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Activity Number: 354 - SPEED: Big Data, Small Area Estimation, and Methodological Innovations Under Development, Part 2
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 11:15 AM
Sponsor: Survey Research Methods Section
Abstract #307746
Title: Small Area Estimates of the Child Population and Poverty in School Districts Using Dirichlet-Multinomial Models
Author(s): Jerry Maples*
Companies: U.S. Census Bureau
Keywords: Small Area; poverty; population; administrative records; SAIPE

The Small Area Income and Poverty Estimates (SAIPE) program produces estimates of the number of children, total and in poverty, for each school district across the United States. Currently, the estimation methodology for school district child population is based on the most recent decennial census school district to county shares that do not change between censuses. The methodology for school district poverty estimates is based on shares determined by the most recent ACS 5-year and Federal Tax data (Maples 2007). Neither method is built on a statistical model framework. Preliminary research has shown that the Dirichlet-Multinomial small area model is a promising model framework for modeling the subcounty to county population shares. We propose a pair of Dirichlet-Multinomial small area models to jointly estimate relevant school-aged child population and poverty. Data from the American Community Survey and Federal Tax records will be used to fit the models. An added improvement in switching to a stochastic model-based form is that prediction errors can now be quantified for both population and poverty estimates.

Authors who are presenting talks have a * after their name.

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