Small-Area Estimates of School-Age Children in Poverty Evaluation of Current Methodology (2000) / Chapter Skim
Currently Skimming:
Research and Development Priorities
Research and Development Priorities
Pages 157-168
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From page 157... ...
In addition, research is needed to take account of likely future developments in the availability and characteristics of data sources that have implications for the modeling effort and to work on longer term modeling issues. Continued work to improve the county model is important not only for county estimates, but also to improve school district estimates that are developed by using the within-county shares estimation procedure.
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From page 158... ...
There is also interest in state and county estimates of poor children for other important public policy uses, such as evaluating the effects of changes in welfare programs. Priorities for short-term and longer term research should consider the imporiThe final report considers the possible role for the SAIPE Program of two new survey data sources with relatively large sample sizes, the 2000 census long-form survey and the planned American Community Survey, as well as two smaller ongoing surveys, the March CPS and the Survey of Income and Program Participation.
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From page 159... ...
, the panel identified seven types of research that should be pursued as a priority to determine if the current estimation procedure for counties can be improved: modeling of CPS county sampling variances; estimation of model error and sampling error variance in the state model; methods to incorporate state effects in the county model; discrete variable models that include counties in the CPS sample that have no sampled households with poor school-age children; ways to reduce the time lag of the estimates; evaluation of food stamp and other input data; and large category differences and residual patterns for the state and county models. Since then, the Census Bureau has made progress in several of these research areas as noted below.
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From page 160... ...
In addition, the Census Bureau should continue to pursue an alternative approach, which is to estimate the CPS sampling variances for counties with adequate sample size on the basis of direct calculations of these variances that take account of the clustered sample design within these counties, and then use a generalized variance function for modeling the sampling variances for all counties with CPS-sampled households. With this approach, the model error variance is then obtained simultaneously with the regression parameter estimates through use of maximum likelihood estimation, as in the state model.
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From page 161... ...
, suggested that a small state random effect may be present and that further research on a random state effects model should be conducted. Discrete Variable Models that Use Counties with No Sampled Poor SchoolAge Children When using a logarithmic transformation of the number of poor school-age children as the dependent variable in the county regression model, all counties in the CPS sample for which none of the sampled households has schoolage children who are poor (262 of 1,247 counties for the 1995 model)
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From page 162... ...
Another possibility is to control the estimates from the county model to the state model estimates for the latest of the 3 years of CPS data used in the county model, instead of to the middle year. These ideas and others (see National Research Council, 2000:Ch.3)
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From page 163... ...
demonstrated that the state and county models are generally well behaved with respect to the estimates for various categories of states and counties. However, it is important to investigate further the residual patterns and category differences to determine if the regression models could be improved either through a modification of the model form or through the addition of predictor variables.5 As an example of a pattern that is worth further investigation, when compared with CPS aggregate estimates, the county model exhibited a tendency in 1989, 1993, and 1995 to underpredict the number of poor school-age children in counties with large percentages of Hispanics.
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From page 164... ...
Three important areas for research are: investigation of methods to reduce the variance of the census estimates of poor school-age children; use of school enrollment data to improve estimates of the total number of school-age children; and investigation of the possible use of National School Lunch Program data to improve estimates of poor school-age children. Reducing the Variance of the Census Estimates of Poor School-Age Children Because so many school districts are so small in size, the census estimates of poor school-age children, which derive from the long-form sample, are subject to high sampling variability.
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From page 165... ...
By carefully constructing smoothed school-district estimates as combinations of school-district and county-level estimates, it might be possible to produce school-district estimates with lower mean square errors than the direct census estimates. It would be desirable to make use of knowledge about model error and sampling variances at the school-district level if available to tailor the degree of smoothing for each school district.
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From page 166... ...
Nonetheless, approval to receive free meals under the National School Lunch Program is an indicator of low income, and it seems worthwhile to pursue for other states the research that the panel undertook for New York and Indiana (see also National Research Council, 2000:Ch.5)
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From page 167... ...
Users should also study the effects of using the estimates for allocations (see National Research Council, 2000:Ch.6, 7~. The Committee on National Statistics is planning to conduct more work in this area.
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