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Ddi For Codebook For The Sipp Synthetic Beta 7.0

Authors: Reeder, Lori B.; Stanley, Jordan C.; Vilhuber, Lars;

Ddi For Codebook For The Sipp Synthetic Beta 7.0

Abstract

The SIPP Synthetic Beta (SSB) is a Census Bureau product that integrates person-level micro-data from a household survey with administrative tax and benefit data. These data link respondents from the Survey of Income and Program Participation (SIPP) to Social Security Administration (SSA)/Internal Revenue Service (IRS) Form W-2 records and SSA records of retirement and disability benefit receipt, and were produced by Census Bureau staff economists and statisticians in collaboration with researchers at Cornell University, the SSA and the IRS. The purpose of the SSB is to provide access to linked data that are usually not publicly available due to confidentiality concerns. To overcome these concerns, Census has synthesized, or modeled, all the variables in a way that changes the record of each individual in a manner designed to preserve the underlying covariate relationships between the variables. The only variables that were not altered by the synthesis process and still contain their original values are gender and a link to the first reported marital partner in the survey. Eight SIPP panels (1990, 1991, 1992, 1993, 1996, 2001, 2004, 2008) form the basis for the SSB, with a large subset of variables available across all the panels selected for inclusion and harmonization across the years. Administrative data were added and some editing was done to correct for logical inconsistencies in the IRS/SSA earnings and benefits data.

Additional funding by Sloan Grant https://sloan.org/grant-detail/6845

Related Organizations
Keywords

administrative data, codebook, SIPP, synthetic data, linked

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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