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Proposed modified probit model incorporating non-parametric density estimation: how to measure asymmetric information in the health insurance market?

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  • Takaaki Aoki

Abstract

On the basis of the theory of Chiappori and Salanie (2000), this paper proposes a simple modified bivariate Probit model incorporating non-parametric kernel density estimation. The model is applied to test asymmetric information in a health insurance market, using MEPS96 data.1 Results show that asymmetric information (whether moral hazard or adverse selection) exists between the contract of insurance coverage, and some non-emergency visits services, which appear to support the conclusions of Cardon and Hendel (2001). It is also shown how this non-parametric approach plays an important role in the delicate task of correctly testing, by computing generalized residuals, the existence of asymmetric information.

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  • Takaaki Aoki, 2005. "Proposed modified probit model incorporating non-parametric density estimation: how to measure asymmetric information in the health insurance market?," Applied Economics Letters, Taylor & Francis Journals, vol. 12(6), pages 347-350.
    • Handle: RePEc:taf:apeclt:v:12:y:2005:i:6:p:347-350
    DOI: 10.1080/13504850500044104
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    Cited by:

    1. Takaaki Aoki, 2009. "Some notes on statistic robustness of nonparametric bivariate probit model in a finite sample," Applied Economics Letters, Taylor & Francis Journals, vol. 16(5), pages 443-447.
    2. Marcelo Resende & Rodrigo Zeidan, 2010. "Adverse selection in the health insurance market: some empirical evidence," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 11(4), pages 413-418, August.

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