner with mortgage.......... 37.97 47.13 61.21
Renter........................... 62.03 52.87 38.79
--------------
Totals..................... 100.00 100.00 100.00
------------------------------------------------------------------------

The CES data were also used to identify which home- maintenance
items to price and to establish the relative importance of those items.
4.2.2 Housing Profiles
To compare housing costs in all locations, six typical housing
profiles are used and are assigned to the three income levels, as shown
in the table below. For Runzheimer's data collection, OPM required that
at least one criterion for the owner profile be the square footage of
the home and at least one criterion for the renter profile be the
number of bedrooms in the rental unit. Runzheimer collected additional
information when available. Unfortunately, the quantity and type of
additional data varied markedly from one area to the next and was
completely unavailable in some areas. Therefore, OPM could not use the
additional data.

Table 4-2.--Housing Profiles
----------------------------------------------------------------------------------------------------------------
Income level Renter profile Owner profile
----------------------------------------------------------------------------------------------------------------
Lower.............................. 3 rooms, 1 BR, 1 bath, 600 sq. ft. 4 rooms, 2 BR, 1 bath, 900 sq. ft.
apartment. condo or detached house.
Middle............................. 4 rooms, 2 BR, 1 bath, 900 sq. ft. 5 rooms, 3 BR, 1 bath, 1,300 sq. ft.
apartment. detached house (rowhouse in NE DC).
Upper.............................. 4 rooms, 2 BR, 2 baths, 1,100 sq. ft. 7 rooms, 3 BR, 2 baths, 1,700 sq.
townhouse or detached house. ft. detached house.
----------------------------------------------------------------------------------------------------------------

The home sizes stated above are the representative sizes used for
certain calculations in the model. They are not, however, the only size
surveyed for each profile. For rentals, Runzheimer obtained rental
rates on any unit, regardless of its size, that otherwise met the
profile characteristics. For home sales, Runzheimer obtained the prices
of homes within size range and otherwise meeting the profile
specifications. The size ranges are shown below:

Table 4-3.--Home Sizes Surveyed
------------------------------------------------------------------------
Income level Range
------------------------------------------------------------------------
Lower............................. 600 to 1,200 sq. ft.
Middle............................ 1,000 to 1,600 sq. ft.
Upper............................. 1,400 to 2,300 sq. ft.
------------------------------------------------------------------------

It should be noted that although the size ranges overlap, no home
sale observation was used at more than one income level. Application of
the other criteria (i.e., number and type of rooms) ensured that each
observation was assigned to the appropriate income level even though
its size was common to two income levels.
4.2.3 Living Community Selection
As discussed briefly in section 2.4.2.1, OPM identified the living
communities to be surveyed based on the results of the 1992 Federal
Employee Housing and Living Patterns Survey. The communities surveyed
are identified in Appendix 9. As with previous surveys, nine homeowner
and nine renter communities were identified for the Washington, DC,
area--one for each income level in each of the three areas (DC,
Maryland, and Virginia). In the allowance areas, up to three homeowner
and three renter communities were identified--one for each income
level.
The three-community owner/renter goal was not achievable in many of
allowance areas due to the relatively few home sales and rental
opportunities in these areas. In such areas, OPM directed Runzheimer to
collect prices for the entire survey area or allowance area rather than
in specific communities. This was done in Fairbanks, Juneau, Nome,
Hilo, Kailua Kona, Kauai, Maui, Guam, St. Croix and St. Thomas. In
these areas, all home sales and/or rental rates meeting the housing
profile characteristics for the particular income group were included
in the analysis.
4.2.4 Housing-Related Expenses
Based on the CES data, housing-related expense items were
categorized into one of five groups in the COLA model. These groups
were--

--utilities,
--real estate taxes,
--owners/renters insurance,
--maintenance, and
--telephone expenses.

4.2.4.1 Utilities
Electricity, oil, gas, water, and sewer were the utilities used in
the model. Most utility companies were able to provide current charges
per unit of consumption and average consumption patterns for all
households. The companies were not, however, able to provide separate
consumption patterns by the size or type of housing.
Because many utility costs vary by size of house, a factor was
needed to derive the utility rates at each of the home profiles. The
table below shows the standard square foot sizes and utility factors
used for each home profile. The factors were calculated by assuming
that utility use increases or decreases at half the rate that square
footage increases or decreases.

Table 4-4.--Utility Factors
------------------------------------------------------------------------
Renter profile Owner profile
Income level -----------------------------------
Sq. ft. Factor Sq. ft. Factor
------------------------------------------------------------------------
Lower............................... 600 .73 900 .85
Middle.............................. 900 .85 1,300 1.00
Upper............................... 1,100 .92 1,700 1.15
------------------------------------------------------------------------

In each area, Runzheimer obtained the price of each of the types of
utilities noted above. Runzheimer used average annual consumption per
household

