3
Adjusting PovertyThresholds

The previous chapter focused on the derivation of a poverty threshold for a reference family of two adults and two children. A poverty threshold that is appropriate for this type of family, however, may not be appropriate for another type of family: a single person obviously needs less money than a family of four, and a family of eight needs more money. These differences are recognized in the current poverty measure, which uses different thresholds for different family types. And even for a given family type, the amount of money needed to stay above the poverty threshold will likely be different in a large city than in a small town, and it may also differ by region of the country. There is therefore an argument for adjusting the thresholds, not only for family size, but also for place of residence. This kind of adjustment is not made in the official poverty thresholds. In this chapter, we consider these adjustments and present our recommended procedures for adjusting the reference family threshold. We first discuss adjustments by family type and then by geographic area of residence.

ADJUSTMENTS BY FAMILY TYPE

The Concept of an Equivalence Scale

Equivalence scales are measures of the relative costs of living of families of different sizes and compositions that are otherwise similar. For example, if a family of two adults can live as well as a family of two adults and two children while spending only two-thirds as much, then relative to the reference family of two adults and two children, the equivalence scale value for a two-adult family is two-thirds. For the purpose of poverty measurement, the use of an



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Measuring Poverty: A New Approach 3 Adjusting PovertyThresholds The previous chapter focused on the derivation of a poverty threshold for a reference family of two adults and two children. A poverty threshold that is appropriate for this type of family, however, may not be appropriate for another type of family: a single person obviously needs less money than a family of four, and a family of eight needs more money. These differences are recognized in the current poverty measure, which uses different thresholds for different family types. And even for a given family type, the amount of money needed to stay above the poverty threshold will likely be different in a large city than in a small town, and it may also differ by region of the country. There is therefore an argument for adjusting the thresholds, not only for family size, but also for place of residence. This kind of adjustment is not made in the official poverty thresholds. In this chapter, we consider these adjustments and present our recommended procedures for adjusting the reference family threshold. We first discuss adjustments by family type and then by geographic area of residence. ADJUSTMENTS BY FAMILY TYPE The Concept of an Equivalence Scale Equivalence scales are measures of the relative costs of living of families of different sizes and compositions that are otherwise similar. For example, if a family of two adults can live as well as a family of two adults and two children while spending only two-thirds as much, then relative to the reference family of two adults and two children, the equivalence scale value for a two-adult family is two-thirds. For the purpose of poverty measurement, the use of an

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Measuring Poverty: A New Approach equivalence scale is to scale up or down the threshold for the reference family to provide corresponding thresholds for other family types. The concept underlying such a scale appears straightforward and is similar in spirit to a standard cost-of-living index number. If it costs twice as much at one time to maintain a given standard of living as it did at an earlier date, then one needs twice as much money to reach the equivalent standard of living. The idea of an equivalence scale is the same, but instead of comparing two different sets of prices, one compares two different family types. In spite of this apparent simplicity, a precise characterization of equivalence scales is elusive, and the many scales proposed in the literature differ not only by the usual margin of empirical uncertainty, but also in their underlying conception: different authors are not always measuring the same thing. As a result, it is possible to find a wide range of scales, which have very different implications for the total number of people in poverty as well as for the distribution of poverty among families of different types. Depending on the scale used, the poverty rate can be substantially higher or lower, and the demographic composition of those considered poor can change dramatically. Overview and Recommendation One simple method of adjusting the reference family threshold by family type is to scale it in proportion to the number of people in a family. In the language of ''equivalence scales," a single person would need one-quarter as much as a family of four, a married couple without children one-half as much as a family of four, and a family of eight twice as much as a family of four. Most people, including the members of the panel, regard this as an extreme position, since it makes no allowance for the fact that children are different from adults, nor for the economies of scale possible for larger families by sharing kitchens, bathrooms, and bedrooms or by buying products in bulk. This straight proportion rule clearly understates the needs of small families relative to large ones, and, hence, it will overestimate the number of poor people in large families relative to those in small families. The opposite extreme is to make no adjustments for family type and to apply the basic poverty threshold to all families irrespective of size or composition. This "zero" adjustment for family size is as unpalatable as is the straight proportion adjustment of multiplying the threshold by family size. It assumes that one adult needs as much as a two-adult/two-child family and also that a four-adult family or a family of two adults and three or more children needs no more than the two-adult/two-child family. There is widespread agreement that the appropriate adjustment lies somewhere between the two extremes; however, there is much less agreement on exactly how much to adjust the threshold for children relative to adults or how to measure economies of scale for larger households.

