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Measuring Poverty: A New Approach (1995)
Commission on Behavioral and Social Sciences and Education (CBASSE)

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Measuring Poverty: A New Approach

5
Effects of the Proposed Poverty Measure

This chapter presents our analysis of the difference it would make to poverty statistics to adopt the proposed measure in place of the current measure. This analysis has several objectives: to demonstrate the feasibility of implementing the proposed measure; to determine the reasons for important differences in the numbers and kinds of poor people between the proposed measure and the current measure; and to identify problems and areas for further research.

We first describe the data sources and procedures that we used. Next, we present the results we obtained for income year 1992, for which we conducted the most extensive analysis. Two aspects that we explore in detail are the effects of using different equivalence scales for the poverty thresholds and the accuracy of our imputations for out-of-pocket medical care costs and their implications for poverty rates. We then briefly review the data, procedures, and results for the more limited analysis that we were able to conduct for earlier years. Finally, we consider the likely effects on poverty rates of using the Survey of Income and Program Participation (SIPP) instead of the March income supplement to the Current Population Survey (CPS).

In conducting this analysis, we had to wrestle with a number of data problems. Hence, in this chapter we also discuss those problems and make recommendations for improvements in data sources that are needed for more accurate measurement of people's poverty status. The discussion covers data sources for deriving and updating the thresholds, as well as data sources for estimating family resources.

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Measuring Poverty: A New Approach 5 Effects of the Proposed Poverty Measure This chapter presents our analysis of the difference it would make to poverty statistics to adopt the proposed measure in place of the current measure. This analysis has several objectives: to demonstrate the feasibility of implementing the proposed measure; to determine the reasons for important differences in the numbers and kinds of poor people between the proposed measure and the current measure; and to identify problems and areas for further research. We first describe the data sources and procedures that we used. Next, we present the results we obtained for income year 1992, for which we conducted the most extensive analysis. Two aspects that we explore in detail are the effects of using different equivalence scales for the poverty thresholds and the accuracy of our imputations for out-of-pocket medical care costs and their implications for poverty rates. We then briefly review the data, procedures, and results for the more limited analysis that we were able to conduct for earlier years. Finally, we consider the likely effects on poverty rates of using the Survey of Income and Program Participation (SIPP) instead of the March income supplement to the Current Population Survey (CPS). In conducting this analysis, we had to wrestle with a number of data problems. Hence, in this chapter we also discuss those problems and make recommendations for improvements in data sources that are needed for more accurate measurement of people's poverty status. The discussion covers data sources for deriving and updating the thresholds, as well as data sources for estimating family resources.

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Measuring Poverty: A New Approach DATA AND PROCEDURES An extract of the March 1993 CPS provided to the panel by the Census Bureau served as the primary database for our analysis for income year 1992. SIPP is an alternate data source and, indeed, we recommend that SIPP become the basis for official poverty statistics in place of the March CPS (see below).1 We did use SIPP data to impute some of the elements for deriving disposable income that are not part of the March CPS. Because we have estimates of aggregate poverty rates from the March CPS and SIPP, using the current gross money income definition of family resources, we are reasonably confident of the type of results that we would have obtained had we used SIPP (see below). Poverty Measure Alternatives For income year 1992, we conducted two analyses that compared the current measure with the official thresholds and the official definition of family resources (namely, gross money income) to the proposed measure. The first analysis was designed to illustrate the effects of the current and proposed measures on the kinds of people who are poor, holding constant the official 1992 poverty rate for the total population. For this exercise, we determined the two-adult/two-child family threshold that, together with the proposed threshold adjustments (with a 0.75 scale economy factor) and the proposed family resource definition, resulted in the same 1992 poverty rate as the official rate of 14.5 percent.2 The official reference family threshold for 1992 was $14,228; the threshold that gave the same result with the proposed measure is $13,175.3 The second analysis was designed to illustrate the effects—for the whole 1   We did not use SIPP in our analysis because the Census Bureau had not completed work to develop procedures for simulating income taxes and valuing in-kind benefits with SIPP (this work will be completed in the near future); we did not have the time or resources to undertake such work ourselves. By using the March CPS, we could take advantage of the Bureau's long-standing procedures for estimating taxes and valuing in-kind benefits with that data source. 2   The 1992 poverty rates that we tabulated from the March 1993 CPS for the current measure are consistent with rates published in Bureau of the Census (1993c). Subsequently, the Census Bureau revised the rates due to the introduction of new population weighting controls derived from the 1990 census results that incorporate an adjustment for the census undercount (see Bureau of the Census, 1995). Thus, the revised official 1992 poverty rate for the total population is 14.8 percent instead of 14.5 percent as previously reported and as we tabulated. 3   The value of $13,175 has no intrinsic meaning as a reference family poverty threshold. It is an artifact of the analysis, including not only the effects of the other threshold adjustments and definition of resources as disposable money and near-money income, but also the effects of the underlying data, including imputations. In other words, it is simply the result of implementing all other proposed changes and calculating what level of the reference family threshold is necessary to achieve the specified rate of 14.5 percent.

