| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w28429 |
| 来源ID | Working 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 |
| URL | https://www.nber.org/papers/w28429 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/586102 |
| 推荐引用方式 GB/T 7714 | Mark Braverman,Sylvain Chassang. Data-Driven Incentive Alignment in Capitation Schemes. 2021. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w28429.pdf（568KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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