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 this information creates and distributes economic value.
I focus on applications in insurance and digital platforms as well as the design of pricing, contracting, and matching mechanisms. I specialize in structural modeling, field experiments, and independent collaboration with firms.
How to Prevent Traffic Accidents? Moral Hazard, Inattention, and Behavioral Data
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 risky driving behavior (Holmström 1979). Our results illustrate the important interaction between bounded rationality and market failure, and how behavioral data allow insurers to expand risk management strategies from selective risk-sharing to risk mitigation.
(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, gain competitive advantage, and 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.
In macro/finance, our model is typically referred to as "rational inattention:" in a market with asymmetric information, firms endogenously acquire information subject to a cost (switching inertia and consumers' unwillingness to be monitored).
(first author | w/ Shosh Vasserman)
We collaborate with a large e-commerce platform, and construct a new dataset of consideration sets by matching consumer search and browsing behavior with seller product and operations information. We also conduct a year-long experiment among new sellers on the platform. The treatment group receives access to a free online entrepreneur training program customized by an AI algorithm. We first document a growth barrier (``GB'') in the competition for consumer attention: new sellers must achieve 10% higher conversion rates to obtain the same consumer exposure as incumbents. The training program lowers this barrier by enhancing sellers' marketing and customer service efforts, hence boosting their revenue. We then estimate an empirical model to capture heterogeneity in consumer demand. This quantifies the effect of the GB: conditional on making a sale, new sellers have 23% higher quality than incumbents. By lowering this barrier, the training program benefits buyers. Although only 0.25% of all sellers on the platform are trained, removing the program would have reduced consumer surplus by 0.07%.
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.