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Activity Number: 161
Type: Topic Contributed
Date/Time: Monday, August 1, 2016 : 10:30 AM to 12:20 PM
Sponsor: Government Statistics Section
Abstract #320571 View Presentation
Title: Using Sample Data to Reduce Nonsampling Error in Unit-Level Tax Administrative Data
Author(s): Tracy Haines* and Victoria Bryant and Kimberly Henry
Companies: IRS/SOI and IRS/SOI and IRS/SOI
Keywords: Survey sampling ; Administrative Tax Records ; Small area Estimation

Tax policy research relies heavily on the availability of accurate and timely income-related statistics on the filing population of the United States. With computing advancements, more researchers are seeking quality income information for smaller subsets of the filing population. While the Statistics of Income (SOI) Division of the Internal Revenue Service's Individual sample has had a long history of providing quality data at the national level, estimates for small domains are subject to sampling variability. Henry et al. (2007) used area-level small area models to produce improved state-level estimates. We extend this research, using rich auxiliary data in unit-level models to reduce error in zip code-level estimates. We apply some alternative estimators to data from SOI's Tax Year 2012 sample and associated population frame data to gauge the estimates' accuracy.

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

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