Working papers
(with Paul Schrimpf)Show/Hide Abstract
This paper investigates regulatory distortion in the incentives to invest in transmission capacity in the United States natural gas pipeline network. We are motivated by the fact that trade of gas between states should temper regional price variation. However, price differences between locations frequently exceed the marginal cost of transmission, indicating that capacity constraints are binding. Gas pipelines are tightly regulated by the federal government, who sets the price for transmission service to target a fixed rate of return on capital. By decoupling firms’ profits from gas prices, this policy mitigates the incentive to withhold capacity but may also distort firms’ incentives to target investment in valuable areas. To combat this distortion, the regulator subjects all investments in the pipeline network to additional regulation through a costly approval process. We develop a structural model of a pipeline firm’s dynamic investment problem, and estimate the model nonparametrically using debiased machine learning. We then construct a measure of the social value of pipeline capital, based on a social planner model that ties the value of capacity expansion to regional price gaps. We find that in most areas, the incentives of firms to invest under fixed rates of return exceed the social value of capital. This highlights the importance of costly approvals as a secondary tool to control investments. Even for a range of discount factors that rationalize the observed policy on average, there are systematic deviations from the optimal policy both spatially and intertemporally. We suggest a welfare improving reallocation of regulatory costs that would streamline the approval process in certain parts of the northeast, but shift focus toward the southeast and parts of the mountain west.
(with Wei Li)Show/Hide Abstract
We study how a planner can optimally counter misinformation in a social network under coarse targeting—she broadcasts the same message to all agents, but chooses their exposure levels. Before messaging begins, the planner chooses a vector of target weights that determine how much each agent is exposed to her message, in order to maximize total discounted utility. Optimal targeting depends jointly on the network structure and the distribution of initial opinions. Counterintuitively, agents with extreme views may sometimes receive less exposure. In stylized opinion-leader networks, optimal weights align with authority centrality. But centrality alone is also not sufficient; in symmetric networks, targeting is uniform only when initial opinions are in consensus. More generally, optimal weights reflect persistent local opinion dispersion among subsets of agents, which slows opinion convergence. We illustrate the model using U.S. Facebook friendship data and climate change opinions. In this example, the planner over-targets Texas and under-targets California despite their similar centrality. This underscores how local disagreements shape optimal targeting.
(with Luke Boosey)Show/Hide Abstract
We examine behavior in an experimental collaboration game that incorporates endogenous network formation. The environment is modeled as a generalization of the voluntary contributions mechanism. By varying the information structure in a controlled laboratory experiment, we examine the underlying mechanisms of reciprocity that generate emergent patterns in linking and contribution decisions. Providing players more detailed information about the sharing behavior of others drastically increases efficiency, and positively affects a number of other key outcomes. To understand the driving causes of these changes in behavior we develop and estimate a structural model for actions and small network panels and identify how social preferences affect behavior. We find that the treatment reduces altruism but stimulates reciprocity, helping players coordinate to reach mutually beneficial outcomes. In a set of counterfactual simulations, we show that increasing trust in the community would encourage higher average contributions at the cost of mildly increased free-riding. Increasing overall reciprocity greatly increases collaborative behavior when there is limited information but can backfire in the treatment, suggesting that negative reciprocity and punishment can reduce efficiency. The largest returns would come from an intervention that drives players away from negative and toward positive reciprocity.