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Asymmetric Information in the Market for Automobile Insurance: Evidence from Germany

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Listed:
  • Spindler, Martin
  • Winter, Joachim
  • Hagmayer, Steffen

    (Munich Center for the Economics of Aging (MEA))

Abstract

Asymmetric information is an important phenomenon in insurance markets, but the empirical evidence on the extent of adverse selection and moral hazard is mixed. Because of its implications for pricing, contract design, and regulation, it is crucial to test for asymmetric information in speci c insurance markets. In this paper, we analyze a recent data set on automobile insurance in Germany, the largest such market in Europe. We present and compare a variety of statistical testing procedures. We find that the extent of asymmetric information depends on coverage levels and on the speci c risks covered which enhances the previous literature. Within the framework of Chiappori et al. (2006), we also test whether drivers have realistic expectations concerning their loss distribution, and we analyze the market structure.

Suggested Citation

  • Spindler, Martin & Winter, Joachim & Hagmayer, Steffen, 2012. "Asymmetric Information in the Market for Automobile Insurance: Evidence from Germany," MEA discussion paper series 201208, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    • Handle: RePEc:mea:meawpa:201208
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    1. Pierre‐André Chiappori & Bruno Jullien & Bernard Salanié & François Salanié, 2006. "Asymmetric information in insurance: general testable implications," RAND Journal of Economics, RAND Corporation, vol. 37(4), pages 783-798, December.
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    20. Kuniyoshi Saito, 2006. "Testing for Asymmetric Information in the Automobile Insurance Market Under Rate Regulation," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 73(2), pages 335-356, June.
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    Cited by:

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    2. David Rowell & Son Nghiem & Luke B Connelly, 2017. "Two Tests for Ex Ante Moral Hazard in a Market for Automobile Insurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 84(4), pages 1103-1126, December.
    3. Casper H. de Jong, 2021. "Risk classification and the balance of information in insurance; an alternative interpretation of the evidence," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 24(4), pages 445-461, December.
    4. Ben‐jiang Ma & Jing‐yu Ye & Yuan‐ji Huang & Muhammad Farhan Bashir, 2020. "Research of two‐period insurance contract model with a low compensation period under adverse selection," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 41(3), pages 293-307, April.
    5. Vijay Aseervatham & Christoph Lex & Spindler, Martin, 2014. "How do unisex rating regulations affect gender differences in insurance premiums?," MEA discussion paper series 201416, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    6. Feng Gao & Michael R. Powers & Jun Wang, 2017. "Decomposing Asymmetric Information in China's Automobile Insurance Market," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 84(4), pages 1269-1293, December.

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    More about this item

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

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