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Several features of the 2000 state estimates should be noted.
Bayesian Estimation Techniques. The models SAIPE used to estimate 2000 income and poverty at the state level employ both direct survey-based estimates of 2000 income and poverty from the 2001 ASEC and regression predictions of income and poverty based on administrative records and Census 2000 data. We combine the regression predictions with the direct sample estimates using Bayesian techniques. The Bayesian techniques weight the contribution of the two components (regression predictions and direct estimates) on the basis of their relative precision.
The regression models used to develop the regression predictions are postulated for the true, unobserved poverty ratios and median income, but they are fitted to the ASEC direct estimates allowing for the sampling errors in the data. If the variance of the error term in a given regression model (the model error variance) were known, then the Bayesian estimate for each state would be a weighted average (shrinkage estimate) of the state's regression prediction and direct ASEC estimate. The two weights in this average add to 1.0, with the weight on the direct estimate computed as the model error variance divided by the total variance (model error variance plus sampling error variance). In this average, the larger the sampling variance of a direct sample estimate, the smaller its contribution to the shrinkage estimate, and the larger the contribution from the regression prediction. Since the model error variance is unknown, the Bayesian approach averages the shrinkage estimates computed over a plausible range of values of the model error variance, weighting the results for each of these values according to the posterior (conditional on the data) probability distribution of the model error variance developed from the Bayesian calculations. The result is generally very close to what one gets by estimating the model error variance by the mean of its posterior distribution and computing the corresponding shrinkage estimate. Technical details of the Bayesian approach are discussed in the paper, "Accounting for Uncertainty About Variances In Small Area Estimation," (Bell 1999) in the Published Papers section of this web site.
Annual Social and Economic Supplement (ASEC sample expansion). Starting in 2001, the sample for the CPS March supplement used to estimate poverty and income statistics, was expanded. Now known as the Annual Social and Economic Supplement or ASEC (formally called the Annual Demographic Supplement), the sample was expanded primarily to improve state estimates of children's health insurance coverage to satisfy data needs of the State Children's Health Insurance Program (SCHIP). Initial estimates from the 2001 sample released in September 2001 used only the traditional March supplement households, but revised estimates later became available that were based on the full 2001 ASEC sample, including poverty and income estimates for IY 2000. These estimates from the full 2001 ASEC sample have substantially lower variances than those from the traditional March supplement sample, and so have been used in the SAIPE state poverty rate and median income models for IY 2000. For survey years 2002 and beyond, CPS annual supplement estimates are all based on the full ASEC sample, and so these estimates will continue to be used in the SAIPE state models. For further information on the ASEC and the SCHIP sample expansion see the Summary of the CPS Sample Expansion.
Poverty Ratios and Numbers of People in Poverty. Deriving state-level estimates of the numbers of people in poverty of various ages involves two steps. The first step is to apply the models and Bayesian estimation techniques to the ASEC direct state estimates of "poverty ratios." The second step is to multiply the resulting model-based poverty ratio estimates by corresponding demographic population estimates to convert the results to estimates of the numbers of people in poverty of various ages.
The poverty ratios used as the dependent variables in the regression models have the ASEC direct-estimated number people in poverty of the given age in the numerator and the CPS direct-estimated noninstitutional population of the given ages in the denominator. These "poverty ratios" differ from official poverty rates which would use the ASEC estimated poverty universes of the given age as the denominators. (For a discussion of the differences between the noninstitutional population and the poverty universe see Denominators for Model-Based State and County Poverty Rates).
We use ASEC estimated numbers in both the numerator and denominator of the poverty ratios because positive correlation between the two estimates generally makes the resulting poverty ratio estimate more precise than one obtained with an ASEC estimated numerator and a demographic population estimate in the denominator. We multiply the model-based poverty ratio estimates by demographic population estimates, however, because the demographic estimates are deemed more reliable than ASEC direct population estimates, which contain substantial sampling error for most states. The ASEC controls survey weights only to estimates of the population age 0-18 and 19 and over at the state level, and we are making estimates for more specific age groups.
Controlling to the National Estimates. After converting the Bayesian estimates of poverty ratios to state estimates of numbers of people in poverty, we control these estimates to the direct national estimate of number people in poverty based on the ASEC. We do not control estimates of state median household income to the national median because the estimation model does not produce the entire household income distribution, which would be required to do so.
Using Estimates from Census 2000 in the Models. The Census 2000 estimates of poverty ratios and median income in 1999 provide regression predictor variables for the corresponding age-specific poverty ratio models and the median income model. The specific variables are documented below. The same variables were used last year in the models for IY 1999. Prior to this, however, the models generally used "census residuals" obtained by regressing the prior (1990) census estimates for 1989 on the 1989 values of the regression predictors from the administrative data. For the census year, the use of census estimates or census residuals for that year as a regression variable yields equivalent models, so the choice to use 2000 census estimates last year in the IY 1999 models was made for other reasons. This year we expected to switch to the use of 2000 census residuals. However, statistical comparisons of the alternative models for IY 2000 favored the use of census estimates rather than census residuals, and so the census estimates have been used in this year's models. We will continue to make such model comparisons in the future and will switch to use of census residuals when this becomes the favored choice, something that becomes more and more likely the further we move beyond the census IY.
The Poverty Ratio Models
The model of 2000 state poverty ratios employs the following predictors:
For further information on these variables, go to Information about Data Inputs.
The dependent variable is the 2000 state estimate of the ratio of the number people in poverty for the relevant age group to the noninstitutional population of that age with both the numerator and denominator estimated from the 2001 CPS ASEC.
Estimating the Total Number of People in Poverty.
We derive the estimate of the total number of people in poverty in a state by summing the separate model-based estimates of the number of people in poverty by age (not limited to related children). The age groups with separate models were 1) people under 5 years of age, 2) people age 5 to 17 years, 3) people age 18 to 64 years, and 4) people age 65 years and over.
The Model For Median Household Income
The regression model for the 2000 median household income for states has the following predictor variables:
The dependent variable is the direct estimate of median household income in 2000 from the