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Reporting of Indian Health Service Coverage in the American Community Survey

Author

Listed:
  • Renuka Bhaskar
  • Rachel M. Shattuck
  • James Noon

Abstract

Response error in surveys affects the quality of data which are relied on for numerous research and policy purposes. We use linked survey and administrative records data to examine reporting of a particular item in the American Community Survey (ACS) - health coverage among American Indians and Alaska Natives (AIANs) through the Indian Health Service (IHS). We compare responses to the IHS portion of the 2014 ACS health insurance question to whether or not individuals are in the 2014 IHS Patient Registration data. We evaluate the extent to which individuals misreport their IHS coverage in the ACS as well as the characteristics associated with misreporting. We also assess whether the ACS estimates of AIANs with IHS coverage represent an undercount. Our results will be of interest to researchers who rely on survey responses in general and specifically the ACS health insurance question. Moreover, our analysis contributes to the literature on using administrative records to measure components of survey error.

Suggested Citation

  • Renuka Bhaskar & Rachel M. Shattuck & James Noon, 2018. "Reporting of Indian Health Service Coverage in the American Community Survey," CARRA Working Papers 2018-04, Center for Economic Studies, U.S. Census Bureau.
    • Handle: RePEc:cen:cpaper:2018-04
    as

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    File URL: https://www.census.gov/content/dam/Census/library/working-papers/2018/adrm/carra-wp-2018-04.pdf
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    References listed on IDEAS

    as
    1. Bruce D. Meyer & Wallace K. C. Mok & James X. Sullivan, 2015. "Household Surveys in Crisis," Journal of Economic Perspectives, American Economic Association, vol. 29(4), pages 199-226, Fall.
    2. Mathiowetz, Nancy A & Duncan, Greg J, 1988. "Out of Work, Out of Mind: Response Errors in Retrospective Reports of Unemployment," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(2), pages 221-229, April.
    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. Zuckerman, S. & Haley, J. & Roubideaux, Y. & Lillie-Blanton, M., 2004. "Health Service Access, Use, and Insurance Coverage among American Indians/Alaska Natives and Whites: What Role Does the Indian Health Service Play?," American Journal of Public Health, American Public Health Association, vol. 94(1), pages 53-59.
    5. Johnson, P.J. & Blewett, L.A. & Call, K.T. & Davern, M., 2010. "American Indian/Alaska native uninsurance disparities: A comparison of 3 surveys," American Journal of Public Health, American Public Health Association, vol. 100(10), pages 1972-1979.
    6. Brittany Bond & J. David Brown & Adela Luque & Amy O’Hara, 2014. "The Nature of the Bias When Studying Only Linkable Person Records: Evidence from the American Community Survey," CARRA Working Papers 2014-08, Center for Economic Studies, U.S. Census Bureau.
    7. Renuka Bhaskar & James Noon & Brett O'Hara & Victoria Velkoff, 2016. "Medicare Coverage and Reporting," CARRA Working Papers 2016-12, Center for Economic Studies, U.S. Census Bureau.
    Full references (including those not matched with items on IDEAS)

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