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Measuring Poverty: A New Approach population and various groups—of raising the poverty threshold in real terms as well as implementing the proposed threshold adjustments and family resource definition. For this exercise, we used a two-adult/two-child family threshold of $14,800, representing the midpoint of our suggested range for that threshold of $13,700 to $15,900 (see Chapter 2). We implemented two versions of the proposed measure with the $14,800 reference family threshold: one with a scale economy factor of 0.75 and one with a scale economy factor of 0.65. Threshold Adjustments Table 5-1 shows the poverty thresholds for 1992 by family size and number of children for the current measure. Table 5-2 shows the thresholds for three versions of the proposed measure: using a $13,175 reference family threshold to keep the overall poverty rate at 14.5 percent; using a $14,800 reference family threshold and a scale economy factor of 0.75; and using a $14,800 threshold and a scale economy factor of 0.65. Unlike the official thresholds, the proposed thresholds do not distinguish one- and two-person families by whether the head is over or under age 65. We adjusted the thresholds in Table 5-2 for estimated differences in the cost of housing by size of metropolitan area within nine regions of the country; see Table 5-3. Imputation Procedures for Proposed Resource Definition For the two analyses, we also implemented the proposed definition of family resources as disposable money and near-money income, adding values for in-kind benefits (food stamps, school lunches, and public housing) to gross money income, and subtracting the following from income: out-of-pocket medical care expenditures (including health insurance premiums), federal and state income and Social Security payroll taxes, child care expenses, and other work-related expenses. Imputations to the March 1993 CPS were the basis for each of these adjustments. The only element of the proposed resource definition that we did not implement was the subtraction of child support payments to another household, because the March CPS does not provide a basis for a reasonable imputation; however, we have an estimate of the likely effect of subtracting child support payments on the aggregate poverty rate from SIPP (see below). This section describes our imputation procedures (in some cases, the Census Bureau's procedures for which we simply adopted the results) for each component used in the derivation of disposable money and near-money income (see Betson, 1995, for a detailed description). Generally, the goal of our procedures was to use the best and most recent data source and to develop a

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Measuring Poverty: A New Approach TABLE 5-1 Official Poverty Thresholds in 1992, by Family Size and Type Number in Family (Age of Head) Number of Related Children Under 18 Years None One Two Three Four Five Six Seven Eight or More Onea                   <65 $7,299                 65+ 6,729                 Two                   <65 9,395 9,670               65+ 8,480 9,634               Three 10,974 11,293 11,304             Four 14,471 14,708 14,228 14,277           Five 17,451 17,705 17,163 16,743 16,487         Six 20,072 20,152 19,737 19,339 18,747 18,396       Seven 23,096 23,240 22,743 22,396 21,751 20,998 20,171     Eight 25,831 26,059 25,590 25,179 24,596 23,855 23,085 22,889   Nine or More 31,073 31,223 30,808 30,459 29,887 29,099 28,387 28,211 27,124 SOURCE: Bureau of the Census (1993c: Table A). NOTE: Weighted average thresholds for families of two or more people (which are those commonly cited) are as follows: $9,137 for all two-person families ($9,443 for such families with the householder under age 65, $8,487 for such families with the householder age 65 and over); $11,186 for three-person families; $14,335 for four-person families; $16,592 for five-person families; $19,137 for six-person families; $21,594 for seven-person families; $24,053 for eight-person families; and $28,745 for families of nine or more people (Bureau of the Census, 1993c: Tables A-3, 23). Weighted average thresholds for each family size are the average of the thresholds for the specific categories (e.g., families of size two with no children or one child), weighted by the proportion that each category represents of all families of that size. a A one-person "family" is an unrelated individual, that is, someone living alone or with others not related to him or her. procedure that preserved as much of the variance and as many of the relationships among key variables as possible. (The preservation of variance and key relationships is particularly important for an indicator such as the poverty measure, which relates to one tail of the income distribution.) However, we were limited in available time and resources.4 4   Readers interested in replicating our results or in conducting additional analyses may obtain a data file (from the Committee on National Statistics) that contains the March 1993 CPS extract file with our imputed variables and poverty status indicators for the current measure and the proposed measure.

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Measuring Poverty: A New Approach TABLE 5-2 Poverty Thresholds in 1992 Under Proposed Measure, by Family Size and Type Number in Family Number of Related Children Under 18 Years   None One Two Three Four Five Six Seven Eight $13,175 Reference Family Threshold: 0.75 Scale Economy Factor Onea $5,262                 Two 8,850 7,834               Three 11,995 11,083 10,147             Four 14,883 14,038 13,175 12,293           Five 17,594 16,796 15,985 15,161 14,322         Six 20,172 19,411 18,640 17,857 17,063 16,258       Seven 22,645 21,912 21,173 20,424 19,665 18,898 18,119     Eight 25,030 24,323 23,608 22,887 22,158 21,420 20,674 19,919   Nine 27,342 26,655 25,963 25,264 24,559 23,848 23,128 22,402 21,667 $14,800 Reference Family Threshold: 0.75 Scale Economy Factor Onea $5,911                 Two 9,941 8,800               Three 13,474 12,450 11,398             Four 16,719 15,769 14,800 13,809           Five 19,764 18,868 17,957 17,031 16,088         Six 22,660 21,805 20,939 20,060 19,168 18,263       Seven 25,438 24,615 23,784 22,943 22,091 21,229 20,354     Eight 28,117 27,323 26,520 25,710 24,891 24,062 23,224 22,376   Nine 30,714 29,943 29,165 28,380 27,588 26,789 25,981 25,165 24,339 $14,800 Reference Family Threshold: 0.65 Scale Economy Factor Onea $6,680                 Two 10,483 9,432               Three 13,644 12,741 11,802             Four 16,449 15,636 14,800 13,938           Five 19,017 18,267 17,500 16,715 15,910         Six 21,409 20,707 19,992 19,263 18,519 17,758       Seven 23,665 23,001 22,326 21,640 20,943 20,232 19,508     Eight 25,811 25,178 24,536 23,885 23,224 22,552 21,870 21,177   Nine 27,865 27,258 26,643 26,021 25,390 24,751 24,103 23,445 22,777 NOTE: The thresholds are adjusted by geographic area; see Table 5-3 and text. a A one-person "family" is an unrelated individual, that is, someone living alone or with others not related to him or her. In-Kind Benefit Values and Taxes We used the 1992 values that the Census Bureau provided on the March 1993 CPS extract file for in-kind benefits (food stamps, school lunches, and public and subsidized housing) and for federal and state income and Social Security payroll taxes. (See Chapter 4 for a description of the Census Bureau's current in-kind benefit valuation procedures, which use the market value approach,

