Report on 1997 Surveys Used to Determine Cost-of-Living Allowances in Nonforeign Areas

SUMMARY: This notice publishes the ``Report on 1997 Surveys Used to

Determine Cost-of-Living Allowances in Nonforeign Areas.'' The results

of the surveys are used to determine cost-of-living allowances (COLAs)

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 an increase in the COLA rate for the

City and County of Honolulu, Hawaii, allowance area being published by

OPM in the interim rulemaking immediately preceding this notice.

DATES: Comments must be received on or before February 18, 1999.

ADDRESSES: Comments may be sent or delivered to Donald J. Winstead,

Assistant Director for Compensation Administration, Workforce

Compensation and Performance Service, Office of Personnel Management,

Room 7H31, 1900 E Street NW., Washington, DC 20415-8200, FAX: (202)

606-4264, or email at [email protected].

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

FAX: (202) 606-4264, or email at [email protected].

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 1997 Surveys

Used to Determine Cost-of-Living Allowances in Nonforeign Areas'' with

this notice. This report explains in detail the methodologies,

calculations, and findings of the 1997 COLA surveys.

Results of Surveys. 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

the Honolulu allowance area should be increased from its current level

of 22.5 percent to 25 percent. The survey results also show that the

COLA rate for one area is currently at the appropriate level and that

the COLA rates in 10 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, 2000.

Therefore, OPM is not proposing any COLA rate reductions.

Comments on 1996 Report. OPM published the report on the 1996

surveys conducted in Alaska, Hawaii, Guam, Puerto Rico, the U.S. Virgin

Islands, and the Washington, DC, area in the Federal Register (62 FR

14190) on March 25, 1997. Twelve respondents submitted comments on the

report.

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;

--House size, selection, necessary features, purchase price, storage

needs, and maintenance as affected 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 the quality of local schools.

OPM is participating in two major initiatives concerning the COLA

program. Many of these and other concerns are being considered under

one or both of these initiatives. These two initiatives are discussed

below.

Memorandum of Understanding and Report to Congress. In 1996, OPM

entered into a memorandum of understanding (MOU) with litigants in the

cases of Alaniz v. Office of Personnel Management and Karamatsu v.

United States. The MOU committed 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 of the Safe Harbor process is to resolve COLA issues that have

long been contested and to assist OPM as it prepares a report to

Congress on the COLA program. That report, required by the Treasury,

Postal Service, and General Government Appropriations Act, 1992 (Public

Law 102-141), as amended, is due by March 1, 2000. OPM anticipates that

the studies will examine many of the issues raised by comments on the

survey reports and will produce a number of valuable recommendations

for improving the COLA program.

COLA Partnership. In November 1996, OPM established a pilot

project to involve agencies and employee representatives directly in a

partnership to help plan and conduct COLA surveys, to 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. Under the 2-year pilot project, five partnership

committees were formed--one each in Alaska, Hawaii, Guam, Puerto Rico,

and the U.S. Virgin Islands. There were also four subcommittees formed

to represent individual allowance areas. Committee/subcommittee

functions 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 will examine some of the issues

raised by the comments on the survey reports and will provide many

recommendations for improving the COLA program.

[[Page 56433]]

Program Changes during MOU Research and Pilot Project

During the Safe Harbor process and the COLA partnership pilot

project, OPM plans generally to avoid making substantive policy changes

in the COLA program. OPM intends to first complete its research,

receive public comment, and deliver its report to Congress. This does

not mean that OPM will make no changes. There are administrative

changes relating to survey coverage that must be made for each survey,

and OPM may implement other improvements in response to comments it

receives. As with the 1996 surveys, OPM has made a few changes in this

year's surveys compared with previous years. These are discussed in the

report.

Comments on Partnership

The Alaska COLA Partnership Committee submitted comments in regard

to its decision not to participate in the 1997 COLA surveys in Alaska.

The Alaska Committee felt that OPM had not provided sufficient time for

the committee to become knowledgeable enough to make sound decisions

and to solve problems related to the survey. They noted that the

Partnership Pilot Project was effective November 21, 1996, but it was

not until May that OPM met with the Committee in advance of the July

survey.

The Alaska Committee, as well as one other commenter, also felt

that OPM was not working with the Committee in good faith or in the

spirit of a partnership. The Committee felt that it was being asked to

``rubber stamp'' a survey that would not reflect actual cost-of-living

differences between Washington, DC, and Alaska. The Committee members

stated that they wanted ``to work towards building a true partnership

with OPM in order to find an equitable COLA process.''

Six other commenters similarly asked that OPM not relegate the

Alaska Committee to an advisory role, but accept the Committee as a

full partner in evaluating COLAs. The commenters requested that OPM

delay the survey until the partnership issues were resolved.

OPM agrees that more lead time between the establishment of the

COLA Partnership Committees and the 1997 surveys would have been

desirable. The amount of time it took to launch the committees was much

greater than OPM had expected. OPM had not anticipated the significant

amount of time required by many agencies and unions to nominate

committee members and/or approve their release for committee work. As a

result, OPM delayed the surveys, originally scheduled to be conducted

during the period January-March, until July. Delaying the surveys

further was not deemed acceptable.

Despite the short lead time, OPM encouraged the partnership

committees to participate in the survey. We believe the local knowledge

and perspective offered by the committees would benefit the surveying

of outlets and items in their region. The committees would also be able

to offer preliminary feedback, based on their experience in assisting

OPM in the survey, on survey procedures. Participation would

additionally provide an opportunity for the committees to familiarize

OPM with COLA issues unique to their area. We also believe that by

participating in the survey, committees would become more knowledgeable

about the survey process and that that knowledge would be valuable in

understanding and examining the various elements of the COLA rate-

setting process.

OPM addressed the role of the partnership committees in the

publication of its final COLA Partnership Pilot Project regulations on

November 21, 1996 (61 FR 59173). The following is excerpted from the

discussion of comments in those regulations:

No two partnerships look exactly alike, and OPM believes that

establishment of these committees will result in a more collaborative

relationship among affected agencies and employees with respect to this

complex and often contentious program. By statute and Executive order,

however, OPM has the final authority for conducting COLA surveys and

administering the COLA program. If a consensus cannot be reached on an

issue or if the views of one COLA committee differ from those of

another on the same issue, OPM must still conduct surveys and set COLA

rates. Nevertheless, this does not mean that we cannot use partnership

to improve the COLA program.

OPM plans to accommodate suggestions whenever practical and

consistent with the laws and regulations that govern the COLA program.

We certainly do not expect the committees to ``rubber stamp'' our

proposals. Instead, we plan to listen carefully to and seriously

consider all of the information and advice that will be provided. We

know there is much we can learn that will help us improve the surveys

and the way we administer the program, and we look forward to having

frank and open discussions with the other committee members. It is our

hope that we can reach a consensus on the vast majority of issues that

will face us. As several commenters said, the partnership process will

not work unless there is a sincere commitment from all parties,

including OPM, to share ideas, listen to others, learn from what is

said, and find areas of agreement. OPM is committed to this process.

Overall Living Cost Model

Several commenters stated that the surveys compare only prices,

not total living costs. Two commenters said the surveys should consider

other factors, such as cultural differences, individual needs,

isolation from friends and family, and other hidden costs. Another

commenter stated that Alaska was unique and should be evaluated based

on Alaska costs and needs.

The COLA model compares the cost of an item in an allowance area

with the cost for the same brand, model, and size of item in the

Washington, DC area. OPM believes this model is consistent with the

settlement of Hector Arana, et al., v. United States, 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.

Intangible influences on living costs, such as cultural

differences and isolation from family, are very difficult to quantify

objectively. This is, however, one of the MOU research topics, and OPM

plans to discuss this issue in its report to Congress.

One commenter said that OPM's price comparison methodology is not

an accurate method for comparing cost-of-living differences. Under the

MOU and as part of the COLA Partnership Pilot Project, OPM is studying

various ways of improving the price comparison methodology for its

report to Congress.

Another commenter suggested an alternative method of cost

comparison under which employees with similar individual and family

situations in the comparison areas would be selected to maintain a

detailed record of expenses for a given period of time. OPM does not

believe this approach is practical.

One commenter disagreed with OPM's inclusion of sale taxes in the

COLA model. The commenter said that taxes are purchases of services,

not part of the price of items, and that areas with

[[Page 56434]]

lower taxes receive fewer services. As such, the commenter argued, OPM

should compare the services being provided in its calculations or, if

the services are not measurable, should not measure the sales tax that

pays for those services.

