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Race, Ethnicity, and Measurement Error

In: Race, Ethnicity, and Economic Statistics for the 21st Century

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  • Bruce D. Meyer
  • Nikolas Mittag
  • Derek Wu

Abstract

Large literatures have analyzed racial and ethnic disparities in economic outcomes and access to the safety net. For such analyses that rely on survey data, it is crucial that survey accuracy does not vary by race and ethnicity. Otherwise, the observed disparities may be confounded by differences in survey error. In this paper, we review existing studies that use linked data to assess the reporting of key programs (including SNAP, Social Security, Unemployment Insurance, TANF, Medicaid, Medicare, and private pensions) in major Census Bureau surveys, aiming to extract the evidence on differences in survey accuracy by race and ethnicity. Our key finding is a strong and robust, but previously largely unnoticed, pattern of greater measurement error for Black and Hispanic individuals and households relative to whites. As the dominant error is under-reporting for a wide variety of programs, samples, and surveys, the implication is that the safety net better supports minority groups than the survey data suggest, through higher program receipt and greater poverty reduction. These biases in survey estimates are large in many cases examined in the literature. We conclude that racial and ethnic minorities are inadequately served by our large household surveys and that researchers should cautiously interpret survey-based estimates of racial and ethnic differences in program receipt and post-benefit income. We briefly discuss paths forward.
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Suggested Citation

  • Bruce D. Meyer & Nikolas Mittag & Derek Wu, 2024. "Race, Ethnicity, and Measurement Error," NBER Chapters, in: Race, Ethnicity, and Economic Statistics for the 21st Century, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberch:14959
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    References listed on IDEAS

    as
    1. Celhay, Pablo & Meyer, Bruce D. & Mittag, Nikolas, 2021. "Errors in Reporting and Imputation of Government Benefits and Their Implications," IZA Discussion Papers 14396, Institute of Labor Economics (IZA).
    2. Charles Courtemanche & Augustine Denteh & Rusty Tchernis, 2019. "Estimating the Associations between SNAP and Food Insecurity, Obesity, and Food Purchases with Imperfect Administrative Measures of Participation," Southern Economic Journal, John Wiley & Sons, vol. 86(1), pages 202-228, July.
    3. Raj Chetty & Nathaniel Hendren & Maggie R Jones & Sonya R Porter, 2020. "Race and Economic Opportunity in the United States: an Intergenerational Perspective [“Intergenerational Mobility of Immigrants in the US Over Two Centuries,”]," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 135(2), pages 711-783.
    4. Hausman, J. A. & Abrevaya, Jason & Scott-Morton, F. M., 1998. "Misclassification of the dependent variable in a discrete-response setting," Journal of Econometrics, Elsevier, vol. 87(2), pages 239-269, September.
    5. Randall Akee & Maggie R. Jones & Sonya R. Porter, 2019. "Race Matters: Income Shares, Income Inequality, and Income Mobility for All U.S. Races," Demography, Springer;Population Association of America (PAA), vol. 56(3), pages 999-1021, June.
    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. Black, Dan & Sanders, Seth & Taylor, Lowell, 2003. "Measurement of Higher Education in the Census and Current Population Survey," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 545-554, January.
    8. Matthew Blackwell & James Honaker & Gary King, 2017. "A Unified Approach to Measurement Error and Missing Data: Details and Extensions," Sociological Methods & Research, , vol. 46(3), pages 342-369, August.
    9. Julian Cristia & Jonathan A. Schwabish, 2007. "Measurement Error in the SIPP: Evidence from Matched Administrative Records: Working Paper 2007-03," Working Papers 18322, Congressional Budget Office.
    10. Elira Kuka & Bryan A. Stuart, 2021. "Racial Inequality in Unemployment Insurance Receipt and Take-Up," NBER Working Papers 29595, National Bureau of Economic Research, Inc.
    11. Matthew Blackwell & James Honaker & Gary King, 2017. "A Unified Approach to Measurement Error and Missing Data: Overview and Applications," Sociological Methods & Research, , vol. 46(3), pages 303-341, August.
    12. AIGNER, Dennis J., 1973. "Regression with a binary independent variable subject to errors of observation," LIDAM Reprints CORE 130, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    13. Nikolas Mittag, 2019. "Correcting for Misreporting of Government Benefits," American Economic Journal: Economic Policy, American Economic Association, vol. 11(2), pages 142-164, May.
    14. Quentin Brummet & Denise Flanagan-Doyle & Joshua Mitchell & John Voorheis & Laura Erhard & Brett McBride, 2018. "Investigating the Use of Administrative Records in the Consumer Expenditure Survey," CARRA Working Papers 2018-01, Center for Economic Studies, U.S. Census Bureau.
    15. Bruce D. Meyer & Nikolas Mittag & Robert M. Goerge, 2022. "Errors in Survey Reporting and Imputation and Their Effects on Estimates of Food Stamp Program Participation," Journal of Human Resources, University of Wisconsin Press, vol. 57(5), pages 1605-1644.
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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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