Shoshana Vasserman

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

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.
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.

Consumer data is increasingly available to firms through private exchanges. 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 data set 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.

We then model the forces of supply and demand shaping the amount of information revealed in equilibrium. We find large profit and welfare gains from introducing monitoring. Requiring the firm to make monitoring data public would have reduced short-term welfare.

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.

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.


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. (IJCAI 2015)

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.

Tutorials, Code and Other Writings​

Fast Bayesian Inference on Large-Scale Random Utility Logit Models

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.