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Measuring Poverty: A New Approach TABLE 5-3 Housing Cost Adjustments for Proposed Poverty Thresholds Area and Population Size Index Value Northeast New England (Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont) Nonmetropolitan areas and metropolitan areas under 250,000 1.128 Metropolitan areas of 250,000-500,000 1.128 Metropolitan areas of 500,000-1,000,000 1.148 Metropolitan areas of 1,000,000-2,500,000 1.141 Metropolitan areas of 2,500,000 or more 1.209 Middle Atlantic (New Jersey, New York, Pennsylvania) Nonmetropolitan areas and metropolitan areas under 250,000 0.908 Metropolitan areas of 250,000-500,000 0.997 Metropolitan areas of 500,000-1,000,000 1.020 Metropolitan areas of 1,000,000-2,500,000 0.975 Metropolitan areas of 2,500,000 or more 1.187 Midwest East North Central (Illinois, Indiana, Michigan, Ohio, Wisconsin) Nonmetropolitan areas and metropolitan areas under 250,000 0.896 Metropolitan areas of 250,000-500,000 0.959 Metropolitan areas of 500,000-1,000,000 0.987 Metropolitan areas of 1,000,000-2,500,000 0.995 Metropolitan areas of 2,500,000 or more 1.059 West North Central (Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, South Dakota) Nonmetropolitan areas and metropolitan areas under 250,000 0.861 Metropolitan areas of 250,000-500,000 0.962 Metropolitan areas of 500,000-1,000,000 0.981 Metropolitan areas of 1,000,000-2,500,000 1.028 Metropolitan areas of 2,500,000 or more N.A. South South Atlantic (Delaware, District of Columbia, Florida, Georgia, Maryland, North Carolina, South Carolina, Virginia, West Virginia) Nonmetropolitan areas and metropolitan areas under 250,000 0.899 Metropolitan areas of 250,000-500,000 0.961 Metropolitan areas of 500,000-1,000,000 1.007 Metropolitan areas of 1,000,000-2,500,000 1.043 Metropolitan areas of 2,500,000 or more 1.119 East South Central (Alabama, Kentucky, Mississippi, Tennessee) Nonmetropolitan areas and metropolitan areas under 250,000 0.827 Metropolitan areas of 250,000-500,000 0.935

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Measuring Poverty: A New Approach Area and Population Size Index Value East South Central—continued Metropolitan areas of 500,000-1,000,000 0.947 Metropolitan areas of 1,000,000-2,500,000 N.A. Metropolitan areas of 2,500,000 or more N.A. West South Central (Arkansas, Louisiana, Oklahoma, Texas) Nonmetropolitan areas and metropolitan areas under 250,000 0.858 Metropolitan areas of 250,000-500,000 0.911 Metropolitan areas of 500,000-1,000,000 0.942 Metropolitan areas of 1,000,000-2,500,000 0.962 Metropolitan areas of 2,500,000 or more 1.005 West Mountain (Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, Wyoming) Nonmetropolitan areas and metropolitan areas under 250,000 0.888 Metropolitan areas of 250,000–500,000 0.976 Metropolitan areas of 500,000–1,000,000 1.039 Metropolitan areas of 1,000,000–2,500,000 1.003 Metropolitan areas of 2,500,000 or more N.A. Pacific (Alaska, California, Hawaii, Oregon, Washington) Nonmetropolitan areas and metropolitan areas under 250,000 0.969 Metropolitan areas of 250,000–500,000 1.018 Metropolitan areas of 500,000–1,000,000 1.028 Metropolitan areas of 1,000,000–2,500,000 1.104 Metropolitan areas of 2,500,000 or more 1.217 NOTES: Housing cost indexes calculated from 1990 census data on gross rent for apartments with specified characteristics, adjusted to reflect the share of housing in the proposed poverty budget; see Chapter 3. Nonmetropolitan areas are combined with metropolitan areas of less than 250,000 population because of restrictions on geographic area coding in the CPS and SIPP N.A., not applicable and for a description of the Census Bureau's tax simulator. Because of the Census Bureau's procedures to protect confidentiality on the public-use March CPS files, care must be taken in subtracting taxes for high-income people so as not to inadvertently move them below the poverty line. Also, the portion of taxes due to realized capital gains should not be subtracted because such gains are not part of the proposed resources definition.) Out-of-Pocket Medical Care Expenditures The March CPS does not contain any information on medical care expenses (out-of-pocket or otherwise), although it does provide some relevant infor

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Measuring Poverty: A New Approach mation that is helpful for imputation purposes, such as age and health insurance coverage. We imputed out-of-pocket expenses by using tabulations provided by the Agency for Health Care Policy and Research (AHCPR) from the 1987 National Medical Expenditure Survey (NMES), aged to represent the 1992 population.5 AHCPR prepared separate multivariate tabulations for families (and unrelated individuals) for which the head was under age 65 or age 65 and older. The tabulation for families headed by someone younger than 65 cross-classified the age of head and type of health insurance coverage (private, public, or no insurance) by family size, family annual income-to-poverty ratio, and race of head. The tabulation for families headed by someone age 65 or older included the same variables, except that the categories for type of insurance coverage were different (Medicare and private, Medicare and public, all other).6 Because of the small sample size of the NMES, we had to combine many of the cells in these two very large multivariate tabulations to have a minimum of 100 observations in each cell. The tabulation that we used for families headed by someone younger than age 65 cross-classified health insurance status (covered, not covered) by family size (one, two-three, four or more people), by race of head (black, other), and by annual income-to-poverty ratio (less than 1.50, greater than or equal to 1.50). The tabulation that we used for families headed by someone aged 65 or older cross-classified the age of head (under 75, 75 and older) by income-to-poverty ratio (under 1.50, greater than or equal to 1.50) and by family size (one, two or more people). For each category in these two tabulations, we had the weighted counts of families with no out-of-pocket medical care expenditures and with non-zero expenditures within each of 10 expenditure ranges. Out-of-pocket expenditures included health insurance premiums, copayments, deductibles, and all other health care expenditures paid directly by the family. The lower bounds for the 10 expenditure ranges were $1, $500, $1,000, $1,500, $2,000, $2,500, $5,000, $7,500, and $12,500. The imputation of out-of-pocket expenditures to the March 1993 CPS was a multistep procedure. The first step was to determine whether the individual CPS record would be imputed to have any out-of-pocket expenditures. For families who reported receiving Medicaid, we assumed that they would have no out-of-pocket medical expenditures.7 For non-Medicaid families, we randomly assigned a fraction of these families to have some out 5   A multiple regression would have been preferable for imputation purposes (because it would then have been possible to introduce more variation), but it could not be obtained within the time and resources available. 6   Although type of health insurance coverage is captured in these tabulations, differences in generosity of coverage within type (e.g., differences among state Medicaid programs) are not. 7   This assumption is an approximation, as the generosity of Medicaid programs varies across states, and some families with Medicaid coverage do incur out-of-pocket medical expenditures. See Taylor and Banthin (1994: Table 2) for estimates from the 1987 NMES of out-of-pocket expenses by type of insurance coverage.

