Optimal taxation and insurance using machine learning - Sufficient statistics and beyond

The proposed methods are applied to the choice of coinsurance rates in health insurance, using data from the RAND health insurance experiment. The key trade-off in this setting is between transfers toward the sick and insurance costs. The key empirical relationship the policy maker needs to learn about is the response of health care expenditures to coinsurance rates. Holding the economic model and distributive preferences constant, we obtain much smaller point estimates of the optimal coinsurance rate (18% vs. 50%) when applying our estimation method instead of the conventional “sufficient statistic” approach.