This issue was originally raised in comments on the 1995 surveys

and responded to by OPM in the 1996 survey notice. As stated in the

notice, OPM believes that the effect on living costs of any area

differences in community programs and services due to differences in

sales tax revenues probably cannot be measured. Revenues for community

services or programs may originate from many sources other than sales

taxes, including State and local income taxes, corporate taxes and

subsidies, property and other taxes, user fees, lottery revenues, civil

penalties, and Federal funds. Furthermore, the sales tax is a direct

consumer expense. Regardless of the services that are supported by the

sales tax, it is a cost that the consumer must pay. For that reason,

OPM continues to believe that it is appropriate to include the sales

tax in the prices of the items surveyed.

The same commenter said that using the Consumer Expenditure Survey

(CES) is inappropriate because it assumes DC and Alaska consumers spend

their money in the same manner. As stated in the report, OPM uses the

nationwide CES data because OPM knows of no other source of

comprehensive consumer expenditure information by income level suitable

for use in the COLA model. One of the topics being researched under the

MOU is the possible use of local CES data, including Anchorage CES

data, in the COLA model. OPM anticipates including the results of this

research in its report to Congress.

One commenter noted how much more expensive it was in Alaska

compared to Wyoming. OPM is required by law to use Washington, DC, as

the reference area for living-cost comparisons.

Goods and Services

One commenter said that there are fewer department stores in

Alaska and that sales at these stores are infrequent. Two commenters

noted that one of the fast food restaurants in the survey advertised a

sale item at one price, but the price in Alaska was much higher. The

survey compares only non-sale prices of identical items from similar

outlets, which we believe is consistent with Arana.

One commenter felt that Alaskans are more likely than Washington,

DC, residents to incur expenses related to snow removal and other

winter conditions. One of the research topics under the MOU concerns

expenses unique to each allowance area and to the Washington, DC, area.

OPM plans to include the results of this MOU research in its report to

Congress.

The same commenter thought OPM should publish with the report the

prices for all items. More than 18,000 prices were collected in the

1997 surveys. Publishing this volume of information is not practical.

One commenter said OPM should examine additional fees charged by

mail order companies to ship to Alaska or Hawaii. OPM included catalog

prices for selected items in the surveys. Additional costs for shipping

and excise taxes, if any, were added to the catalog pricing where

applicable.

The same commenter said that in Alaska the cost of lettuce is by

the pound, not by the head, as is charged elsewhere in the U.S. For

comparison purposes, where lettuce is sold by the head, OPM collects

the price and weight of an average head and converts the price to price

per pound. The commenter also said OPM should examine the cost of dairy

products in Alaska. OPM collects price data for milk, cheese, eggs, ice

cream, and margarine in each of the allowance areas for use in the

comparisons.

Two commenters noted the high cost of goods and services on Prince

of Wales Island in Alaska. Prince of Wales Island is in the Rest of

Alaska allowance area, and OPM notes that, as have the previous

surveys, the results of the 1997 survey show that the maximum allowable

COLA rate (25 percent) should continue to be paid in this allowance

area.

Housing

One commenter felt that OPM's calculations should allow for

Alaskans having larger homes because of Arctic entrances and extra

storage needs. The home purchase price data collected reflect local

home sales, which in turn should reflect the cost of any special

features common to dwellings in each area.

The same commenter stated that Alaskan homes require more frequent

maintenance because of the harsh winters and the composition of houses.

The commenter also stated that house heating systems wear out more

quickly in Alaska. One of the key research topics under the MOU is

housing costs, and the possible application of a ``rental equivalence

approach,'' which is the approach the Bureau of Labor Statistics uses

for measuring change in housing costs for the Consumer Price Index. OPM

will include the findings of this MOU research in its report to

Congress.

One commenter noted that housing is scarce and thereby expensive

in Thorne Bay, Alaska. Thorne Bay is in the Rest of Alaska allowance

area, and as OPM noted earlier, COLA surveys have consistently shown

that payment of the maximum COLA rate is warranted in that area.

Transportation Component

One commenter stated that Alaskans have a higher accident rate and

incur higher insurance and repair costs because of icy roads. The same

commenter felt that a fuel adjustment should be made because Alaskans

need to warm up their cars in the morning, using more fuel. The

commenter also said that OPM should include the cost of changing to and

from snow tires in its calculations.

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. For the 1997

surveys, OPM also priced the cost of mounting and balancing snow tires

and the cost of switching mounted snow tires and street tires on a

semi-annual basis.

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.

Although OPM does not take into consideration the effect of

extended periods of idling on fuel consumption,

[[Page 56435]]

OPM does take into consideration the effect of climate on gas mileage.

(See section 5.2.3.1 of the report.) In the case of Alaska, the COLA

model assumes that automobiles there generally get fewer miles per

gallon than equivalent automobiles in the Washington, DC, area.

Several commenters stated that travel by air is more necessary,

and therefore more frequent, in Alaska. The current model assumes that

the typical Federal employee puts 15,000 miles per year on a car. 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.

Transportation is one of the MOU research topics, and OPM plans to

include this research in its report to Congress.

One commenter noted that DC residents have Metro costs subsidized

by tax dollars. 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 roundtrip airfares from the allowance areas with the cost of

roundtrip airfares from the Washington, DC, area to the same

destinations.

Miscellaneous Component

Several commenters felt that the medical expense portion of the

Miscellaneous Component fails to reflect high out-of-pocket expenses

they believe 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. Medical

expense is one of the research topics under the MOU, and OPM plans to

include this research in its report to Congress. OPM also notes that in

the analysis of the results of the 1997 survey, OPM used average

employee Federal health benefit expense by area. These data indicate

that, with the exception of Puerto Rico, these expenses are higher in

the allowance areas than in the Washington, DC, area.

General Comments

One commenter asked that OPM consider the effect significant

reductions would have on the local economy of the allowance area.

Another commenter believed that the results of the survey would end

COLAs in the more populous areas of Alaska. This is not quite accurate.

If COLA rates were based on the results of the 1997 survey, employees

in both Juneau and Fairbanks would continue to receive COLAs, though at

a lower rate. However, as noted earlier, COLA reductions are prohibited

by law until December 31, 2000. In addition, OPM has the authority to

reduce COLA rates gradually.

Two commenters cited the scarcity of higher education choices in

Alaska and the expenses of having family members attend out-of-state

schools. Education is an MOU research topic, and OPM will report on

this research in its report to Congress.

One commenter noted that DC residents have free access to many

recreational opportunities on the Mall in Washington, DC, such as

museums and concerts. OPM believes each area offers recreational

opportunities that are unique to that area, such as beaches, rivers,

mountains, parks, or museums, as well as various leisure activities.

Some of the recreational choices require paid admission, and others are

free. Surveying everything is not feasible. OPM surveys the cost

related to a number of recreational activities for which a fee is

charged, including movie theaters, video rentals, golf, and bowling.

Two commenters noted that they had only a short time in which to

prepare comments on the notice. In response to similar comments on the

previous survey, OPM had increased the comment period for the notice

from 60 to 90 days. For this report, OPM is further increasing the

comment period from 90 to 120 days.

One commenter requested to be placed on a mailing list and

notified of COLA publications. OPM does not maintain a mailing list for

employee notification on COLA issues. OPM does employ several other

means outside Federal Register publication for disseminating this

information to Federal employees. These include agency, union, and

Partnership Committee notification; agency postings; and publication on

OPM's Internet web page (www.opm.gov) and the nonforeign area COLA web

page (www.opm.gov/cola).

Clarification and Correction of the 1996 Report

In preparing its report on the 1997 surveys, OPM discovered

discrepancies in section 4.2.2, section 5.2.5, and Appendix 8 of the

1996 report. These discrepancies are discussed below, and OPM addressed

them in the 1997 report. OPM notes that the clarifications and

corrections had no effect on any COLA rate.

Section 4.2.2 did not fully describe the procedures used to assign

home sales observations to the appropriate income level. As stated in

the report, Runzheimer was unable to obtain on a consistent basis

across areas information on number and types of rooms for home sales.

