Title

Data-Driven Incentive Alignment in Capitation Schemes

Author(s)

Mark Braverman Mark Braverman (Princeton University)

Sylvain Chassang Sylvain Chassang (Princeton University and NBER)

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.

Creation Date

2021-02

Section URL ID

Paper Number

282

URL

https://gceps.princeton.edu/wp-content/uploads/2021/07/282_Chassang.pdf

File Function

Jel

C55, D82, H51, I11, I13

Keyword(s)

adverse selection, big data, capitation, health-care regulation, detail-free mechanism design, delegated model selection

Suppress

false

Series

3