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来源类型Working Paper
规范类型报告
DOI10.3386/w28429
来源IDWorking Paper 28429
Data-Driven Incentive Alignment in Capitation Schemes
Mark Braverman; Sylvain Chassang
发表日期2021-02-08
出版年2021
语种英语
摘要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.
主题Econometrics ; Estimation Methods ; Microeconomics ; Economics of Information ; Public Economics ; National Fiscal Issues ; Health, Education, and Welfare ; Health
URLhttps://www.nber.org/papers/w28429
来源智库National Bureau of Economic Research (United States)
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条目标识符http://119.78.100.153/handle/2XGU8XDN/586102
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Mark Braverman,Sylvain Chassang. Data-Driven Incentive Alignment in Capitation Schemes. 2021.
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