Therefore, in assigning home sales observations to each income level,

Runzheimer relied primarily on living community and home size. In areas

where discrete communities were assigned to each income level,

Runzheimer used all observations, regardless of room count and type,

that met the size range specification shown in Table 4-3. As shown in

table 4-3, these size ranges overlap. Therefore, in areas where the

same communities were used at more than one income level, Runzheimer

relied on room count and type to assign home sales in the size range

overlap to the appropriate income level. When such information was not

available, as was the case in St. Thomas, Runzheimer assigned homes in

the 600 to 1,100 square foot range to the lower income level, homes in

the 1,101 to 1,500 square foot range to the middle income level, and

homes in the 1,501 to 2,300 square foot range to the upper income

level.

Table 4-2 also implied that OPM used the prices of condominiums

and rowhouses at the lower and middle income levels. This was not

correct. To allow the comparison of the same type of housing across

areas, OPM used the prices only of detached, single family homes in all

areas. Some of these homes, particularly in the Virgin Islands,

probably had apartment units within them, but this level of detail was

not available.

Section 5.2.5 stated that, in addition to the price of studded

snow tires, Runzheimer surveyed the extra cost of wheels (i.e., rims)

in each of the Alaska COLA areas. In comparing the results of the 1997

survey with those of the 1996 survey, OPM found that the extra cost of

rims was not obtained in the 1996 survey. In the 1997 survey, OPM did

price rims in Alaska, as well as the cost of mounting and balancing

snow tires and the cost of switching mounted snow tires and street

tires on a semi-annual basis, although the quantity of data was

limited. For the coming surveys, OPM is improving the item description,

which will address this problem.

In Appendix 8, the Consumption Goods and Services indexes for

[[Page 56436]]

Honolulu, HI, did not agree with the indexes in Appendix 22. The

Honolulu indexes in Appendix 22 were the correct indexes and were used

to determine the final index for Honolulu. Therefore, the final total

comparative cost index for Honolulu was correct.

Office of Personnel Management.

Janice R. Lachance,

Director.

Table of Contents

Executive Summary

1. Introduction

1.1 Report Objectives

1.2 The COLA Partnership Pilot Project and 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

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 Data Collection

2.5.2 Inclusion of Sales and Excise Taxes

2.5.3 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 Private Education (K-12) Category

6.3.3 Contributions Category

6.3.4 Personal Insurance and Retirement Category

6.4 Miscellaneous Expense Analyses

7. Final Results

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: Consumption Goods and Services Analysis and Summary

Appendix 8: OPM Living Community List

Appendix 9: Historical Home Market Values and Interest Rates

Appendix 10: Historical Housing Data

Appendix 11: Rental Data Analyses

Appendix 12: Housing Cost Analysis

Appendix 13: Housing Summary

Appendix 14: Private Transportation Cost Analysis

Appendix 15: Auto Insurance Calculation Worksheet

Appendix 16: Air Fares Cost Analysis

Appendix 17: Transportation Analysis

Appendix 18: Transportation Summary

Appendix 19: Miscellaneous Expense Analysis--Category Index

Development

Appendix 20: Miscellaneous Expense Summary

Appendix 21: Component Expenditures

Appendix 22: Final Indexes

Executive Summary

Cost-of-living allowances (COLAs) are paid to Federal employees in

nonforeign areas in consideration of living costs higher than in the

Washington, DC, area. OPM conducts living-cost surveys in order to set

the COLA rates. This report provides the results of the summer 1997

living-cost surveys and compares living costs in nonforeign COLA areas

to those in the Washington, DC, area.

Survey data were collected by the Office of Personnel Management

(OPM) under the COLA Partnership Pilot Project, a 2-year pilot project

that was established to test and evaluate a new approach in the

administration of the COLA program, including the conduct of living-

cost surveys. Surveys were conducted 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. In the interest of

expediting COLA rate increases, OPM is publishing this report at the

same time it is discussing the survey results with the COLA Partnership

Pilot Project Committees and Subcommittees. If, as a result of these

discussions, OPM implements changes that affect the results of the 1997

survey, OPM will describe these changes and the results in a future

Federal Register notice.

For this study, over 3,500 outlets were contacted and over 18,000

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............................................. 102.93

Fairbanks, Alaska............................................. 107.57

Juneau, Alaska................................................ 111.54

The rest of the State of Alaska............................... 126.64

City and County of Honolulu, Hawaii........................... 126.78

Hawaii County, Hawaii......................................... 110.85

Kauai County, Hawaii.......................................... 114.92

Maui County, Hawaii........................................... 118.84

Guam/CNMI*, Local Retail...................................... 121.77

Guam/CNMI, Commissary/Exchange................................ 118.23

Puerto Rico................................................... 105.42

U.S. Virgin Islands........................................... 119.09

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

*CNMI=Commonwealth of the Northern Mariana Islands

1. Introduction

1.1 Report Objectives

This report provides the results of the Summer 1997 surveys. A

listing of

[[Page 56437]]

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 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

In the interest of expediting COLA rate increases, OPM is

publishing this report at the same time it is discussing the survey

results with the committees and subcommittees established under the

COLA Partnership Pilot Project. OPM will have these discussions in the

near future. If, as a result of these discussions, OPM implements

changes that affect the results of the 1997 survey, OPM will describe

these changes in a future Federal Register notice.

1.2. The COLA Partnership Pilot Project and Changes in This Year's

Survey

In November 1996, OPM established the COLA Partnership Pilot

Project, a 2-year pilot project designed to assist OPM in the

administration of the COLA program. (See 61 FR 59173.) Under the pilot

project, COLA Partnership Pilot Project Committees and Subcommittees

were established in Alaska, Hawaii, Guam, Puerto Rico, and the U.S.

Virgin Islands. The committees and subcommittees are composed of four

representatives of Federal unions, four representatives from Federal

agencies in each local area, plus two OPM representatives.

All of the Committees and Subcommittees, except the Alaska

Committee, worked with OPM in planning the COLA surveys, observing OPM

data collection, and advising OPM on the COLA program and on

compensation issues relating to the COLA areas. The Alaska COLA

Partnership Committee elected not to be involved in survey planning and

data collection observation because it believed there had not been

sufficient time to become knowledgeable about the COLA program and to

resolve issues prior to the survey. Agency and employee representatives

in some Alaska areas, however, worked with OPM on an informal basis.

Prior to the surveys, OPM central office staff traveled to each of

the COLA areas to discuss with the Committees and Subcommittees survey

plans and specifications. OPM adopted several changes in response to

Committee/Subcommittee recommendations. Appendix 6 lists significant

changes made for this survey relative to the previous survey. Among the

key changes are the following:

--Private education (K-12) was surveyed in all areas, and ``use

factors'' derived from the results of the 1992/93 Federal Employee

Housing and Living Patterns Survey were used to reflect the mix by area

of Federal employees whose children attend private schools and those

who attend public schools.

--Average employee Federal health benefit expense was estimated by area

and used in place of the fixed amount used in previous surveys.

--Several other new survey items were added, including windshield

replacement, cellular phone service, hospital attendant, and air

ambulance insurance.\1\

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

\1\Hospital attendant and air ambulance insurance were surveyed

in all areas, but were used in index calculations only in two areas

because these services were not available in other areas. Hospital

attendant prices were added to the cost of the hospital room in

Puerto Rico, and air ambulance insurance premiums were added to the

cost of Federal health benefits premiums in the U.S. Virgin Islands.

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

--Omaha, NE, was added to the list of destinations for pricing air

fares.

--Rental and home sales data were collected for new housing communities

on Oahu.

--Outlet specifications were changed for certain items, such as

restaurant meals, to provide a more consistent mix of outlet types

across areas.

Another change compared with previous surveys is that a private

contractor no longer collected price data. Instead, under the COLA

Partnership Pilot Project, OPM central office staff collected these

data, usually with the assistance of local observers from the COLA

Partnership Committees and Subcommittees. OPM found this to be a very

beneficial and informative process. OPM staff has gained a much better

understanding of local conditions and issues and believes that the

Committees, Subcommittees, and observers also have gained a better

understanding of the COLA program.

In addition to the above changes, OPM collected data on several

test items and in two test areas: the Waimea/Waikoloa area on the

Island of Hawaii and on St. John, U.S. Virgin Islands. OPM will be

discussing the results of these tests with the Committees and

Subcommittees in the near future. Since these test data were not used

in the calculation of living-cost indexes, they are not discussed in

this report.

