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Nonparametric Testing for Asymmetric Information

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  • Liangjun Su
  • Martin Spindler

Abstract

Asymmetric information is an important phenomenon in many markets and in particular in insurance markets. Testing for asymmetric information has become a very important issue in the literature in the last two decades. Almost all testing procedures that are used in empirical studies are parametric, which may yield misleading conclusions in the case of misspecification of either functional or distributional relationships among the variables of interest. Motivated by the literature on testing conditional independence, we propose a new nonparametric test for asymmetric information, which is applicable in a variety of situations. We demonstrate that the test works reasonably well through Monte Carlo simulations and apply it to an automobile insurance dataset and a long-term care insurance (LTCI) dataset. Our empirical results consolidate Chiappori and Salanié's findings that there is no evidence for the presence of asymmetric information in the French automobile insurance market. While Finkelstein and McGarry found no positive correlation between risk and coverage in the LTCI market in the United States, our test detects asymmetric information using only the information that is available to the insurance company, and our investigation of the source of asymmetric information suggests some sort of asymmetric information that is related to risk preferences as opposed to risk types and thus lends support to Finkelstein and McGarry.

Suggested Citation

  • Liangjun Su & Martin Spindler, 2013. "Nonparametric Testing for Asymmetric Information," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 208-225, April.
  • Handle: RePEc:taf:jnlbes:v:31:y:2013:i:2:p:208-225 DOI: 10.1080/07350015.2012.755127
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    References listed on IDEAS

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    Cited by:

    1. Georges Dionne, 2012. "The Empirical Measure of Information Problems with Emphasis on Insurance Fraud and Dynamic Data," Cahiers de recherche 1233, CIRPEE.
    2. Choi Yun Jeong & Chen Joe & Sawada Yasuyuki, 2015. "Life Insurance and Suicide: Asymmetric Information Revisited," The B.E. Journal of Economic Analysis & Policy, De Gruyter, pages 1127-1149.
    3. Rothlauf, Franz & Schunk, Daniel & Pfeiffer, Jella, 2005. "Classification of human decision behavior : finding modular decision rules with genetic algorithms," Papers 05-04, Sonderforschungsbreich 504.
    4. Su, Liangjun & White, Halbert, 2014. "Testing conditional independence via empirical likelihood," Journal of Econometrics, Elsevier, vol. 182(1), pages 27-44.
    5. Polanski, Arnold & Stoja, Evarist, 2015. "Extreme risk interdependence," Bank of England working papers 563, Bank of England.
    6. Spindler, Martin, 2013. "“They do know what they are doing... at least most of them.†Asymmetric Information in the (private) Disability Insurance," MEA discussion paper series 201209, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    7. repec:mea:meawpa:12259 is not listed on IDEAS
    8. Martin Spindler & Joachim Winter & Steffen Hagmayer, 2014. "Asymmetric Information in the Market for Automobile Insurance: Evidence From Germany," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 81(4), pages 781-801, December.
    9. Dionne, Georges & Michaud, Pierre-Carl & Pinquet, Jean, 2013. "A review of recent theoretical and empirical analyses of asymmetric information in road safety and automobile insurance," Research in Transportation Economics, Elsevier, vol. 43(1), pages 85-97.
    10. repec:eee:intfor:v:33:y:2017:i:4:p:958-969 is not listed on IDEAS
    11. Arnold Polanski & Evarist Stoja, 2016. "Extreme risk interdependence," ESRB Working Paper Series 12, European Systemic Risk Board.
    12. Spindler, M., 2014. "“They do know what they are doing ... at least most of them.†Asymmetric Information in the (private) Disability Insurance," Health, Econometrics and Data Group (HEDG) Working Papers 14/16, HEDG, c/o Department of Economics, University of York.
    13. Polanski, Arnold & Stoja, Evarist, 2017. "Forecasting multidimensional tail risk at short and long horizons," Bank of England working papers 660, Bank of England.
    14. repec:mea:meawpa:12260 is not listed on IDEAS

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