[[Page 14201]]

information gathered from utility companies serving each area to
compute average annual utility costs. The above factors were then used
to adjust the total annual utility costs for each of the various
housing profiles.
In the DC area, Runzheimer was unable to obtain estimates for
electricity usage for houses heated by gas or oil. However, Runzheimer
was able to obtain kilowatt usage for all-electric houses. In order to
avoid potential double counting of utility costs, OPM used the all-
electric data for the DC area. This was not a problem in the warm-area
COLA areas where there is little heat expense. It also was not a
problem in Alaska where most consumers use gas or oil heat, not
electric heat.
4.2.4.2 Real Estate Taxes
For this study, Runzheimer contacted the city assessors in each
allowance area and in the Washington, DC, area to obtain real estate
tax information on the living communities surveyed. Real estate tax
formulas were obtained for all living communities and applied to the
home values for each income level.
4.2.4.3 Owners/Renters Insurance
Homeowners' insurance rates were gathered for each of the survey
areas for both renter and owner profiles. For renters, the following
estimated content values were used: $25,000 at the lower and middle
income levels and $30,000 at the upper income level. For homeowners,
the cost of insurance was dependent on the median home values
calculated as part of this survey. In most areas, it was assumed that
the structure was equal to 80 percent of the total home value. In
Hawaii, where the land represents a greater proportion of property
value, 50 percent was used.
Previous research conducted by Runzheimer International for OPM
found that insurance coverage for disasters, such as floods and
earthquakes, were not widely purchased in the allowance areas.
Therefore, the COLA model does not include these additional riders.
(See Report to OPM on Living Costs in Selected NonForeign Areas and in
the Washington, DC, Area, June 1992, at 57 FR 58556). Hurricane
insurance was priced for all of the allowance areas in Hawaii and in
Guam, Puerto Rico, and the U.S. Virgin Islands.
4.2.4.4 Home Maintenance
Estimated home maintenance expense was computed for each of the
homeowner profiles. Maintenance costs were not added in the three
renter profiles because most, if not all, maintenance expenses are
covered by the landlord.
As done in previous surveys, Runzheimer priced both home
maintenance services as well as home maintenance commodities using the
CES information to identify items to price and the weights associated
with these items. The maintenance service items priced were interior
painting, plumbing repair, electrical repair, and pest control. In the
Nome area, however, pest control was not priced because local sources
indicated it is not necessary. The maintenance commodities priced were
bathroom caulking, a kitchen faucet set, an electrical outlet, latex
interior paint, and a fire extinguisher.
To compute home maintenance cost differences between each allowance
area and the Washington, DC, area for the homeowner profiles, an index
was computed for each maintenance item by comparing the allowance area
price to the DC area price. As with the Goods and Services component
items, the CES data were used to weight these maintenance indexes into
an overall home maintenance index for each area.
To combine the maintenance indexes with the other homeowner costs,
which were expressed in dollar amounts, OPM converted the indexes to
dollars by multiplying the index for each area by the average
maintenance expense reported in the CES. This cost was assigned to the
middle-income homeowner profile. Logically, maintenance costs for
larger homes would generally be greater than costs for middle-sized
homes, while costs for smaller homes would generally be less.
Therefore, the same homeowner multi pliers used in the utilities model
for the lower and upper income profiles (.85 and 1.15 respectively) are
applied to recognize differences in maintenance costs due to house size
at these income levels.
4.2.4.5 Telephone Expenses
Telephone expenses consisted of local service charges, additional
charges for local calls (if applicable), and charges for long distance
calls. To measure estimated expenses for local service and local calls,
Runzheimer surveyed the cost of touch- tone service with unlimited
calling in each area.
To estimate long distance charges in all areas, Runzheimer surveyed
the cost of three 10-minute direct dial calls per month to large U.S.
mainland cities (i.e., Los Angeles, Chicago, and New York City).
Runzheimer measured the price of a call placed in the survey area at
the time of day necessary to be received in the respective city at 8:00
p.m. local time. In many areas, this resulted in pricing a combination
of daytime and evening-rate calls.

4.3 Housing Data Collection Procedures

As done in previous years, Runzheimer collected housing information
mainly from real estate professionals, various listing services, and
advertisements. In addition, Runzheimer personnel traveled to each of
the surveyed communities to assess the compatibility of the housing
community with the income level for which the data were used and to
ensure that homes in these communities were comparable to those
surveyed in the Washington, DC, area.
4.3.1 Homeowner Data Collection
Runzheimer surveyed selling prices of homes that matched the
housing profiles in each living community and obtained as many of these
selling prices as possible for sales that occurred during the 12-month
period prior to the date of the survey. The amount of data obtained
depended on the number of home sales in the community and the
availability of square footage and other housing profile information.
This in turn depended on the size of the community, economic
conditions, quality and quantity of the realty data available, and the
willingness and ability of local realty professionals and assessor
offices to provide data. If sales data obtained from the preliminary
data sources did not meet specified contract minimums, Runzheimer
contacted additional data sources in the area to attempt to secure more
sales data, if practical. In this manner, either all or a sizeable
portion of the home sales in each area was surveyed.
4.3.2 Renter Data Collection
Rental data also were obtained from a variety of sources, e.g.,
brokers, rental management firms, property managers, newspaper
advertisements, and other listings. Analyses of these data revealed
what appeared to be two separate rental markets: a broker market and a
non-broker market. Rental rates and estimates provided by brokers
generally exceeded those obtained from other sources. The methodology
used to analyze these two data sets is discussed in section 4.4.2.