1.3 Pricing Period

Although OPM implemented the COLA Partnership Pilot Project in

November 1996, it took much longer than expected to establish the COLA

Partnership Committees and Subcommittees. Therefore, it was necessary

to delay the surveys from the February time frame in which OPM

originally planned to conduct the survey. The Committees and

Subcommittees were established in early spring, 1997; and in April and

May 1997, OPM central office staff traveled to each of the COLA areas

to discuss with the Committees and Subcommittees plans for the 1997

living-cost surveys. As noted above, OPM adopted several changes in

response to Committee and Subcommittee recommendations. In July and

August 1997, OPM central office staff returned to the COLA areas to

collect living-cost data. During roughly the same time frame, OPM staff

collected data in the Washington, DC, area. The prices of some items--

those dependent upon the pricing of other items--were collected later.

Limitations on OPM staffing resources and budget allocations also

extended the pricing period on these few items.

As 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, summer catalogs were used for all catalog items

surveyed. Because the surveys were conducted during the summer months,

winter items, such as downhill skiing, were not 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

[[Page 56438]]

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-collar employees who have annual base salaries between

approximately $12,400 and $90,100, the range of the 1996 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 1996

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 identified 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

$22,300, $34,000, and $51,500. 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 1996 distribution of General Schedule employees by salary in each

allowance area to derive employment weights. These were combined with

similar data from 1994 and 1995 to produce a moving average. (OPM uses

moving averages to lessen index changes caused by the introduction of

new weights over time.) 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 21 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

1992, 1994, and 1995. 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: (1) to identify appropriate items

for the survey and (2) 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 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.\2\ 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.

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

\2\The midpoint of the moving average of CES data was 1994.

Therefore, for the purposes of these regressions, OPM adjusted

Federal salaries to reflect 1994 pay rates. OPM used the pay

increases for 1995 (2.0%) and 1996 (2.0%) to deflate the 1996

salaries. This produced adjusted Federal salaries of $21,450,

$32,700, and $49,500 for use in the regression equations.

Table 2-1.--Component Expenses Expressed as a Percentage of Total Expenses

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

1994

adjusted Goods and Housing Transportation Misc. Total

1996 income level income services (percent) (percent) (percent) (percent)

level* (percent)

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

$22,300........................ $21,450 38.90 26.03 18.72 16.34 100.00

34,000......................... 32,700 38.18 24.67 18.54 18.61 100.00

51,500......................... 49,500 37.52 23.43 18.38 20.68 100.00

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

*Income levels are adjusted as described in footnote 2.

(Values may not total because of rounding.)

Goods and Services Component items were further separated into 10

categories, and linear regression techniques were used to estimate

expenditures on these 10 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.

[[Page 56439]]

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 consumers purchase (e.g., edam, gouda, jack,

swiss, etc). The market basket that OPM 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.

On St. Thomas, for example, essentially the whole island is surveyed

because the island is not that large and Federal employees live

throughout the island. For other areas, specific communities had to be

identified. To do this, OPM relied mainly on 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, in consultation with the COLA

Partnership Committees and Subcommittees, to select the living

communities in which housing costs were priced. OPM, again in

consultation with the Committees and Subcommittees, 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, OPM and the Committees/Subcommittees selected

outlets that were popular with consumers and that were comparable to

outlets in other areas. For example, grocery items were surveyed at

supermarkets in all areas because most people purchase their groceries

at such stores and because supermarkets are found in nearly all

areas.\3\ 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).

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

\3\Groceries were surveyed at two kinds of supermarkets (i.e.,

full-service supermarkets and ``warehouse-type'' supermarkets) in

areas where both types of supermarkets were common and within a

normal shopping radius of the living communities surveyed. OPM

notes, however, that some areas do not have warehouse-type

supermarkets. Membership stores, such as Costco, were not surveyed

in any area.

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

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.

As noted earlier, OPM obtained over 18,000 prices on about 200

items from over 3,500 outlets. In each survey area, OPM attempted to

get three price quotes for most items. There were certain exceptions.

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, OPM

attempted to get 18 price quotes for most items in this area.

2.5.1 Data Collection

To avoid possible conflicts of interest, price data were collected

in each area by OPM central office staff. In all of the COLA areas,

except Anchorage, a data collection observer, usually designated by the

local COLA Partnership Committee or Subcommittee, accompanied OPM staff

and advised and assisted in contacting outlets, matching items,

selecting substitutes, and generally informing OPM staff on living

costs and related compensation issues. OPM found this to be a very

informative process.

Most data were collected onsite in stores, repair shops, etc.

However, many items, such as insurance, home maintenance services, and

private education expenses, were priced by telephone. Some items, such

as property tax rates, were collected from web sites on the Internet.

OPM also purchased home sales and some rental data from various

sources.

2.5.2 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. OPM gathered

applicable information on taxes by contacting appropriate sources of

information in the allowance areas and the Washington, DC, area.

2.5.3 Surveying the Washington, DC, Area

As noted above, OPM attempted 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

[[Page 56440]]

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 8. Survey data from each of the six DC survey

areas were combined using equal weights.

As in the COLA areas, OPM central office staff collected data

onsite and by phone in the DC area. Due to funding limitations,

allowance area data collection observers did not travel to the DC area

to observe and assist in data collection.

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 components,

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 21. 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....................................... $8,675 $5,805 $4,175 $3,644 $22,300

Middle...................................... 12,981 8,388 6,304 6,327 34,000

Upper....................................... 19,323 12,066 9,466 10,650 51,500

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

(Note: Values may not total because of rounding here and in Table 2-1.)

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), total relative costs were produced separately for owners

and 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 22.

3. Consumption Goods and Services

3.1 Categories and Category Weights

Based on the CES data, OPM identified 10 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 consumer spends in each

category at various income levels. The categories and the relative

expenditures are shown in the table below:

[[Page 56441]]

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..................... 26.85 23.89 21.11

Food Away from Home.............. 13.59 14.26 14.88

Tobacco.......................... 2.91 2.41 1.95

Alcohol.......................... 2.49 2.52 2.54

Furnishings and Household

Operations...................... 15.19 16.35 17.45

Clothing......................... 13.34 13.95 14.53

Domestic Service................. 1.80 2.03 2.23

Professional Services............ 6.97 6.81 6.66

Personal Care.................... 3.58 3.49 3.41

Recreation....................... 13.28 14.29 15.24

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

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 7 shows for each allowance area 10 category indexes, the

weights used at each of the 3 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, some employees

have such access, so OPM priced the same market basket 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 market

basket item found in these facilities.

Employees who have access to military facilities make some of

their purchases in these facilities and make other purchases elsewhere.

Therefore, OPM used the results of a survey of Federal employees 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 rental weights shown

below and in Appendix 22. OPM excluded data for homeowning families

without a mortgage because they were not typical of Federal homeowners

in the base area or in the allowance areas.

[[Page 56442]]

Table 4-1.--Owner/Renter Weights

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

Income levels

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

Category Lower Middle Upper

(percent) (percent) (percent)

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

Homeowner with mortgage.......... 38.60 48.05 62.17

Renter........................... 61.40 51.95 37.83

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

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--three for homeowners and three for renters. These

profiles are shown in Table 4.2. One owner and one renter profile was

assigned to each income level. OPM attempted to collect information on

the living area, numbers and types of rooms, and other information that

might influence home sale or rental prices. This information was rarely

available for rental units, so OPM relied on bedroom count and living

community to segregate rental prices by income level. The additional

information shown in Table 4.2, however, was used during the interview

of rental brokers to collect broker data.

Information about characteristics of houses sold was also

difficult to collect on a consistent basis across all areas. Although

detailed information about the houses sold was available for many

areas, it was not available for other areas, including the District of

Columbia and the Maryland suburbs of the Washington, DC, area. The only

housing characteristics that were consistently available across all

areas were house type and size. OPM surveyed only the prices of single

family detached houses in each area and relied mainly on house size and

living community to segregate homes sales by income level.\4\ As shown

in Table 4.2, these size ranges overlap. Therefore, when housing was

priced in the same living community at two or more income levels, the

additional information was used to separate home sales observations

into the appropriate income level so that no single home sale

observation was used at more than one income level.

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

\4\ In the U.S. Virgin Islands, many of the houses surveyed had

apartments within them. Since this is a very common characteristic

of housing in that area, exclusion of the price of housing with

apartments was not feasible. It is also likely that some of the home

sale prices obtained in other areas, including the Washington, DC,

area were for housing that had basement or ``mother-in-law''

apartments, although the sources OPM used did not provide that

information.

Table 4-2.--Housing Profiles

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

Renters Owners

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

Income level Additional Additional

Key Characteristic Information Key Characteristic Information

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

Lower......................... 1 bedroom apartment 3 rooms total, 1 Detached house, 4 rooms total, 2

bath; Reference 600 to 1,200 bedrooms, 1 bath;

size: 600 sq. ft.. sq.ft.. Reference size:

900 sq. ft.

