Small-Area Income and Poverty Estimates Priorities for 2000 and Beyond (2000) / Chapter Skim
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Executive Summary
Executive Summary
Pages 1-10
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From page 1... ...
The 1994 "Improving America's Schools Act" called for the use of the SAIPE estimates of poor school-age children for counties and school districts to allocate more than $7 billion annually for programs for educationally disadvantaged children under Title I of the Elementary and Secondary Education Act. The 1994 act also required a panel of the Committee on National Statistics at the National Research Council to determine if the estimates were sufficiently reliable for Title I allocations and to make recommendations for their use and future development.
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Because there is no one data source that can provide the SAIPE estimates, the Census Bureau develops them by using statistical modeling techniques that combine data from household surveys, the decennial census, and administrative records. The SAIPE estimates, consequently, are "indirect," and, as such, their quality depends on the choice of a suitable statistical model.
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Producers The producing agency for a program of model-dependent estimates, such as SAIPE, should, first of all, have adequate staff and other resources for all the component operations. The producing agency should also: · maintain regular contact with key users, so that the estimation program produces those estimates that are most needed and appropriate for important program uses within the constraints of available resources; · as a matter of routine practice every time a new round of estimates is prepared, check the input data for errors and assess each data source for its continued suitability for use in estimation models; · search for possible new data sources whose use might lead to im
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RECOMMENDATIONS FOR SAIPE Internal and external evaluations of the 1993 and 1995 estimates of poor school-age children for small areas from the SAIPE program found that the state and county models are working reasonably well but identi
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Marked improvements in the estimates for school districts and other subcounty areas will require new sources of data for use in models. Research and Development for Current Models The Census Bureau's SAIPE Program estimates poverty and income for states and counties by combining the estimates from statistical regression models that are based on the March Current Population Survey (CPS)
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Work should proceed on estimation techniques, such as generalized linear mixed models, that would include all counties with households in the CPS sample. · Both the state and county models have problems in estimating the relative weights that are used to combine the regression predictions and the direct CPS estimates.
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From page 7... ...
Another use is for ACS estimates to serve as the dependent variable in county models, which could thereby include all, or nearly all, counties in the estimation. The Census Bureau should also conduct research on using ACS estimates for school districts and other subcounty areas to form within-county shares or proportions to apply to updated county model poverty estimates.
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From page 8... ...
Role of Administrative Data SAIPE estimates for school districts and other subcounty areas cannot currently be produced by using regression models similar to the state and county models, although such models would likely improve upon the current shares procedure. No administrative records data sources currently exist that can provide consistently measured predictor variables for a subcounty model, in the way that tax return and food stamp data are used in the state and county models.
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From page 9... ...
Although issues of comparability across areas and the current lack of a centralized source for the data present problems in using school lunch counts to estimate poverty, the panel concludes that further evaluation may be warranted to determine the usefulness of those data for the SAIPE school district estimates. Estimates of total population and population by age are required for many uses of small-area income and poverty estimates from SAIPE; postcensal population estimates are developed by using administrative records such as tax returns.
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