Adjusting Federal Benefits for Geographic Differences in the Cost of Living
Order Code RS20942
Updated August 20, 2007
Adjusting Federal Benefits for Geographic
Differences in the Cost of Living
Brian W. Cashell
Specialist in Macroeconomic Policy
Government and Finance Division
Summary
By indexing various benefits and transfer payments to the consumer price index
(CPI), policymakers intended that the real value, or purchasing power, of those
payments not be eroded by increases in the general level of prices. Although such
indexing provisions may compensate for changing economic conditions over time, there
is no allowance for substantial geographic differences in the cost of living. A separate
CPI is published for each of a number of metropolitan areas, but those figures allow only
a comparison of inflation rates experienced by residents of those areas. The CPI does
not allow interarea cost of living comparisons. The federal government does not
currently publish any statistics allowing comparison of differences in the overall cost of
living in different areas of the country. The most widely used data to estimate
geographic cost of living differences are the ACCRA indexes, published by the Council
for Community and Economic Research. An examination of ACCRA data for the first
quarter of 2007 reveals considerable variation in the cost of living across different areas
of the country. The greatest portion of the variation from place to place in the cost of
living was attributable to differences in the cost of housing. This report will not be
updated.
By indexing various benefits and transfer payments to the consumer price index
(CPI), policymakers intended that the real value, or purchasing power, of those payments
not be eroded by increases in the general level of prices. Poverty thresholds are also
updated annually based on changes in the CPI. But while such indexing provisions
compensate for changing economic conditions over time, there is no allowance for
substantial differences in the cost of living, at any given time, depending on geographic
location. Simply put, some places are more expensive than others in which to live.
Some measure of the geographic variation in the cost of living might be useful for
a number of reasons. It would make possible the adjustment of a variety of income
support payments for local living costs. It would also make possible an adjustment to
CRS-2
existing measures of income to provide a clearer view of variations in living standards
across the country.
The federal government produces several different measures of change in the cost
of living. The CPI measures change in the prices paid for goods and services by
households. There are a number of price indexes associated with the goods and services
that make up gross domestic product (GDP), and the producer price index measures price
change at the wholesale level and for raw materials. Although each of these indicators
measures changes in prices over time, they are useless when it comes to measuring
variations in the cost of living in different parts of the country at any one time.
A separate CPI is published for each of a number of metropolitan areas, but those
figures only allow a comparison of inflation rates experienced by residents of those areas.
For each of the individual city price indexes the value of the index is set equal to 100 for
the years 1982 to 1984 (the base period). Thus, even if the cost of living in one city is
substantially higher than it is in another, the index numbers for those two cities are equal
in the base year. Subsequent index numbers can only indicate if there is any difference
in the inflation rates experienced by residents of those cities. The CPI does not allow
interarea cost-of-living comparisons.
The federal government makes annual adjustments to white-collar pay, and a
locality-based comparability payment. Although the locality-based payment is sometimes
referred to as a cost-of-living adjustment, it is not based on any measure of the cost of
living. Rather, it is based on the Employment Cost Index (ECI), which is a measure of
the rate of change in private sector wages and salaries.1
From a theoretical standpoint, goods that are sold nationwide and are easily
transported might be expected to exhibit little geographic variation in price. The main
reason for prices to vary for such goods would be transportation costs, and variations in
the rents and salaries paid by the stores in which they are sold. Otherwise there would
be a tendency for the prices of those goods to converge. If there were a premium for a
given good in one area of the country, there would be an incentive to producers of that
good to make more of it available in that area, either through increased production or a
redistribution of current production. Any increase in the supply of that good in an area
where there is a premium would tend to reduce that premium, and bring the good’s price
closer in line with its price elsewhere.