Middle........................ 2 bedroom apartment 4 rooms total, 2 Detached house, 5 rooms total, 3

baths; Reference 1,000 to 1,600 bedrooms, 1 bath;

size: 900 sq. ft.. sq.ft.. Reference size:

1,300 sq. ft.

Upper......................... 2 bedroom townhouse 4 rooms total, 2 Detached house, 7 rooms total, 3

or detached house. baths; Reference 1,400 to 2,300 bedrooms, 2

size: 1,100 sq. sq.ft.. baths; Reference

ft.. size: 1700 sq.

ft.

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

The reference sizes in Table 4.2 are used for the calculation of

utility costs in the model. (See section 4.2.4.1.) As noted above, they

are not the only sizes surveyed for each profile.

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 and in consultation with

the COLA Partnership Committees and Subcommittees. The communities

surveyed are identified in Appendix 8. 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 collected 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 characteristics for

the particular income group were included in the analysis.

For most areas in which discrete living communities were

identified, OPM used zip code boundaries. The exceptions were Anchorage

and San Juan. In Anchorage, OPM used the multiple listing service

location codes that realtors commonly use in that area. In San Juan,

OPM used the name of the municipio or community.

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,

[[Page 56443]]

--Maintenance, and

--Telephone expenses.

4.2.4.1 Utilities

Electricity, oil, gas, and water were the utilities used in the

model. Many 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 4-3.--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, OPM obtained the price of each of the types of

utilities noted above. Where available, OPM also gathered from local

utility companies average annual consumption data per household

information. The local rates and consumption information were used 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, OPM was unable to obtain estimates for electricity

usage for houses heated by gas or oil. However, OPM 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, OPM contacted the local tax assessors or municipal

web sites on the Internet to obtain real estate tax information on the

living communities surveyed. These real estate tax formulas were

applied to the median home values for each income level to estimate

annual real estate taxes. For San Juan, however, OPM was able to obtain

only general information about home assessment values. This information

verified data collected during the 1996 survey, which indicated that

property taxes were very low in Puerto Rico. Therefore, OPM used the

1996 San Juan property tax expense in this year's calculations.

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. At the request of

the Guam COLA Partnership Committee, OPM also collected, on a test

basis, renter insurance rates at other levels of coverage. OPM has not

had the opportunity to examine these test data in detail. Therefore,

they were not used in these calculations. OPM may test price such

coverage again in the coming survey.

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.

Hurricane insurance was priced for all of the allowance areas in

Hawaii and in Guam, Puerto Rico, and the U.S. Virgin Islands. This

year, at the request of the Hawaii COLA Partnership Committee, OPM

attempted to collect flood insurance information in Hawaii,

particularly information on how frequently this type of coverage is

required by lenders. The information OPM obtained was sparse and

inconclusive. OPM will attempt to collect more information in the

coming survey. In research previously conducted for OPM, the contractor

found that insurance coverage for disasters, such as floods and

earthquakes, was not widely purchased in the allowance areas.

Therefore, the COLA model does not include these additional riders.

(See section 4.2.4.3 of the Report to OPM on Living Costs in Selected

Nonforeign Areas and in the Washington, DC, Area, December 10, 1992, at

57 FR 58556).

4.2.4.4 Home Maintenance

Estimated home maintenance expense was computed for each of the

homeowner and renter profiles. In previous surveys, OPM used

maintenance costs for owners only on the premise that most, if not all,

maintenance expenses are covered by the landlord. It was pointed out,

however, that this assumption resulted in a mathematical error, albeit

a very small one, because of the way OPM uses CES data. Therefore, this

year OPM derived from the CES separate home maintenance expenditure

amounts for both owners and renters. Not surprisingly, the CES

indicates that renters spend relatively little on home maintenance

compared with homeowners.

As done in previous surveys, OPM 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.

At the request of the Hawaii COLA Partnership Committee, OPM also

attempted to collect, on a test basis, the cost of termite bait

treatment systems. OPM found that this service is not common in some

allowance areas nor in the Washington, DC, area. Therefore, the test

data were not used. OPM may test price this service again in future

surveys.

To compute home maintenance cost differences between each

allowance area and the Washington, DC, area for the homeowner and

renter 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 and

renter 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 for owners and renters

separately. This cost was assigned to the middle-income homeowner and

renter 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 owner and

renter multipliers used in the utilities model were applied to

recognize differences in maintenance costs due to house size at the

various income levels.

[[Page 56444]]

4.2.4.5 Telephone Expenses

Telephone expenses consisted of local service charges, additional

charges for local calls (if applicable), charges for long distance

calls, and basic cellular phone service. To measure estimated expenses

for local service and local calls, OPM surveyed the cost of touch-tone

service with unlimited calling in each area. To estimate long distance

charges in all areas, OPM priced from a major long distance provider

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). As in

previous surveys, OPM priced 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.

This year, OPM also priced cellular phone service. In each area,

OPM priced the basic monthly plan for such service. Weights were

derived from CES data to account for the portion consumers spend on

regular phone service and cellular phone service. These weights were

then used to combine the prices of these two types of phone service.

4.3 Housing Data Collection Procedures

OPM collected home sales information from multiple listing type

services and rental information mainly from rental brokers and

advertisements.

4.3.1 Homeowner Data Collection

OPM obtained the selling prices of homes that matched the housing

profiles in each living community for home sales that occurred roughly

during the 12-month period preceding and including the survey month.

The amount of data obtained depended on the number of home sales in the

community and the availability of square footage and other information

on housing characteristics. 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 to provide data.

Relatively large quantities of home sales data were obtained in

all areas except Nome and St. Thomas. In Nome, home sales were

extremely limited because Nome is not very large. In St. Thomas, home

sales were limited because, at the time of the survey, there was no

readily available and comprehensive source of home sales data that

provided home size (i.e., square footage) information. OPM obtained a

limited amount of St. Thomas home sales information, as well as more

general home sale trend information. Analysis of the home sales

information indicated that prices on St. Thomas had fallen sharply, but

the more general trend information indicated that lower average prices

were probably caused by the sale of hurricane damaged properties. It is

not OPM's policy to price uninhabitable or severely damaged homes.

Therefore, OPM held home prices on St. Thomas constant by using the

previous year's data.

Identifying houses that were uninhabitable, severely damaged, or

otherwise in need of significant repairs was impossible for most areas,

given the limited amount of information available from the listing

services. As discussed in section 4.4.1 below, OPM uses the median

rather than the average home value to compute housing costs. (The

median is the middle value in a rank-ordered set of observations and

tends to be less sensitive than the average to unusually low or high

values at the ends of a range of data.) Nevertheless, in some of the

data bases OPM purchased, the quantity of exceptionally low priced

homes had a significant effect on the median. Therefore, in all areas

OPM trimmed home sale prices that were $30,000 or less, recognizing

that $30,000 was probably a conservative price threshold for most

areas. No trimming was done at the upper end of the data, even though

there were a few very expensive homes in some of the data bases,

particularly in Hawaii. OPM plans to review the issue of data trimming

with the COLA Partnership Committees and Subcommittees.

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. For each income profile

in each allowance area and the Washington, DC, area, OPM computed price

per square foot for each of the comparables and determined the median

price per square foot. The median was used 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. The median price per square foot was then multiplied by

the reference square footage for the income level to determine the home

purchase price.

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 9 of this report. For all areas except Oahu, 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.

For Oahu, OPM obtained additional historical housing data. As

discussed earlier in this report, OPM, at the recommendation of the

Hawaii COLA Partnership Committee, surveyed housing prices in new

living communities on Oahu. Because OPM's historical data did not cover

these communities, OPM obtained and used this additional historical

price data.\5\

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

\5\The Honolulu historical data covered the period from 1988 to

1997. For this year's calculations, OPM needed data for 1987 as

well. These data were extrapolated using the relationship of the

newly obtained historical data to the previously obtained historical

data for 1988.

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

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 10.

4.4.2 Rental Data Analysis

OPM assigned each rental quote to a single income level based on

the criteria shown in Table 4-2. 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.

[[Page 56445]]

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 11. As noted in that appendix, OPM found inexplicable rental

price trends in some of the data, particularly in the broker data. For

example, the median broker rental price at the middle income level was

sometimes less than that quoted at the lower income level. Therefore,

OPM adjusted the rental data to address these anomalies.

