Data-Driven Incentive Alignment in Capitation Schemes

This paper explores whether Big Data, taking the form of extensive but high dimensional records, can reduce the cost of adverse selection in government-run capitation schemes. 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. This gives an informed private provider 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 provider’s gains from selection.