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Measuring Poverty: A New Approach We have reviewed the adjustments for family type that are embodied in the official poverty thresholds, as well as those that are implicit in other government programs. We have also considered numerous other proposals in the literature, including those that use empirical analysis in an attempt to establish an objective adjustment on the basis of comparing the behavior of families of different types. Although the empirical evidence helps determine the limits of what makes sense, there is no objective procedure for measuring the different needs for different family types. As with the determination of the reference family poverty threshold itself, for which empirical evidence can inform but not prescribe what is fundamentally a social or political judgement, so with the adjustments for different family types. Thus, similarly, we have opted for a procedure that, while taking into account the empirical evidence and previous experience, recognizes that the decision is based on judgement and seeks to make the process as transparent as possible. Our recommended procedure follows from our conclusion that the equivalence scale implicit in the official poverty thresholds is problematic and should be replaced. We say "implicit" because the official thresholds were developed separately for each family type rather than by the application of a formal scale to a reference family threshold. The basis for the official thresholds was a set of estimates of different food requirements for adults and children of various ages in families of different sizes. The assumptions underlying the differences are questionable, as is the assumption that differences in food needs adequately capture differences in needs for housing and other goods. One particularly questionable assumption is that people aged 65 and older need less to eat and so should have lower poverty thresholds than younger people; this assumption underlies the official thresholds for unrelated individuals and members of two-person families. Also, the implicit scale (which can be calculated by comparing the differences among the official thresholds for various family types) exhibits a number of irregularities and anomalies: for example, the second child in a family adds more costs than the first child. We propose that poverty thresholds for different family types be developed by applying an explicit scale to the reference family poverty threshold. The scale should distinguish the needs of children under 18 and adults but not make other distinctions by age; the scale should also recognize economies of scale for larger families. A scale of this type is the following: where A is the number of adults in the family, K is the number of children, each of whom is treated as a proportion P of an adult, and F is the scale economy factor. The formula calculates the number of adult equivalents (A + PK) and raises the result to a power F that reflects economies of scale for larger families. We recommend values for both P and F near 0.70; to be specific, we recommend setting P at 0.70 (i.e., each child is treated as 70% of an adult) and

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Measuring Poverty: A New Approach F in the range of 0.65 to 0.75. To calculate the actual thresholds, the ratio of the scale value from the formula for each family type to the value for the reference family type is applied to the reference family threshold. RECOMMENDATION 3.1. The four-person (two adult/two child) poverty threshold should be adjusted for other family types by means of an equivalence scale that reflects differences in consumption by adults and children under 18 and economies of scale for larger families. A scale that meets these criteria is the following: children under 18 are treated as consuming 70 percent as much as adults on average; economies of scale are computed by taking the number of adult equivalents in a family (i.e., the number of adults plus 0.70 times the number of children), and then by raising this number to a power of from 0.65 to 0.75. To explain the basis for our recommendation, we review types of equivalence scales, including the scale inherent in the official thresholds. In the discussion, we present our reasons for recommending that children be treated as needing 70 percent as much, on average, as adults, and for suggesting a range of 0.65 to 0.75 for the factor used to adjust for economies of scale for larger families. The Current Equivalence Scale During the 1960s, when there was keen interest in developing a poverty measure for the United States, one widely cited measure did not employ an equivalence scale. The 1964 report of the Council of Economic Advisers (CEA) set the poverty line for 1962 at $3,000 for a family (of any size) and $1,500 for unrelated individuals. It is hard to defend the proposition that a family of five can live as cheaply as a family of two, and although some might argue that parents who have chosen to have larger families should not be regarded as poor simply because of that choice, the same can hardly be said of the children, who played no part in their parents' decision. If one is to construct a sensible measure of poverty, some equivalence scale must be used. Mollie Orshansky, working at the Social Security Administration in the early 1960s, developed the poverty measure that was ultimately adopted for official use. Her central poverty threshold for a family of four was about the same as the CEA family threshold of $3,000, but she developed a whole range of thresholds that took family size and composition into account (Orshansky, 1963, 1965a). She thereby defined an equivalence scale, not directly, but by constructing a set of thresholds for different family types. Orshansky's thresholds were derived from looking at food budgets, and the equivalence scale that is implicit in them is a consequence of her judgements about needs for food and other goods. The underpinning for Orshansky's thresholds was the U.S. Department of

