Data-Driven Incentive Alignment in Capitation Schemes

Mark Braverman and Sylvain Chassang

No 28429, NBER Working Papers from National Bureau of Economic Research, Inc

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.

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
Note: EH PE
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Published as Mark Braverman & Sylvain Chassang, 2022. "Data-driven incentive alignment in capitation schemes," Journal of Public Economics, vol 207.

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