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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. I specialize in structural modeling, field experiments, and independent collaboration with firms.



Firms in many markets directly elicit large amounts of data from consumers. The data is used to mitigate information problems, gain a 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).

(corresp. author | w/ Shosh Vasserman)


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. 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.


Risk mitigation is an essential aspect of risk management, but it has largely evaded attention by auto insurers and researchers that optimize selective risk-sharing and claim mitigation instead. We use novel sensor data to track drivers’ handheld phone use behavior (“HPU”), a generalization of text-and-drive in the smartphone age, and demonstrate how a behavior-based insurance contract can prevent accidents. Based on a prior experiment conducted in Jin (Forthcoming), we develop a structural model to distinguish inattention, risk aversion, and the price elasticity of HPU. This facilitates counterfactual simulations of the “first-best” auto insurance contract with full insurance and a direct price on HPU (Holmstr  ̈om 1979). The contract features a 40-cent average charge per mile of HPU. A 62-cent charge can resolve additional externalities on traffic congestion and injuries.

(corresp. author | w/ Thomas Yu)


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We conduct a year-long experiment among new sellers on a large e-commerce platform. The treatment group receives access to a free and customized entrepreneur training program, in which an AI algorithm assigns training materials to sellers based on their real-time operating statistics. With a 24% take-up rate, new sellers that are eligible for training see 1.7% higher revenue on average (6.6% ATT), driven largely by higher traffic and enhanced advertising.  To dissect the economic mechanisms, we propose a model of platform-mediated search and construct a panel dataset of consideration sets. We show that the entry barrier primarily manifests through higher search frictions among entrants -- a sign of platform ``gatekeeping.'' Advertising directly addresses the resulting traffic deficiency and effectively signals quality, leading to market expansion and welfare improvement.

(corresp. author | w/ Zhengyun Sun)


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.

(w/ Zhengyun Sun)

Discussions and Other Writings

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Discussion of Cosconati (2021):

The Effect of Insurance Telematics and Financial Penalties on Market-wide Moral Hazard

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Telematics Data in U.S. Auto Insurance: Evidence from Telematics Contracts and an Accident Prevention Program

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