I am an IO economist with interests spanning a number of policy settings such as public procurement, pharmaceutical pricing and auto-insurance. My work leverages theory, empirics and modern computation to better understand the equilibrium implications of policies and proposals involving information revelation, risk sharing and commitment.

Research Papers

Robust Bounds for Welfare Analysis

Robust Bounds for Welfare Analysis

with Zi Yang Kang

Economists routinely make functional form assumptions about consumer demand to obtain welfare estimates—often for convenience, tractability, or both. How sensitive are welfare estimates to these assumptions? In this paper, we answer this question by providing bounds on welfare that hold for families of demand curves commonly considered in different literatures.

We show that typical functional forms—such as linear, exponential and CES demand—are extremal in different families: they yield either the highest or lowest welfare estimate among all demand curves in those families. To illustrate the flexibility of our approach, we apply our results to the welfare analysis of trade tariffs, income taxation and surge pricing.

with Yizhou Jin

New technologies have enabled firms to elicit granular behavioral data from consumers in exchange for lower prices and better experiences. This data can mitigate asymmetric information and moral hazard, but it may also increase firms’ market power if kept proprietary.

We study a voluntary monitoring program by a major U.S. auto insurer, in which drivers accept short-term tracking in exchange for potential discounts on future premiums. Using a proprietary dataset matched with competitor price menus, we document that safer drivers self-select into monitoring, and those who opt-in become yet 30% safer while monitored. Using an equilibrium model of consumer choice and firm pricing for insurance and monitoring, we find that the monitoring program generates large profit and welfare gains.

However, large demand frictions hurt monitoring adoption, forcing the firm to offer large discounts to induce opt-in while preventing the unmonitored pool from unraveling given the competitive environment. A counterfactual policy requiring the firm to make monitoring data public would thus further reduce the firm’s incentive to elicit monitoring data, leading to less monitoring and lower consumer welfare in equilibrium.

Bridge under construction

Most U.S. government spending on highways and bridges is done through “scaling” procurement auctions, in which private construction firms submit unit price bids for each piece of material required to complete a project.

Using data on bridge maintenance projects undertaken by the Massachusetts Department of Transportation (MassDOT), we present evidence that firm bidding behavior in this context is consistent with optimal skewing under risk aversion: firms limit their risk exposure by placing lower unit bids on items with greater uncertainty.

We estimate bidders’ risk aversion, the risk in each auction, and the distribution of bidders’ private costs. Simulating equilibrium item-level bids under counterfactual settings, we estimate the fraction of project spending that is due to risk and evaluate auction mechanisms under consideration by policymakers. We find that scaling auctions provide substantial savings relative to lump sum auctions and show how our framework can be used to evaluate alternative auction designs.

with Nageeb Ali and Greg Lewis

A concern central to the economics of privacy is that firms may use consumer data to price discriminate. A common response is that consumers should have control over their data and the ability to choose how firms access it. Since firms draw inferences based on both the data seen as well as the consumer’s disclosure choices, the strategic implications of this proposal are unclear.

We study whether such measures improve consumer welfare in monopolistic and competitive markets. We find that consumer control can improve consumer welfare relative to both perfect price discrimination and no personalized pricing.

First, consumers can use disclosure to amplify competitive forces. Second, consumers can disclose information to induce even a monopolist to lower prices. Whether consumer control improves welfare depends both on the disclosure technology and on market competitiveness. Simple disclosure technologies suffice in competitive markets. When facing a monopolist, a consumer needs the ability to disclose partial information to obtain any welfare gains.
We develop and implement a heterogeneous-agents network-based empirical model to analyze alternative policies during a pandemic outbreak.
We combine several data sources, including information on individuals’ mobility and encounters across metropolitan areas, information on health records for  millions of individuals, and information on the possibility to be productive while working from home.
This rich combination of data sources allows us to build a framework in which the severity of a disease outbreak varies across locations and industries, and across individuals who differ by age, occupation, and preexisting health conditions.

We use this framework to analyze the impact of different social distancing policies in the context of the COVID-19 outbreaks across US metropolitan areas.
Our  results highlight how outcomes vary across areas in relation to the underlying heterogeneity in population density, social network structures, population health, and employment characteristics.

We find that policies by which individuals who can work from home continue to do so, or in which schools and firms alternate schedules across different groups of students and employees, can be  effective in limiting the health and healthcare costs of the pandemic outbreak while also reducing employment losses.

The United States spends twice as much per person on pharmaceuticals as European countries, in large part because prices are higher in the US. This has led policymakers in the US to consider legislation for price controls. This paper assesses the effects of a hypothetical US reference pricing policy that would cap prices in US markets by those offered in Canada.