4.4 Housing Analysis

4.4.1 Homeowner Data Analysis
One of the most important factors relating to the price of a home
is the number of square feet of living space. As was done last year,
OPM used the median home value. The median is the middle value in a
rank-ordered set of

[[Page 14202]]

observations. OPM used this approach to reduce the volatility of the
housing data from one survey to the next because a relatively few
extremely high or low home prices could significantly influence average
housing prices.
For each income profile in each allowance area and the Washington,
DC, area, OPM computed the median price per square foot for the
comparables. This value was then multiplied by the reference square
footage for the profile to determine the home purchase price for the
profile.
As was done last year, OPM also used historical housing data in
addition to data collected in this survey. These data are found in
Appendix 10 of this report. The historical data are from previous
living-cost surveys that were published in the Federal Register
beginning with the 1990 report. (See Appendix 1 for a listing of these
publications). The data for the period prior to 1990 were published
with the results of the 1991-1992 living-cost surveys at 57 FR 58617.
All housing values are based on the community selections and analytical
methodologies used at the time of each respective survey.
The historical housing data used were estimated annual principal
plus interest payments by income level in each area. To combine these
data, OPM used weights that were derived from the 1992 Federal Employee
Housing and Living Patterns Survey. These weights reflect the
proportion of Federal employee homeowners by year of purchase in all
allowance areas and in the Washington, DC, area. The historical housing
weights and analyses are shown in Appendix 11.
4.4.2 Rental Data Analysis
OPM assigned each rental quote to a single income level based on
the criteria stated in section 4.2.2. As discussed earlier, there were
essentially two sources of rental information: broker and non-broker
sources. In each area, the quantity of data obtained from either source
varied significantly. Therefore, analyzing all of the rental data (both
broker and non-broker) together for an area and income level was
undesirable. Instead, OPM analyzed broker and non-broker data
separately by income level. As with the housing data analyses, OPM used
the median rental values. For each income level, OPM separately ranked
rental rates from low to high for broker and non-broker data. The
median values for broker and non-broker data for each group were
determined and then averaged to compute a single rental value for each
income level. Because OPM has no information on how the Federal
employees who rent generally secure their lodgings, OPM applied equal
weights to the broker and non-broker data to compute an overall average
rental rate for the area and income level. The broker and non-broker
medians and final results are shown in Appendix 12.

4.5 Housing Survey Results

In the above sections, the processes used for determining the costs
for maintenance, insurance, utilities, real estate taxes, rents, and
homeowner mortgages were described. Appendix 13 shows the cost of each
of these items for renters and homeowners in each allowance area and in
the Washington, DC, area. Appendix 14 compares the total cost of these
items by income level in each allowance area with the total cost of the
same items by income level in the DC area. Again, there are separate
comparisons for renters and homeowners. The final housing-cost
comparisons take the form of indexes that are used in Appendix 22 to
derive the total, overall indexes for owners and renters.

5. Transportation

5.1 Component Overview

The transportation component consists of two categories: Automobile
Expense and Other Transportation Costs. The Automobile Expense Category
reflects costs relating to owning and operating a car in each area. The
Other Transportation Costs Category is represented by the cost of air
travel from each location to common points within the contiguous 48
States.

5.2 Private Transportation Methodology

As done in previous surveys, OPM analyzed automobile trans
portation costs for three commonly purchased vehicles: a domestic auto,
an import auto, and a utility vehicle. New car costs were used for
these analyses because it was believed that pricing used vehicles of
equivalent quality in each area could introduce inconsistencies because
of the value judgments that would be required.
5.2.1 Vehicle Selection and Pricing
The same three models of automobiles that were surveyed in previous
years were surveyed again this year:

Domestic-Ford Taurus GL 4-door sedan 3.0L 6 cyl.
Import-Honda Civic DX 4-door sedan 1.5L 4 cyl.
Utility-Chevrolet S10 Blazer 4X4 2 door 4.3L 6 cyl.

For each model car, Runzheimer collected new vehicle prices at
dealerships in each area and from secondary sources, such as the Kelly
Blue Book. All prices were based on the manufacturers' suggested retail
prices (MSRP) for 1996. All vehicles were equipped with standard
options, such as automatic transmission, AM/FM stereo radio, and air
conditioning. In Alaska locations, special additional equipment was
included in new-vehicle prices (i.e., engine-block heaters and heavy-
duty batteries). Snow tires were also priced in Alaska. (See section
5.2.5.) In addition to the MSRP, the price included additional charges
such as shipping, dealer preparation, additional dealer markup, excise
tax, sales tax, and any other one-time taxes or charges. In Anchorage,
for example, documentation fees were also included as part of the new-
vehicle costs.
5.2.2 Vehicle Trade Cycle
Calculating the cost of owning and operating a vehicle requires
knowing the miles driven and how long the car is owned. In the
automobile industry, these two factors are known collectively as a
vehicle's ``trade cycle.'' The trade cycle is stated as a length of
time (in months or years) and the total number of miles driven in that
time period. This information is used in the model to compute annual
costs related to fuel, oil, tires, maintenance, and depreciation. As
with the previous living-cost analyses, OPM used a four-year, 60,000-
mile trade cycle in all areas.
5.2.3 Fuel Performance and Type
All vehicles included in this study used regular unleaded fuel.
Runzheimer surveyed self-service cash prices of unleaded regular
gasoline at name-brand gas stations in the Washington, DC, area and in
all allowance areas, except those in Alaska. In consideration of the
harsh climate in the Alaska allowance areas, full-service cash prices
were surveyed.
To establish average fuel-performance ratings, the COLA model uses
the ``city driving'' figures published by the U.S. Environmental
Protection Agency (EPA). The ``city'' figures instead of ``highway''
figures are used because all locations contained considerable stop-and-
go driving conditions. As in previous COLA surveys, OPM included in its
analysis the following fuel-performance factors: temperature, road
surface, and gradient. These factors are based on research previously
conducted for OPM. This research and the factors are discussed below.
5.2.3.1 Impact of Temperature upon Fuel Performance
Gas mileage is affected by temperature. The lower the temperature,

