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ADJUSTING POVERTY THRESHOLDS 194 different results, but researchers have also estimated differing index values for the same areas even when using similar methods and data (e.g., compare Blackley, Follain, and Lee, 1986, and Thibodeau, 1989). The work at BLS to extend and improve the hedonic methodology so that the results are more stable with respect to such factors as the choice of reference area or independent variables is very promising, but this effort is still developmental. Moreover, data problems remain: the data source with the largest sample size and coverage (the decennial census) has limited information on housing characteristics, while other data sources that are richer in content (the CPI database and the American Housing Survey) are smaller in size and restricted in the areas they cover.11 Yet despite all the methodological problems and uncertainties, it is clear that the cost of housing differs across geographic location. For example, HUD fair market rents differ significantly across areas even when they are adjusted for the median income of the area. Overall, we believe the findings support the importance of an adjustment of the poverty thresholds for geographic variations in housing costs. Furthermore, despite the problems and uncertainties, the literature helps indicate the size of geographic area for which an adjustment would be feasible and appropriate. Data are not available with which to develop housing cost indexes for every city and town in the United States, but an adjustment for areas classified by population size within region would accord with findings that intraregional differences are highly correlated with population: larger cities or metropolitan areas within a region are more expensive than smaller areas. This pattern is evident in the results from Kokoski, Cardiff, and Moulton (1992, 1994), and in other studies as well (e.g., Thibodeau, 1989); Ruggles (1990) recommends an adjustment of this type. Recommended Approach At the current state of knowledge, we conclude that a feasible way to move toward a comprehensive interarea price index with which to adjust the poverty thresholds is first to develop an interarea price index for shelter. Not only are housing costs a large component of a poverty budget, but housing cost 11 The national component of the American Housing Survey is conducted every two years and currently includes about 57,000 housing units; the sample is designed to produce national estimates, and the geographic identification made available to users is limited to four regions and central city-suburb and urban-rural classifications. The metropolitan component currently includes samples of about 5,000 housing units in each of 44 metropolitan areas; 11 areas are surveyed each year on a rotating cycle. The CPI database (described above) obtains price data for about 85 areas, most of which are combined for publication into size classes within each of four regions.
ADJUSTING POVERTY THRESHOLDS 195 variations are also significant across areas, and there are data and methods available with which to develop a reasonable index. Such an index should take account of differences by region and size of place. For constructing housing cost index values for the purpose of adjusting the poverty thresholds for all families, not just urban families or families in selected areas, we conclude that it is almost a necessity to turn to the decennial census, despite its limited data content. Given a decision to use census data, the HUD methodology for developing fair market rents has appeal. This methodology is subject to criticism because of its use of a limited number of characteristics to define a ''standard" rental apartment unit for comparing rental costs across areas. But until more sophisticated methods are fully developed and, more important, improvements effected in the underlying database with which to apply these methods, the HUD methodology appears to offer a reasonable alternative that is easy to understand and straightforward to implement. We implemented a modified version of the HUD approach with 1990 census data to determine whether we could develop interarea housing cost index values that accorded reasonably well with major findings in the literature.12 We obtained a copy of an extract of 1990 census data for every U.S. county (originally prepared for HUD). This extract provided the distribution of rents for two-bedroom apartments that had complete plumbing facilities, kitchen facilities, and electricity and in which the occupant had moved in within the last 5 years. (Units for which no cash rent was paid or for which the rent covered one or more meals were excluded.) Using these data, we first produced index values (relative to 1.0 for the nation as a whole) for each of the 341 metropolitan areas in the country and for nonmetropolitan areas within each state. Compared to the 32 metropolitan areas for which Kokoski, Cardiff, and Moulton (1992) also computed index values by using hedonic techniques with the CPI database, our index showed similar patterns, although less variation. For these 32 areas, our index values ranged from 1.67 to 0.88; the Kokoski, Cardiff, and Moulton values ranged from 1.83 to 0.69.13 The rank-order correlation of our index values with those of Kokoski, Cardiff, and Moulton is very high (.897 computed using Spearman's r). We next grouped the metropolitan areas into six population size categories within each of the nine census regions (divisions), aggregated the nonmetropolitan areas by region, and recomputed the index values. Following 12 The modification was that, for reasons of feasibility and consistency of estimates across the nation, we used decennial census data exclusively rather than a combination of census, AHS, and random digit dialing survey data. 13 One reason for the difference may be that our index values included utilities, which Kokoski, Cardiff, and Moulton found in a separate analysis varied somewhat less than shelter costs per se.
