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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E. FUTURE RESEARCH
E. FUTURE RESEARCH
Pages 72-79
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From page 72... ...
among the predictor variables in the county-level model; the effects of implementing a constrained model; the possible advantages of using other models, such as a model of change in poverty over time or a model of poverty rates or ratios; and two issues raised by the use of a logarithmic transformation of the predictor and dependent variables. MODEL SPECIFICATION The model used by the Census Bureau to produce updated estimates of poor school-age children for states is of a form that appears in the literature, and the estimation procedures are those that have been used previously.
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From page 73... ...
The use of counts rather than rates in the countylevel model is justified on the basis that the chosen model permits estimation of standard errors of model predictions. The Census Bureau has argued that a model for rates would not permit estimation of standard errors since there are no standard errors for the demographic population estimates.
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From page 74... ...
It is not a requirement that the state- and county-level models be consistent, but inconsistent models should be used only if there is a good reason to do so. In this case, the decision to use distinct models may have been primarily driven by the administrative organiza TABLE E-1 Ratios of State Estimates of the Number of School-Age Children in Poverty in 1993 to the Sum of Uncontrolled County Estimates for 1993, Selected States State Ratio of State Estimate to Sum of County Estimates Alaska Connecticut Michigan Massachusetts West Virginia New Jersey Arizona New York Florida California Wyoming Texas Mississippi Alabama minois Nebraska Idaho 1.33 1.24 1.22 1.21 1.16 1.12 1.11 1.11 1.05 1.01 1.01 0.98 0.98 0.97 0.97 0.94 0.89 SOURCE: Calculated by the panel from data that were made available to the panel in January 1997.
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From page 75... ...
TABLE E-2 Correlation Matrix of Independent Variables Used in 1993 County-Level Model Vanable x ~x2 x3 x4 x5 x~ (tax returns, poor, < 21) 1.000 0.959 0.948 0.950 0.971 x2 (food stamp recipients)
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From page 76... ...
The fact that this sum exceeds 1 implies that the model estimates a higher poverty rate for large counties than for small counties, as is shown in Table E-3 for three hypothetical counties of different sizes, where each of the predictor variables increases across the counties directly in the proportions 1:5:25. As is shown in the final row of the table, the Census Bureau's county-level model estimates a poverty rate that is 15 percent higher for the large county than for the small county.
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From page 77... ...
However, the sum of the coefficients for the unrestricted model is 1.041, close to the 1 for the restricted model. The fitted models were used to estimate the number of school-age children in poverty in 1989 for every county; the averages of the relative differences (model estimate minus census estimate, divided by census estimate)
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From page 78... ...
A closely related procedure, which is used in the state-level model, is to include residuals from a model fit during an earlier "control" period as an explanatory variable. A change model assumes that administrative procedures have been relatively constant within any given county over the study period.
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From page 79... ...
Also, it would be desirable to investigate the use of a generalized linear model as an alternative modeling approach that does not require removing counties with no school-age children in poverty from the estimation of the regression coefficients.
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