We estimate a structural model of demand and supply for pharmaceuticals in the US and Canada, in which Canadian prices are set through a negotiation process between pharmaceutical companies and the Canadian government. We then simulate the impacts of the counterfactual international reference pricing rule, allowing firms to internalize the cross-country impacts of their prices both when setting prices in the US and when negotiating prices in Canada.

We find that such a policy results in a slight decrease in US prices and a substantial increase in Canadian prices. The magnitude of these effects depends on the particular structure of the policy. Overall, we find modest consumer welfare gains in the US, but substantial consumer welfare losses in Canada. Moreover, we find that pharmaceutical profits increase in net, suggesting that reference pricing of this form would constitute a net transfer from consumers to firms.


with Eray Turkel, Anish Saha, Rhett Owen and  Greg Martin.

Major changes to the operation of local newsrooms — ownership restructuring, layoffs, and a reorientation away from print advertising — have become commonplace in the last decades. However, there have been few systematic attempts to characterize the impact of these changes on the types of reporting that local newsrooms produce. In this paper, we propose a method to measure the investigative content of news articles based on article text and influence on subsequent articles. We use our method to examine over-time and cross-sectional patterns in news production by local newspapers in the United States over the past decade. We find surprising stability in the quantity of investigative articles produced over most of the time period examined, but a notable decline in the last two years of the decade, corresponding to a recent wave of newsroom layoffs.

with Muhamet Yildiz. RAND Journal of Economics, Vol. 50, No. 2 (Summer 2019)

Empirical evidence shows that equally informed, experienced negotiators may refuse to settle because they fundamentally disagree on each one’s probability of success.

We study the dynamics of agreement to settle in pretrial negotiations when the negotiating parties are both optimistic and new information may arrive at any point.

We characterize the conditions under which the negotiators do or do not reach an agreement at every period of negotiation and discuss the implications for policy design such as timing periods of discovery and jury selection, and whether or not to allow the winning party to recover the legal costs incurred from the losing party.

with Avinatan Hassidim and Michal Feldman. Proceedings of the 24th International Conference on Artificial Intelligence (IJCAI’15)

How well can an informed central planner like Waze do at routing drivers on paths with uncertain wait times using an incentive compatible protocol?

We find that the mediation ratio is at most 8/7 in the case of two links with affine cost functions, and remains strictly smaller than the price of anarchy of 4/3 for any fixed m. However, it approaches the price of anarchy as m grows. For general (monotone) cost functions, the mediation ratio is at most m, a significant improvement over the unbounded price of anarchy.

Surveys, Tutorials, Code and Other Writings

with Mitchell Watt. International Journal of Industrial Organization (2021): 102758.

Auctions are inherently risky: bidders face uncertainty about their prospects of winning and payments, while sellers are unsure about revenue and chances of a successful sale. Auction rules influence the allocation of risk among agents and the behavior of risk-averse bidders, leading to a breakdown of payoff and revenue equivalence and a heightened significance of auction design decisions by sellers. In this paper, we review the literature on risk aversion in auctions, with an emphasis on what can be learned about auction design from theoretical modeling and empirical studies. We survey theoretical results relating to the behavior of risk-averse agents in auctions, the comparison of standard auction formats in the presence of risk aversion and implications for auction design. We discuss standard and more recent approaches to identifying risk preferences in empirical studies and evidence for the significance of risk aversion in auction applications. Finally, we identify areas where existing evidence is relatively scant and ask what questions empirical research might ask given the theory and where further theoretical research may be beneficial given existing empirical results.

Fast Bayesian Inference on Large-Scale Random Utility Logit Models

with James Savage, Presented at StanCon ’18

Random coefficients logit is a benchmark model for discrete choice, widely used in marketing and industrial organization. In “conjoint analysis” conducted in experimental marketing, it has historically been estimated using a Metropolis-within Gibbs method, or with simulated likelihoods (Train 2009). In economic problems where only aggregate data are available, it is estimated using GMM using the BLP algorithm of Berry Levinsohn and Pakes (1995).

We propose a latent variable form of the model as in Yang, Chen and Allenby (2003) for both individual choice and aggregate data, which can be estimated efficiently using Hamiltonian Monte Carlo. This offers several benefits over the current standards. Relative to Metropolis-within-Gibbs, HMC allows efficient estimation of enormous parameter spaces, allowing much larger (and even context-dependent choice) models to be fit in hours (rather than days or weeks). The proposed approach models aggregate sales, not shares, and so unlike BLP, the method allows for measurement error due to differences in market size. Priors also regularize the loss surface, leading to estimates that are robust when GMM objectives would be susceptible to local minima.