Measuring a Changing Nation Modern Methods for the 2000 Census (1999) / Chapter Skim
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4 Evaluation of Some Common Arguments Against ICM-Based Adjustment of the Census
4 Evaluation of Some Common Arguments Against ICM-Based Adjustment of the Census
Pages 68-85
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From page 68... ...
This substantial statistical literature especially portions of Survey Methodology for June 1992, the Journal of the American Statistical Association for September 1993, and Statistical Science for November 1994 specifically addresses the questions of whether to use adjusted counts for the 1980 and 1990 censuses. It contains analyses that both support and oppose the panel's position, that is, that use of integrated coverage measurement in the 2000 census will in all likelihood result in counts that are preferable to the "unadjusted" counts for key uses of census data.1 In this chapter the panel discusses the results and arguments presented in this literature, providing a detailed argument supporting the panel's position on the likely effectiveness of integrated coverage measurement in the 2000 census.
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From page 69... ...
matching error and the bias from imputation of match status for unresolved cases, (2) unmodeled heterogeneity in census undercoverage for lower levels of geographic aggregation (violation of the so-called synthetic assumption)
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From page 70... ...
Most of the key uses of census counts are to allocate a "fixed pie," and therefore loss functions that measure how close the estimated shares are to the true shares at some level of geographic aggregation are more important than loss functions that measure how close the estimated counts are to the true counts. No one can directly measure loss since a set of true counts does not exist.
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From page 71... ...
To support integrated coverage measurement, one must be convinced that the amount of matching bias and variance is small enough that the adjusted counts are still more accurate than the unadjusted counts. (Assessment of the effect of matching error in combinations with other sources of bias is then conducted using a total error model.)
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From page 72... ...
Although simpler imputation methods are planned for the 2000 census to substitute for the use of logistic regression, the argument that this source of error will remain limited in 2000 is similar given the sensitivity analysis work cited below. Given that at least 3 percent of P-sample cases in 1990 had unresolved match status, an inadequate imputation model would make it difficult to use integrated coverage measurement.
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From page 73... ...
Breiman (1994) focuses attention on the disagreement rates from this study: he points out that for P-sample cases the average disagreement rate across enumeration strata between the original match status and that of the rematch staff for those cases originally classified as unresolved matches, weighted to the total population, was 23.8 percent.
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From page 74... ...
could have been given an imputed match status probability of close to 1.0, thereby contributing little to differences in estimated undercount. Furthermore, it must be understood that this result involves only 10 percent of less than 25 percent, or less than 2.5 percent of the cases; it does not directly measure matching error; and it does not allow for offsetting errors.
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From page 75... ...
is not critical since the key uses of decennial census counts are for purposes such as apportionment, redistricting, fund allocation, and public and private planning, which typically make use of census counts at higher levels of aggregation. The estimates at lower levels of aggregation are used primarily as "building blocks." However, there are some uses of census counts at lower levels of aggregation than the poststrata, so it is important to determine whether adjusted counts are at least as good as unadjusted counts at lower levels of aggregation.
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From page 76... ...
Using an empirical approach, they demonstrated that counts produced using synthetic estimation were preferred to unadjusted census counts in a wide variety of simulated circumstances. The benefits are acknowledged to be relatively modest which is only to be expected since no new information is being provided at that level of aggregation but the preference for adjusted counts to unadjusted counts occurs with relatively high probability.
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From page 77... ...
Second, the simulation results from Schirm and Preston and Hartigan, as well as Tukey's results, assume that the adjusted estimates for the poststrata have less error than the corresponding unadjusted estimates. A more complete and realistic simulation would assume that estimates for various poststrata are subject to error of various magnitudes probabilistically, and then see whether synthetic estimation does result in counts with reduced loss as measured by typical loss functions.
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From page 78... ...
So some heterogeneity will exist. However, the question is instead whether the counts resulting from the use of this assumption are inferior to the unadjusted counts with respect to sensible loss functions.
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From page 79... ...
Second, the analyses conducted by Tukey, Schirm and Preston, Hartigan, and Wolter and Causey (cited above) , considered both loss functions for population counts and loss functions for population shares.
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From page 80... ...
Some of this bias is reduced through use of poststrata that have people with similar characteristics, who thus have similar probabilities of enumeration. The extent to which correlation bias, widely accepted as the largest source of bias in dual-system estimation when used in the decennial census, remains after posts/ratification, and the effect of any remaining correlation bias on the relative preference of adjusted to unadjusted census counts and shares is the main topic of this section.
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From page 81... ...
This argument is at least somewhat dependent on the use of a loss function based on population counts, rather than population shares. For "small" adjustments, Taylor series arguments can be made to show that similar benefits would transfer to share loss functions.
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From page 82... ...
(Although more theory would be desirable, this question may be more of an empirical than a theoretical one.) A greater understanding of the magnitude of correlation bias in the various poststrata would help to inform a decision as to whether adjusted counts are preferred for share loss functions.
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From page 83... ...
Second, what would be the effect on adjusted counts from hard undercoverage? Like the argument with respect to the impact of heterogeneity of enumeration probability, the hard undercoverage problem results in a situation at aggregate levels in which the adjusted counts, while not a perfect solution, are still preferred to the unadjusted census counts using loss functions for population counts.9 However, it is more difficult to assert the same for a loss function for population shares, which relates to the key uses of census data for apportionment, most fund allocation, etc.
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From page 84... ...
(Of course, census undercoverage at the block level is very indirectly measured, which complicates the interpretation of their findings.) This finding suggests that there could be geographically based clustering of the undercounted population that might reduce the effectiveness of adjustment for share loss functions.
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From page 85... ...
The panel discusses this literature to further support its endorsement of integrated coverage measurement. It argues that these three issues are not sufficiently compelling to shift the panel's position supporting the use of integrated coverage measurement as a reliable method for reducing census differential undercoverage and, more broadly, for improving the quality of census counts for the key purposes for which they are used.
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