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The Measurement of Medicaid Coverage in the SIPP : Evidence from a Comparison of Matched Records

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Abstract

This paper studies the accuracy of reported Medicaid coverage in the Survey of Income and Program Participation (SIPP) using a unique data set formed by matching SIPP survey responses to administrative records from the State of California. Overall, we estimate that the SIPP underestimates Medicaid coverage in the California population by about 10 percent. The probability that a SIPP respondent who is covered by Medicaid in a given month correctly reports their coverage is around 85 percent. The corresponding probability for low-income children is higher -- around 90 percent. Under-reporting by those who are actually in the Medicaid system is partially offset by over-reporting of coverage by people who are not. Some of these false positive responses are attributable to errors and missing data in the administrative system, rather than to problems in the SIPP. Taking account of these errors, the estimated false positive rate for the population as a whole is about 1.5 percent, and 4-5 percent for poor children.

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  • David Card & Andrew K.G. Hildreth & Lara D. Shore-Sheppard, 2002. "The Measurement of Medicaid Coverage in the SIPP : Evidence from a Comparison of Matched Records," Department of Economics Working Papers 2003-11, Department of Economics, Williams College.
    • Handle: RePEc:wil:wileco:2003-11
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    1. Jerry Hausman, 2001. "Mismeasured Variables in Econometric Analysis: Problems from the Right and Problems from the Left," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 57-67, Fall.
    2. Janet Currie & Jonathan Gruber, 1996. "Health Insurance Eligibility, Utilization of Medical Care, and Child Health," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 111(2), pages 431-466.
    3. Card, David, 1996. "The Effect of Unions on the Structure of Wages: A Longitudinal Analysis," Econometrica, Econometric Society, vol. 64(4), pages 957-979, July.
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    1. Zhuan Pei, 2017. "Eligibility Recertification and Dynamic Opt-In Incentives in Income-Tested Social Programs: Evidence from Medicaid/CHIP," American Economic Journal: Economic Policy, American Economic Association, vol. 9(1), pages 241-276, February.
    2. Shore-Sheppard Lara D., 2008. "Stemming the Tide? The Effect of Expanding Medicaid Eligibility On Health Insurance Coverage," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 8(2), pages 1-35, July.
    3. Johnston, David W. & Propper, Carol & Shields, Michael A., 2009. "Comparing subjective and objective measures of health: Evidence from hypertension for the income/health gradient," Journal of Health Economics, Elsevier, vol. 28(3), pages 540-552, May.
    4. 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.
    5. Amy O’Hara & Rachel M. Shattuck & Robert M. Goerge, 2017. "Linking Federal Surveys with Administrative Data to Improve Research on Families," The ANNALS of the American Academy of Political and Social Science, , vol. 669(1), pages 63-74, January.
    6. Meijer, Erik & Karoly, Lynn A., 2017. "Representativeness of the low-income population in the Health and Retirement Study," The Journal of the Economics of Ageing, Elsevier, vol. 9(C), pages 90-99.
    7. Rosemary Hyson & Alice Zawacki, 2008. "Health-Related Research Using Confidential U.S. Census Bureau Data," Working Papers 08-21, Center for Economic Studies, U.S. Census Bureau.
    8. R. Vincent Pohl, 2018. "Medicaid And The Labor Supply Of Single Mothers: Implications For Health Care Reform," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(3), pages 1283-1313, August.
    9. Mazzolari, Francesca & Ragusa, Giuseppe, 2012. "Time Limits: The Effects on Welfare Use and Other Consumption-Smoothing Mechanisms," IZA Discussion Papers 6993, Institute of Labor Economics (IZA).
    10. Zhuan Pei & Yi Shen, 2017. "The Devil is in the Tails: Regression Discontinuity Design with Measurement Error in the Assignment Variable," Advances in Econometrics, in: Regression Discontinuity Designs, volume 38, pages 455-502, Emerald Group Publishing Limited.
    11. Kalyan Kolukuluri, 2022. "Healthcare utilization and outcomes for insured dependent children: evidence from Indonesia," Empirical Economics, Springer, vol. 63(2), pages 945-977, August.
    12. Cascio, Elizabeth U., 2005. "School Progression and the Grade Distribution of Students: Evidence from the Current Population Survey," IZA Discussion Papers 1747, Institute of Labor Economics (IZA).
    13. East, Chloe N. & Simon, David, 2024. "The safety net and job loss: How much insurance do public programs provide?," Journal of Public Economics, Elsevier, vol. 238(C).
    14. Cuccaro-Alamin, Stephanie & Eastman, Andrea Lane & Foust, Regan & McCroskey, Jacquelyn & Nghiem, Huy Tran & Putnam-Hornstein, Emily, 2021. "Strategies for constructing household and family units with linked administrative records," Children and Youth Services Review, Elsevier, vol. 120(C).
    15. Brachet, Tanguy, 2008. "Maternal Smoking, Misclassification, and Infant Health," MPRA Paper 21466, University Library of Munich, Germany.
    16. Erik Meijer & Lynn A. Karoly & Pierre-Carl Michaud, 2010. "Using Matched Survey and Administrative Data to Estimate Eligibility for the Medicare Part D Low Income Subsidy Program," Working Papers WR-743, RAND Corporation.
    17. Bronchetti, Erin Todd, 2014. "Public insurance expansions and the health of immigrant and native children," Journal of Public Economics, Elsevier, vol. 120(C), pages 205-219.

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