Abstract: This paper studies the impact of uncertainty about underlying demand process on investment and welfare in container shipping. In particular, I explore how such uncertainty interacts with demand fluctuations and firms' strategic incentives. I construct and estimate a dynamic oligopoly model in which agents do not know the true parameters in the demand process, but form and revise their beliefs based on information available at each decision-making moment. I find that uncertainty about the demand process amplifies investment cycles through (i) leading firms to revise beliefs more drastically as they experience demand fluctuations, and (ii) intensifying strategic incentives among firms.
A Computational Framework for Analyzing Dynamic Procurement Auctions: The Market Impact of Information Sharing (with John Asker, Chaim Fershtman, and Ariel Pakes)
NBER Working Paper No. 22836.
Abstract: This paper develops a computational framework to analyze dynamic auctions and uses it to investigate the impact of information sharing among bidders. We show that allowing for the dynamics implicit in many auction environments enables the emergence of equilibrium states that can only be reached when firms are responding to dynamic incentives. The impact of information sharing depends on the extent of dynamics and provides support for the claim that information sharing, even of strategically important data, need not be welfare reducing. Our methodological contribution is to show how to adapt the Experience Based Equilibrium concept to a dynamic auction environment and to provide an implementable boundary consistency condition that mitigates the extent of multiple equilibria.
Firms’ Beliefs and Learning: Models, Identification, and Empirical Evidence (with Victor Aguirregabiria)
Abstract: This paper reviews recent literature on structural models of oligopoly competition where firms have biased beliefs about primitives of the model (e.g. demand, costs) or about the strategic behavior of other firms in the market. We describe different structural models that have been proposed to study this phenomenon and examine the approaches used to identify firms' beliefs. We discuss empirical results in recent studies and show that accounting for firms' biased beliefs and learning can have important implications on our measures and interpretation of market efficiency.
Endogenous Information Acquisition and Insurance Choice (with Zach Y. Brown)
Abstract: Insurance contracts are complicated and individuals may choose how much time and effort to spend understanding potential out-of-pocket costs and comparing plans. Building on the rational inattention literature, we develop a parsimonious demand model in which individ- uals choose how much to research difficult to observe characteristics, affecting the accuracy of their beliefs and subsequent choices. The model predicts that individuals acquire more information when the stakes are higher. Using prescription drug insurance data, we exploit within-individual variation in the stakes and show that the model provides an explanation for behavior that is inconsistent with standard demand models. Based on our framework, we estimate an empirical model of insurance demand and find that the marginal cost of acquiring information is higher for older enrollees and those with less prior experience with Medicare Part D plans. Counterfactual analysis sheds light on the welfare losses due to information frictions and how policy makers can restrict plan choice to simplify decision- making and raise welfare. Policies that decrease cost sharing also reduce equilibrium in- formation costs. Overall, we argue that endogenous information acquisition has important implications for counterfactuals and welfare, in addition to supply-side incentives.
Work in Progress
Patient Costs and Physicians’ Information (with Michael J. Dickstein and Eduardo Morales)