RESEARCH
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.
INSURANCE
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.
How can insurance design mitigate risk as opposed to just claims? 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, 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 (Holmstrom 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)
DIGITAL PLATFORM
Millions of entrepreneurs enter large digital platforms every year, amplifying the well-known "cold-start" problem. How do platforms explore entrant quality in practice? Can entrepreneurs improve their odds of successful entry?
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 comprehensive online entrepreneur training program. With a 24% take-up rate, training raised seller revenue (1.7% ITT and 6.6% ATT) largely by inducing advertising upfront. This allowed sellers to purchase sampling and overcome the ``cold-start'' problem. To understand the net effect of training-induced ads, we develop a model of advertising under platform-guided consumer search and information friction; we also construct consideration-set data that matches the modeled environment. Overall, our results reveal the information friction on---and the signaling value of---advertising for entrepreneurs on increasingly congested digital platforms.
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.
Discussions and Other Writings
Discussion of Cosconati (2021):
The Effect of Insurance Telematics and Financial Penalties on Market-wide Moral Hazard
Telematics Data in U.S. Auto Insurance: Evidence from Telematics Contracts and an Accident Prevention Program
Discussion of "Data, Privacy Laws, and Firm Production: Evidence from GDPR" by Demirer, Jimenez Hernandez, Li, and Peng (2023)