Data-driven incentive alignment in capitation schemes

Mark Braverman and Sylvain Chassang

Journal of Public Economics, 2022, vol. 207, issue C

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.

Keywords: Adverse selection; Big data; Capitation; Health-care regulation; Detail-free mechanism design; Delegated model selection (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0047272721002206
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:pubeco:v:207:y:2022:i:c:s0047272721002206

DOI: 10.1016/j.jpubeco.2021.104584

Access Statistics for this article

Journal of Public Economics is currently edited by R. Boadway and J. Poterba

More articles in Journal of Public Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().