Partially Identifying the Prevalence of Health Insurance Given Contaminated Sampling Response Error
AbstractThis 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.
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Bibliographic InfoPaper provided by Iowa State University, Department of Economics in its series Staff General Research Papers with number 12588.
Date of creation: 15 Apr 2006
Date of revision:
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Postal: Iowa State University, Dept. of Economics, 260 Heady Hall, Ames, IA 50011-1070
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More information through EDIRC
partial identification; nonparametric bounds; contaminated sampling; classification error;
Find related papers by 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-04-29 (All new papers)
- NEP-ECM-2006-04-29 (Econometrics)
- NEP-HEA-2006-04-29 (Health Economics)
- NEP-IAS-2006-04-29 (Insurance Economics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Bollinger, Christopher R., 1996. "Bounding mean regressions when a binary regressor is mismeasured," Journal of Econometrics, Elsevier, vol. 73(2), pages 387-399, August.
- 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.
- 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).
- Kreider, Brent & Hill, Steven C., 2005. "Partially Identifying Treatment Effects with an Application to Covering the Uninsured," Staff General Research Papers 12296, Iowa State University, Department of Economics.
- 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, 07.
- 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.
- Molinari, Francesca, 2008.
"Partial identification of probability distributions with misclassified data,"
Journal of Econometrics,
Elsevier, vol. 144(1), pages 81-117, May.
- Molinari, Francesca, 2005. "Partial Identification of Probability Distributions with Misclassified Data," Working Papers 05-10, Cornell University, Center for Analytic Economics.
- Kreider, Brent & Pepper, John V., 2003.
"Inferring Disability Status from Corrupt Data,"
Staff General Research Papers
10228, Iowa State University, Department of Economics.
- 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, Department of Economics, University of Kent.
- Saloniki, E-C.; & Gosling, A.;, 2013. "Point identification in the presence of measurement error in discrete variables: application - wages and disability," Health, Econometrics and Data Group (HEDG) Working Papers 13/16, HEDG, c/o Department of Economics, University of York.
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