[[Page 14203]]

the fewer miles-per-gallon achieved and vice versa. According to the
EPA's Passenger Car Fuel Economy: EPA and Road, the temperature at
which no adjustments to fuel performance occur is 77 deg.F. Below that
temperature, miles-per- gallon achieved drops. Above 77 deg.F miles-
per-gallon achieved improves. The model uses the average monthly
temperatures for each allowance area and the DC area as reported in The
Weather Almanac, published by Ruffner and Blair. For each location and
month, the model uses the appropriate factor from the EPA study based
on the average monthly temperature for the area. These factors are then
averaged to derive a single overall factor for each location. The
results of these calculations are shown in Table 5-1.
5.2.3.2 Impact of Road Surface upon Fuel Performance
For the model, it is assumed that Federally controlled roadways are
typically composed of concrete and/or high-load asphalt and that
locally controlled roadways are typically composed of low-load asphalt.
EPA's research indicates that cars are generally more fuel-efficient on
the firmer, high-load surfaces than on the softer, low-load surfaces.
Although traffic patterns and road usage vary among areas, previous
research conducted for OPM produced no relevant findings regarding this
issue. Therefore, the model uses the assumption that Federally-
controlled roadways generally support twice the traffic of, or are used
at least twice as much as, locally controlled roadways.
In each allowance area, the total mileage falling into either the
Federal or local categories was collected. For example, Alaska contains
5,512 miles of Federally controlled roads and 7,120 miles of locally
controlled roads. The usage assumption increased Federal road mileage
by a factor of two for the Alaska allowance areas.
The average low-load asphalt factor (which reflects dry, wet, and
snowy conditions) was applied to the local mileage percentage, and the
average concrete and/or high-load asphalt factor was applied to the
Federal mileage percentage to produce two weighted average factors--one
for the Alaskan allowance areas and another for the other allowance
areas. These factors are shown in Table 5-1. The Washington, DC, area
was assigned a factor of 1.00 on the premise that the vast majority of
traffic in that area travels on dry, high-load surfaces. The
application of these factors is described in Section 5.2.3.4.
5.2.3.3 Impact of Gradient Upon Fuel Performance
The effect of gradient on gas mileage is also estimated from EPA's
Passenger Car Fuel Economy: EPA and Road. Local topography (i.e.,
gradient) affects fuel efficiency. EPA provides mileage factors based
upon various gradients ranging from less than 0.5% (essentially flat)
to greater than 6% (steep).
In research previously conducted for OPM, the contractor reviewed
the topographic features of each area and found a wide range of road
conditions. However, the contractor was unable to find relevant
information on the types of terrain drivers typically encounter in each
area or the number of miles drivers travel in each type of terrain.
Lacking such information, the contractor assumed that drivers in the
allowance areas generally traveled roads having approximately the same
gradients that are found on average in the United States.
Applying the information from EPA's research, a fuel- performance
factor of 0.98 was computed for this type of driving. This factor was
assigned to each allowance area. For the DC area, a factor of 1.00 was
used on the premise that the vast majority of traffic in that area
travels on major freeways and highways that are relatively flat. The
application of these factors is described in the next section.
5.2.3.4 Overall Impact upon Fuel Performance
OPM applied the factors described above to make adjustments in the
average gas mileage ratings for each type of automobile surveyed for
each allowance area and for the Washington, DC, area. The adjustment
factors compound-- that is, the total adjustment is the result of
multiplying the three individual factors together for each area.
In the table below, the factor 1.00 means that no adjustment in EPA
fuel performance is appropriate. A factor of less than 1.00 means that
the estimated gasoline mileage in the area is less than the EPA
average. For example, the total adjustment factor for Juneau is 0.84.
This means that the estimated gasoline mileage in Juneau is 84 percent
of the EPA estimated average. Note that the adjustment factor for the
DC area (0.94) indicates that average gasoline mileage in that area is
also below the EPA estimate.

Table 5-1.--Summary of Fuel-Performance Adjustments
----------------------------------------------------------------------------------------------------------------
Road
Location Temperature surface Gradient Total
----------------------------------------------------------------------------------------------------------------
Anchorage................................................... 0.88 0.96 0.98 0.83
Fairbanks................................................... 0.85 0.96 0.98 0.80
Juneau...................................................... 0.89 0.96 0.98 0.84
Nome........................................................ 0.85 0.96 0.98 0.80
Hawaii...................................................... 0.99 0.98 0.98 0.95
Virgin Islands.............................................. 1.01 0.98 0.98 0.97
Puerto Rico................................................. 1.01 0.98 0.98 0.97
Guam........................................................ 0.99 0.98 0.98 0.95
Washington, DC.............................................. 0.94 1.00 1.00 0.94
----------------------------------------------------------------------------------------------------------------

5.2.4 Vehicle Maintenance
As done in the previous surveys, Runzheimer surveyed the cost of
five common maintenance services and repairs performed on the vehicles
surveyed. The services and repairs were--

--Tuneup,
--Oil change,
--Automatic transmission fluid change,
--Flush/fill coolant, and
--Muffler/exhaust pipe replacement.

