IDEAS home Printed from https://ideas.repec.org/p/isu/genres/12588.html
   My bibliography  Save this paper

Partially Identifying the Prevalence of Health Insurance Given Contaminated Sampling Response Error

Author

Listed:
  • Kreider, Brent

Abstract

This paper derives simple closed-form identification regions for the U.S. nonelderly population's prevalence of health insurance coverage in the presence of household reporting errors. The methods extend Horowitz and Manski's (1995) nonparametric analysis of contaminated samples for the case that the outcome is binary. In this case, draws from the alternative distribution (i.e., not the distribution of interest) might naturally be defined as response errors. The derived identification regions can dramatically reduce the degree of uncertainty about the outcome distribution compared with the contaminated sampling bounds. These regions are estimated using data from the Medical Expenditure Panel Survey (MEPS) combined with health insurance validation data available for a nonrandom portion of the sample.

Suggested Citation

  • Kreider, Brent, 2006. "Partially Identifying the Prevalence of Health Insurance Given Contaminated Sampling Response Error," Staff General Research Papers Archive 12588, Iowa State University, Department of Economics.
    • Handle: RePEc:isu:genres:12588
    as

    Download full text from publisher

    File URL: http://www2.econ.iastate.edu/papers/p3849-2006-04-15.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mark C. Berger & Dan A. Black & Frank A. Scott, 1998. "How Well Do We Measure Employer‐Provided Health Insurance Coverage?," Contemporary Economic Policy, Western Economic Association International, vol. 16(3), pages 356-367, July.
    2. Horowitz, Joel L & Manski, Charles F, 1995. "Identification and Robustness with Contaminated and Corrupted Data," Econometrica, Econometric Society, vol. 63(2), pages 281-302, March.
    3. Bollinger, Christopher R., 1996. "Bounding mean regressions when a binary regressor is mismeasured," Journal of Econometrics, Elsevier, vol. 73(2), pages 387-399, August.
    4. Brent Kreider & Steven C. Hill, 2009. "Partially Identifying Treatment Effects with an Application to Covering the Uninsured," Journal of Human Resources, University of Wisconsin Press, vol. 44(2).
    5. John V. Pepper, 2000. "The Intergenerational Transmission Of Welfare Receipt: A Nonparametric Bounds Analysis," The Review of Economics and Statistics, MIT Press, vol. 82(3), pages 472-488, August.
    6. Molinari, Francesca, 2008. "Partial identification of probability distributions with misclassified data," Journal of Econometrics, Elsevier, vol. 144(1), pages 81-117, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Brent Kreider & John Pepper, 2008. "Inferring disability status from corrupt data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(3), pages 329-349.
    2. Eirini-Christina Saloniki & Amanda Gosling, 2012. "Point identification in the presence of measurement error in discrete variables: application - wages and disability," Studies in Economics 1214, School of Economics, University of Kent.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Craig Gundersen & Brent Kreider, 2008. "Food Stamps and Food Insecurity: What Can Be Learned in the Presence of Nonclassical Measurement Error?," Journal of Human Resources, University of Wisconsin Press, vol. 43(2), pages 352-382.
    2. 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.
    3. 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.
    4. Brent Kreider & John V. Pepper & Manan Roy, 2016. "Identifying the Effects of WIC on Food Insecurity Among Infants and Children," Southern Economic Journal, John Wiley & Sons, vol. 82(4), pages 1106-1122, April.
    5. 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.
    6. Kreider, Brent & Pepper, John V., 2011. "Identification of Expected Outcomes in a Data Error Mixing Model With Multiplicative Mean Independence," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 49-60.
    7. Brent Kreider & Steven C. Hill, 2009. "Partially Identifying Treatment Effects with an Application to Covering the Uninsured," Journal of Human Resources, University of Wisconsin Press, vol. 44(2).
    8. Acerenza, Santiago & Ban, Kyunghoon & Kedagni, Desire, 2021. "Marginal Treatment Effects with Misclassified Treatment," ISU General Staff Papers 202106180700001132, Iowa State University, Department of Economics.
    9. Guido W. Imbens & Charles F. Manski, 2004. "Confidence Intervals for Partially Identified Parameters," Econometrica, Econometric Society, vol. 72(6), pages 1845-1857, November.
    10. Brent Kreider & John Pepper, 2008. "Inferring disability status from corrupt data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(3), pages 329-349.
    11. Giovanni Compiani & Yuichi Kitamura, 2016. "Using mixtures in econometric models: a brief review and some new results," Econometrics Journal, Royal Economic Society, vol. 19(3), pages 95-127, October.
    12. Manan Roy, 2012. "Identifying the Effect of WIC on Infant Health When Participation is Endogenous and Misreported," Departmental Working Papers 1202, Southern Methodist University, Department of Economics.
    13. Takahide Yanagi, 2019. "Inference on local average treatment effects for misclassified treatment," Econometric Reviews, Taylor & Francis Journals, vol. 38(8), pages 938-960, September.
    14. Brent Kreider & John V. Pepper & Craig Gundersen & Dean Jolliffe, 2012. "Identifying the Effects of SNAP (Food Stamps) on Child Health Outcomes When Participation Is Endogenous and Misreported," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 958-975, September.
    15. Robert Paul Hartley & Carlos Lamarche & James P. Ziliak, 2022. "Welfare Reform and the Intergenerational Transmission of Dependence," Journal of Political Economy, University of Chicago Press, vol. 130(3), pages 523-565.
    16. Punarjit Roychowdhury & Gaurav Dhamija, 2022. "Don't cross the line: Bounding the causal effect of hypergamy violation on domestic violence in India," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1952-1978, October.
    17. Meyer, Bruce D. & Mittag, Nikolas, 2017. "Misclassification in binary choice models," Journal of Econometrics, Elsevier, vol. 200(2), pages 295-311.
    18. Christian Bontemps & Thierry Magnac & Eric Maurin, 2012. "Set Identified Linear Models," Econometrica, Econometric Society, vol. 80(3), pages 1129-1155, May.
    19. DiTraglia, Francis J. & García-Jimeno, Camilo, 2019. "Identifying the effect of a mis-classified, binary, endogenous regressor," Journal of Econometrics, Elsevier, vol. 209(2), pages 376-390.
    20. German Blanco & Carlos A. Flores & Alfonso Flores-Lagunes, 2013. "Bounds on Average and Quantile Treatment Effects of Job Corps Training on Wages," Journal of Human Resources, University of Wisconsin Press, vol. 48(3), pages 659-701.

    More about this item

    Keywords

    partial identification; nonparametric bounds; contaminated sampling; classification error;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:isu:genres:12588. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Curtis Balmer (email available below). General contact details of provider: https://edirc.repec.org/data/deiasus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.