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Regression Coefficient Identification Decay in the Presence of Infrequent Classification Errors

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  • Kreider, Brent

Abstract

Recent evidence from Bound et al. (2001) and Black et al. (2003) suggests that reporting errors in survey data routinely violate all of the classical measurement error assumptions. The econometrics literature has not considered the consequences of arbitrary measurement error for identification of regression coefficients. This paper highlights the severity of the identification problem given the presence of even infrequent arbitrary errors in a binary regressor. In the empirical component, health insurance misclassification rates of less than 1.3 percent generate double-digit percentage point ranges of uncertainty about the variable's true marginal effect on the use of health services.

Suggested Citation

  • Kreider, Brent, 2007. "Regression Coefficient Identification Decay in the Presence of Infrequent Classification Errors," Staff General Research Papers Archive 12822, Iowa State University, Department of Economics.
    • Handle: RePEc:isu:genres:12822
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    Cited by:

    1. Nguimkeu, Pierre & Denteh, Augustine & Tchernis, Rusty, 2019. "On the estimation of treatment effects with endogenous misreporting," Journal of Econometrics, Elsevier, vol. 208(2), pages 487-506.
    2. Lorenzo Almada & Ian McCarthy & Rusty Tchernis, 2016. "What Can We Learn about the Effects of Food Stamps on Obesity in the Presence of Misreporting?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 98(4), pages 997-1017.
    3. Patrick Hullegie & Tobias J. Klein, 2010. "The effect of private health insurance on medical care utilization and self‐assessed health in Germany," Health Economics, John Wiley & Sons, Ltd., vol. 19(9), pages 1048-1062, September.
    4. 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.
    5. Acerenza, Santiago & Ban, Kyunghoon & Kedagni, Desire, 2021. "Marginal Treatment Effects with Misclassified Treatment," ISU General Staff Papers 202106180700001132, Iowa State University, Department of Economics.
    6. Ha Trong Nguyen & Huong Thu Le & Luke Connelly & Francis Mitrou, 2023. "Accuracy of self‐reported private health insurance coverage," Health Economics, John Wiley & Sons, Ltd., vol. 32(12), pages 2709-2729, December.
    7. Santiago Acerenza & Kyunghoon Ban & D'esir'e K'edagni, 2021. "Local Average and Marginal Treatment Effects with a Misclassified Treatment," Papers 2105.00358, arXiv.org, revised Sep 2024.
    8. Daniel Kaufmann, 2020. "Is deflation costly after all? The perils of erroneous historical classifications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 614-628, August.
    9. Tommasi, Denni & Zhang, Lina, 2024. "Bounding program benefits when participation is misreported," Journal of Econometrics, Elsevier, vol. 238(1).
    10. Akanksha Negi & Digvijay Singh Negi, 2022. "Difference-in-Differences with a Misclassified Treatment," Papers 2208.02412, arXiv.org.
    11. Kone, Zovanga L., 2018. "Intergenerational assimilation of UK immigrants in the labour market: A minor assumption with enormous implications for inference," Economics Letters, Elsevier, vol. 164(C), pages 94-99.
    12. Brent Kreider & John V. Pepper & Manan Roy, 2020. "Does The Women, Infants, And Children Program Improve Infant Health Outcomes?," Economic Inquiry, Western Economic Association International, vol. 58(4), pages 1731-1756, October.
    13. Helen H. Jensen & Brent Kreider & Oleksandr Zhylyevskyy, 2019. "Investigating Treatment Effects of Participating Jointly in SNAP and WIC when the Treatment Is Validated Only for SNAP," Southern Economic Journal, John Wiley & Sons, vol. 86(1), pages 124-155, July.
    14. Kyung Min Kang & Robert A. Moffitt, 2019. "The Effect of SNAP and School Food Programs on Food Security, Diet Quality, and Food Spending: Sensitivity to Program Reporting Error," Southern Economic Journal, John Wiley & Sons, vol. 86(1), pages 156-201, July.
    15. Brent Kreider & Richard J. Manski & John Moeller & John Pepper, 2015. "The Effect of Dental Insurance on the Use of Dental Care for Older Adults: A Partial Identification Analysis," Health Economics, John Wiley & Sons, Ltd., vol. 24(7), pages 840-858, July.
    16. Gundersen, Craig & Kreider, Brent & Pepper, John, 2012. "The impact of the National School Lunch Program on child health: A nonparametric bounds analysis," Journal of Econometrics, Elsevier, vol. 166(1), pages 79-91.
    17. Gundersen, Craig & Kreider, Brent, 2009. "Bounding the effects of food insecurity on children's health outcomes," Journal of Health Economics, Elsevier, vol. 28(5), pages 971-983, September.
    18. Kreider, Brent & Pepper, John V. & Roy, Manan, 2018. "Does the Women, Infants, and Children Program (WIC) Improve Infant Health Outcomes?," ISU General Staff Papers 201805010700001055, Iowa State University, Department of Economics.
    19. Engzell, Per, 2017. "What Do Books in the Home Proxy For? A Cautionary Tale," Working Paper Series 1/2016, Stockholm University, Swedish Institute for Social Research.

    More about this item

    Keywords

    nonclassical measurement error; classification error; health insurance; corrupt sampling; binary regressor;
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