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Measuring Poverty: A New Approach Agriculture (USDA) Economy Food Plan, which provided the estimated cost of a minimally adequate diet for adults and children of various ages and for families of different sizes. (The latter estimates reflect assumptions about economies of scale on food; see Peterkin et al., 1983.) Orshansky's food budgets were based on the USDA estimates, coupled with assumptions about the ages of the children in each size and type of family. She developed separate budgets for families on the basis of the sex of the family head, the family size, the number of family members under the age of 18, and, for one- and two-person units, the age of the family head (under age 65 or 65 and older). According to the 1955 Household Food Consumption Survey, the average family of three or more spent approximately one-third of its after-tax money income on food. On the basis of this evidence, Orshansky created thresholds for families of three or more by multiplying her estimated food costs by three. She examined families of two separately, however, on the grounds that smaller families are less able to take advantage of economies of scale and so must absorb higher per capita fixed costs. The average family of two spent 27 percent of its income on food, so the multiplier for families of this size was set at 3.70 (1.00/0.27). Without using a food plan and a multiplier, she set thresholds for unrelated individuals, characterized by sex and age, at 80 percent of the corresponding threshold for two-person families. 1 This figure implies that two adults can live as well as one person on 125 percent as much income (1.0/0.8). Finally, she took 70 percent of her thresholds as the thresholds for farm families. In 1969 the Bureau of the Budget adopted Orshansky's thresholds (and thereby her equivalence scale) for the official measure of poverty, with the modification that the farm thresholds were raised from 70 to 85 percent of the nonfarm thresholds. In 1981 the nonfarm thresholds were applied also to farm families; the thresholds for families headed by women and men were averaged; and the largest family size category for the thresholds was raised from families of seven or more to families of nine or more. With the exception of these fairly minor changes, the current equivalence scale comes directly from Orshansky's original work. Because of the way it was constructed, the scale has as many categories as the official poverty thresholds and is thus quite detailed. (There are 48 categories at present, reduced from 124 categories prior to 1981.) Most presentations summarize it using weighted averages: see Table 3-1, which expresses the weighted average thresholds for families of size two to size seven relative to the threshold for a single adult under age 65. A key point to note is the essential arbitrariness of the equivalence scale 1   Unrelated individuals aged 15 and older are treated as separate one-person "families" in the U.S. poverty measure. Some of them live alone in their own households, but others live with other people not related to them (e.g., they may board with a family or live with one or more unrelated roommates).

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Measuring Poverty: A New Approach TABLE 3-1 Equivalence Scale Implicit in Official Weighted Average Poverty Thresholds for 1992 Family Size Scale Value Relative to a Single Adult (Under Age 65) Increment in the Scale for Each Added Family Member (Relative to Single Adult Under Age 65)a One person under age 65 1.000 0.00 One person aged 65 or over 0.922 -0.08 Two persons, head aged 65 or over 1.163 +0.16b Two persons, head under age 65 1.294 +0.29 Three persons 1.533 +0.24 Four persons 1.964 +0.43 Six persons 2.273 +0.31 Six persons 2.622 +0.35 Seven persons 2.958 +0.34 SOURCE: Bureau of the Census (1993c: Table A). a The values in this column represent the marginal effect of adding one more person to a family. For example, the figure of 0.24 for a three-person family category is the added amount for the third person, computed as the difference between the aggregate scale values in the first column for three-person families and two-person families relative to the scale value for a single adult. b The value shown is for the increment in the scale for the second person in an elderly family relative to a single adult under age 65. The increment in the scale for a second person in an elderly family relative to a single adult aged 65 or over is 0.24—the difference between the scale values of 1.163 and 0.922. that underlies the current poverty measure. Even if one accepts the scientific validity of the Economy Food Plan—itself a controversial matter since the plan is based on a compromise between expert nutritional advice and actual behavior—the derivation of the thresholds, and hence the equivalence scale, rests on a chain of ad hoc adjustments. The scientific basis for them is elusive or controversial, and, consequently, the scale is largely arbitrary. There are numerous specific criticisms of the current scale, that is, of the way in which the poverty thresholds vary across family types. For example, it seems unlikely that economies of scale in food are similar to those for other goods, especially given the presumption that many economies of scale operate through housing (see Nelson, 1993; Orshansky, 1968a). This criticism was especially pertinent for the pre-1981 thresholds for farm and nonfarm families, in which farm families, because they spend less on food on average than nonfarm families, had lower thresholds. This distinction would make sense only if less is also needed for all necessities other than food, such as clothing and shelter, something for which there is no clear evidence. Although the farm-nonfarm distinction no longer exists, a similar situation occurs for elderly

