TY - JOUR
T1 - Data-driven incentive alignment in capitation schemes
AU - Braverman, Mark
AU - Chassang, Sylvain
N1 - Funding Information:
Braverman acknowledges support from NSF Award CCF-1215990, NSF CAREER award CCF-1149888, a Turing Centenary Fellowship, and a Packard Fellowship in Science and Engineering. Chassang acknowledges support from the Alfred P. Sloan Foundation.
Funding Information:
Braverman acknowledges support from NSF Award CCF-1215990, NSF CAREER award CCF-1149888, NSF Alan T. Waterman Award, Grant No. 1933331, a Turing Centenary Fellowship, and a Packard Fellowship in Science and Engineering. Chassang acknowledges support from the Alfred P. Sloan Foundation.
Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/3
Y1 - 2022/3
N2 - 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.
AB - 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.
KW - Adverse selection
KW - Big data
KW - Capitation
KW - Delegated model selection
KW - Detail-free mechanism design
KW - Health-care regulation
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U2 - 10.1016/j.jpubeco.2021.104584
DO - 10.1016/j.jpubeco.2021.104584
M3 - Article
AN - SCOPUS:85124479419
SN - 0047-2727
VL - 207
JO - Journal of Public Economics
JF - Journal of Public Economics
M1 - 104584
ER -