Small-Area Estimates of School-Age Children in Poverty Evaluation of Current Methodology (2000) / Chapter Skim
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Alternative County Models
Alternative County Models
Pages 44-56
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From page 44... ...
In selecting a specific model for developing small-area poverty estimates that are to be used for such an important public purpose as allocating funds, it is important to compare the selected model to competing models that may have specific advantages. When the original county estimates of poor school-age children in 1993 were released in early 1997, the Census Bureau had not had time to undertake a thorough assessment of the performance of the model used or to compare its performance to that of other models.
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From page 45... ...
. Treatment of Information from the Previous Census The revised county model that the Census Bureau used to produce estimates of poor school-age children in 1993 and 1995 is a single-equation model in which the dependent variable is from the CPS and one of the predictor variables is the estimated number of poor school-age children from the previous census.
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From page 46... ...
As a way to explore this problem, the Census Bureau developed a fixed state effects model by including an indicator, or dummy, variable for each state. The purpose of these state indicator variables is to enable the model to more accurately capture the variation among counties within each state by accounting for differences in the level of the dependent variable by state.
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From page 47... ...
The dependent variable is the CPS estimate of the log number of poor school-age children, derived by multiplying for each county the 3-year weighted average poverty rate for related children aged 5-17 by the 3-year weighted average of total related children aged 5-17. The predictor variables, all of which are transformed to logarithms, are the number of child exemptions (assumed to be under age 21)
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From page 48... ...
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From page 49... ...
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From page 50... ...
The dependent variable is the CPS estimate of the log proportion poor, or log poverty rate, for school-age children: more precisely, a poverty ratio-similar to the state model-in which for each county the numerator is the sum over 3 years of the estimated number of poor related children aged 5-17 and the denominator is the sum over 3 years of the estimated total number of CPS children aged 5-17. The predictor variables are also ratios: the ratio of the number of child exemptions reported by families in poverty on tax returns to the total number of child exemptions on tax returns; the ratio of the number of people receiving food stamps to the total population (all ages)
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From page 51... ...
Each model was also estimated for 1989: for the dependent variable, by averaging 3 years of CPS data (March 1989,1990, and 1991, covering income years 1988, 1989, and 1990~; for the predictor variables, by using appropriate data from IRS and food stamp records for 1989,1990 population estimates of school-age children, and 1980 census estimates of poor school-age children. The 1989 models were estimated to permit comparisons with 1990 census estimates of poor school-age children in 1989 for evaluation purposes (see Chapter 6~.
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From page 52... ...
, 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 53... ...
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 9~. 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 54... ...
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From page 56... ...
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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