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Demand Heterogeneity in Insurance Markets: Implications for Equity and Efficiency

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  • Michael Geruso
Abstract
In many markets insurers are barred from price discrimination based on consumer characteristics like age, gender, and medical history. In this paper, I build on a recent literature to show why such policies are inefficient if consumers differ in their willingness-to-pay for insurance conditional on the insured losses they generate. Using administrative claims data, I then show that this type of demand heterogeneity is empirically relevant in a consumer health plan setting. Younger and older consumers and men and women reveal strikingly different demand for health insurance, conditional on their objective medical spending risk. This implies that these groups must face different prices in order to sort themselves efficiently across insurance contracts. The theoretical and empirical analysis highlights a fundamental tradeoff between equity and efficiency that is unique to selection markets.

Suggested Citation

  • Michael Geruso, 2016. "Demand Heterogeneity in Insurance Markets: Implications for Equity and Efficiency," NBER Working Papers 22440, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:22440
    Note: AG EH PE
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    References listed on IDEAS

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    Cited by:

    1. De-Lei Sheng & Linfeng Shi & Danping Li & Yanping Zhao, 2022. "Manage Pension Deficit with Heterogeneous Insurance," Methodology and Computing in Applied Probability, Springer, vol. 24(2), pages 1119-1141, June.
    2. Drake, Coleman & Ryan, Conor & Dowd, Bryan, 2022. "Sources of inertia in the individual health insurance market," Journal of Public Economics, Elsevier, vol. 208(C).
    3. Pietro Tebaldi & Alexander Torgovitsky & Hanbin Yang, 2023. "Nonparametric Estimates of Demand in the California Health Insurance Exchange," Econometrica, Econometric Society, vol. 91(1), pages 107-146, January.
    4. Boonen, Tim J. & Liu, Fangda, 2022. "Insurance with heterogeneous preferences," Journal of Mathematical Economics, Elsevier, vol. 102(C).
    5. Drake, Coleman, 2019. "What are consumers willing to pay for a broad network health plan?: Evidence from covered California," Journal of Health Economics, Elsevier, vol. 65(C), pages 63-77.
    6. Michael Geruso & Timothy J. Layton & Grace McCormack & Mark Shepard, 2023. "The Two-Margin Problem in Insurance Markets," The Review of Economics and Statistics, MIT Press, vol. 105(2), pages 237-257, March.
    7. Layton, Timothy J. & McGuire, Thomas G. & van Kleef, Richard C., 2018. "Deriving risk adjustment payment weights to maximize efficiency of health insurance markets," Journal of Health Economics, Elsevier, vol. 61(C), pages 93-110.
    8. de Meza, David & Reito, Francesco & Reyniers, Diane, 2021. "Too much trade: The hidden problem of adverse selection," Journal of Public Economics, Elsevier, vol. 204(C).
    9. Michael Geruso & Timothy J. Layton, 2017. "Selection in Health Insurance Markets and Its Policy Remedies," Journal of Economic Perspectives, American Economic Association, vol. 31(4), pages 23-50, Fall.
    10. Ignacio Ibarra López & Juan Antonio Tapia Cortés, 2022. "El uso de productos financieros en la demanda de seguros en México," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 17(3), pages 1-26, Julio - S.
    11. Adachi, Takanori, 2023. "A sufficient statistics approach for welfare analysis of oligopolistic third‐degree price discrimination," International Journal of Industrial Organization, Elsevier, vol. 86(C).
    12. Wiseman, Thomas, 2018. "Competitive long-term health insurance," Journal of Health Economics, Elsevier, vol. 58(C), pages 144-150.
    13. Fleitas, Sebastian & Gowrisankaran, Gautam & Lo Sasso, Anthony, 2022. "Incumbent regulation and adverse selection: You can keep your health plan, but at what cost?," Journal of Public Economics, Elsevier, vol. 205(C).
    14. Jisang Yu & Edward D. Perry, 2024. "Premium subsidies and selection in the federal crop insurance program," Journal of Agricultural Economics, Wiley Blackwell, vol. 75(1), pages 280-297, February.
    15. Andreas Fuster & Paul Goldsmith‐Pinkham & Tarun Ramadorai & Ansgar Walther, 2022. "Predictably Unequal? The Effects of Machine Learning on Credit Markets," Journal of Finance, American Finance Association, vol. 77(1), pages 5-47, February.
    16. Levon Barseghyan & Francesca Molinari & Darcy Steeg Morris & Joshua C. Teitelbaum, 2020. "The Cost of Legal Restrictions on Experience Rating," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 17(1), pages 38-70, March.
    17. Olivier Darmouni & Dan Zeltzer, 2022. "Horizon effects and adverse selection in health insurance markets," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 55(2), pages 800-827, May.

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    More about this item

    JEL classification:

    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
    • I13 - Health, Education, and Welfare - - Health - - - Health Insurance, Public and Private

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