Health and Healthcare

‘The Empirical Content of Models with Multiple Equilibria in Economies with Social Interactions,’ Bisin, A. (with A. Moro and G. Topa), 2011.

We study a general class of models with social interactions that might display multiple equilibria. We propose an estimation procedure for these models and evaluate its efficiency and computational feasibility relative to different approaches taken to the curse of dimensionality implied by the multiplicity. Using data on smoking among teenagers, we implement the proposed estimation procedure to understand how group interactions affect health-related choices. We and that interaction effects are strong both at the school level and at the smaller friends-network level. Multiplicity of equilibria is pervasive at the estimated parameter values, and equilibrium selection accounts for about 15 percent of the observed smoking behavior. Counterfactuals show that student interactions, surprisingly, reduce smoking by approximately 70 percent with respect to the equilibrium smoking that would occur without interactions.

‘Data-Driven Incentive Alignment in Capitation Schemes,’ Chassang, S. (with M. Braverman), 2015.

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.