Small-Area Estimates of School-Age Children in Poverty Interim Report 1, Evaluation of 1993 County Estimates for Title I Allocations (1997) / Chapter Skim
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4. PANEL ASSESSMENT OF THE METHODOLOGY
4. PANEL ASSESSMENT OF THE METHODOLOGY
Pages 25-35
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From page 25... ...
For the purposes of Title I allocations, the primary concern is with the quality of the estimates of poverty among school-age children for counties. Thus, the discussion in this section focuses largely on the county-level model, but it also considers the state-level model and the Census Bureau's population estimates, both of which enter into the final county estimates.
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From page 26... ...
Such models include a county-level model that predicts rates instead of numbers of school-age children in poverty; models that predict change in poverty over time or that use change-related predictor variables (e.g., changes in the number or proportion of child exemptions reported by families in poverty on tax returns) ; models that include additional predictor variables constructed from the available data; and models that allow a more flexible approach by using such statistical estimation procedures as generalized linear modeling.
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From page 27... ...
The expressed rationale for this decision is that, although there are postcensal estimates of the school-age population by county with which to connect the estimated rates to estimated numbers of poor school-age children, there are no variance estimates for these population estimates (Coder et al., 1996~. The consequence of this decision is that changes in the number of children in poverty due to changing poverty rates and due to changing overall population growth (or decline)
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From page 28... ...
The rationale for this specification is that the administrative data used in the model are more consistently measured across different areas than across time. It is certainly true that changes in tax and transfer program rules will affect the comparability of administrative data over time, but differences in program participation rates and administration may affect comparability across areas.3 Although the quantitative evaluation summaries we have seen of variations of the Census Bureau's basic model did not demonstrate superior accuracy for models that used more change variables, the fact that a type of change variable was important in the state-level model makes us believe that it would have been helpful in some form in the county-level model as well.
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From page 29... ...
A formal hypothesis test performed for the state-level model (Fay, 1996) supported the conclusion that the state-level regression model using administrative records data improved on the estimates of poverty rates for 1993 that could be obtained by using only 1989 poverty rates from the decennial cenSUS.5 Thus, this test provided evidence to support basing estimates on a statistical model rather than on decennial census data.
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From page 30... ...
in January 1997. Subsequently, the Census Bureau discovered errors in the input data for a few counties that somewhat changed the 1993 estimates from the county-level model.
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From page 31... ...
Subsequently, the Census Bureau discovered errors in the input data for a few counties that somewhat changed the estimates from its 1989 county-level model. However, the general findings continue to hold.
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From page 32... ...
(Because we averaged over large enough groups of counties, the CPS sampling error is not excessively large.) Statistical hypothesis tests suggest that there is a tendency for the model to underpredict for counties with smaller populations, although the pattern is not completely consistent for the years (1989 and 1993)
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From page 33... ...
Yet preliminary tests by the panel have not been able to establish statistically significant differences between the CPS-census ratios among groups of counties defined by population size and other characteristics. (As an example, there does not appear to be a statistically significant difference between the CPScensus ratio for counties with population size 100,000-499,999 population and the ratios for counties with smaller or larger population sizes.)
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From page 34... ...
For county estimates of the total population, the average absolute percentage error improved between 1980 and 1990: in 1980, it was 4.1 percent unweighted and 3.1 percent weighted (by size of county) ; in 1990, it was 3.6 percent un i°More precisely, the denominator of the poverty rate in the allocation formula is the estimated population of related children aged 5-17 in each county.
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From page 35... ...
The pattern is not monotonic: counties that lost population had a somewhat larger average absolute error, 3.5 percent (Davis, 1994~. The Census Bureau has not yet completed its evaluation of county-level estimates for the population group aged 5-17 and the one under age 21.
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