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A major overall change from 2004 and previous years is the switch to using data from the American Community Survey (ACS) as the basis for the SAIPE state and county models, replacing the data from the Current Population Survey (CPS) Annual Social and Economic Supplement (ASEC) that was used previously. This change was made for essentially two reasons. In 2006 the Census Bureau changed the basis of its official direct state poverty estimates from CPS ASEC data to ACS data. Since SAIPE focuses on estimates at state and lower levels of geography, changing to ACS as the basis for SAIPE is consistent with this change made for the official direct survey estimates. Additionally, the much larger sample size in the ACS (about 3,000,000 addresses nationally) than in the CPS ASEC (about 100,000 addresses nationally) conveys some significant advantages for small area estimation. In general, the larger ACS sample sizes lead to substantially lower variances of the direct survey estimates and to mostly lower variances for the resulting model-based estimates.
In addition to the reduced sampling error in the ACS based estimates, definitional and data collection differences in the two surveys produce differences in direct income and poverty estimates. The 2006 reports Evaluation of Poverty Estimates: A Comparison of the American Community Survey and the Current Population Survey [PDF 74k] and What Do We Know About Differences Between CPS and ACS Income and Poverty Estimates? [PDF 713k] discuss some of the methodological differences and make some comparisons between ACS and CPS ASEC income and poverty estimates.
We can identify certain differences between the surveys that may produce differences in the income and poverty estimates. The following considerations are worth noting.
As an example of the implications of the last two points, consider the college dormitory population. The CPS ASEC includes college dormitory residents in the poverty universe and generally counts them at their parents' home addresses, but the 2005 ACS did not include college dormitory residents in the survey. Thus, for example, a family of four with one child living in a college dormitory would have its poverty threshold for ACS 2005 computed for a family with three members, but for CPS ASEC the computation would be based on a family with four members.
Some particular implications of the differences between the ACS and the CPS ASEC as sources of data for the SAIPE state and county models are discussed in the sections below. Further details are given in a 2007 SAIPE report Use of ACS Data to Produce SAIPE Model-Based Estimates of Poverty for Counties [PDF 3.4M].
State Model Changes
The forms of the state poverty ratio and median income models for 2005 remained the same as for the 2004 estimates. The switch to use of ACS data, however, had some implications for how the state models were implemented.
County Model Changes
The timing of the regressor variables corresponds with the discussion of the regressor variables in the state model: Since the 2005 ACS collected income responses covering overlapping 12-month periods from January of 2004 through November of 2005, the ACS 2005 income and poverty estimates do not refer clearly to a single income year. In fact, the regression predictor variables defined for either 2004 or 2005 have about equal correspondence to the ACS 2005 estimates. Both were tried in the models and the resulting model-based estimates were very similar. The predictor variables for 2004 were chosen for this year's model. This means that they are the same as the predictor variables used last year when modeling the CPS ASEC estimates for income year 2004.
Using the CPS ASEC, sampling and model error variance had to be calculated using indirect methods. Sampling error variance was derived by assuming the variance is proportional to the square root of the sample size. Model error variance was estimated from a census auxiliary equation. Using ACS, data changed the way these variances are calculated. The sampling error variance was directly estimated by using replicate weights. The model error variance was estimated using the ACS data, not an auxiliary equation.
Prior to the 2005 estimates, the most recent census results were used to estimate the proportions (shares) of the numbers of school-age children in poverty in each county for each school district that is wholly or partially contained in that county. These shares remained constant through the decade. The shares were used to produce school district piece poverty estimates for a given year by multiplying the SAIPE model-based county estimates of the numbers of school-age children in poverty for that year. Other than districts that experienced a boundary change, this approach provided updated information on poverty only at the county level. For intercensal years, there was no updated information on the distribution of poverty within counties, since the school district to county poverty shares remained constant.
For the current 2005 estimates, recent tabulations of IRS income tax data for school districts provide updated information on the distribution of poverty across school districts within counties. Tabulations of poor exemptions (exemptions on returns with adjusted gross income below the poverty threshold) provide a variable related to poverty, while tabulations of total exemptions (all exemptions on tax returns assigned to a given geographic area) provide a variable related to population,. From these tabulations, we produce a tax-based share of school district to county poverty, then employ an algorithm that minimizes the difference between the tax-based shares and the corresponding census-based shares. For more details see Overview of School District Estimates 2005.