Data-driven incentive alignment in capitation schemes
Data-driven incentive alignment in capitation schemes
复制标题
按人头付费计划中数据驱动的激励调整
DOI：
10.1016/j.jpubeco.2021.104584
复制
发表时间：
2022
影响因子：
9.8
通讯作者：
Chassang, Sylvain
中科院分区：
文献类型：
--
作者：
Braverman, Mark;Chassang, Sylvain
本文探讨了大数据，采取广泛的高维记录的形式，是否可以减少私营保险公司在政府经营的按人头计算的计划，如医疗保险优势的逆向选择的成本。我们认为，使用数据来提高人头回归的事前精度是不太可能有帮助的。即使类型变得本质上可观察，协变量的高维性也使得精确估计服务给定类型的成本变得不可行：大数据使类型变得可观察，但不一定是可解释的。这使知情的私人经营者有机会选择服务成本相对较低的类型。相反，我们认为，数据可以用来调整激励措施，形成无偏见的和不可操纵的事后估计的私人经营者的收益选择。
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.