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Measuring Poverty: A New Approach individuals living in one- and two-person units who have somewhat lower thresholds than do the nonelderly because they are assumed to need less food. There are also a number of disturbing irregularities in the current scale. If there are economies of scale as family size increases, then the increment in the scale for an additional person should be lower for larger families. Yet as Ruggles (1990:66) has pointed out, this is not true of the current scale: on a weighted average basis relative to a single adult (as seen in Table 3-1), a second person in a family adds 0.29 to the scale, a third person adds only 0.24, a fourth person adds 0.43, and a fifth person adds 0.31. In some cases, single-parent families have higher thresholds than married-couple families of the same size, implying that children cost more than adults in certain size families. As one example, the child in a two-person single-parent family adds more to the family's costs than does the spouse in a married-couple family: see Figure 3-1, which graphs—separately for married-couple and single-parent families—the increment in the scale for each added family member relative to a single FIGURE 3-1 Equivalence scale implicit in the current poverty thresholds: increment for each added family member (relative to a scale value of 1.00 for a single adult under age 65). SOURCE: Data from Bureau of the Census (1993c: Table A).

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Measuring Poverty: A New Approach adult under age 65. These irregularities come in part from the assumptions that Orshansky had to make about the ages of children in families when using the food plans. We believe that these sorts of difficulties are always likely to be present in any method that is based on the construction of "ideal" or "expert" budgets for different family types, whether the budgets derive from food, as in Orshansky's procedure, or from a wider basket of goods as, for example, proposed by Ruggles (1990) and implemented by Renwick (1993a, 1993b).2 Expert poverty budgets are inevitably the result of families' actual spending patterns and a series of adjustments that reflect judgements about what a low-income family ''ought" to purchase. Because these budgets are always at least somewhat arbitrary, they impart no legitimacy to the equivalence scales that are implicit within them. We prefer a more direct approach that recognizes the arbitrariness by setting an equivalence scale formula directly and transparently and then using it to scale the threshold for a reference family type to derive poverty thresholds for other family types. Alternative Equivalence Scales Although there is wide agreement that different family types should have different poverty thresholds, that children have different needs from adults, and that larger households can benefit from economies of scale by sharing some items of consumption, there is little agreement about how the differences should be measured, and there is a wide range of scales in the literature. This section discusses some of these scales, as well as their conceptual and empirical basis. Programmatic Equivalence Scales In addition to the scale implicit in the official poverty thresholds, there are a number of other scales embodied in government programs or official pronouncements; see Table 3-2. The Bureau of Labor Statistics (BLS) estimated its own scale for the Family Budgets Program.3 For this program, BLS estimated higher, intermediate, and lower budgets for two types of reference families: (1) a four-person family living in an urban area and comprising a husband aged 38 and employed full-time, a homemaker wife (no age speci- 2   Renwick (1993b: Table 6) presents budgets for single-parent families of size two to size seven, consisting of separately developed estimates (including assumptions about scale economies) for food, housing, household operations, health care, transportation, clothing, and personal care. One key assumption that shapes her implicit equivalence scale is that a parent needs her or his own bedroom and that only two children can share a bedroom. 3   BLS last respecified the family budgets for 1966-1967 and last published them, updated for price changes, for 1981.

