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(M)oral Hazard?

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Would you go to the dentist more often if it were free? Observational data is here used to analyze the impact of full-coverage insurance on dental care utilization using different identification strategies. The challenge of assessing the bite of moral hazard without an experimental study design is to separate it from adverse selection, as agents act on private and generally unobservable information. By utilizing a quasi-experimental feature of the insurance scheme the moral hazard effect is identified on observables, and by having access to an instrument the effect is identified with IV. Moral hazard is assessed using both difference-in-differences and cross-sectional estimations.

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  • Grönqvist, Erik, 2006. "(M)oral Hazard?," SSE/EFI Working Paper Series in Economics and Finance 642, Stockholm School of Economics.
    • Handle: RePEc:hhs:hastef:0642
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    1. Manning, Willard G, et al, 1987. "Health Insurance and the Demand for Medical Care: Evidence from a Randomized Experiment," American Economic Review, American Economic Association, vol. 77(3), pages 251-277, June.
    2. Angrist, Joshua D. & Krueger, Alan B., 1999. "Empirical strategies in labor economics," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 23, pages 1277-1366, Elsevier.
    3. Frank Windmeijer, 2002. "ExpEnd, A Gauss programme for non-linear GMM estimation of exponential models with endogenous regressors for cross section and panel data," CeMMAP working papers CWP14/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Lechner, Michael, 1999. "Identification and Estimation of Causal Effects of Multiple Treatments Under the Conditional Independence Assumption," IZA Discussion Papers 91, Institute of Labor Economics (IZA).
    5. Grönqvist, Erik, 2004. "Does Adverse Selection Matter? Evidence from a Natural Experiment," SSE/EFI Working Paper Series in Economics and Finance 575, Stockholm School of Economics.
    6. Georges Dionne & Christian Gourieroux & Charles Vanasse, 2001. "Testing for Evidence of Adverse Selection in the Automobile Insurance Market: A Comment," Journal of Political Economy, University of Chicago Press, vol. 109(2), pages 444-473, April.
    7. Heckman, James J. & Lalonde, Robert J. & Smith, Jeffrey A., 1999. "The economics and econometrics of active labor market programs," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 31, pages 1865-2097, Elsevier.
    8. Jaap H. Abbring & Pierre-André Chiappori & Jean Pinquet, 2003. "Moral Hazard and Dynamic Insurance Data," Journal of the European Economic Association, MIT Press, vol. 1(4), pages 767-820, June.
    9. Jaap H. Abbring & Pierre-André Chiappori & Jean Pinquet, 2003. "Moral Hazard and Dynamic Insurance Data," Journal of the European Economic Association, MIT Press, vol. 1(4), pages 767-820, June.
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    More about this item

    Keywords

    Asymmetric information; Moral Hazard; Health Insurance; Porpensity Score Matching; IV;
    All these keywords.

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets

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