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An Empirical Total Survey Error Decomposition Using Data Combination

Author

Listed:
  • Meyer, Bruce D.

    () (University of Chicago)

  • Mittag, Nikolas

    () (CERGE-EI)

Abstract

Survey error is known to be pervasive and to bias even simple, but important estimates of means, rates, and totals, such as poverty statistics and the unemployment rate. To summarize and analyze the extent, sources, and consequences of survey error, we define empirical counterparts of key components of the Total Survey Error Framework that can be estimated using data combination. Specifically, we estimate total survey error and decompose it into three high level sources of error: representation error, item non-response error and measurement error. We further decompose these sources into lower level sources such as a failure to report a positive amount and errors in amounts conditional on reporting a positive value. For error in dollars paid by two large government transfer programs, we use administrative records on the universe of program payments in New York State linked to three major household surveys to estimate the error components we define. We find that total survey error is large and varies in its size and composition, but measurement error is always by far the largest source of error. Our application shows that data combination makes it possible to routinely measure total survey error and its components. The results allow survey producers to assess error reduction strategies and survey users to mitigate the consequences of survey errors or gauge the reliability of their conclusions.

Suggested Citation

  • Meyer, Bruce D. & Mittag, Nikolas, 2019. "An Empirical Total Survey Error Decomposition Using Data Combination," IZA Discussion Papers 12151, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp12151
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    References listed on IDEAS

    as
    1. Bruce Meyer & Robert Goerge, 2011. "Errors in Survey Reporting and Imputation and Their Effects on Estimates of Food Stamp Program Participation," Working Papers 11-14, Center for Economic Studies, U.S. Census Bureau.
    2. C. Adam Bee & Joshua Mitchell, 2017. "The Hidden Resources of Women Working Longer: Evidence from Linked Survey-Administrative Data," NBER Chapters, in: Women Working Longer: Increased Employment at Older Ages, pages 269-296, National Bureau of Economic Research, Inc.
    3. Bollinger, Christopher R & David, Martin H, 2001. "Estimation with Response Error and Nonresponse: Food-Stamp Participation in the SIPP," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(2), pages 129-141, April.
    4. Deborah Wagner & Mary Lane, 2014. "The Person Identification Validation System (PVS): Applying the Center for Administrative Records Research and Applications’ (CARRA) Record Linkage Software," CARRA Working Papers 2014-01, Center for Economic Studies, U.S. Census Bureau.
    5. Barry T. Hirsch & Edward J. Schumacher, 2004. "Match Bias in Wage Gap Estimates Due to Earnings Imputation," Journal of Labor Economics, University of Chicago Press, vol. 22(3), pages 689-722, July.
    6. Wooldridge, Jeffrey M., 2007. "Inverse probability weighted estimation for general missing data problems," Journal of Econometrics, Elsevier, vol. 141(2), pages 1281-1301, December.
    7. Meyer, Bruce D. & Mittag, Nikolas & Goerge, Robert M., 2018. "Errors in Survey Reporting and Imputation and Their Effects on Estimates of Food Stamp Program Participation," IZA Discussion Papers 11776, Institute of Labor Economics (IZA).
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    total survey error; administrative data; measurement error; linked data; data combination;

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs

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