JSM 2005 - Toronto

Abstract #303336

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 228
Type: Contributed
Date/Time: Tuesday, August 9, 2005 : 8:30 AM to 10:20 AM
Sponsor: Section on Government Statistics
Abstract - #303336
Title: Evaluation of School District Poverty Estimates: Predictive Models Using IRS Income Tax Data
Author(s): Jerry Maples*+
Companies: U.S. Census Bureau
Address: 4201 Logteal Dr, Waldorf, MD, 20603, United States
Keywords: Small area estimates ; administrative records ; school districts ; poverty estimates
Abstract:

The Small Area Income and Poverty Estimates (SAIPE) program provides estimates for selected income and poverty statistics for states, counties, and school districts. School districts are decomposed into pieces to avoid overlapping county boundaries. In the current school district poverty model, within-county shares of poverty are created for each school district piece based on the most recent census long form data. These shares allocate the county's child poverty to each school district piece, and the estimate of the school district is the sum of the poverty in its pieces. New models using IRS income tax data have been proposed and model fit examined. To evaluate the predictive properties of the new estimates of school district poverty, the models are fitted to the 1990 census and income year 1989 IRS income tax data and then used to predict the number of poor children for income year 1999 using the 1999 IRS income tax data. The long form poverty estimates from Census 2000 are the standard of comparison for the model predictions. However, this evaluation must take into account the sampling error variance of the long form estimates.

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Revised March 2005