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Activity Number:
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191
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Type:
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Topic Contributed
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Date/Time:
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Monday, July 30, 2007 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Survey Research Methods
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| Abstract - #309138 |
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Title:
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Microdata Simulation for Confidentiality Protection Using Regression Quantiles and Hot Deck
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Author(s):
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Jennifer Huckett*+ and Michael D. Larsen
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Companies:
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Iowa State University and Iowa State University
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Address:
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114 Snedecor, Ames, IA, 50011,
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Keywords:
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disclosure control ; disclosure avoidance ; synthetic data ; multiple imputation ; tax return data
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Abstract:
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Government agencies must simultaneously maintain confidentiality of individual records and disseminate useful microdata. Iowa's Legislative Services Agency (LSA) needs predicted state tax revenue based on proposed policy changes calculated from individual income tax returns. Iowa's Department of Revenue (IDR) cannot provide individual records to LSA by law. Currently, LSA submits requests to IDR that IDR computes and reports to LSA. This is inefficient for both agencies. We study options for IDR creating a full synthetic tax return file for release to LSA. Specifically, we study combining quantile regression, hot deck imputation, and additional confidentiality-preserving methods to produce releasable, usable data. Several versions of microdata can be multiply imputed to assess uncertainty. Measures of disclosure risk to evaluate confidentiality protection are considered.
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