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Data-Driven Incentive Alignment in Capitation Schemes

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  • Mark Braverman
  • Sylvain Chassang

Abstract

This paper explores whether Big Data, taking the form of extensive high dimensional records, can reduce the cost of adverse selection by private service providers in government-run capitation schemes, such as Medicare Advantage. We argue that using data to improve the ex ante precision of capitation regressions is unlikely to be helpful. Even if types become essentially observable, the high dimensionality of covariates makes it infeasible to precisely estimate the cost of serving a given type: Big Data makes types observable, but not necessarily interpretable. This gives an informed private operator scope to select types that are relatively cheap to serve. Instead, we argue that data can be used to align incentives by forming unbiased and non-manipulable ex post estimates of a private operator’s gains from selection.

Suggested Citation

  • Mark Braverman & Sylvain Chassang, 2021. "Data-Driven Incentive Alignment in Capitation Schemes," NBER Working Papers 28429, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:28429
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    More about this item

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • H51 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Health
    • 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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