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Measuring Poverty: A New Approach of-pocket medical expenditures on the basis of their characteristics and the computed probabilities from the NMES tabulations. If the family was assigned to have out-of-pocket medical expenditures, we devised an imputation procedure so that these families were assigned a level of expenditures consistent with the distribution of expenditures tabulated with their characteristics from the NMES. The object of this two-step procedure was to impute a set of medical expenditures that would reflect the entire distribution of expenditures and not to impute to all families the average level of expenditures consistent with their characteristics (see Betson, 1995). Child Care Expenses The March CPS does not contain any information on child care expenses, although it does have information on the number and age of children and employment status and weeks worked for the parents, which is needed for imputation purposes. We imputed child care expenses by using four regression equations from the 1990 SIPP panel. Two logit regressions estimated, respectively, the probability that a single parent who worked and a two-parent family in which both parents worked would pay for child care. Then, two ordinary-least-squares regressions estimated, for those single-parent and two-parent working families who paid for care, the total weekly amount. The single-parent working family equations included as independent variables the race of the head, the number of children of various ages, the region of residence, and the log of total family income. The two-parent working family equations included the same variables plus the proportion of family earnings accounted for by the earnings of the mother. (A number of model specifications were tested before deciding on these regression models.) For weekly child care amounts, the probability that a family would have paid for child care was computed using the estimated logit equations. On the basis of this probability, the family was randomly assigned either to have or to have not paid for child care. If the family was imputed to have paid for child care, the second estimated equation and the family's characteristics were used to predict an average amount of child care for the family. A random ''shock," whose standard deviation was derived from the standard error of the estimated equation, was then added to this average amount. This weekly amount was then multiplied by the number of weeks worked by the head of single-parent families or by the secondary worker of two-earner families. A cap was imposed so that the annual amount imputed could not exceed the earnings of the parent with the lower earnings or the value of the ceiling on eligible expenses for the dependent care tax credit of $2,400 per year for one child and $4,800 for two or more children. Other Work-Related Expenses The March CPS does not contain any information on work-related expenses,

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Measuring Poverty: A New Approach although it does report the employment status and weeks worked of each adult. We imputed work expenses to each worker aged 18 and over. For each week worked, we assigned a work expense value of $14.42, representing an annual amount of $750 for a 52-week work-year (or $720 for a 50-week work-year—see Chapter 4). The amount assigned was not allowed to exceed the worker's annual earnings. Also, for any parent for whom child care expenses were imputed (the parent in each family with the lower annual earnings), the combined child care and other work expense deduction was not allowed to exceed the parent's annual earnings. The value of the work expense deduction was derived on the basis of analyzing work expense data from Wave 3 of the 1987 SIPP. We computed median weekly work expenses for the first job reported for all workers aged 18 and over (including those reporting zero values). The estimated median weekly value in 1992 dollars was $17 (see Chapter 4 for details of the calculation). The amount that we deducted from earnings for each week worked ($14.42) is 85 percent of the median value. Distribution of Imputed Values On average, we imputed $2,872 in deductions for out-of-pocket medical care expenses, child care expenses, and other work-related expenses, or 8.5 percent of gross money income for the average unit (families and unrelated individuals). As would be expected, the dollar amount imputed increased linearly with gross money income and decreased on a percentage basis. As shown in Table 5-4, the imputed deduction for the sum of these three expense categories is $669 for the family at the 10th percentile of the distribution (10.7% of gross money income); $3,007 for the family at the 50th percentile (median) (11.1% of gross money income); and $4,898 for the family at the 95th percentile (5.2% of gross money income). Higher amounts, both in dollars and as a percentage of gross money income, were imputed for these expenses for the reference family of two adults and two children (see Table 5-4); this results from the high proportion of workers among this family type. RESULTS Effects with a Constant Poverty Rate In our first analysis, we implemented the current measure with the official 1992 threshold of $14,228 for a two-adult/two-child family and the proposed measure with a threshold of $13,175 for this family type and a scale economy factor of 0.75. By design, the proposed measure under this scenario produces about the same 1992 poverty rate (14.54%) and number of poor people (36.9 million) as the current measure (14.52% and 36.9 million). However, they are not all the same people.

