I am an economist trained in empirical industrial organization. I study how data and AI technologies generate useful information in imperfect markets, and the mechanisms through which they create and distribute economic value.
I focus on applications in insurance and digital platforms as well as the design of pricing and matching mechanisms. I specialize in structural modeling, field experiments, and independent collaboration with firms.
In many economic settings -- e.g. consumer lending, criminal justice, labor contracts -- the intuition of moral hazard has given rise to a doctrine of deterring bad behaviors by increasing punishment. In auto insurance, firms combat risky driving almost exclusively by raising accident punishment. However, not only does this undermine risk-sharing, this paper shows that it fails at deterring risky driving due to drivers' inattention to risk. We do so with novel sensor data as well as observational and experimental methods. On the other hand, these data make risky driving behavior contractable, so we develop and estimate a structural model to simulate the "first-best" contract, which features full insurance and a direct and hence salient price on behavior (Holmström 1979).
(first author | w/ Thomas Yu)
Firms in many markets directly elicit large amounts of data from consumers. The data is used to mitigate information problems, to gain competitive advantage, and to extract rent from consumers. We develop an equilibrium framework to trade-off these countervailing forces, and use it to study the creation of detailed driving data in U.S. auto insurance through a voluntary monitoring program. Surprisingly, the incentive effect from the data collection process, not the data itself, is the main source of social value creation. Despite large switching inertia, the product market is still competitive enough so that forcing the firm to make the data public hurts short-term consumer welfare by discouraging data creation.
(first author | w/ Shosh Vasserman)
We conduct a large experiment where we randomize access to a digital training program among e-commerce entrants. We document high growth barriers in the competition for consumer attention, and that the training program boosts entrant traffic by closing the knowledge gap on practical operational skills. But the training data are far more valuable as it can identify more high-quality entrants. We develop a consideration-set based equilibrium model to capture the welfare benefit of reallocating consumer attention to these higher-quality entrants. Counterfactual analyses show that, at social optima, the platform should expand digital training and value entrant traffic significantly more when ranking firms and assigning traffic.
(Zhengyun Sun's job market paper)
What kind of firms collect what types of data? Can such data facilitate growth? If so, by influencing what strategies? We analyze the adoption and the effect of analytics tools by firms on an e-commerce platform. We then conduct a high-stake experiment among non-adopting stores and found evidence of information friction despite low take-up.