Data-Driven Incentive Alignment in Capitation Schemes

55 Pages Posted: 30 Oct 2015

See all articles by Mark Braverman

Mark Braverman

Princeton University

Sylvain Chassang

New York University (NYU) - Department of Economics

Date Written: September 10, 2015

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, observable but not interpretable, health-care regulation, detail-free mechanism design, model selection

Suggested Citation

Braverman, Mark and Chassang, Sylvain, Data-Driven Incentive Alignment in Capitation Schemes (September 10, 2015). Princeton University William S. Dietrich II Economic Theory Center Research Paper No. 073_2015. Available at SSRN: https://ssrn.com/abstract=2683542 or http://dx.doi.org/10.2139/ssrn.2683542

Mark Braverman

Princeton University ( email )

22 Chambers Street
Princeton, NJ 08544-0708
United States

Sylvain Chassang (Contact Author)

New York University (NYU) - Department of Economics ( email )

19 West 4th Street
New York, NY 10012
United States

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