In contrast, geographic differences in the cost of land are likely to persist, since the
supply of land in a given area is fixed. The local supply of land may vary somewhat, in
a sense, due to changes in zoning for example, but ultimately land price increases cannot
induce an increase in its supply. Differences in land values, and thus rents, might
therefore be expected to account for a significant share of any geographic variation in the
cost of living. Variation in rents may affect relative living costs directly through its effect
on housing costs, or indirectly through its effect on the costs of doing business.
1 See CRS Report RL33732, Federal White-Collar Pay: FY2008 Salary Adjustments, by Barbara
L. Schwemle.
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Measuring Geographic Differences in the Cost of Living
The federal government does not currently publish any statistics allowing
comparison of differences in the overall cost of living in different areas of the country.2
But there are some regularly available data from a private source which can be useful in
getting an idea how much the cost of living varies across the country.
Perhaps the most widely used data are the ACCRA indexes (formerly the American
Chamber of Commerce Researchers Association). These are published by the Council
for Community and Economic Research (C2ER). The data in the ACCRA cost-of-living
index were originally collected by participating chambers of commerce, but may be
collected by any organization. The data are put in the form of a set of index numbers that
compare the cost of living in more than 300 urban areas across the country.3
The ACCRA data are intended to measure the relative cost, in different areas of the
country, of the standard of living “appropriate for professional and managerial households
in the top income quintile.”4 The actual market basket is smaller than the one for which
BLS collects price data to calculate the CPI, and many of the items priced tend to be name
brands sold at large chain stores. For the goods and services priced in the ACCRA index
considerable effort is expended to maintain consistency in the market basket.
Considerable detail, for example, goes into the selection of the house to be priced in each
area.5
The Missouri Economic Research and Information Center (MERIC) has taken the
ACCRA index numbers for the first quarter of 2007, and averaged the data for all of the
cities represented in each state. An examination of those data reveals considerable
variation in the cost of living across the states.6 According to the MERIC indexes, Hawaii
was the most expensive state at 165.3% of the national average, and Oklahoma was the
least expensive state at 89.4% of the national average. Table 1 presents the aggregate
MERIC cost-of-living indexes as well as the ranking for each state. For each state, the
index is relative to the national average, which equals 100.
2 There has been some work done by BLS towards the development of an interarea cost-of-living
index using data collected for the CPI. See Bettina H. Aten, “Interarea Price Levels: An
Experimental Methodology,” Monthly Labor Review, September 2006, pp. 47-61.
3 Only cities with populations larger than 50,000 are eligible to participate in the ACCRA index
program. Rather than surveying what households purchase in each area, the ACCRA index tracks
the prices of goods that are representative of larger categories. Those prices are then aggregated
using weights based on the Survey of Consumer Expenditures, published by BLS. The effects
of taxes are not included.
4 From the C2ER website [http://www.c2er.org].
5 The house selected is supposed to be one convenient to schools and shopping, with three
bedrooms and roughly 2,400 square feet of living space and utilities typical of similar houses in
the area. In contrast to the CPI, the cost of housing is based on the cost of home purchase and
thus is affected by the level of interest rates.
6 New Hampshire is not represented in the first quarter 2007 survey.
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Table 1. Relative Cost of Living by State, First Quarter of 2007
State
Ranka
Cost of
State
Ranka
Cost of
Living Index
Living Index
Alabama
11
91.9
Missouri
7
90.8
Alaska
46
129.0
Montana
29
101.5
Arizona
34
104.7
Nebraska
5
90.5
Arkansas
4
90.1
Nevada
37
107.7
California
49
138.9
New Jersey
47
130.0
Colorado
30
101.6
New Mexico
27
100.0
Connecticut
42
123.1
New York
44
125.4
Delaware
36
105.3
North Carolina
17
94.3
District of Columbia
48
136.9
North Dakota
15
93.7
Florida
35
105.3
Ohio
16
94.0
Georgia
9
91.6
Oklahoma
1
89.4
Hawaii
50
165.3
Oregon
39
111.7
Idaho
21
95.4
Pennsylvania
31
101.8
Illinois
23
97.2
Rhode Island
43
124.1
Indiana
12
92.9
South Carolina
13
93.2
Iowa
14
93.3
South Dakota
6
90.7
Kansas
8
91.1
Tennessee
3
89.6
Kentucky
20
95.2
Texas
2
89.6
Louisiana
19
94.7
Utah
24
98.6
Maine
38
109.3
Vermont
41
117.4
Maryland
45
126.4
Virginia
32
103.9
Massachusetts
40
116.3
Washington
33
104.6
Michigan
26
99.3
West Virginia
22
96.5
Minnesota
25
99.1
Wisconsin
18
94.4
Mississippi
10
91.9
Wyoming
28
101.4
Source: Missouri Economic Research and Information Center.
