Data-driven incentive alignment in capitation schemes

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number104584
JournalJournal of Public Economics
Volume207
DOIs
StatePublished - Mar 2022

All Science Journal Classification (ASJC) codes

  • Finance
  • Economics and Econometrics

Keywords

  • Adverse selection
  • Big data
  • Capitation
  • Delegated model selection
  • Detail-free mechanism design
  • Health-care regulation

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