ADJUSTING POVERTY THRESHOLDS 196 TABLE 3-6 Cost-of-Housing Index Values (Relative to 1.00 for the United States as a Whole) by Region (Census Division) and Size of Metropolitan Area Region and Population Size Index Value New England (Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont) Nonmetropolitan areas 1.062 Metropolitan areas under 250,000 1.368 Metropolitan areas 250,000â500,000 1.290 Metropolitan areas 500,000â1,000,000 1.335 Metropolitan areas 1,000,000â2,500,000 1.321 Metropolitan areas 2,500,000 or more 1.475 Middle Atlantic (New Jersey, New York, Pennsylvania) Nonmetropolitan areas 0.797 Metropolitan areas under 250,000 0.771 Metropolitan areas 250,000â500,000 0.992 Metropolitan areas 500,000â1,000,000 1.045 Metropolitan areas 1,000,000â2,500,000 0.943 Metropolitan areas 2,500,000 or more 1.424 East North Central (Illinois, Indiana, Michigan, Ohio, Wisconsin) Nonmetropolitan areas 0.713 Metropolitan areas under 250,000 0.864 Metropolitan areas 250,000â500,000 0.906 Metropolitan areas 500,000â1,000,000 0.969 Metropolitan areas 1,000,000â2,500,000 0.988 Metropolitan areas 2,500,000 or more 1.133 West North Central (Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, South Dakota) Nonmetropolitan areas 0.630 Metropolitan areas under 250,000 0.817 Metropolitan areas 250,000â500,000 0.913 Metropolitan areas 500,000â1,000,000 0.956 Metropolitan areas 1,000,000â2,500,000 1.063 Metropolitan areas 2,500,000 or more N.A. South Atlantic (Delaware, District of Columbia, Florida, Georgia, Maryland, North Carolina, South Carolina, Virginia, West Virginia) Nonmetropolitan areas 0.713 Metropolitan areas under 250,000 0.873 Metropolitan areas 250,000â500,000 0.911 Metropolitan areas 500,000â1,000,000 1.016 Metropolitan areas 1,000,000â2,500,000 1.097 Metropolitan areas 2,500,000 or more 1.270 East South Central (Alabama, Kentucky, Mississippi, Tennessee) Nonmetropolitan areas 0.564 Metropolitan areas under 250,000 0.757 Metropolitan areas 250,000â500,000 0.852 Metropolitan areas 500,000â1,000,000 0.878
ADJUSTING POVERTY THRESHOLDS 197 Region and Population Size Index Value East South Centralâcontinued Metropolitan areas 1,000,000â2,500,000 N.A. Metropolitan areas 2,500,000 or more N.A. West South Central (Arkansas, Louisiana, Oklahoma, Texas) Nonmetropolitan areas 0.617 Metropolitan areas under 250,000 0.780 Metropolitan areas 250,000â500,000 0.797 Metropolitan areas 500,000â1,000,000 0.868 Metropolitan areas 1,000,000â2,500,000 0.914 Metropolitan areas 2,500,000 or more 1.011 Mountain (Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, Wyoming) Nonmetropolitan areas 0.713 Metropolitan areas under 250,000 0.841 Metropolitan areas 250,000â500,000 0.946 Metropolitan areas 500,000â1,000,000 1.090 Metropolitan areas 1,000,000â2,500,000 1.006 Metropolitan areas 2,500,000 or more N.A. Pacific (Alaska, California, Hawaii, Oregon, Washington) Nonmetropolitan areas 0.891 Metropolitan areas under 250,000 0.978 Metropolitan areas 250,000â500,000 1.041 Metropolitan areas 500,000â1,000,000 1.063 Metropolitan areas 1,000,000â2,500,000 1.236 Metropolitan areas 2,500,000 or more 1.492 Low index value 0.564 Median index value 0.951 High index value 1.492 NOTE: Housing cost indexes calculated from 1990 census data on gross rent for two-bedroom apartments with specified characteristics; index values drawn from the 45th percentile of the gross rent distribution (see text). N.A., Not applicable: no such areas in the region. the HUD approach, the index values were based on the cost of housing at the 45th percentile of the value of the distribution for each area. The results of our calculations produced the expected findings of higher index values in the Northeast and West and higher index values for larger relative to smaller areas; see Table 3-6. We further adjusted these index values for the estimated fraction of the poverty budget accounted for by housing (including utilities), which we set at 44 percent. In effect, we produced a fixed-weight interarea price index with two componentsâhousing and all other goods and servicesâin which the