Note: New Hampshire is not represented in this survey.
a. 1st is the least expensive, 50th is the most expensive.
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Analysis of the various component indexes allows for some interesting
observations. As was expected, the greatest portion of the variation from place to place
in the cost of living was attributable to differences in the cost of housing.7 The variance
in the housing component of the cost-of-living index was more than five times that of the
overall index. In contrast, health care costs had only about one-third of the variation of
the overall cost of living.
Policy Considerations
Data showing geographic differences in the cost of living are limited. If
policymakers want to make adjustments to income support payments, or the official
poverty thresholds, there is currently no official measure on which to base them. Those
data that are available are from private sources, and there is no way to ensure their
consistency or continued availability. Even the ACCRA indexes may not be ideal for
adjusting income support payments, because they are limited to urban areas and only
track the cost of living for fairly well-to-do households. Moreover, participation in the
survey is entirely voluntary, and there is no guarantee that an area that is currently part
of the survey will continue to be.
The areas for which data are collected for the ACCRA indexes were not selected to
be representative of the overall distribution of the population. If the purpose of an official
interarea cost-of-living measure were to adjust benefits for those who are relatively less
well off, it would be important for it to reflect the areas in which that population tended
to live. Whether there is as much geographic variation in the cost of living of that
population as there is in the ACCRA data is an open question.
The ACCRA indexes also show that the variation in cost depends on what goods and
services are under consideration. There appears to be much less variation in medical care
costs than there is in housing costs, for example. If that is true, there might be a less
compelling case for geographic adjustments to medical care subsidies than for housing
subsidies.
It may be that a measure could be constructed making use of data already collected
for the calculation of the CPI. But like the ACCRA index, CPI data are only collected
in urban areas. Although there is considerable variance in the cost of living across urban
areas, there may also be substantial differences between urban and rural areas, and among
rural areas.
There may be considerable practical obstacles to any effort to adjust benefits
automatically to compensate for variations in the cost of living. An important question
is how would the different areas be defined? It is conceivable that such small areas
would be needed to account for all the significant differences in living costs, that the data
collection required would be immense.
7 Variance is a statistic which measures how much a set of numbers deviates from its mean. It
is not expressed in any particular unit. The variance of a set of numbers is calculated by
summing the squares of the differences between each observation and the mean of the entire set
of numbers.
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One way of reducing the cost of constructing an interarea cost-of-living index might
be to limit the range of items that are included. The income level that defines the poverty
population was based entirely on food prices. An interarea measure that focused on just
the price of food and housing, for example, would be much less complicated than one that
reflected all the items that currently make up the marketbasket for the CPI.
A possible alternative to the use of a measure that tracked price differences in
different areas might be a measure based on income differences. The most important
reason for geographic differences in the cost of living is variation in the cost of housing.
Housing costs are considerably influenced by income and so a measure based on income
might be a reasonable substitute for one based on prices.8
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8 The Census Bureau publishes estimates of median household income by state, see the U.S.
Census Bureau’s website at [http://www.census.gov/hhes/www/income/statemedfaminc.html].
The Bureau of Economic Analysis publishes regular estimates of personal income by
metropolitan area, state, and county, see the Bureau of Economic Analysis’s website at
[http://www.bea.gov/regional/index.htm].