Small-Area Estimates of School-Age Children in Poverty Interim Report 2, Evaluation of Revised 1993 County Estimates for Title I Allocations (1998) / Chapter Skim
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3. ALTERNATIVE COUNTY MODELS
3. ALTERNATIVE COUNTY MODELS
Pages 20-32
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From page 20... ...
When the original county estimates of poor school-age children in 1993 were provided to the panel, the Census Bureau had not had time to undertake a thorough assessment of the performance of that model or to compare it to other models. Subsequently, the panel and the Census Bureau developed a range of alternative county models to evaluate.
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From page 21... ...
. In the bivariate formulation, the 1993 county model jointly estimates two separate equations for March 1993-1995 CPS data and 1990 census data, respectively, in which the model errors of the two equations are allowed to be correlated (see below, "Bivariate Models".
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From page 22... ...
suggested that the county model may not adequately account for differences among states in the relationship of the predictor variables to the dependent variable and, consequently, that the county model may not adequately account for the variation among counties within a state. As a way to explore this problem, the Census Bureau developed a fixed state effects model.
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From page 23... ...
The predictor variables are the number of child exemptions (assumed to be under age 21) reported by families in poverty on tax returns; the number of people receiving food stamps; the estimated population under age 21; the total number of child exemptions on tax returns; and the estimated number of poor school-age children in the 1990 census.
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o 4= clot ~ A, .-~ t,.4 ~ o 4= ca an x x - ~ ca ~ ·0 ~ ~ o ~ 4~;)
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From page 26... ...
; the ratio of the total number of child exemptions on tax returns to the total population under age 21;2 and the ratio of the estimated number of poor related children aged 5-17 to the estimated total number of related children aged 5-17 from the 1990 census. All variables are transformed to logarithms.
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From page 27... ...
, XCENli · · · XCEN4i = the predictor variables in county i, UCENi = model error for county i, and eCENi = sampling error of YCENi for county i. The formulation with fixed state effects adds a dummy variable for each state, which is 1 for all counties in the state and O otherwise.
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From page 29... ...
29 ~4- ~, ~ Hi ~ ca ~ ca ~ o ~ ~ ~o so ,= so ,= ~ ca 0 4= 0 4= ca o ~ o ~ o o o so C)
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From page 30... ...
, except that the 1990 census estimated poverty rate for school-age children is dropped from the equation. In the 1990 census equation, the dependent variable is the estimated log poverty ratio for school-age children from the census; the predictor variables are the same as in the CPS equation, except that the IRS and food stamp data pertain to 1989 instead of 1993 and the population data are from the 1990 census rather than from the population estimates program.
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From page 31... ...
However, further development of bivariate and multivariate models, which might include CPS equations for more than 1 year, as well as a census equation, is worth pursuing for the longer run (see Chapter 6~. Evaluation results indicated that the county model would likely benefit from taking account of state effects in some way.
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From page 32... ...
This model is the same as model (c) except that the ratio of total child exemptions on tax returns to the total population under 18 replaces the ratio of total child exemptions on tax returns to the total population under age 21 as a predictor variable.
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