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Synthetic Data For Small Area Estimation In The American Community Survey

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  • Joseph W. Sakshaug
  • Trivellore E. Raghunathan

Abstract

Small area estimates provide a critical source of information used to study local populations. Statistical agencies regularly collect data from small areas but are prevented from releasing detailed geographical identifiers in public-use data sets due to disclosure concerns. Alternative data dissemination methods used in practice include releasing summary/aggregate tables, suppressing detailed geographic information in public-use data sets, and accessing restricted data via Research Data Centers. This research examines an alternative method for disseminating microdata that contains more geographical details than are currently being released in public-use data files. Specifically, the method replaces the observed survey values with imputed, or synthetic, values simulated from a hierarchical Bayesian model. Confidentiality protection is enhanced because no actual values are released. The method is demonstrated using restricted data from the 2005-2009 American Community Survey. The analytic validity of the synthetic data is assessed by comparing small area estimates obtained from the synthetic data with those obtained from the observed data.

Suggested Citation

  • Joseph W. Sakshaug & Trivellore E. Raghunathan, 2013. "Synthetic Data For Small Area Estimation In The American Community Survey," Working Papers 13-19, Center for Economic Studies, U.S. Census Bureau.
    • Handle: RePEc:cen:wpaper:13-19
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    File URL: https://www2.census.gov/ces/wp/2013/CES-WP-13-19.pdf
    File Function: First version, 2013
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    Cited by:

    1. Javier Miranda & Lars Vilhuber, 2016. "Using Partially Synthetic Microdata to Protect Sensitive Cells in Business Statistics," Working Papers 16-10, Center for Economic Studies, U.S. Census Bureau.

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