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Combining Administrative and Survey Data to Improve Income Measurement

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  • Bruce D. Meyer
  • Nikolas Mittag

Abstract

We describe methods of combining administrative and survey data to improve the measurement of income. We begin by decomposing the total survey error in the mean of survey reports of dollars received from a government transfer program. We decompose this error into three parts, generalized coverage error (which combines coverage and unit non-response error and any error from weighting), item non-response or imputation error, and measurement error. We then discuss these three sources of error in turn and how linked administrative and survey data can assess and reduce each of these sources. We then illustrate the potential of linked data by showing how using linked administrative variables improves the measurement of income and poverty in the Current Population Survey, focusing on the substitution of administrative for survey data for three government transfer programs. Finally, we discuss how one can examine the accuracy of the underlying links used in the combined data.

Suggested Citation

  • Bruce D. Meyer & Nikolas Mittag, 2019. "Combining Administrative and Survey Data to Improve Income Measurement," NBER Working Papers 25738, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:25738
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    References listed on IDEAS

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    1. Bruce D. Meyer & Nikolas Mittag, 2019. "Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness, and Holes in the Safety Net," American Economic Journal: Applied Economics, American Economic Association, vol. 11(2), pages 176-204, April.
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    3. Bruce D. Meyer & Nikolas Mittag, 2015. "Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net," Upjohn Working Papers 15-242, W.E. Upjohn Institute for Employment Research.
    4. 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.
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    Cited by:

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    2. Christian Awuku-Budu & Dirk van Duym, 2022. "Developing Statistics on the Distribution of State Personal Income: Methodology and Preliminary Results," BEA Working Papers 0197, Bureau of Economic Analysis.
    3. Dennis Fixler & Marina Gindelsky & David Johnson, 2020. "Measuring Inequality in the National Accounts," BEA Working Papers 0175, Bureau of Economic Analysis.
    4. Korenman, Sanders & Remler, Dahlia K. & Hyson, Rosemary T., 2021. "Health insurance and poverty of the older population in the United States: The importance of a health inclusive poverty measure," The Journal of the Economics of Ageing, Elsevier, vol. 18(C).
    5. Nora Lustig, 2019. "The “Missing Rich” in Household Surveys: Causes and Correction Approaches," Commitment to Equity (CEQ) Working Paper Series 75, Tulane University, Department of Economics.
    6. Nora Lustig, 2020. "The ``missing rich'' in household surveys: causes and correction approaches," Working Papers 520, ECINEQ, Society for the Study of Economic Inequality.

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    More about this item

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs

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