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Measuring Poverty: A New Approach TABLE 3-2 Selected Alternative Equivalence Scales: Increment in the Scale Value for a Spouse and Each Added Child (Relative to a Scale Value of 1.00 for a Single-Adult Family) Source or Type of Scale Family Size   2 3 4 5 6 Per capita 1.00 1.00 1.00 1.00 1.00 Official U.S. poverty thresholdsa 0.29 0.26 0.40 0.35 0.27 Bureau of Labor Statistics Family Budgets Programb 0.67 0.61 0.50 0.50 0.56 U.S. Department of Agriculture (food only)c,d 0.83 0.80 0.70 0.63 0.80 Organization for Economic Cooperation and Developmente 0.70 0.50 0.50 0.50 0.50 Canadian low-income cut-offs (LICOs) (1986 base)f 0.36 0.37 0.26 0.18 N.A. Lazear-Michael (1980a)g 0.06 0.24 0.18 0.22 N.A. Lazear-Michael (1988)h 1.00 0.40 0.40 0.40 0.40 Jorgenson-Slesnii 0.76 0.60 0.73 0.34 1.28 Van der Gaag and Smolenskyj 0.45 0.10 0.17 0.10 0.09 Income Survey Development Program (ISDP)k 0.47 0.18 0.16 0.13 0.11 Rainwater (1990)c,l 0.26 0.18 0.15 0.12 0.11 Statistics Canadac,m 0.17 0.27 0.23 0.00 N.A. NOTE: Add values across, plus 1.00 for the first adult, to obtain the scale value for a particular size family. a Calculated from the thresholds for a married-couple family of the specified family size compared to the threshold for an unrelated individual under age 65 (Bureau of the Census, 1993c: Table A). b Derived on the basis of Engel curves and food shares. The scale values shown are for a family in which the head is aged 35–54 (in Sherwood, 1977: Table 7). c Scale values do not distinguish between adults and children. d Derived by adding the costs of individual food plans and adjusting for household economies of scale in the use of food (Peterkin et al., 1983:15). e Derived on the basis that a second adult adds 70 percent to the single adult's budget and each child adds another 50 percent (Organization for Economic Cooperation and Development, 1982). f Derived using a method similar to the iso-prop method (in Wolfson and Evans, 1989:55); see text. g Derived using a variant of the Barten model. h Derived using a variant of the Rothbarth model; see text. i Derived using a variant of the Barten model, which also distinguishes by the age, race, and sex of the household head, geographic region, and farm-nonfarm residence. The scale values shown are for a family headed by a nonfarm white male between the ages of 25 and 34 and living in the Northeast (in Jorgenson and Slesnik, 1987: Table 2). j A subjective scale applying to households in which the head is under age 65 (in Danziger et al., 1984: Table 2).

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Measuring Poverty: A New Approach k A subjective scale applying to households in which the head is under age 65, derived from the 1979 ISDP Research Panel by estimating the log of the answer to a survey question regressed on the log of income, the log of family size, and the age and sex of the family head (in Danziger et al., 1984: Table 2). l A subjective scale derived from Gallup Poll data on the amount needed to get-along by estimating the log of the annualized get-along income amount regressed on the log of income, the log of family size, and the respondent's age (Rainwater, 1990:19). m A subjective scale based on 1986 data (in Wolfson and Evans, 1989:55). fied), a girl of 8, and a boy of 13; and (2) a retired couple aged 65 or older, in reasonably good health and living independently. BLS developed an equivalence scale to adjust these budgets for other family types, by applying the Engel methodology (discussed below) to data from the 1960-1961 Consumer Expenditure Survey (CEX). The key assumption of this methodology is that families spending an equal proportion of income on food have attained an equivalent level of living. The USDA also developed its own equivalence scale to determine adjustments to its food plans for the economies of scale of larger families. (The food plans themselves were constructed for adults and children of different sexes and ages.) The resulting scale values, applied to the cost of the Thrifty Food Plan for a reference family of four persons (husband and wife aged 20-54 and two children aged 6-8 and 9-11) are used in setting benefit levels in the Food Stamp Program. (The Thrifty Food Plan is the successor to the Economy Food Plan that formed the basis of the original poverty thresholds.) The USDA scale was originally developed in 1962 and revised in 1975 on the basis of data from a 1965 survey of food consumption of nonfarm households (Kerr and Peterkin, 1975). The scale has not been changed since 1975 because, according to an evaluation study (Greger, 1985:26), "the superiority of alternate adjustment factors was not clear." The USDA scale, which applies to food consumption only, is more generous for larger families than the BLS scale, which, in turn, is more generous than the scale implicit in the official poverty thresholds (see Table 3-2). Other organizations have dealt with the equivalence scale issue by proposing simple formulas, in the same general spirit as our own recommendation. Most notably, the Organization for Economic Cooperation and Development (OECD) (1982) has used an administratively convenient scale in which the first adult counts as 1.0, an additional adult counts as 0.7, and children count as 0.5 of an adult (see O'Higgins and Jenkins, 1990, for an application of the OECD poverty measure). Although there is no explicit recognition of economies of scale in these numbers, they are built into the scale, most obviously in the "discount" for the second adult.

