Working Paper: NBER ID: w28429
Authors: Mark Braverman; Sylvain Chassang
Abstract: 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.
Keywords: Adverse Selection; Big Data; Capitation; Healthcare; Regulation; Detail-Free Mechanism Design; Delegated Model Selection
JEL Codes: C55; D82; H51; I11; I13
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| high-dimensional covariates (C39) | estimation of costs (C13) |
| estimation of costs (C13) | selection behavior of private plans (I13) |
| big data (C55) | interpretable cost estimates (C51) |
| lack of interpretable cost estimates (C82) | strategic selection by private plans (I13) |
| strategic selection by private plans (I13) | increased costs for the overall population (H59) |
| traditional capitation schemes (G22) | efficient selection under certain conditions (C52) |
| illegitimate selection characteristics (P37) | compromised efficiency (D61) |
| data usage (L96) | improved incentive alignment (D82) |
| public plan's capitation payments (I18) | private plan's selection behavior (G52) |