APPLIED MICRO: Designing Monitoring Programs; Dr Samuel Goldberg (Stanford Graduate School)

Abstract

I study the design of voluntary consumer monitoring programs in auto-insurance, using data from more than 2 million unique drivers. Monitoring technology tracks driving behaviors and provides performance incentives for improved driving, in the form of discounts on future premiums. The efficacy of these programs depends on who chooses to participate and their ability to influence driver risk. Because drivers anticipate performance incentives when making their participation decision, these goals are fundamentally tied. I estimate a model with with both selection and moral hazard using unique variation in both performance and participation discounts. Through the lens of the model, I non-parametrically recover the joint distribution of drivers risk characteristics. I use my model to perform two counterfactuals of interest. First, I separate the screening and risk adjustment effects and estimate the welfare of monitoring programs in this setting. Second, I use my model to examine counterfactual incentive schemes that illustrate how different incentive structures screen different drivers in and out of the program.