The automobile manufacturers' recommended maintenance schedules
were used to determine the frequency of performing each of these
maintenance jobs. Maintenance schedules vary, depending on the driving
conditions typically encountered. Consistent with the assumptions used
for fuel economy and tire mileage, it was assumed that driving
conditions in the allowance areas are generally severe, and the
maintenance schedules used reflected that kind of driving. For the DC
area, it was assumed that driving conditions were normal, and the
maintenance

[[Page 14204]]

schedules used for that area reflected that kind of driving.
The recommended frequency of performing each of these jobs was
combined with the prices charged by local dealers and service stations
to compute an estimated annual maintenance expense. Runzheimer
collected the cost of the complete maintenance service or repair job
for each vehicle. For example, the cost of a complete oil change was
collected for each vehicle including the total charge for parts and the
total charge for labor.
In the Alaska and DC areas, constant velocity joint (CVJ) boots
replacement was also included in the cost of vehicle maintenance.
Previous research conducted for OPM revealed varying replacement cycles
among the Alaska allowance areas and between the Alaska areas and the
DC area: Anchorage and Juneau-- every 45,000 miles (3 years), Nome--
every 30,000 miles (2 years), Fairbanks--every 15,000 miles (1 year),
and the Washington, DC, area--every 60,000 miles (4 years). The cost of
replacement for all three vehicle types was factored into the indexes
based upon the frequency of the replacement. In Fairbanks, for example,
100 percent of the cost was included because previous research
indicated annual replacement was the norm.
5.2.5 Tires
Research previously conducted for OPM revealed that various factors
(e.g., road quality/state of repair, road composition) appeared to
reduce tread life (i.e., the average number of miles a tire is expected
to last) in the allowance areas compared with the Washington, DC, area.
Based on this research, the model uses tire expense based on a 40,000-
mile tread life in allowance areas and a 55,000-mile tread life in the
DC area.
Runzheimer priced the cost of a new set of tires, including
mounting and balancing and all applicable taxes, in each area. This
cost was converted into an annual cost by dividing the estimated number
of annual miles driven by the expected tread life and multiplying this
by the new tire price. Previous research indicated that four extra
studded snow tires would be required for all three vehicles in the
Alaska allowance areas. Therefore, Runzheimer surveyed the cost of
extra wheels, extra tires, and installing studs for all vehicles in
Anchorage, Fairbanks, Juneau, and Nome.
5.2.6 License and Registration Fees and Miscellaneous Taxes
Runzheimer obtained information regarding license registration
fees, miscellaneous taxes, and personal property taxes (where
applicable). License and registration fees were included as part of the
annual cost of owning an automobile. Miscellaneous and personal-
property taxes were computed for each year of the vehicle's 4-year
trade cycle using the vehicle's estimated used-car value for each year.
The resulting four personal property tax values were then averaged, and
that average was included as part of the annual cost of owning an
automobile. As stated in section 5.2.1, sales and excise taxes were
included in the purchase price of the vehicle and were accounted for
under the annual vehicle purchase and finance costs.
5.2.7 Depreciation
The single largest annual expense related to owning and operating a
new car is depreciation--the lost value of the vehicle as it ages and
is driven. In the COLA model, total depreciation is calculated by
subtracting from the purchase price the estimated residual value (used
car value) 4 years later. This value is then divided by four to produce
an annual depreciation amount.
As described earlier, the new car price was the manufacturer's
suggested retail price plus any additional charges, such as shipping,
dealer prep, additional dealer markup, excise tax, and sales tax. As
done in previous surveys, the used car value was based on information
from sources such as the Black Book Official Finance/Lease Guide for
1994. Although such sources only track prices of vehicles sold in the
contiguous 48 States, previous research performed by Runzheimer did not
indicate that used cars in allowance areas were (on average) worth more
or less than used cars in the DC area, except for Fairbanks and Nome.
For Fairbanks and Nome, 90 percent of the projected residual values
were used to reflect the more severe conditions.
It should be noted that identical residual values did not result in
identical depreciation amounts. Depreciation amounts were generally
higher in the allowance areas than in the Washington, DC, area because
new car prices were generally higher in the allowance areas.
5.2.8 Finance Expense
The COLA model assumes that new car purchases are financed.
Therefore, Runzheimer surveyed banks in all areas to obtain their auto-
loan interest rates for a 48-month loan with 80 percent financing. OPM
computed the finance cost for each vehicle in each area and included it
in the annual cost of owning and operating an automobile.
5.2.9 Vehicle Insurance
Runzheimer surveyed the cost of car insurance in each location.
Consistent with the previous year's survey, Runzheimer used the
following common coverages, limits, and deductibles:

Bodily Injury............................. $100,000/$300,000.
Property Damage........................... $50,000.
Medical................................... $5,000.
Uninsured Motorist........................ $100,000/300,000.
Comprehensive............................. $100 Deductible.
Collision................................. $250 Deductible.

