|
Activity Number:
|
71
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Sunday, July 29, 2007 : 4:00 PM to 5:50 PM
|
|
Sponsor:
|
Section on Survey Research Methods
|
| Abstract - #309683 |
|
Title:
|
Using the Statistics of Income Division's Sample Data To Reduce Measurement and Processing Errors in Small-Area Estimates Produced from Administrative Tax Records
|
|
Author(s):
|
Kimberly A. Henry*+ and Robin Fisher and Partha Lahiri
|
|
Companies:
|
Internal Revenue Service and U.S. Department of the Treasury and University of Maryland
|
|
Address:
|
PO Box 2608, Washington, DC, 20013,
|
|
Keywords:
|
Survey Sampling ; Empirical Bayes Estimation ; Variance Smoothing
|
|
Abstract:
|
The large Individual Master File constructed by the Internal Revenue Service (IRS) has been used in the past to produce various income-related statistics for small geographic areas. Previous research using the Statistics of Income Division's (SOI) Form 1040 sample, a large national sample of cleaned administrative tax records, suggests the IRS data are subject to various measurement and processing errors. Thus, small-area estimates based on the IRS data, though free from the usual sampling error problem typical in small area estimation, are subject to various nonsampling errors. The SOI sample can be potentially used to reduce nonsampling errors in the IRS-based small area estimates. We propose an empirical best prediction (EBP) method to improve the IRS-based small area estimates by exploiting complementary strengths of IRS and SOI data.
|