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Measuring Poverty: A New Approach An even simpler scale underlies the poverty guidelines, which were originally developed by the Office of Economic Opportunity and are issued annually by the U.S. Department of Health and Human Services (see Burke, 1993: Table 12) and used to determine eligibility for many government assistance programs (see Chapter 7). They are constructed by smoothing the official thresholds for different size families: the resulting implicit equivalence scale counts the first adult as 1.0 and each additional adult or child as 0.35. Behavioral Scales Simple weighting schemes, like the OECD or our own recommendation, have the obvious merit of transparency, but they take no account of actual behavior except insofar as their plausibility is anchored in everyday experience. For at least a century, economists and others have tried to provide a more solid foundation for equivalence scales, analyzing patterns of household behavior in an attempt to measure the differential needs of adults and children, as well as economies of scale. At its simplest, one might attempt to measure the costs of children by looking at family budgets and identifying how much a poor family spends on such child-related expenditure items as food, clothing, and education. There are many such attempts in the literature: see, for example, Dublin and Lotka (1946), who wanted to calculate the "money value of a man" and needed to deduct the cost of bringing him to maturity; more recently, Lindert (1978) wanted to use child costs to predict fertility. The fundamental problem with such attempts is that adding children to a family without adding additional resources can only cause the family to rear-range its purchases. If a family spends more on child goods, it must spend less on something else. Consequently, a complete accounting of the "additional" expenditures associated with children would lead to the inevitable conclusion that children cost nothing. Although the children come with needs, which cause additional expenditures on some goods, those needs are paid for out of the same resources, which makes the family as a whole worse off, causing a reduction in expenditures in other goods. If one is going to calculate the cost of the children from the data, one must compare families of different types but at the same level of living. That is, in order to calculate measures of the cost of the children, or, indeed, of the extent of household economies of scale, one must have some procedure for knowing when two families of different types are equally well off; only in that way will a comparison of their expenditure patterns reveal what is the cost of the children or the extent of economies of scale. These arguments suggest that in order to calculate the equivalence scale by comparing expenditure patterns, one needs to know the equivalence scale to start with, so that one can be sure of comparing two households at the same level of well-being. If so, there is essentially no hope of using behavior to

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Measuring Poverty: A New Approach TABLE 3-5 Hedonic Model Price Indexes for Rent and Rental Equivalence, and Combined Multilateral Index, Selected Areas, July 1988–June 1989 Area or Population Size Index for Renters Index for Owners Combined Index Rank Northeast         New York City 1.216 1.877 1.818 2 New York-Connecticut suburbs 1.404 1.711 1.830 1 New Jersey suburbs 1.329 1.514 1.635 6 Philadelphia-Wilmington-Trenton 1.000 1.000 1.117 13 Boston-Lawrence-Salem 1.326 1.613 1.712 3 Pittsburgh-Beaver Valley 0.726 0.698 0.786 36 Buffalo-Niagara Falls 0.783 0.821 0.903 25 Areas of 500,000–1,200,000 0.987 0.952 1.068 15 Areas of 100,000–500,000 0.786 0.758 0.850 28 Areas under 100,000 0.802 0.912 0.982 21 Midwest         Chicago-Gary-Lake County 1.004 1.034 1.143 12 Detroit-Ann Arbor 0.928 0.873 0.985 20 Cleveland-Akron-Lorain 0.758 0.753 0.839 30 Minneapolis-St. Paul 0.954 0.886 1.004 18 St. Louis-East St. Louis 0.740 0.729 0.812 33 Cincinnati-Hamilton 0.765 0.742 0.833 31 Kansas City, Mo.-Kan. 0.713 0.702 0.784 37 Milwaukee 0.887 0.892 0.993 19 Areas of 500,000–1,200,000 0.716 0.707 0.789 35 Areas of 100,000–500,000 0.667 0.651 0.729 40 Areas under 100,000 0.522 0.449 0.518 44 South         Washington, D.C. 1.049 1.165 1.266 11 Dallas-Fort Worth 0.673 0.745 0.807 34 Houston-Galveston-Brazoria 0.555 0.639 0.685 41 Miami-Fort Lauderdale 0.939 0.905 1.020 17 Atlanta 0.794 0.868 0.945 23 Baltimore 0.861 0.954 1.035 16 Tampa-St. Petersburg-Clearwater 0.755 0.684 0.782 38 New Orleans 0.776 0.810 0.892 26 Areas of 500,000–1,200,000 0.682 0.704 0.778 39 Areas of 100,000–500,000 0.557 0.583 0.642 42 Areas under 100,000 0.551 0.516 0.585 43 West         Los Angeles County 1.427 1.551 1.690 4 Greater Los Angeles 1.375 1.286 1.462 9 San Francisco-Oakland-San Jose 1.423 1.535 1.676 5 Seattle-Tacoma 0.927 0.976 1.073 14 San Diego 1.153 1.426 1.498 8 Denver-Boulder 0.758 0.898 0.959 22 Portland-Vancouver 0.858 0.830 0.935 24 Honolulu 1.184 1.470 1.550 7