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 12 shows the cost of

each of these items for renters and homeowners in each allowance area

and in the Washington, DC, area. Appendix 13 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 21 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 in previous surveys, OPM analyzed automobile transportation

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, OPM collected new vehicle prices at

dealerships in each area. 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 4-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.

OPM collected self-service cash prices of unleaded regular gasoline at

name-brand gas stations in the Washington, DC, area and in all

allowance areas. In Alaska, OPM obtained both the full-service and

self-service gasoline prices at stations that offered both and averaged

the prices.

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,

the fewer miles-per-gallon achieved, and vice versa. According to 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

[[Page 56446]]

concrete and/or high-load asphalt factor was applied to the Federal

mileage percentage to produce two weighted average factors--one for the

Alaska 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 table 5-1, 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

OPM surveyed the cost of 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,

--Muffler/exhaust pipe replacement,

--Constant velocity joint (CVJ) boot replacement, and

--Windshield replacement.

The automobile manufacturers' recommended maintenance schedules

were used to determine the frequency of performing each of the first

five 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 are normal, and the

maintenance 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. OPM 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.

Previous research conducted for OPM revealed varying replacement

cycles for constant velocity joint (CVJ) boots 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). OPM used the Washington, DC, area

frequency of repair for the other (i.e., non-Alaska) COLA areas. In

each area, 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.

To determine the frequency of replacement of windshields, OPM

contacted local dealers and automobile repair shops. Based on the

information obtained, OPM determined that windshield replacement was

much more frequent in Alaska than in the other allowance areas or the

Washington, DC, area. Therefore, OPM assumed that windshields had to be

replaced every 2 years in the Alaska areas but rarely (i.e., never) in

the other areas or in the DC area during the 4-year trade cycle used in

the COLA model. Windshield replacement, however, is normally covered by

the owner's automotive insurance. Therefore, OPM used the deductible

rather than the

[[Page 56447]]

surveyed price of windshield replacement, since the deductible was

always less than the replacement prices.

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.

OPM 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 (but not in the DC area). Therefore, OPM surveyed the

prices of studded snow tires for all vehicles in Anchorage, Fairbanks,

Juneau, and Nome. OPM also priced the cost of rims and switching snow

and street tires semi-annually in these Alaska areas.

5.2.6 License and Registration Fees and Miscellaneous Taxes

OPM obtained information regarding license and 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. The

used car value was based on information from sources such as the Kelly

Blue Book. Although such sources track prices of vehicles sold only in

the contiguous 48 States, previous research performed by a contractor

for OPM 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 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, OPM 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

OPM surveyed the cost of car insurance in each location.

Consistent with the previous year's survey, the following common

coverages, limits, and deductibles were used:

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, OPM 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.

As had been reported in previous surveys, OPM 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 surveyed 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 coverage 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 15.

The procedure used this year was much simpler than that used in

previous surveys. Sensitivity analysis indicated that the new procedure

produced essentially the same results, and the simpler procedure

requires less information from the insurance companies. Therefore, it

reduces the public burden of the survey.

5.2.10 Overall Annual Costs

As described above, OPM 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 14 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 surveyed 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,

St. Louis, and Omaha, NE. These cities were selected to 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

16.

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 17. 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 16 and 18. OPM used national

average expenditure data to derive weights that

[[Page 56448]]

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 18.

6. Miscellaneous Expenses

6.1 Component Overview

The Miscellaneous Expense component consists of four categories of

expenses:

--Medical care.

--Private education (K-12).

--Contributions (including gifts to non-family members).

--Personal insurance and retirement contributions/investments.

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 20. Item

weights are shown in Appendix 19.

Table 6-1.--Miscellaneous Expense Categories and Weights

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

Income level

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

Categories Lower Middle Upper

(percent) (percent) (percent)

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

Medical Care..................... 40.74 30.79 23.66

Private Education (K-12)......... .87 1.23 1.48

Contributions.................... 16.07 16.56 16.91

Personal Insurance and Retirement

Contributions................... 42.31 51.42 57.95

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

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

OPM 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

--Federal health insurance

In addition, OPM surveyed the price of hospital attendant services

and air ambulance insurance on a test basis in each area. OPM found

that hospital attendant services were only available in Puerto Rico,

where hospital services are significantly different from those in the

Washington, DC, area. Therefore, OPM added the price of daily hospital

attendant service to that of a hospital room in Puerto Rico. Air

ambulance insurance was found to be available only in the Virgin

Islands, where on-island hospital services are limited. Therefore, OPM

added the price of air ambulance insurance to the price of health

insurance in the Virgin Islands.

To address comments OPM had received on previous surveys and to

allow the use of air ambulance insurance in this fashion, OPM dropped

the constant $100 that had been used for health insurance in previous

surveys.\6\ Instead, OPM used Federal employee health benefit

enrollment information from OPM's Central Personnel Data File along

with Federal health benefit premiums to compute average health benefit

expense by areas. These expenses varied by area, and OPM used these

averages rather than assuming that costs were constant among areas.

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

\6\In previous surveys, it had been assumed that the cost of

health insurance was constant among areas because the choice of

Federal health coverage was considered to be, by and large, a matter

of personal preference. Therefore, in those surveys, the index for

this item was 100.00.

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

OPM surveyed the cost of the health care 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.

6.3.2 Private Education (K-12) Category

Private education (K-12) was added this year at the recommendation

of the Puerto Rico COLA Partnership Committee. Since not everyone sends

their children to private school, OPM derived use factors from the

results of the 1992/93 Federal Employee Housing and Living Patterns

Survey. The following table shows these factors and the resulting

adjustment of price indexes by area. The factors reflect the relative

extent to which Federal employees make use of private education in the

COLA areas compared with the Washington, DC, area. For example, the

table indicates a use factor of 4.1066 for Puerto Rico because about 54

percent of Federal employees with school age children there send at

least one child to private school compared with about 13 percent for

the DC area.

Table 6-2.--Summary of Private Education Use Factors and Indexes

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

Employees w/children in

private schools Price index

Location -------------------------- Use factor Price index w/use

Local area DC area factor

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

Anchorage...................................... 10.34 13.23 0.7816 55.53 43.40

Fairbanks...................................... 8.56 13.23 0.6470 41.59 26.91

Juneau......................................... 12.43 13.23 0.9395 57.30 53.84

[[Page 56449]]

Nome........................................... 8.08 13.23 0.6107 38.42 23.46

Honolulu....................................... 26.86 13.23 2.0302 113.03 229.48

Hilo*.......................................... 18.94 13.23 1.4316 44.23 63.32

Kona*.......................................... 18.94 13.23 1.4316 87.03 124.59

Kauai.......................................... 22.46 13.23 1.6977 95.72 162.50

Maui........................................... 20.39 13.23 1.5412 89.05 137.24

Guam........................................... 42.26 13.23 3.1943 90.95 290.52

Puerto Rico.................................... 54.33 13.23 4.1066 66.85 274.52

St. Croix...................................... 57.27 13.23 4.3288 90.26 390.72

St. Thomas..................................... 51.90 13.23 3.9229 95.78 375.74

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

*Use data available only for Hawaii County.

6.3.3 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.

6.3.4 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 20. 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 22 shows

how each index was derived from the component indexes.

Table 7-1.--Final Cost Comparison Indexes

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

Allowance area Index

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

Anchorage, Alaska............................................ 102.93

Fairbanks, Alaska............................................ 107.57

Juneau, Alaska............................................... 111.54

The rest of Alaska........................................... 126.64

City and County of Honolulu, Hawaii.......................... 126.78

Hawaii County, Hawaii........................................ 110.85

Kauai County, Hawaii......................................... 114.92

Maui County, Hawaii.......................................... 118.84

Guam/CNMI*, Local Retail..................................... 121.77

Guam/CNMI, Commissary/Exchange............................... 118.23

Puerto Rico.................................................. 105.42

U.S. Virgin Islands.......................................... 119.09

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

*CNMI=Commonwealth of the Northern Mariana Islands

Appendix 1.--Publication in the Federal Register of Results of

Nonforeign Area Living-Cost Surveys: 1990-1997

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

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.

[[Page 56450]]

62 FR 14190............... Office of Personnel Results of 1996

Management: Report living-cost surveys

on 1996 Surveys Used conducted in Alaska,

to Determine Cost-of- Hawaii, Guam, Puerto

Living Allowances in Rico, and the U.S.

Nonforeign Areas. Virgin Islands.

