Adjusting Federal Benefits for Geographic Differences in the Cost of Living

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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.

Measuring Geographic Differences in the Cost of Living........................................................2
Policy Considerations................................................................................................................4
Table 1. Relative Cost of Living by State, First Quarter of 2007....................................................3
Author Contact Information............................................................................................................5

y 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 B

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 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 ¹
and salaries.
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

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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.
The federal government does not currently publish any statistics allowing comparison of ²
differences in the overall cost of living in different areas of the country. 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 ³
urban areas across the country.
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 ⁴
quintile.” 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 ⁵
selection of the house to be priced in each area.
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 ⁶
the states. 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.

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Table 1. Relative Cost of Living by State, First Quarter of 2007
^{State Rank}^a^{Cost of}^{Living Index}^{State Rank}^a^{Cost of}^{Living Index}
^{Alabama 11}^91.9^Missouri⁷^90.8
^{Alaska 46}^129.⁰^Montana²⁹^101.⁵
^{Arizona 34}^104.⁷^Nebras^ka⁵^90.5
^{Arkansas 4}^90.1^Nevada³⁷^107.⁷
^{California 49}^138.⁹^New^Jers^ey⁴⁷^130.⁰
^Colorad^{o 30}^101.⁶^New^Me^xico²⁷^100.⁰
^{Connecticut 42}^123.¹^New^York⁴⁴^125.⁴
^{Delaware 36}^105.³^North^Carolina¹⁷^94.3
^{District of Columbia}⁴⁸^136.⁹^{North Dakota}¹⁵^93.7
^{Florida 35}^105.³^Ohio¹⁶^94.0
^{Georgia 9}^91.6^Oklahoma¹^89.4
^{Hawaii 50}^165.³^Oregon³⁹^111.⁷
^{Idaho 21}^95.4^Pennsylvania³¹^101.⁸
^{Illinois 23}^97.2^Rhode^Island⁴³^124.¹
^{Indiana 12}^92.9^South^Carolina¹³^93.2
^{Iowa 14}^93.3^South^Dakota⁶^90.7
^{Kansas 8}^91.1^Tennessee³^89.6
^{Kentucky 20}^95.2^Texas²^89.6
^{Louisiana 19}^94.7^Utah²⁴^98.6
^{Maine 38}^109.³^Vermont⁴¹^117.⁴
^{Maryland 45}^126.⁴^Virginia³²^103.⁹
^Massach^{usetts 40}^116.³^Washington³³^104.⁶
^{Michigan 26}^99.3^West^Virginia²²^96.5
^{Minnesota 25}^99.1^Wisconsin¹⁸^94.4
^{Mississippi 10}^91.9^Wyoming²⁸^101.⁴
^Source:^Missouri^{Economic Research a}^{nd In}^{formation Center.}
^Note:^{New Hampshire}^{is not re}^prese^{nted in this s}^urvey.
^a.¹^st^{is the least e}^xpe^{nsive, 5}⁰^th^{is t}^{he most ex}^pen^sive.
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 ⁷
attributable to differences in the cost of housing. The variance in the housing component of the

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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.
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.
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 ⁸
based on prices.
^B^r^ian^W^.^Cash^ell
^Specialis^{t i}ⁿ^Macroecon^o^mⁱ^c^P^o^licy
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