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Measuring Poverty: A New Approach Area or Population Size Index for Renters Index for Owners Combined Index Rank West—continued Anchorage 1.004 1.219 1.289 10 Areas of 500,000-1,200,000 0.705 0.803 0.863 27 Areas of 100,000-500,000 0.727 0.774 0.848 29 Areas under 100,000 0.718 0.742 0.820 32 Low index value 0.522 0.449 0.518   Median index value 0.798 0.871 0.952   High index value 1.427 1.877 1.830   SOURCE: Kokoski, Cardiff, and Moulton (1992: Table 2.4). NOTE: Areas are ordered within region by population size as of the 1990 census; rankings are assigned to the combined index values from 1 (highest cost) to 44 (lowest cost). equations included some 33 attributes of housing units and neighborhoods.) They created bilateral interarea price indexes from the resulting antilogs of the estimated coefficients on the area dummy variables, and then created "multilateral" indexes from the bilateral indexes. The authors claim that the resulting multilateral indexes are independent of the choice of reference area and, hence, that the rankings for areas are stable. The results obtained by Kokoski, Cardiff, and Moulton (1992) for July 1988-June 1989 tend to accord with common expectations about the location and magnitudes of high- and low-cost areas; see Table 3-5 . The major cities in the Northeast (Boston and New York City) and the West (Los Angeles, San Francisco, and San Diego) have the highest shelter costs, with index values between 1.46 and 1.83. Washington, D.C., Philadelphia, and Chicago have mid-range index values, while other major cities in the Midwest (e.g., St. Louis, Cleveland) and the South (e.g., Houston and Dallas) have substantially lower shelter costs, with index values between 0.69 and 0.84. Small urban areas generally have lower shelter costs than larger metropolitan areas in the same region. Indexes for rent and owners' equivalent rent tend to be highly correlated. In areas in which rent control is important (e.g., New York, Los Angeles, and San Francisco), the index for owners' equivalent rent is substantially higher than the rent index. Discussion What can one conclude from the work to date to develop interarea housing cost indexes? Clearly, there are no easy answers to the question of how to develop a reliable index. Not only does the use of different methods yield

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Measuring Poverty: A New Approach 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.

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Measuring Poverty: A New Approach 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.

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Measuring Poverty: A New Approach 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 250,000–500,000 1.290 Metropolitan areas 500,000–1,000,000 1.335 Metropolitan areas 1,000,000–2,500,000 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 250,000–500,000 0.992 Metropolitan areas 500,000–1,000,000 1.045 Metropolitan areas 1,000,000–2,500,000 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 250,000–500,000 0.906 Metropolitan areas 500,000–1,000,000 0.969 Metropolitan areas 1,000,000–2,500,000 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 250,000–500,000 0.913 Metropolitan areas 500,000–1,000,000 0.956 Metropolitan areas 1,000,000–2,500,000 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 250,000–500,000 0.911 Metropolitan areas 500,000–1,000,000 1.016 Metropolitan areas 1,000,000–2,500,000 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 250,000–500,000 0.852 Metropolitan areas 500,000–1,000,000 0.878

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Measuring Poverty: A New Approach Region and Population Size Index Value East South Central—continued   Metropolitan areas 1,000,000–2,500,000 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 250,000–500,000 0.797 Metropolitan areas 500,000–1,000,000 0.868 Metropolitan areas 1,000,000–2,500,000 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 250,000–500,000 0.946 Metropolitan areas 500,000–1,000,000 1.090 Metropolitan areas 1,000,000–2,500,000 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 250,000–500,000 1.041 Metropolitan areas 500,000–1,000,000 1.063 Metropolitan areas 1,000,000–2,500,000 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 components—housing and all other goods and services—in which the

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Measuring Poverty: A New Approach 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 misclassified—relative to the individual metropolitan and nonmetropolitan area index values—by 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 adjust 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.