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Measuring Poverty: A New Approach TABLE 5-4 Distribution of Gross Money Income, with Amounts Deducted for Out-of-Pocket Medical Care Expenditures, Child Care Expenses, and Other Work-Related Expenses, 1992, in Dollars   All Familiesa Two-Adult/Two-Child Families Percentile of Gross Money Income Gross Money Income Deductionsb Gross Money Income Deductionsb     Dollar Amount Percent   Dollar Amount Percent 10th 6,282 669 10.7 15,798 2,648 16.8 20th 10,768 1,429 13.3 24,364 4,142 17.0 30th 15,544 2,042 13.1 31,005 4,629 14.9 40th 20,971 2,518 12.0 37,275 5,656 15.2 50th (median) 27,088 3,007 11.1 43,387 5,894 13.6 60th 34,210 3,516 10.3 49,816 5,669 11.4 70th 42,916 3,956 9.2 56,993 6,108 10.7 80th 54,538 4,416 8.1 66,633 6,926 10.4 90th 74,240 4,651 6.3 86,667 6,641 7.7 95th 93,818 4,898 5.2 99,451 6,946 7.0 Average 33,857 2,872 8.5 46,583 5,243 11.3 a Includes unrelated individuals. b Average of imputed out-of-pocket medical care expenses (including health insurance premiums), child care expenses, and work-related expenses for families with gross money income 2.5 percentiles below to 2.5 percentiles above each percentile value (e.g., deductions for families at the 10th percentile are averaged over families with gross money income between the 7.5 and 12.5 percentiles). The proposed measure moves 7.4 million people out of poverty, and it moves about 7.4 million people into poverty. (A total of 29.5 million people, 80% of the poverty population, are poor under both measures.) Most of the movement occurs near the poverty line. Thus, 87 percent of the 7.4 million people who are no longer categorized as poor move from the category of income between 50 and 100 percent of the poverty line to the category of income between 100 and 150 percent of the poverty line. Similarly, 79 percent of the 7.4 million people who are newly categorized as poor move from the category of income between 100 and 150 percent of the poverty line to the category of income between 50 and 100 percent of the poverty line; see Table 5-5. Table 5-6 shows the effect of the proposed poverty measure on the composition of the poor population. By age, somewhat more poor people are adults aged 18-64 and somewhat fewer poor people are adults aged 65 and older under the proposed measure in comparison with the current measure, while the proportion of children under age 18 among the poverty population is about the same under both measures. By race, somewhat more poor people

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Measuring Poverty: A New Approach A particularly important problem to address is population undercoverage, particularly of low-income minority groups. RECOMMENDATION 5.2. To facilitate the transition to SIPP, the Census Bureau should produce concurrent time series of poverty rates from both SIPP and the March CPS by using the proposed revised threshold concept and updating procedure and the proposed definition of family resources as disposable income. The concurrent series should be developed starting with 1984, when SIPP was first introduced. RECOMMENDATION 5.3. The Census Bureau should routinely issue public-use files from both SIPP and the March CPS that include the Bureau's best estimate of disposable income and its components (taxes, in-kind benefits, child care expenses, etc.) so that researchers can obtain poverty rates consistent with the new threshold concept from either survey. Data Sources for Income The March CPS The March CPS has several important advantages: large sample size (over 60,000 households); timeliness (reports and data files are typically available within 6 months of data collection); and the fact that analysts both inside and outside the Census Bureau are comfortable with the data. However, the March CPS has many limitations for measuring poverty with the proposed resource definition.21 The March CPS collects information for each adult household member on previous year's money income from a large number of sources and also asks about participation in the major in-kind benefit programs. However, its coverage of in-kind programs is not complete. Moreover, it does not ask about expenses that we propose to deduct from income, such as out-of-pocket medical care expenditures, child care costs, other work-related expenses, and child support payments. The March CPS also does not ask questions that would facilitate accurate estimation of income taxes, such as number of dependents (including those outside the household), whether the household itemizes deductions, etc. The March CPS does not ascertain characteristics of rented housing needed to value public subsidies or characteristics of owned housing needed to impute equivalent rents. Finally, it does not ask about assets or lump-sum receipts, which may be needed for supplementary short-term poverty measures, if not for the official annual measure. 21   For a detailed description of the March CPS, see Appendix B.

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Measuring Poverty: A New Approach Indeed, the March CPS cannot be used to construct poverty measures for shorter (or longer) periods than a year. Moreover, the annual data it provides present a number of technical difficulties. In particular, family composition as defined in March may not reflect the composition during the income reference year, which can result in an erroneous assignment of poverty status. With regard to data quality, many income questions in the March CPS have high nonresponse rates: overall, 20 percent of estimated total income from the CPS represents imputed rather than reported values. There are other kinds of reporting errors as well. The problems with the March CPS are tractable in principle (e.g., more questions could be added or steps taken to improve quality). In practice, however, it would be difficult to effect further improvements because the March CPS is a supplement to the monthly labor force survey that is the basis of the nation's monthly unemployment statistics. The primary focus of the Bureau of Labor Statistics (BLS), which sponsors the monthly CPS, and the Census Bureau, which collects it, is to maintain and enhance the quality of the monthly labor force data. All of the supplements, including the March income supplement, are of secondary priority. One consequence is that fairly high nonresponse rates to the income supplement are tolerated so as not to reduce the likelihood that households will cooperate with the next month's employment questions. Also, the recent major redesign of the CPS, involving a new sample, revised questionnaire, and revised data collection and processing systems, focused on the main labor force component and not the supplements. The income supplement will benefit from some of the changes, such as the introduction of computer-assisted interviewing, but no special effort was made to revisit the questionnaire or other features of the income supplement itself. The Alternative of SIPP Recognizing the inherent limitations of the March CPS as long ago as the early-1970s, a federal interagency committee sponsored by the U.S. Office of Management and Budget proposed that a new income survey be fielded to improve the scope and quality of the information available on income and the effects of government assistance programs. This proposal ultimately led to the creation of SIPP, which began in 1983 (see Committee on National Statistics, 1989:Ch. 4). Currently, SIPP is designed as a longitudinal survey that follows the adult members of samples or "panels" of about 20,000 households. A new panel is introduced every February and followed over a period of 32 months, with interviews at 4-month intervals. The survey is scheduled for a major redesign beginning in 1996.22 22   See Appendix B for a detailed description of SIPP.