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

Appendix 2.--Multiple Survey Areas:1997 Survey

[Federal Employment Weights Within a Single Allowance Area]

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

Location 1994 1995 1996 Average Weights

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

Hawaii County

Hilo............................................ 310 304 308 307 75.99

Kona............................................ 99 97 96 97 24.01

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

Total....................................... .......... .......... .......... 404 100.00

===========================================================

Virgin Islands

St. Croix....................................... 151 154 166 157 48.76

St. Thomas/St. John............................. 166 160 170 165 51.24

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

Total....................................... .......... .......... .......... 322 100.00

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

Multiple Income Levels: 1997 Survey

[Federal Employment Weights Within a Single Allowance Area]

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

Location and income level 1994 1995 1996 Average Weights

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

Anchorage:

Lower........................................... 1,609 1,540 1,445 1,531 26.11

Middle.......................................... 1,971 1,754 1,719 1,815 30.95

Upper........................................... 2,583 2,522 2,448 2,518 42.94

Totals........................................ .......... .......... .......... 5,864 100.00

===========================================================

Fairbanks:

Lower........................................... 444 388 449 427 33.54

Middle.......................................... 442 446 456 448 35.19

Upper........................................... 392 405 397 398 31.26

Totals........................................ .......... .......... .......... 1,273 99.99

===========================================================

Juneau:

Lower........................................... 145 139 126 137 19.77

Middle.......................................... 220 203 199 207 29.87

Upper........................................... 360 341 346 349 50.36

Totals........................................ .......... .......... .......... 693 100.00

===========================================================

Rest of Alaska:

Lower........................................... 414 349 363 375 24.32

Middle.......................................... 722 703 687 704 45.65

Upper........................................... 445 481 462 463 30.03

Totals........................................ .......... .......... .......... 1,542 100.00

===========================================================

Honolulu:

Lower........................................... 4,239 4,140 4,453 4,277 33.20

Middle.......................................... 4,171 3,952 4,009 4,044 31.40

Upper........................................... 4,689 4,514 4,476 4,560 35.40

Totals........................................ .......... .......... .......... 12,881 100.00

===========================================================

Hawaii:

Lower........................................... 165 139 152 152 37.16

Middle.......................................... 154 164 163 160 39.12

Upper........................................... 91 98 101 97 23.72

Totals........................................ .......... .......... .......... 409 100.00

===========================================================

Kauai:

Lower........................................... 81 73 59 71 29.10

Middle.......................................... 84 76 80 80 32.79

Upper........................................... 89 97 92 93 38.11

Totals........................................ .......... .......... .......... 244 100.00

===========================================================

[[Page 56451]]

Maui:

Lower........................................... 39 35 35 36 24.66

Middle.......................................... 56 59 62 59 40.41

Upper........................................... 51 51 51 51 34.93

Totals........................................ .......... .......... .......... 146 100.00

===========================================================

Guam/CNMI:

Lower........................................... 1,060 947 873 960 46.00

Middle.......................................... 681 669 640 663 31.77

Upper........................................... 498 464 430 464 22.23

Totals........................................ .......... .......... .......... 2,087 100.00

===========================================================

Puerto Rico:

Lower........................................... 2,428 2,370 2,281 2,360 40.42

Middle.......................................... 2,184 2,166 2,177 2,176 37.27

Upper........................................... 1,321 1,303 1,286 1,303 22.32

Totals........................................ .......... .......... .......... 5,839 100.01

===========================================================

Virgin Islands:

Lower........................................... 114 98 123 112 34.67

Middle.......................................... 128 133 137 133 41.18

Upper........................................... 75 83 76 78 24.15

Totals........................................ .......... .......... .......... 323 100.00

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

Appendix 3--Consumer Expenditure Surveys

[Pre-published Data for All Consumer Units Nationwide*]

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

Total complete reporting

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

1992 1994 1995 Average

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

Average Before Tax Income....................... 33,854.00 36,838.00 36,948.00 35,880.00

Average annual expenditures..................... 30,527.49 32,762.99 33,610.38 32,300.29

Food.......................................... 4,358.56 4,526.94 4,690.51 4,525.34

Food at home................................ 2,684.35 2,764.21 2,885.98 2,778.18

Cereals and bakery products............... 418.15 439.36 454.64 437.38

Cereals and cereal products............. 144.15 166.94 169.16 160.08

Flour................................. 7.21 7.93 8.93 8.02

Prepared flour mixes.................. 13.62 13.20 13.29 13.37

Ready-to-eat and cooked cereals....... 88.39 102.02 99.83 96.75

Rice.................................. 12.67 15.47 19.43 15.86

Pasta, cornmeal and other cereal

products............................. 22.27 28.32 27.68 26.09

Bakery products......................... 274.00 272.42 285.49 277.30

Bread................................. 77.58 77.20 78.18 77.65

White bread......................... 38.04 38.02 38.37 38.14

Bread, other than white............. 39.54 39.17 39.81 39.51

Crackers and cookies.................. 67.10 64.36 70.09 67.18

Cookies............................. 40.75 43.78 46.76 43.76

Crackers............................ 26.34 20.58 23.33 23.42

Frozen and refrigerated bakery

products............................. 21.06 22.16 22.42 21.88

Other bakery products................. 108.27 108.70 114.79 110.59

Biscuits and rolls.................. 35.55 37.26 39.48 37.43

Cakes and cupcakes.................. 31.67 31.12 36.15 32.98

Bread and cracker products.......... 4.70 4.68 4.45 4.61

Sweetrolls, coffee cakes, doughnuts. 24.93 23.08 21.57 23.19

Pies, tarts, turnovers.............. 11.41 12.55 13.14 12.37

Meats, poultry, fish, and eggs............ 687.17 728.89 758.30 724.79

Beef.................................... 210.36 226.73 232.15 223.08

Ground beef........................... 87.67 89.79 87.81 88.42

Roast................................. 37.74 37.79 40.70 38.74

Chuck roast......................... 13.48 12.10 12.54 12.71

Round roast......................... 12.96 14.18 13.55 13.56

Other roast......................... 11.30 11.51 14.62 12.48

Steak................................. 69.00 85.81 87.57 80.79

Round steak......................... 14.63 16.44 18.92 16.66

Sirloin steak....................... 17.72 24.09 22.70 21.50

Other steak......................... 36.65 45.28 45.95 42.63

Other beef............................ 15.95 13.34 16.06 15.12

Pork.................................... 155.56 154.66 157.51 155.91

Bacon................................. 20.47 23.01 20.26 21.25

[[Page 56452]]

