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I am an economist trained in empirical industrial organization and information economics.  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.


This paper develops a method to empirically analyze competitive equilibrium in selection markets when firms offer differentiated products while having different cost structures and information precision. We apply this method to study the Italian auto insurance industry using an administrative panel dataset covering liability contracts and claims from 2010 to 2020. We uncover substantial differences in the precision of risk rating across firms. However, heterogeneity in variable costs and brand values also strongly influences market outcomes. Importantly, firms that suffer lower information precision tend to have lower costs. This means that policies that reduce heterogeneous information can have surprising consequences. For instance, a counterfactual information policy that moves every firm to the information frontier would generate a 3% increase in prices and a 3% decrease in consumer welfare.

(w/ Marco Cosconati and Yi Xin)

Draft available soon

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 conducted a year-long experiment among millions of new sellers on an e-commerce platform. The treatment group received access to a free and customized entrepreneur training program, in which an AI algorithm assigned training materials to sellers based on their real-time operating statistics. With a 24% take-up rate, new sellers eligible for training saw 1.7% higher revenue on average (6.6% ATT), primarily driven by higher traffic and enhanced advertising. To understand the welfare impact, we present an equilibrium model in which consumers search sequentially based on platform ranking, while new sellers can advertise at a cost to signal their quality, subject to information friction. Using panel data of consideration sets, we validate model predictions and document how training improved screening among new sellers, expanding the market.  Overall, this study reveals the information friction on—and the signaling value of—advertising among platform-dependent entrepreneurs.

(corresp. author | w/ Zhengyun Sun)


Many large digital platforms have begun to commercialize the enormous amount of data they have collected, both a byproduct of their expansion over the last two decades and a critical engine for their continued growth. We obtain data from a large e-commerce platform to study the efficacy of such a market-based solution for platforms' data and its implications on seller revenues and concentration.

(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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Discussion of "Data, Privacy Laws, and Firm Production: Evidence from GDPR" by Demirer, Jimenez Hernandez, Li, and Peng (2023)

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