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Measuring Poverty: A New Approach SIPP has already made important contributions to knowledge about the dynamics of income receipt and program participation, health insurance coverage, asset holdings, and other topics related to material and other dimensions of well-being. SIPP has also made important strides toward obtaining higher quality income data than in the March CPS (e.g., nonresponse rates for many income sources are significantly lower), although there are still problems to overcome. With specific regard to poverty measurement, SIPP asks (or has asked) questions to obtain virtually all of the information needed to implement the proposed family resource definition. On the negative side, SIPP experienced significant start-up problems, including delays in release of data products and budget cuts that necessitated reductions in sample size and number of interviews. A panel of the Committee on National Statistics (CNSTAT) recently completed a thorough review and evaluation of SIPP, recommending changes to begin with the 1996 panel (Citro and Kalton, 1993). These changes, taken together, promise to significantly improve the usefulness of the survey for both longitudinal and cross-sectional analyses of income, program participation, and related topics. They include: extending the length of each panel (i.e., each new sample of households whose members are followed over time) from 32 to 48 months; following children as well as adult members of the households originally included in each panel, even if they move to other households; introducing new panels every 2 years, so as to reduce the complexity of the survey (compared with the current design of introducing a new panel every year) and still maintain the ability to produce yearly time series for income, poverty, program participation, and other statistics; enlarging the sample size of each panel so that about 55,000 households are available for cross-sectional estimates by combining two panels, compared with 38,000 under the current SIPP design (for fully funded panels) and 62,000 in the March CPS;23 and making maximum use of the planned introduction of computer-assisted interviewing and database management system technology to improve data quality and timeliness. The CNSTAT Panel to Evaluate SIPP concluded that these changes would make it possible for SIPP to produce timely income statistics of high reliability. Noting the limited ability to make further improvements to the March CPS, the SIPP panel recommended that, over time, SIPP replace the March CPS for purposes of producing income, poverty, and related statistics.24 23   The CNSTAT SIPP panel believed that further expansion of sample size would be possible once planned improvements in data collection and processing are put into place. 24   The CPS would, of course, continue to include income items for use in labor force analyses.

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Measuring Poverty: A New Approach We are in full agreement with the recommendation that SIPP become the basis for the nation's official poverty and related statistics. The March CPS does not collect all of the information needed for poverty measurement, has problems with the quality of the information that it collects, and does not have much room for further improvement. In contrast, SIPP collects most of the needed information, has achieved quality improvements, and, because of its focus on income, has ample opportunity for further improvements in both the scope and the quality of income-related data. The best time to put this recommendation into effect would be in 1996, when other changes to the survey are made. Orienting SIPP to Poverty Measurement A decision to use SIPP to produce the official poverty data means that all aspects of the survey should be reviewed to determine their suitability for providing the most accurate statistics possible under the proposed measure. A key aspect for review is the proposed redesign of the survey. Although the Census Bureau has accepted many of the recommendations of the CNSTAT Panel to Evaluate SIPP, it has decided against the recommendation for a design that would have two panels of about 27,000 households each in the field each year, with new panels introduced every 2 years. Instead, the Census Bureau has proposed a design that would have one large panel of 50,000 households in the field each year, with new panels introduced every 4 years. The Census Bureau's design has the advantage of maximum sample size in a single panel for purposes of longitudinal analysis. For cross-sectional analysis, the two designs are equivalent: the two panels in the field each year under the CNSTAT SIPP panel's design can readily be combined to produce the same sample size as the single, larger panel of the Census Bureau's design. Longitudinal estimates are important, but we believe that the time series of annual poverty rates and other statistics is paramount and that the design must support the production of reliable annual estimates. In this regard, the Census Bureau's proposed design provides no overlap between panels. Hence, every 4 years, it will be hard to determine if changes in the poverty rate are real or due to the introduction of a new panel in place of an old panel that may have uncorrected attrition bias or other problems.25 Since most attrition of sample cases from SIPP occurs by the end of the first year of a panel, there may be problems of attrition bias with the CNSTAT SIPP panel's design as well as the Census Bureau's, as the former does not 25   Attrition bias can occur when attrition rates differ between groups: for example, higher rates of attrition for low-income people could produce a downward bias in the poverty rates. Adjustments to the survey weights are usually made to compensate for attrition bias, but the adjustments may not be adequate.

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Measuring Poverty: A New Approach refresh the sample for cross-sectional estimates more frequently than every 2 years. Research on attrition and the most appropriate corrective actions is obviously needed, whichever design is used, and the Census Bureau has stated its commitment to such research for SIPP. However, it is still the case that attrition bias or other problems with a panel that may affect the poverty estimates cannot be fully assessed with a nonoverlapping design. Indeed, a nonoverlapping design also limits the possibility of using SIPP for longitudinal analysis of important policy changes, such as changes in the welfare or health care systems. Ideally for such analysis, one wants information for a sufficient length of time before a change in order to accurately characterize people's behavior under the old policy regime. One then wants information for as long as possible after the policy change to assess the effects on behavior. However, if policy changes take effect near the beginning or end of a 4-year panel under the Census Bureau's design, information either before or after the change will be limited, reducing the ability to adequately evaluate the effects. In contrast, under the design of the CNSTAT Panel to Evaluate SIPP, there will likely always be a panel in the field that is suitable for analysis of before-and-after effects, albeit with a smaller sample size. In addition to considering the best survey design for purposes of poverty measurement, the SIPP questionnaire should be reviewed to determine what changes may be required. Thus, some questions may need to be added at least occasionally (e.g., work expenses) or asked more frequently (e.g., child care expenses or child support payments), while others may need to be modified. In some cases, such as the estimation of tax liabilities, it may make sense to collect a limited set of variables that will enhance the Census Bureau's simulation model rather than to try to collect detailed information directly. 26 Finally, from the perspective of improved poverty measurement, we urge that high priority be given to several areas of methodological research for SIPP. First, questionnaire research should be pursued to develop ways to improve the quality of reporting of wage and salary income in SIPP, which falls short of independent estimates (very likely because many people report net rather than gross pay). Second, research should be conducted to improve the weighting process so that the weights adequately account for the higher rates of attrition evidenced by low-income population groups (see Appendix B on both these points). Third, and very important, research should be conducted to improve population coverage in SIPP. A problem that affects all household surveys, including SIPP and the March CPS, is that not all people who are associated with sample households are in fact listed as household residents. Particularly subject to undercoverage are low-income minority groups. For example, it is 26   See Citro and Kalton (1993:Chap. 3) for suggestions of content changes to SIPP that generally comport with the proposed resource definition for the poverty measure.