In each survey area, Runzheimer identified the common automobile
insurance companies and attempted to obtain three insurance price
quotes for each type of car surveyed. These quotes were averaged by
type of car to produce estimated insurance costs for each area.
Runzheimer found that some insurance companies in Guam, Puerto
Rico, and the Virgin Islands did not offer the coverages, limits, and
deductibles shown above. To allow the comparison of the cost of these
different policies with DC costs, OPM directed Runzheimer also to
survey in the DC area the cost of insurance that was comparable to that
offered in these allowance areas. The costs of these equivalent
policies were then compared to derive adjustment factors that could be
applied to the cost of the standard coverages, limits, and deductibles
shown above. By applying these factors to the DC area average price,
the cost of equivalent coverage was estimated for these particular
allowance areas. The factors and their derivation are shown in Appendix
16.
5.2.10 Overall Annual Costs
As described above, Runzheimer surveyed the annual costs for fuel,
maintenance and oil, tires, licensing, taxes, depreciation, finance,
and insurance for three types of automobiles in each allowance area and
in the Washington, DC, area. These costs were then summed to determine
the overall annual costs by area for owning and operating each type of
automobile. Appendix 15 shows these costs for each area by type of
vehicle.

5.3 Other Transportation Costs--Air Fares

Air fare is the only item priced for the Other Transportation Costs
Category. For this item, OPM priced the lowest priced round-trip air
fare on a major carrier with a 2-week advance purchase and a 1-week
stay over. Trips were priced from each allowance area and the
Washington, DC, area to Chicago, Los Angeles, Miami, New York, Seattle,
and St. Louis. These cities were selected to

[[Page 14205]]

represent a range of travel destinations coast- to-coast for COLA area
and DC area Federal employees. The costs of the trips from each
allowance area were averaged and compared with the average cost of the
trips from the DC area to compute the category indexes. The fares are
shown in Appendix 17.

5.4 Transportation Component Analyses

OPM compared the total cost of private auto transportation for each
vehicle in each allowance area with the total cost for the same vehicle
in the DC area. These comparisons are expressed as indexes and are
shown in Appendix 19. Likewise, OPM compared the cost of air fares for
each area with those for the DC area and computed a cost index. These
indexes are shown in Appendices 17 and 19. OPM used national average
expenditure data to derive weights that reflected how much consumers
typically spend to own and operate an automobile versus other
transportation expenses. These weights vary by income level and were
used to combine the Automobile Expense Category index with the Other
Transportation Costs index by area to derive the overall Transportation
Component index for the area. The weights, computations, and final
Transportation Component indexes are shown in Appendix 19.

6. Miscellaneous Expenses

6.1 Component Overview

The Miscellaneous Expense component consists of three categories of
expenses:

--Medical care.
--Contributions (including gifts to non-family members).
--Personal insurance and retirement contributions/investments.

OPM used an approach similar to that used for the Goods and
Services Component to derive the indexes for each of these categories
and the Miscellaneous Component overall.

6.2 Component Weights

OPM used CES data to determine the appropriate weights for each of
the items and categories in the Miscellaneous Component. The category
weights are shown in the following table and in Appendix 21. Item
weights are shown in Appendix 20.

Table 6-1.--Miscellaneous Expense Categories and Weights
------------------------------------------------------------------------
Income level
--------------------------------------
Categories Lower Middle Upper
(percent) (percent) (percent)
------------------------------------------------------------------------
Medical Care..................... 41.36 31.40 24.04
Contributions.................... 16.52 17.18 17.67
Personal Insurance and Retirement
Contributions................... 42.11 51.42 58.29
--------------
Totals..................... 100.00 100.00 100.00
------------------------------------------------------------------------
Note: Values may not total because of rounding.

6.3 Component Categories

6.3.1 Medical Expense Category
Runzheimer surveyed the price of medical care items using
essentially the same approach it used for the Goods and Services
component items. The following medical care items were priced in each
allowance area and in the Washington, DC, area:

--nonprescription pain reliever
--prescription drugs
--contact lenses
--dental service
--doctor visit
--hospital room
--health insurance

Runzheimer surveyed the cost of these items in both the allowance
areas and in the DC area. OPM compared the prices to produce an index
for each item in each area, then combined these indexes using CES
weights to produce a single Medical Care Category index for each area.
The COLA model assumes that the cost of health insurance is constant
among areas because the choice of Federal health coverage is to a large
extent a matter of personal preference. Therefore, the index for this
item is 100.00.
6.3.2 Contributions Category
The index for the Contributions Category is the Goods and Services
Component index for the area. The use of the Goods and Services index
is based on the assumption that the relative level of contributions is
roughly equivalent to that reflected by the Goods and Services index.
6.3.3 Personal Insurance and Retirement Category
The index for personal insurance and retirement contributions and
investments is assumed to be constant among areas. The cost of Federal
Employees Group Life Insurance is a matter of personal preference and
is constant in all areas for the same age, salary, and benefit option
combinations. Likewise, retirement contributions are a matter of
personal preference, and the minimum contribution requirements are
constant among areas for equivalent salary levels.

6.4 Miscellaneous Expense Analyses

As with the Goods and Services Component, the indexes for each of
the Miscellaneous Component categories were combined using CES weights
to produce component indexes by income level for each area. These
indexes are shown in Appendix 21. Section 2.6 describes how the
miscellaneous expense component indexes are combined with the other
component indexes to derive the final index for each area.

7. Final Results

7.1 Total Comparative Cost Indexes

The total comparative cost indexes appear below. Appendix 23 shows
how each index was derived from the component indexes.

