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Beyond Statistics: The Economic Content of Risk Scores

Author

Listed:
  • Liran Einav
  • Amy Finkelstein
  • Raymond Kluender
  • Paul Schrimpf

Abstract

"Big data" and statistical techniques to score potential transactions have transformed insurance and credit markets. In this paper, we observe that these widely-used statistical scores summarize a much richer heterogeneity, and may be endogenous to the context in which they get applied. We demonstrate this point empirically using data from Medicare Part D, showing that risk scores confound underlying health and endogenous spending response to insurance. We then illustrate theoretically that when individuals have heterogeneous behavioral responses to contracts, strategic incentives for cream-skimming can still exist, even in the presence of "perfect" risk scoring under a given contract. (JEL C55, G22, G28, H51, I13)

Suggested Citation

  • Liran Einav & Amy Finkelstein & Raymond Kluender & Paul Schrimpf, 2016. "Beyond Statistics: The Economic Content of Risk Scores," American Economic Journal: Applied Economics, American Economic Association, vol. 8(2), pages 195-224, April.
    • Handle: RePEc:aea:aejapp:v:8:y:2016:i:2:p:195-224
    Note: DOI: 10.1257/app.20150131
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    References listed on IDEAS

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    9. Liran Einav & Mark Jenkins & Jonathan Levin, 2013. "The impact of credit scoring on consumer lending," RAND Journal of Economics, RAND Corporation, vol. 44(2), pages 249-274, June.
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    Cited by:

    1. Sungchul Park & Anirban Basu, 2018. "Alternative evaluation metrics for risk adjustment methods," Health Economics, John Wiley & Sons, Ltd., vol. 27(6), pages 984-1010, June.
    2. Michael Geruso & Timothy Layton, 2020. "Upcoding: Evidence from Medicare on Squishy Risk Adjustment," Journal of Political Economy, University of Chicago Press, vol. 128(3), pages 984-1026.
    3. Carey, Colleen, 2021. "Sharing the burden of subsidization: Evidence on pass-through from a subsidy revision in Medicare Part D," Journal of Public Economics, Elsevier, vol. 198(C).
    4. Moiz Bhai & Danny R. Hughes, 2026. "Estimating Self‐Selection in Medicare Advantage," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 45(2), March.
    5. De Giorgi, Giacomo & Drenik, Andres & Seira Bejarano, Enrique, 2017. "Sequential Banking: Direct and Externality Effects on Delinquency," CEPR Discussion Papers 12280, C.E.P.R. Discussion Papers.
    6. Delphine Roy, 2025. "Strategic Coding in the Assessment of Long‐Term Care Needs: Evidence From France," Health Economics, John Wiley & Sons, Ltd., vol. 34(6), pages 1035-1063, June.
    7. Sriubaite, I. & Harris, A. & Jones, A.M. & Gabbe, B., 2020. "Economic Consequences of Road Traffic Injuries. Application of the Super Learner algorithm," Health, Econometrics and Data Group (HEDG) Working Papers 20/20, HEDG, c/o Department of Economics, University of York.
    8. Michele Fioretti & Hongming Wang, 2023. "Performance Pay in Insurance Markets: Evidence from Medicare," The Review of Economics and Statistics, MIT Press, vol. 105(5), pages 1128-1144, September.
    9. Zwart, Gijsbert, 2025. "Moral hazard and risk adjustment," Journal of Health Economics, Elsevier, vol. 99(C).
    10. Martin Eling & Irina Gemmo & Danjela Guxha & Hato Schmeiser, 2024. "Big data, risk classification, and privacy in insurance markets," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 49(1), pages 75-126, March.
    11. Mark Shepard, 2016. "Hospital Network Competition and Adverse Selection: Evidence from the Massachusetts Health Insurance Exchange," NBER Working Papers 22600, National Bureau of Economic Research, Inc.
    12. Daniel Montanera & Abhay Nath Mishra & T. S. Raghu, 2022. "Mitigating Risk Selection in Healthcare Entitlement Programs: A Beneficiary-Level Competitive Bidding Approach," Information Systems Research, INFORMS, vol. 33(4), pages 1221-1247, December.

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    More about this item

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • H51 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Health
    • I13 - Health, Education, and Welfare - - Health - - - Health Insurance, Public and Private

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