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Measuring Poverty: A New Approach estimated that as many as 20 percent of black men are missed in the March CPS and SIPP, relative to the population counted in the decennial census. Undercoverage rates are even higher for young black men (Citro and Kalton, 1993: Table 3-12; see also Appendix B). The Census Bureau has initiated a program of coverage research to better understand coverage problems and develop effective countermeasures (Shapiro and Bettin, 1992), and we urge that this work go forward. We note, however, that household surveys, by their nature, overlook some population groups, including the homeless and people in institutions. The decennial population census (see below) includes these groups, although coverage is far from complete. Transition We are reasonably confident that use of SIPP data will show the same effects of the proposed poverty measure as shown in the March CPS, with the exception of lower overall rates. However, its use as the official source of poverty statistics represents another change in addition to the significant changes that we propose in the measure itself. To aid in making the transition and to help evaluate the SIPP-based estimates, it would be helpful for the Census Bureau to produce, for some period, concurrent time series of poverty rates from the March CPS and SIPP by using the proposed revised thresholds (updated each year with new CEX data) and the proposed disposable income resource definition. Admittedly, the construction of disposable income with the March CPS is complicated by the necessity for extensive imputations: in addition to imputation procedures for taxes and nonmedical in-kind benefits that already exist, the Census Bureau would need to develop imputation procedures for out-of-pocket medical expenditures, child care expenses, and child support payments.27 However, we believe that such procedures can be developed, using data from such sources as SIPP and NMES, and that it would be very useful for researchers and policy analysts to have concurrent series. Any imputations that are performed, whether on the March CPS or SIPP, should be evaluated as to their quality and the sensitivity of the resulting poverty rates to the form of the imputation. The concurrent series should be developed going forward from 1996 when the new SIPP design is implemented, and also going backward to 1984 when SIPP was first introduced. In the case of the latter estimates, some imputations will be required for SIPP as well as for the March CPS; also, small sample size for many SIPP panels will be a problem. Nevertheless, the 27   For child support payments, adequate imputations will require the addition of a question to the March CPS that asks whether families provide support to children outside their household (ideally, the question would ask the amount as well, obviating the need for an imputation procedure).

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Measuring Poverty: A New Approach "backcasting" exercise should provide results that are helpful to analysts in historically assessing poverty trends under the proposed measure. Finally, for the foreseeable future, the Census Bureau should routinely issue public-use files from both the March CPS and SIPP that include the Bureau's best estimate of disposable income and its components (taxes, in-kind benefits, child care expenses, etc.). Although many researchers will make the transition to using SIPP for analysis purposes, it is likely that others will continue to use the March CPS for some kinds of poverty analysis, particularly analyses related to labor force behavior (which is the focus of the regular CPS). Hence, it is important that researchers have ready access in the March CPS data files to income variables constructed under the new resource definition as well as variables for the new thresholds: to use the new thresholds with income variables that represent the old resource definition would result in inappropriate estimates of poverty. Research Recommendations Income Data in Other Surveys Many federally sponsored surveys in addition to the March CPS and SIPP (e.g., the American Housing Survey, Consumer Expenditure Survey, National Health Interview Survey, National Medical Expenditure Survey) collect income data. Because the focus of these surveys is on some other topic, they cannot typically afford the questionnaire space to collect detailed income information, although they need to obtain some income measures as background variables for analysis purposes. Often, income-to-poverty ratios are desired because such measures adjust for differences in family size and composition. Our recommendation to measure poverty on the basis of families' disposable money and near-money income may present a problem for surveys with limited room for questions not directly germane to their primary focus. We encourage work by agencies to determine the best set of questions to include in surveys that require income and poverty measures as background variables. Given limited questionnaire space, we believe that it is more important to include questions that will permit estimating disposable income (e.g., questions on net pay, child care costs, and food stamp benefits) than it is to include questions to distinguish among a large number of components of gross money income (e.g., types of cash transfers or property income).28 We also encourage research by agencies on adjustments that may be needed for the greater extent of income underreporting that is likely to occur 28   In 1990, the Interagency Forum on Aging-Related Statistics issued a set of guidelines for income questions to include in surveys of the elderly. That effort might serve as a model for work to develop guidelines for survey questions to support measurement of disposable income.

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Measuring Poverty: A New Approach because a survey cannot ask about as many income components as are included in SIPP or the March CPS. Research with the March CPS, SIPP (and its predecessor, the Income Survey Development Program) has demonstrated that probing for more different sources of income elicits higher levels of reporting compared with asking broad categories (see Appendix B). Finally, and most important, we urge research by agencies on methods to develop poverty estimates for surveys with limited income information that are comparable to the estimates that would result from having complete information with which to calculate disposable money and near-money income. Comparisons of poverty rates from SIPP-based on a full implementation of the disposable income concept with rates based on a partial implementation (e.g., based on money income only, or money income, taxes, and nonmedical in-kind benefits only) could form the basis for developing appropriate adjustment factors for other surveys. Alternatively, agencies might come up with some rough-and-ready imputation procedures to use for estimating disposable income from limited survey information (e.g., a table for imputing out-of-pocket medical care expenditures based on type of health insurance and the number and age of family members). RECOMMENDATION 5.4. Appropriate agencies should conduct research on methods to develop poverty estimates from household surveys with limited income information that are comparable to the estimates that would be obtained from a fully implemented disposable income definition of family resources. Income Data in the Decennial Census Another source of income information is the decennial census, which provides data every 10 years for small geographic areas for which reliable estimates cannot be obtained in household surveys. The census also includes population groups, such as the institutionalized and the homeless, that are typically excluded from household surveys (although census estimates of the homeless are of doubtful quality). Income and poverty data from the census are used in many kinds of analyses; they also serve such important governmental purposes as allocation of federal funds to states and localities. For example, census estimates of the number of school-age children in poverty are used to allocate federal funds to school districts for programs to aid disadvantaged children. Questionnaire space in the decennial census is even more limited than in most surveys. Over the decades, the number of income questions has been expanded, but, in the 1990 census, only 8 types of income were ascertained, compared with more than 30 in the March CPS and more than 60 in SIPP. No information was obtained about taxes, in-kind benefits, medical costs, work expenses, child support payments, or assets. Consequently, it is not

