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APPENDIX B 415 young adults, males, minority groups, never-married people, poor people, and people with lower educational attainment. In addition, more limited evidence suggests that the current noninterview weighting adjustments do not fully compensate for differential attrition across groups. One evaluation of the procedures to adjust for household nonresponse at each wave developed two sets of weights for Wave 2 households in the 1984 panelâone set based on all Wave 2 households and one set based just on those Wave 2 households that provided interviews at Wave 6. Comparing Wave 2 estimates from these two samples showed that the latter set produced higher estimates of median income and fewer households with low monthly income than those produced with the former set, evidence that the weights do not adequately adjust for higher attrition rates among low-income households (Petroni and King, 1988). A subsequent study that compared samples from the 1985 panel of all Wave 2 households and those that provided interviews at Wave 6 obtained similar findings (King et al., 1990). With regard to annual estimates of poverty from SIPP, one study (Lamas, Tin, and Eargle, 1994) found that the inclusion of people with missing waves, using an imputation process, produced somewhat higher poverty rates than the use of complete reporters. Approximately one-sixth of the difference between annual poverty rates in SIPP and the March CPS is apparently due to attrition bias. It is important to note that the current cross-sectional nonresponse adjustments in SIPP make only minimal use of the information that is available from previous waves for many current nonrespondents. Also, in constructing longitudinal files from SIPP panels, the Census Bureau assigns zero weights to original sample members who missed only one or a few waves in addition to those who missed all or most waves. The Census Bureau has recently committed itself to an intensive program of research to improve the weighting adjustments for attrition as part of the decision to move to 4-year panels for SIPP with no overlap (Weinberg and Petroni, 1992). Item Nonresponse In addition to household and person nonresponse, there is substantial item nonresponse in the March CPS. The Census Bureau imputes as much as 20 percent of the total income in the CPS. For some income sources, imputation rates are even higherâas much as one-third of nonfarm self-employment income, interest, and dividend payments are imputed (Bureau of the Census, 1989a: Table A-2; Bureau of the Census, 1992b: Table C-1). SIPP compares favorably with the March CPS on item nonresponse rates: overall, only 11 percent of total regular money income for 1984 was imputed in SIPP, compared with 20 percent in the March CPS. The SIPP and March CPS imputation rates for earnings were 10 percent and 19 percent, respectively;
APPENDIX B 416 for public and private transfers, 12 percent and 21 percent, respectively; and for property income, 24 percent and 32 percent, respectively (Jabine, King, and Petroni, 1990: Table 10.8; see also Citro and Kalton, 1993: Tables 3-4, 3-5 for comparisons of nonresponse rates for such specific income sources as AFDC and SSI). The imputation process maximizes the available sample size for analysis from a survey by providing filled-in records for respondents whose records would otherwise have to be discarded if key analytical variables were missing. However, the process can introduce error. No definitive evaluation has been conducted of the imputation procedures used in the March CPS or SIPP; however, available evidence suggests that the procedures are a source of error and could be improved. The Census Bureau currently applies very complex procedures, which it refers to as statistical matches, to impute values in the March CPS for whole groups of variables, such as income and employment-related items. The records are classified by a number of characteristics, and the record that is the best match is selected as the "donor" to supply the missing values to the record requiring imputation (the "host"). The Census Bureau's statistical matching procedures have, over the years, replaced somewhat less complex "hot-deck" imputations for more and more items. In the hot-deck method, the data records are arrayed by geographic area and processed sequentially, and the reported values are used to update matrices of characteristics. A record with a missing item has the most recently updated value assigned from the appropriate matrix. Hot-deck methods are largely used for imputation in SIPP. David et al. (1986) compared the Census Bureau's imputations of earnings in the March CPS with a regression-based imputationâusing data from the Internal Revenue Service from a 1981 exact-match CPS-IRS file as the measure of truthâand found that the CPS methods performed quite well in reproducing the overall shape of the earnings distribution. However, they and other analysts have determined that the CPS imputations are less successful for small groups, such as minorities and specific occupations (Coder, no date: Lillard, Smith, and Welch, 1986). Coder (1991), in an exact match of the March 1986 CPS with IRS records for married couples with earnings, found that records with imputations for CPS earnings contributed significantly to the overall underestimate of wages and salaries in the CPS in comparison with the IRS tax returns. Thus, while mean CPS earnings in cases with no imputations were 98 percent of mean IRS earnings, mean CPS earnings in cases with imputations were only 89 percent of mean IRS earnings. Also, while 95 percent of cases with no imputations had CPS earnings within 1 decile of IRS earnings, only 66 percent of cases with imputations were in this close agreement. The available evidence suggests that the SIPP imputation procedures could also be improved. Several studies have focused on the population eligible for assistance programs and have identified problems because the current procedures