Data-Driven Incentive Alignment in Capitation Schemes

Mark Braverman and Sylvain Chassang
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Mark Braverman: Princeton University
Sylvain Chassang: New York University

Working Papers from Princeton University. Economics Department.

Abstract: This paper explores whether Big Data, taking the form of extensive but high dimensional records, can reduce the cost of adverse selection in government-run capitation schemes. 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. This gives an informed private provider 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 provider’s gains from selection.

Keywords: adverse selection; big data; capitation; health-care regulation; detailfree 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: 2020-03
New Economics Papers: this item is included in nep-ban and nep-big
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https://www.sylvainchassang.org/assets/papers/strategic_capitation.pdf

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Persistent link: https://EconPapers.repec.org/RePEc:pri:econom:2020-60

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