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Measuring Poverty: A New Approach possible to construct poverty estimates from census data with the proposed disposable income definition of families' resources. Yet, as we have demonstrated, poverty statistics that are based on gross money income cannot distinguish between groups that differ in important ways (e.g., working versus nonworking families) or capture the effects of important government policy changes. Hence, we believe it is critical for agencies to conduct research on methods to adjust census small-area poverty estimates to more closely approximate the estimates that would obtain with a disposable income resource definition. Again, the basis for such adjustments could be analysis of poverty rates with SIPP: for example, comparing rates estimated with a disposable money and near-money income definition to rates estimated with a gross money income definition for various groups. If key population groups (e.g., the elderly, minorities) were distributed about equally across the country instead of residing disproportionately in some areas, then it might not be necessary to conduct research on methods for adjusting census small-area poverty estimates to approximate a disposable income definition of resources. The reason is that most uses of census poverty statistics are relative in nature: for example, allocating shares of a fixed total amount of federal funding to areas according to their poverty rate relative to the nation as a whole. Also, while recognizing the constraints on the census questionnaire, we urge serious consideration of adding perhaps one or two simple yes-no questions that would facilitate adjusting the census poverty estimates. For example, questions on whether a family received food stamps or paid for child care in the past year or had health insurance coverage would be very helpful in developing appropriate adjustment factors.29 RECOMMENDATION 5.5. Appropriate agencies should conduct research on methods to construct small-area poverty estimates from the limited information in the decennial census that are comparable with the estimates that would be obtained under a fully implemented disposable income concept. In addition, serious consideration should be given to adding one or two questions to the decennial census to assist in the development of comparable estimates. Expenditure Data Unlike many other developed countries, the United States does not have adequate data with which to develop a poverty measure that uses a consump- 29   At present, planning for the year 2000 census is exploring ways to reduce the content of the census questionnaire and to determine alternative sources of data, such as a continuing large-scale sample survey with most of the census content (see Edmonston and Schultze, 1995). Whether income questions are included in the census or in a census-like questionnaire that is fielded at more frequent intervals, the issue of obtaining information for developing appropriate poverty estimates remains.

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Measuring Poverty: A New Approach tion- or expenditure-based definition of resources; hence, there is virtually no practical alternative to using an income-based definition. Of course, there are many arguments in favor of an income definition, but there are also strong arguments in favor of a consumption or expenditure definition. We believe it is important to consider improvements to the Consumer Expenditure Survey that would permit its use in estimating resources for poverty measurement purposes.30 We propose use of the current CEX for deriving and updating the poverty thresholds, for which the data requirements are not as demanding as they are for estimating resources (e.g., sample sizes can be smaller). However, even for this purpose, we believe it is important to consider improvements to the survey. In general, improvements to the CEX would be very useful to support research and policy analysis on consumption and savings behavior and the relationship of consumption, income, and wealth. The most costly improvement to explore would be an expansion of the sample size. A major expansion, from 5,000 households or consumer units (the number provided for analysis purposes by the Interview Survey component of the CEX) to 50,000-60,000 households (i.e., the sample size of SIPP or the March CPS) would be required for the CEX to serve as the vehicle for estimating resources. A more modest expansion—perhaps doubling the current sample size—would improve the quality of the data for updating the poverty thresholds under the proposed procedure. More generally, such an expansion would make the data more useful for analyzing trends in expenditures and consumption patterns across population groups. Another area to explore is the development of methods to reduce recall and other reporting errors and to improve the survey's response rate. We surmise that the length and complexity of the questionnaire may be major factors in impairing response. The CEX questionnaire is far more complex than the SIPP questionnaire. The latter has often been criticized for length and complexity, but the burden it poses is less than it would appear for the many people who have relatively few sources of income. In contrast, most people spend money on a wide variety of goods and services and hence must answer most of the detailed questions in the CEX. We understand that the current level of detail may be needed for purposes of respecifying the market basket for the CPI (which is done about once every 10 years); however, a more streamlined questionnaire might be more effective for the purposes of poverty measurement and other analytical uses of expenditure data. One possibility could be to embed a more detailed survey for a subsample of respondents within a larger, more streamlined survey. Yet another area to explore concerns the overall CEX design, which currently consists of two separate surveys (the Diary Survey and the House 30   See Appendix B for details about the CEX.

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Measuring Poverty: A New Approach hold Interview Survey) that comprise separate samples and cannot be linked at the individual respondent level. It would be very useful to consider designs that provide more complete reporting of expenditures for individual families in the sample. Also, it would be useful to explore designs that follow family members over time, so that complete expenditure patterns are obtained on an annual basis. Currently, families that move are not followed; instead, interviews are conducted with the new residents. The kinds of changes to the CEX that could improve its usefulness for poverty measurement and other analysis purposes would not be easy to implement and would likely be expensive (particularly in the case of an increased sample size); however, the potential benefits could be great. A useful first step would be for BLS to conduct or commission a study that evaluates the CEX and assesses the costs and benefits of changes to the survey that could make it more useful for poverty measurement and other purposes. We urge prompt undertaking of such a study. Furthermore, we hope that improvements to the survey that stem from the review can be implemented in time to provide useful input to the next 10-year review of the poverty measure. RECOMMENDATION 5.6. The Bureau of Labor Statistics should undertake a comprehensive review of the Consumer Expenditure Survey to assess the costs and benefits of changes to the survey design, questionnaire, sample size, and other features that could improve the quality and usefulness of the data. The review should consider ways to improve the CEX for the purpose of developing poverty thresholds, for making it possible at a future date to measure poverty on the basis of a consumption or expenditure concept of family resources, and for other analytic purposes related to the measurement of consumption, income, and savings.

Representative terms from entire chapter:

march cps