National Academies Press: OpenBook

Measuring Poverty: A New Approach (1995)

Chapter: Aggregate Comparisons

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Suggested Citation:"Aggregate Comparisons." National Research Council. 1995. Measuring Poverty: A New Approach. Washington, DC: The National Academies Press. doi: 10.17226/4759.
Page 419
Suggested Citation:"Aggregate Comparisons." National Research Council. 1995. Measuring Poverty: A New Approach. Washington, DC: The National Academies Press. doi: 10.17226/4759.
Page 420

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APPENDIX B 419 (e.g., going off or on a welfare program) between pairs of months that span two interviews (e.g., for SIPP, months 4-5, 8-9, 12-13, etc.) than between pairs of months for which data are collected from the same interview. The seam problem affects most variables for which monthly data are collected in SIPP— often strongly. For example, in the first year of the 1984 SIPP panel, over twice as many nonparticipants reported entering the Social Security program between seam months than nonseam months (Jabine, King, and Petroni, 1990: Table 6.2). The reasons for the occurrence and extent of the seam phenomenon are not well understood, but it clearly results in errors in the timing of transitions in SIPP and the duration of spells of program participation (and perhaps of poverty). It may or may not result in errors in the number of transitions that occur within a given period. For example, in the case of food stamps, total exits and entrances from SIPP are close to the rates derived from food stamp administrative records. In contrast, whether due to the seam effect or other factors, entrance rates from SIPP for SSI are significantly higher than those shown by program records (Jabine, King, and Petroni, 1990:59-60). The Census Bureau has pursued research and testing of alternative questionnaire designs and interviewing procedures that could reduce the seam problem and produce more accurate income reporting overall (see, e.g., Marquis, Moore, and Bogen, 1991). To date, there have been few positive results. Aggregate Comparisons Aggregate comparisons of income estimates from SIPP and CPS, like comparisons of internal indicators of data quality, show a mixed picture. On balance, SIPP seems to be doing a somewhat better job of income reporting, but not for all income types. Moreover, it may be that the gains in SIPP are not holding up over time. Comparisons of 1984 estimates from the 1984 SIPP and March 1985 CPS showed SIPP as a percentage of CPS as follows (Jabine, King, and Petroni, 1990: Table 10.8): Total money income 100.1 Regular money income 99.9 Earnings 98.2 All other 106.0 Public and private transfers 111.6 Property income 103.1 All other regular money income 37.0 Lump-sum payments N.A. (not collected in CPS) SIPP performed better than the March CPS with the notable exception of earnings. (The low ratio for all other regular money income is presumably due to higher levels of reporting of specific income types in SIPP than in the

APPENDIX B 420 March CPS.) Census Bureau analysts assume that many SIPP respondents are reporting their net paychecks rather than their gross earnings as requested by the survey. Coder and Scoon-Rogers (1994) reported comparisons for detailed income sources for 1984 and 1990. These comparisons indicate that some of the gains in income reporting seen in SIPP at the outset of the survey may no longer be occurring. However, they noted that the 1990 SIPP panel may not be comparable to the 1984 panel because it contained an added sample, carried over from the 1989 panel, of households headed by single mothers and minorities. The weighting adjustments for these added cases may be problematic. As with the review of internal indicators of data quality, it is difficult from the available comparisons of aggregates to draw conclusions about the implications for estimates of poverty and related income statistics. Perhaps the most telling summary indicator available is the fact, noted above, that SIPP poverty estimates are consistently several percentage points below those from the March CPS. Lamas, Tin, and Eargle (1994) found that only about one-sixth of this difference could be explained by attrition bias in SIPP. Another one- sixth of the difference appears due to more accurate measurement of family composition during the income reporting year in SIPP than in the March CPS. The remaining two-thirds difference, it is hypothesized, is explained by more complete reporting of income in SIPP for the lower end of the income distribution. In that regard, respondents to SIPP report more sources of income than respondents to the March CPS; they also report higher amounts for such income sources as Social Security, Railroad Retirement, SSI, unemployment compensation, veterans' payments, and child support payments, all of which are important to the low-income population. However, reporting of AFDC and other cash welfare is currently no more complete in SIPP than in the March CPS (Coder and Scoon-Rogers, 1994: Table 1). Clearly, much more analytical work needs to be done, including work to look at differences in income reporting among population groups within and across the surveys and the development of a complete time series of poverty and related income statistics from SIPP for comparison with the March CPS.

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Each year's poverty figures are anxiously awaited by policymakers, analysts, and the media. Yet questions are increasing about the 30-year-old measure as social and economic conditions change.

In Measuring Poverty a distinguished panel provides policymakers with an up-to-date evaluation of:

  • Concepts and procedures for deriving the poverty threshold, including adjustments for different family circumstances.
  • Definitions of family resources.
  • Procedures for annual updates of poverty measures.

The volume explores specific issues underlying the poverty measure, analyzes the likely effects of any changes on poverty rates, and discusses the impact on eligibility for public benefits. In supporting its recommendations the panel provides insightful recognition of the political and social dimensions of this key economic indicator.

Measuring Poverty will be important to government officials, policy analysts, statisticians, economists, researchers, and others involved in virtually all poverty and social welfare issues.

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