Pork chops............................ 34.88 37.47 39.03 37.13

Ham................................... 42.73 36.74 38.51 39.33

Ham, not canned..................... 38.98 33.91 36.23 36.37

Canned ham.......................... 3.75 2.84 2.28 2.96

Sausage............................... 23.29 22.63 21.35 22.42

Other pork............................ 34.19 34.80 38.36 35.78

Other meats............................. 94.58 94.34 105.31 98.08

Frankfurters.......................... 21.19 19.13 22.78 21.03

Lunch meats (cold cuts)............... 63.56 65.67 71.55 66.93

Bologna, liverwurst, salami......... 22.91 23.25 25.15 23.77

Other lunch meats................... 40.65 42.41 46.40 43.15

Lamb, organ meats and others.......... 9.84 9.54 10.98 10.12

Lamb and organ meats................ 8.74 9.31 8.92 8.99

Mutton, goat and game............... 1.10 0.24 2.06 1.13

Poultry................................. 123.39 135.32 136.43 131.71

Fresh and frozen chickens............. 91.28 107.49 105.79 101.52

Fresh whole chicken................. 19.61 NA NA NA

Fresh and frozen whole chicken...... NA 29.05 28.37 25.68

Fresh and frozen chicken parts...... 71.67 78.44 77.43 75.85

Other poultry, incl. whole frozen

chickens............................. 32.10 NA NA NA

Other poultry......................... NA 27.83 30.64 30.19

Fish and seafood........................ 74.99 87.13 95.34 85.82

Canned fish and seafood............... 17.46 15.60 17.95 17.00

Fresh and frozen shellfish............ 21.36 NA NA 21.36

Fresh and frozen finfish.............. 36.17 NA NA 36.17

Fresh fish and shellfish.............. NA 48.29 50.11 49.20

Frozen fish and shellfish............. NA 23.23 27.28 25.26

Eggs.................................... 28.30 30.72 31.55 30.19

Dairy products............................ 307.10 297.87 311.48 305.48

Fresh milk and cream.................... 136.59 131.98 129.41 132.66

Whole milk............................ 47.69 NA NA NA

Other milk and cream.................. 88.90 NA NA NA

Fresh milk, all types................. NA 123.44 119.84 121.64

Cream................................. NA 8.55 9.56 9.06

Other dairy products.................... 170.52 165.88 182.07 172.82

Butter................................ 9.71 11.78 13.03 11.51

Cheese................................ 87.72 84.78 93.13 88.54

Ice cream and related products........ 51.93 48.15 53.06 51.05

Miscellaneous dairy products.......... 21.16 21.17 22.85 21.73

Fruits and vegetables..................... 435.20 446.10 467.45 449.58

Fresh fruits............................ 129.17 135.12 148.22 137.50

Apples................................ 26.64 25.34 29.98 27.32

Bananas............................... 26.48 30.25 31.09 29.27

Oranges............................... 13.23 16.05 16.21 15.16

Other fresh fruits.................... 62.82 63.49 70.94 65.75

Fresh vegetables........................ 127.84 138.99 140.83 135.89

Potatoes.............................. 24.56 28.24 28.75 27.18

Lettuce............................... 16.33 17.65 18.31 17.43

Tomatoes.............................. 19.85 21.59 21.89 21.11

Other fresh vegetables................ 67.10 71.52 71.89 70.17

Processed fruits........................ 102.67 95.31 96.98 98.32

Frozen fruits and fruit juices........ 21.35 16.38 17.35 18.36

Frozen orange juice................. 13.34 9.57 9.19 10.70

Other frozen fruits and juices...... 8.01 6.81 8.15 7.66

Canned and dried fruits............... 23.48 21.11 20.11 21.57

Fresh, canned or bottled fruit juices. 57.83 57.83 59.52 58.39

Processed vegetables.................... 75.53 76.68 81.42 77.88

Frozen vegetables..................... 25.46 24.78 29.55 26.60

Canned and dried vegetables and juices 50.07 51.90 51.88 51.28

Canned beans........................ 10.09 10.61 11.26 10.65

Canned corn......................... 7.40 6.99 6.80 7.06

Other canned and dried veg. and

juices............................. 32.59 34.30 33.80 33.56

Other food at home........................ 836.73 851.99 894.10 860.94

Sugar and other sweets.................. 106.24 110.67 119.49 112.13

Candy and chewing gum................. 62.86 66.52 73.02 67.47

Sugar................................. 18.12 18.30 17.88 18.10

Artificial sweeteners................. 3.24 3.57 4.56 3.79

Jams, preserves, other sweets......... 22.02 22.28 24.02 22.77

Fats and oils........................... 73.79 80.76 83.63 79.39

[[Page 56453]]

Margarine............................. 14.56 14.68 13.13 14.12

Other fats, oils, and salad dressing.. 40.94 47.48 51.88 46.77

Nondairy cream and imitation milk..... 6.75 6.71 6.96 6.81

Peanut butter......................... 11.53 11.89 11.66 11.69

Miscellaneous foods..................... 393.26 369.77 394.39 385.81

Frozen prepared foods................. 73.99 65.79 69.94 69.91

Frozen meals........................ 22.99 20.54 21.71 21.75

Other frozen prepared foods......... 51.01 45.25 48.22 48.16

Canned and packaged soups............. 25.44 30.21 31.92 29.19

Potato chips, nuts, and other snacks.. 78.63 75.91 84.32 79.62

Potato chips and other snacks....... 62.34 59.81 65.63 62.59

Nuts................................ 16.29 16.10 18.69 17.03

Condiments and seasonings............. 90.44 82.47 89.18 87.36

Salt, spices, other seasonings...... 20.79 19.68 20.55 20.34

Olives, pickles, relishes........... 10.82 10.76 10.13 10.57

Sauces and gravies.................. 43.55 38.05 41.78 41.13

Baking needs and misc. products..... 15.29 13.98 16.71 15.33

Other canned and packaged prepared

foods................................ 124.75 115.39 119.03 119.72

Salads and desserts................. 20.42 19.30 23.19 20.97

Baby food........................... 24.11 27.68 25.42 25.74

Miscellaneous prepared foods........ 80.22 68.41 70.42 73.02

Nonalcoholic beverages.................. 219.33 241.81 250.31 237.15

Cola.................................. 86.71 93.27 94.76 91.58

Other carbonated drinks............... 40.41 40.20 43.28 41.30

Coffee................................ 40.13 43.29 47.76 43.73

Roasted coffee...................... 24.56 29.20 32.11 28.62

Instant and freeze dried coffee..... 15.57 14.09 15.65 15.10

Noncarbonated fruit flavored drinks... 20.15 NA NA NA

Noncarbonated fruit flavored drinks,

inc. non-frozen lemonade............. NA 23.02 25.18 22.78

Tea................................... 14.26 16.75 16.01 15.67

Nonalcoholic beer.................... NA 0.76 1.17 0.97

Other nonalcoholic beverages.......... 17.68 24.52 22.13 21.44

Food prepared by consumer unit on out-of-

town trips............................. 44.12 48.98 46.29 46.46

Food away from home......................... 1,674.21 1,762.72 1,804.53 1,747.15

Meals at restaurants, carry-outs and other 1,344.40 1,363.26 1,426.22 1,377.96

Lunch................................... 476.89 475.88 499.50 484.09

Dinner.................................. 619.67 668.88 691.44 660.00

Snacks and nonalcoholic beverages....... 141.35 110.46 126.30 126.04

Breakfast and brunch.................... 106.49 108.05 108.98 107.84

Board (including at school)............... 46.92 50.40 58.40 51.91

Catered affairs........................... 40.77 55.38 37.05 44.40

Food on out-of-town trips................. 167.14 213.45 204.85 195.15

School lunches............................ 47.40 54.93 49.47 50.60

Meals as pay.............................. 27.58 25.30 28.53 27.14

Alcoholic beverages........................... 321.12 296.57 301.83 306.51

At home..................................... 177.01 175.40 179.33 177.25

Beer and ale.............................. 99.54 108.74 94.20 100.83

Whiskey................................... 14.23 14.25 12.83 13.77

Wine...................................... 43.11 36.06 54.77 44.65

Other alcoholic beverages................. 20.13 16.36 17.53 18.01

Away from home.............................. 144.11 121.17 122.51 129.26

Beer and ale.............................. 48.77 42.50 36.61 42.63

Wine...................................... 22.95 16.74 22.55 20.75

Other alcoholic beverages................. 47.06 30.22 33.33 36.87

Alcoholic beverages purchased on trips.... 25.34 31.71 30.02 29.02

Housing....................................... 9,528.41 10,189.41 10,576.98 10,098.27

Shelter..................................... 5,431.78 5,695.83 5,912.61 5,680.07

Owned dwellings........................... 3,307.24 3,464.04 3,750.08 3,507.12

Mortgage interest and charges........... 1,984.40 1,925.26 2,120.77 2,010.14

Mortgage interest..................... 1,856.78 1,825.30 1,997.99 1,893.36

Interest paid, home equity loan....... 63.99 44.67 56.26 54.97

Interest paid, home equity line of

credit............................... 63.32 54.73 66.06 61.37

Prepayment penalty charges............ 0.31 0.56 0.46 0.44

Property taxes.......................... 760.97 879.41 909.28 849.89

Maintenance, repairs, insurance, other

expenses............................... 561.86 659.37 720.02 647.08

Homeowners and related insurance...... 176.37 209.07 224.86 203.43

Fire and extended coverage.......... 5.02 6.34 7.31 6.22

Homeowners insurance................ 171.35 202.73 217.55 197.21

Ground rent........................... 33.40 40.26 33.61 35.76

[[Page 56454]]

Maintenance and repair services....... 268.09 312.65 366.16 315.63

Painting and papering............... 37.27 43.27 38.26 39.60

Plumbing and water heating.......... 34.02 36.45 32.01 34.16

Heat, a/c, electrical work.......... 53.14 55.08 75.83 61.35

Roofing and gutters................. 40.98 48.91 66.13 52.01

Other repair and maintenance

services (old)..................... 91.16 NA NA NA

Other repair and maintenance

services........................... NA 112.39 136.51 113.35

Repair and replacement of hard

surface flooring................... 10.16 14.76 15.56 13.49

Repair of built-in appliances....... 1.36 1.78 1.86

This text is long and has been trimmed here. Open the source document for the complete record.