Data-Driven Incentive Alignment in Capitation Schemes

Mark Braverman and Sylvain Chassang
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Mark Braverman: Princeton University
Sylvain Chassang: Princeton University and NBER
Authors registered in the RePEc Author Service: Juan Herreño

Working Papers from Princeton University, Department of Economics, Center for Economic Policy Studies.

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.

Keywords: adverse selection; big data; capitation; health-care regulation; detail-free mechanism design; delegated model selection (search for similar items in EconPapers)
JEL-codes: C55 D82 H51 I11 I13 (search for similar items in EconPapers)
Date: 2021-02
New Economics Papers: this item is included in nep-big
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:pri:cepsud:282

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