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Abstract Details
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Activity Number:
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174
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Type:
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Contributed
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Date/Time:
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Monday, August 1, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Survey Research Methods
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| Abstract - #301689 |
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Title:
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Estimation of Poverty at the School District Level Using Hierarchical Bayes Modeling
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Author(s):
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Sam Hawala*+ and Partha Lahiri
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Companies:
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U.S. Census Bureau and University of Maryland
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Address:
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4600 Silver Hill Road, Room 6H124F, Suitland, MD, 20233,
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Keywords:
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Small Area Estimation ;
Hierarchical Bayes ;
SAIPE ;
MCMC
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Abstract:
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Direct estimates of the poverty level for more than 25% of the school-districts in the U.S., are generally not available from the American Community Survey (ACS) data that are compiled annually. The Small Area Income and Poverty Estimates (SAIPE) program has depended on coalescing information from the decennial census, the Internal Revenue Service (IRS), from linking IRS records to Census geography, and from poverty estimates at the county level, to produce poverty estimates for every school district. Income data from the decennial census in particular played an important role, but are no longer available starting from the 2010 census. This research is part of the Census Bureau's effort to consider alternative estimation procedures. We consider Hierarchical Bayes models that combine information from the ACS, and from the IRS, to obtain annual estimates of most current and most relevan
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