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Data-Driven Incentive Alignment in Capitation Schemes

Author(s): Braverman, Mark; Chassang, Sylvain

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Abstract: This paper explores whether big data, taking the form of extensive high dimensional records, can reduce the cost of adverse selection by private insurers 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.
Publication Date: Mar-2022
Citation: Braverman, Mark, and Sylvain Chassang. "Data-driven incentive alignment in capitation schemes." Journal of Public Economics 207 (2022): 104584. doi:10.1016/j.jpubeco.2021.104584
DOI: 10.1016/j.jpubeco.2021.104584
ISSN: 0047-2727
Pages: 104584
Type of Material: Journal Article
Journal/Proceeding Title: Journal of Public Economics
Version: Author's manuscript

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