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Measuring Poverty: A New Approach ing the poverty thresholds for cost-of-housing variations across areas) would result in 55 percent of the population having an index value that differed by more than 20 percent from its own metropolitan (or nonmetropolitan) area-specific index. The use of regional index values (for the nine census divisions) would result in 45 percent of the population having an index value that differed by more than 20 percent from its own area-specific index. The use of state index values would result in 33 percent of the population having an index value that differed by more than 20 percent from its own area-specific index. In contrast, the use of the proposed index values for metropolitan area size categories within regions would result in only 9 percent of the population having an index value that differed by more than 20 percent from its own area-specific index. In other words, a higher fraction of the population would be assigned a more accurate index value with our proposal than with a regional or state housing cost index. These results demonstrate the superiority of our proposal compared with the alternatives of adjusting solely for regional variations in the cost of housing or of adjusting for variations across states. 18 The proposed procedure should not be viewed as the last word on the issue of adjusting poverty thresholds for area differences in the cost of living, but rather as a modest step in the right direction. The procedure only takes account of housing cost differences and, even for those differences, will assign index values to people in some areas that are considerably in error. The procedure also does not take account of housing cost variations within areas (e.g., differences in costs between central cities, suburbs, and exurbs of, say, large metropolitan areas). And it does not take account of special circumstances, such as significantly higher housing costs for areas in Alaska and Hawaii than are reflected in the index values for the Pacific region as a whole.19 Finally, the proposed method is a crude instrument for attempting to measure housing price differences that do not also reflect quality differences. Nonetheless, within the constraints of available data, we believe that the proposed procedure is a significant improvement over the current situation of no adjustment. The methodology is understandable, operationally feasible, and produces results that conform well with other findings from research. Updating the Housing Cost Index The index values for cost-of-housing differences can readily be revised as necessary every 10 years as new decennial census data become available. How 18   For some purposes, it may still be desirable to use state index values to adjust poverty thresholds for differences in the cost of housing (or the cost of living generally). For example, this type of adjustment may make sense when the poverty thresholds are used as the need standard for such assistance programs as AFDC (see Chapter 8). 19   It would certainly be possible to make some ad hoc adjustments to our index, but we did not believe it desirable for us to attempt such an effort.

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Measuring Poverty: A New Approach ever, revising the index as infrequently as every 10 years could result in a blip in the poverty rates in many areas because of changing housing markets. For example, an area that was experiencing a housing "boom" at the time of one census could experience a housing "bust" at the next census and vice versa. It would be preferable to revise the index on a more frequent basis. Indeed, such a revision in the index values that we developed from 1990 census data would be desirable for the initial implementation of the proposed poverty measure. HUD faces a similar need to update its fair market rents on a regular basis. To make annual adjustments, HUD uses data from several sources, (described above), including the American Housing Survey, local area CPI shelter cost indexes, and random digit dialing surveys. We encourage an assessment of the appropriateness of the HUD methods for updating the housing cost index values from the decennial census for use, in turn, in adjusting the poverty thresholds. We also encourage research on the usefulness and cost-effectiveness of other methods that could be considered. Further Research Obviously, the issue of how best to adjust poverty thresholds for geographic differences in the cost of housing and in the cost of living more broadly is an area for further research and development. We have argued that the proposed procedure for taking account of housing cost differences for metropolitan areas categorized by size of population within region represents an improvement over the current method of no adjustment at all. We have also noted the limitations of the procedure, which represents a step, but only a step, in the right direction. We encourage appropriate agencies, such as BLS and HUD, to undertake research on improved methods for determining area price differences. Ideally, the research would include other goods besides housing and would consider such issues as the types of geographic areas (cities, counties, larger areas) for which an adjustment is feasible and appropriate. It would also address methodological issues, such as refinements to the hedonic regression models under development at BLS that appear so promising. To effect much additional improvement in the methodology and the reliability of interarea price indexes, new data collection may be required. For example, expanding the sample for the American Housing Survey, which provides more detailed information on housing characteristics than the decennial census, would be one way to develop improved cost-of-housing indexes (whether using the proposed adaptation of the HUD methodology or hedonic methods). Even more broadly, expanding the BLS price samples for housing and other goods would be a way to develop comprehensive cost-of-living indexes that represent valid indicators of differences across areas in prices at a point in time and not just differences in the rate of price changes. However,

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Measuring Poverty: A New Approach these kinds of expanded data collection efforts would entail considerable cost. We believe it is worth investigating the cost-effectiveness of additional data collection, in terms of the expected improvements in the data for such purposes as adjusting the poverty thresholds. In general, we believe that data related to consumer expenditures and prices need to be improved in the United States. Not only is the CPI database limited in sample size and area coverage, but the CEX, which is used to determine the CPI market basket, is very limited—in sample size and in other ways—for purposes of measuring and understanding poverty, consumption, and savings. We discuss issues of needed data improvements for poverty measurement, including improvements in the CEX, in Chapter 5. Before that discussion, in Chapter 4, we consider an appropriate definition of family resources to compare with the poverty thresholds for determination of poverty rates for the nation, geographic areas, and population groups.

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