Table 7-1.--Final Cost Comparison Indexes
------------------------------------------------------------------------
Allowance area Index
------------------------------------------------------------------------
Anchorage, Alaska............................................ 104.84
Fairbanks, Alaska............................................ 109.90
Juneau, Alaska............................................... 110.57
The rest of Alaska........................................... 129.24
City and County of Honolulu, Hawaii.......................... 121.95
Hawaii County, Hawaii........................................ 111.89
Kauai County, Hawaii......................................... 121.36
Maui County, Hawaii.......................................... 119.53
Guam/CNMI*, Local Retail..................................... 121.88
Guam/CNMI, Commissary/Exchange............................... 116.06
Puerto Rico.................................................. 102.01

[[Page 14206]]

U.S. Virgin Islands.......................................... 119.25
------------------------------------------------------------------------
*CNMI=Commonwealth of the Northern Mariana Islands

Appendix 1.--Publication in the Federal Register of Results of
Nonforeign Area Living-Cost Surveys: 1990-1996
------------------------------------------------------------------------
Citation Title Contents
------------------------------------------------------------------------
56 FR 7902................ Office of Personnel Results of summer
Management: Cost-of- 1990 living-cost
Living Allowances surveys conducted in
and Post Alaska, Hawaii,
Differentials Guam, Puerto Rico,
(Nonforeign Areas). and the U.S. Virgin
Islands.
57 FR 58556............... Office of Personnel Results of summer
Management: Report 1991 and winter 1992
on 1991/1992 Surveys living-cost surveys
Used to Determine conducted in Alaska,
Cost-of-Living Hawaii, Guam, Puerto
Allowances in Rico, and the U.S.
Nonforeign Areas. Virgin Islands.
58 FR 45558............... Office of Personnel Results of summer
Management: Report 1992 and winter 1993
on 1992/1993 Surveys living-cost surveys
Used to Determine conducted in Alaska,
Cost-of-Living Hawaii, Guam, Puerto
Allowances in Rico, and the U.S.
Nonforeign Areas. Virgin Islands.
58 FR 27316............... Office of Personnel Results of summer
Management: Report 1993 living-cost
on Summer 1993 surveys conducted in
Surveys Used to Hawaii, Guam, Puerto
Determine Cost-of- Rico, and the U.S.
Living Allowances in Virgin Islands.
Nonforeign Areas.
59 FR 45066............... Office of Personnel Results of winter
Management: Report 1994 living-cost
on Winter 1994 surveys conducted in
Surveys Used to Alaska.
Determine Cost-of-
Living Allowances in
Alaska.
60 FR 61332............... Office of Personnel Results of summer
Management: Report 1994 living-cost
on Summer 1994 surveys conducted in
Surveys Used to Hawaii, Guam, Puerto
Determine Cost-of- Rico, and the U.S.
Living Allowances in Virgin Islands.
Selected Nonforeign
Areas.
61 FR 4070................ Office of Personnel Results of winter
Management: Report 1995 living-cost
on Winter 1995 surveys conducted in
Surveys Used to Alaska.
Determine Cost-of-
Living Allowances in
Alaska.
------------------------------------------------------------------------

Appendix 2.--Multiple Survey Areas: 1996 Survey
[Federal Employment Weights Within a Single Allowance Area]
----------------------------------------------------------------------------------------------------------------
Location 1993 1994 1995 Average Weights
----------------------------------------------------------------------------------------------------------------
Hawaii County
Hilo............................................ 250 292 286 276 82.88
Kona............................................ 52 60 58 57 17.12
-------------
Total....................................... .......... .......... .......... 333 100.00
=============
Virgin Islands
St. Croix....................................... 142 151 154 149 46.42
St. Thomas/St. John............................. 190 166 160 172 53.58
-------------
Total....................................... .......... .......... .......... 321 100.00
----------------------------------------------------------------------------------------------------------------

Multiple Income Levels: 1996 Survey
[Federal Employment Weights Within a Single Allowance Area]
----------------------------------------------------------------------------------------------------------------
Location and income level 1993 1994 1995 Average Weights
----------------------------------------------------------------------------------------------------------------
Anchorage:
Lower........................................... 1,638 1,609 1,540 1,596 26.44
Middle.......................................... 2,090 1,971 1,754 1,938 32.11
Upper........................................... 2,400 2,583 2,522 2,502 41.45
Totals........................................ .......... .......... .......... 6,036 100.00
=============
Fairbanks:
Lower........................................... 400 444 388 411 33.28
Middle.......................................... 467 442 446 452 36.60
Upper........................................... 318 392 405 372 30.12
Totals........................................ .......... .......... .......... 1,235 100.00
=============
Juneau:
Lower........................................... 139 145 139 141 19.89

[[Page 14207]]

