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Adverse Selection, Moral Hazard and the Demand for Medigap Insurance, and

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  • Keane, M.;
  • Stavrunova, O.;

Abstract

This paper studies selection and moral hazard in the US Medigap health insurance market. It develops an econometric model for insurance demand and health care expenditure, in which the degree of selection is measured by the sensitivity of insurance demand to expected expenditure in uninsured state, conditional on other variables. The model allows for correlation between unobserved determinants of health care expenditure and the demand for insurance. To capture the complex shape of the distribution of the expenditure, a smooth mixture of Tobit models is employed (a generalization of the smoothly mixing regressions framework of Geweke and Keane (2007)). The model is estimated using a MCMC algorithm with data augmentation. The results suggest that conditionally on income, education, risk attitudes, cognitive ability, Financial planning horizon and longevity expectations there is an adverse selection into Medigap insurance, but the effect is small: a one standard deviation increase in the expected expenditure in uninsured state increases the probability of buying insurance by 0.01. The model allows estimation of the sample distribution of the moral hazard effect of Medigap on health care expenditure. On average, an insured individual spends about $1,600 more on health care than her uninsured counterpart and the size of this effect is lower for healthier individuals as well as for blacks and hispanics. The smooth mixture of Tobits model developed in this paper predicts the conditional expectation of health care expenditure in our data better than the five standard models selected for comparison, and also correctly captures the complex shape of the expenditure distribution. Creation-Date: 2010-07

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Paper provided by HEDG, c/o Department of Economics, University of York in its series Health, Econometrics and Data Group (HEDG) Working Papers with number 10/14.

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Handle: RePEc:yor:hectdg:10/14

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  1. Willard G. Manning & John Mullahy, 1999. "Estimating Log Models: To Transform or Not to Transform?," NBER Technical Working Papers 0246, National Bureau of Economic Research, Inc.
  2. Hanming Fang & Michael P. Keane & Dan Silverman, 2006. "Sources of Advantageous Selection: Evidence from the Medigap Insurance Market," NBER Working Papers 12289, National Bureau of Economic Research, Inc.
  3. Chib, Siddhartha, 1992. "Bayes inference in the Tobit censored regression model," Journal of Econometrics, Elsevier, vol. 51(1-2), pages 79-99.
  4. Blough, David K. & Madden, Carolyn W. & Hornbrook, Mark C., 1999. "Modeling risk using generalized linear models," Journal of Health Economics, Elsevier, vol. 18(2), pages 153-171, April.
  5. Willard G. Manning & Anirban Basu & John Mullahy, 2003. "Generalized Modeling Approaches to Risk Adjustment of Skewed Outcomes Data," Working Papers 0313, Harris School of Public Policy Studies, University of Chicago.
  6. Murat K. Munkin & Partha Deb & Pravin K. Trivedi, 2006. "Bayesian analysis of the two-part model with endogeneity: application to health care expenditure," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(7), pages 1081-1099.
  7. Deb, Partha & Munkin, Murat K. & Trivedi, Pravin K., 2006. "Private Insurance, Selection, and Health Care Use: A Bayesian Analysis of a Roy-Type Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 403-415, October.
  8. Tomas Philipson & John Cawley, 1999. "An Empirical Examination of Information Barriers to Trade in Insurance," American Economic Review, American Economic Association, vol. 89(4), pages 827-846, September.
  9. Ettner, Susan L., 1997. "Adverse selection and the purchase of Medigap insurance by the elderly," Journal of Health Economics, Elsevier, vol. 16(5), pages 543-562, October.
  10. Manning, Willard G., 1998. "The logged dependent variable, heteroscedasticity, and the retransformation problem," Journal of Health Economics, Elsevier, vol. 17(3), pages 283-295, June.
  11. Gilleskie, Donna B. & Mroz, Thomas A., 2004. "A flexible approach for estimating the effects of covariates on health expenditures," Journal of Health Economics, Elsevier, vol. 23(2), pages 391-418, March.
  12. Geweke, John & Keane, Michael, 2007. "Smoothly mixing regressions," Journal of Econometrics, Elsevier, vol. 138(1), pages 252-290, May.
  13. Buntin, Melinda Beeuwkes & Zaslavsky, Alan M., 2004. "Too much ado about two-part models and transformation?: Comparing methods of modeling Medicare expenditures," Journal of Health Economics, Elsevier, vol. 23(3), pages 525-542, May.
  14. Miles S. Kimball & Claudia R. Sahm & Matthew D. Shapiro, 2007. "Imputing Risk Tolerance from Survey Responses," NBER Working Papers 13337, National Bureau of Economic Research, Inc.
  15. Alma Cohen, 2005. "Asymmetric Information and Learning: Evidence from the Automobile Insurance Market," The Review of Economics and Statistics, MIT Press, vol. 87(2), pages 197-207, May.
  16. Munkin M & Trivedi P. K, 2009. "Incentives and Selection Effects of Drug Coverage on Total Drug Expenditure: a Finite Mixture Approach," Health, Econometrics and Data Group (HEDG) Working Papers 09/22, HEDG, c/o Department of Economics, University of York.
  17. Wolfe, John R. & Goddeeris, John H., 1991. "Adverse selection, moral hazard, and wealth effects in the medigap insurance market," Journal of Health Economics, Elsevier, vol. 10(4), pages 433-459.
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