ADJUSTING POVERTY THRESHOLDS 198 price of other goods and services is assumed not to vary.14 This adjustment narrowed the range of index values (and, hence, the range of poverty thresholds: for example, the adjusted index value for metropolitan areas with 2,500,000 or more population in New England dropped from 1.475 to 1.209; conversely, the adjusted index value for metropolitan areas with 250,000-500,000 population in the West South Central division rose from 0.797 to 0.911. Finally, we collapsed the index values for geographic areas smaller than 250,000 population because of restrictions on area identification in the surveys that are available for estimating poverty rates (the Current Population Survey and the Survey of Income and Program Participation). The final set of 41 index values that we used for our analysis of the likely effects of implementing our proposed poverty measure is provided in Table 5-3 in Chapter 5.15 Before deciding on a set of index values by metropolitan area size category within region, we looked at index values produced in the same manner for each of the 50 states and the District of Columbia. There has been interest expressed in adjusting the poverty thresholds for state cost-of-living differences for such purposes as allocating funds to disadvantaged school districts under the Elementary and Secondary Education Act. To compare the set of state index values and our proposed set, we assumed that the index values we originally calculated for each of the 341 individual metropolitan areas and for the nonmetropolitan components of each state were the "truth."16 We then determined what fraction of the population would be misclassifiedârelative to the individual metropolitan and nonmetropolitan area index valuesâby using a single index value for the nation as a whole or separate index values for the nine regions (divisions), for states, and for the proposed classification by metropolitan area population size category within region.17 We found that the use of the national index value of 1.0 (i.e., not adjusting 14 The estimate of 44 percent comes from CEX tabulations of expenditures of two-adult/ two-child families. We looked at families spending at the 35th percentile of the distribution on food, housing, and clothing, determined the share of housing of that total, and converted that share to a fraction of the total poverty budget, including food, housing, and clothing times a multiplier of 1.15. Clearly, one could derive somewhat different values of the fraction of housing in the budget, depending on the percentile or multiplier chosen. 15 The figure of 41 index values represents nine regions (census divisions) by five size classes of metropolitan areas, minus four categories that have zero population: the West North Central, East South Central, and Mountain divisions lack any metropolitan areas larger than 2,500,000 population, and the East South Central division lacks any metropolitan areas of 1,000,000 to 2,500,000 population. 16 In practice, however, we do not believe that it makes sense to develop such a large number of separate indexes for adjusting the poverty thresholds for several reasons: one is that there is a problem of small sample size for rental units with the specified characteristics in smaller metropolitan areas. 17 The analysis was carried out using index values for the population size categories shown in Table 3-6 before any collapsing.