Middle.......................................... 245 220 203 223 31.45
Upper........................................... 334 360 341 345 48.66
Totals........................................ .......... .......... .......... 709 100.00
=============
Rest of Alaska:
Lower........................................... 444 414 349 402 25.62
Middle.......................................... 759 722 703 728 46.40
Upper........................................... 391 445 481 439 27.98
Totals........................................ .......... .......... .......... 1,569 100.00
=============
Honolulu:
Lower........................................... 4,346 4,239 4,140 4,242 32.68
Middle.......................................... 4,540 4,171 3,952 4,221 32.52
Upper........................................... 4,344 4,689 4,514 4,516 34.80
Totals........................................ .......... .......... .......... 12,979 100.00
=============
Hawaii:
Lower........................................... 122 165 139 142 36.69
Middle.......................................... 145 154 164 154 39.79
Upper........................................... 85 91 98 91 23.52
Totals........................................ .......... .......... .......... 387 100.00
=============
Kauai:
Lower........................................... 71 81 73 75 30.24
Middle.......................................... 94 84 76 85 34.28
Upper........................................... 78 89 97 88 35.48
Totals........................................ .......... .......... .......... 248 100.00
=============
Maui:
Lower........................................... 37 39 35 37 25.52
Middle.......................................... 56 56 59 57 39.31
Upper........................................... 51 51 51 51 35.17
Totals........................................ .......... .......... .......... 145 100.00
=============
Guam/CNMI:
Lower........................................... 1,061 1,060 947 1,023 47.12
Middle.......................................... 696 681 669 682 31.41
Upper........................................... 437 498 464 466 21.47
Totals........................................ .......... .......... .......... 2,171 100.00
=============
Puerto Rico:
Lower........................................... 2,330 2,428 2,370 2,376 40.66
Middle.......................................... 2,287 2,184 2,166 2,212 37.86
Upper........................................... 1,140 1,321 1,303 1,255 21.48
Totals........................................ .......... .......... .......... 5,843 100.00
=============
Virgin Islands:
Lower........................................... 128 114 98 113 35.31
Middle.......................................... 133 128 133 131 40.94
Upper........................................... 71 75 83 76 23.75
Totals........................................ .......... .......... .......... 320 100.00
----------------------------------------------------------------------------------------------------------------

Appendix 3--Consumer Expenditure Surveys
[Pre-published Data for All Consumer Units Nationwide*]
----------------------------------------------------------------------------------------------------------------
Total complete reporting
---------------------------------------------------------------
1991 1992 1994 Average
----------------------------------------------------------------------------------------------------------------
Average Before Tax Income....................... 33,901.00 33,854.00 36,838.00 34,864.33
Average annual expenditures..................... 30,487.29 30,527.49 32,762.99 31,259.26
Food.......................................... 4,366.88 4,358.56 4,526.94 4,417.46
Food at home................................ 2,724.89 2,684.35 2,764.21 2,724.48
Cereals and bakery products............... 413.81 418.15 439.36 423.77
Cereals and cereal products............. 149.01 144.15 166.94 153.37
Flour................................. 6.61 7.21 7.93 7.25
Prepared flour mixes.................. 14.67 13.62 13.20 13.83
Ready-to-eat and cooked cereals....... 90.13 88.39 102.02 93.51
Rice.................................. 14.49 12.67 15.47 14.21
Pasta, cornmeal and other cereal
products............................. 23.11 22.27 28.32 24.57

[[Page 14208]]

Bakery products......................... 264.80 274.00 272.42 270.41
Bread................................. 76.98 77.58 77.20 77.25
White bread......................... 38.93 38.04 38.02 38.33
Bread, other than white............. 38.04 39.54 39.17 38.92
Crackers and cookies.................. 65.09 67.10 64.36 65.52
Cookies............................. 41.15 40.75 43.78 41.89
Crackers............................ 23.94 26.34 20.58 23.62
Frozen and refrigerated bakery
products............................. 19.33 21.06 22.16 20.85
Other bakery products................. 103.40 108.27 108.70 106.79
Biscuits and rolls.................. 34.12 35.55 37.26 35.64
Cakes and cupcakes.................. 29.49 31.67 31.12 30.76
Bread and cracker products.......... 4.14 4.70 4.68 4.51
Sweetrolls, coffee cakes, doughnuts. 24.05 24.93 23.08 24.02
Pies, tarts, turnovers.............. 11.61 11.41 12.55 11.86
Meats, poultry, fish, and eggs............ 725.06 687.17 728.89 713.71
Beef.................................... 238.59 210.36 226.73 225.23
Ground beef........................... 89.66 87.67 89.79 89.04
Roast................................. 42.62 37.74 37.79 39.38
Chuck roast......................... 16.81 13.48 12.10 14.13
Round roast......................... 12.63 12.96 14.18 13.26
Other roast......................... 13.18 11.30 11.51 12.00
Steak................................. 87.83 69.00 85.81 80.88
Round steak......................... 16.56 14.63 16.44 15.88
Sirloin steak....................... 23.58 17.72 24.09 21.80
Other steak......................... 47.68 36.65 45.28 43.20
Other beef............................ 18.47 15.95 13.34 15.92
Pork.................................... 146.62 155.56 154.66 152.28
Bacon................................. 21.28 20.47 23.01 21.59
Pork chops............................ 35.26 34.88 37.47 35.87
Ham................................... 38.92 42.73 36.74 39.46
Ham, not canned..................... 35.84 38.98 33.91 36.24
Canned ham.......................... 3.08 3.75 2.84 3.22
Sausage............................... 21.01 23.29 22.63 22.31
Other pork............................ 30.15 34.19 34.80 33.05
Other meats............................. 102.91 94.58 94.34 97.28
Frankfurters.......................... 23.87 21.19 19.13 21.40
Lunch meats (cold cuts)............... 70.13 63.56 65.67 66.45
Bologna, liverwurst, salami......... 23.75 22.91 23.25 23.30
Other lunchmeats.................... 46.39 40.65 42.41 43.15
Lamb, organ meats and ot

[Text truncated at 120,000 characters. The full text is on the page linked above.]

---

Source: Frix Law Library, https://www.frixlaw.com/law-library/documents/fr%3A97-7362. Public record. Not legal advice.