Investment and misallocation in infrastructure networks: The case of U.S. natural gas pipelines

(with Paul Schrimpf)

  • 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.
    Philip Solimine
    Philip Solimine
    PhD Economist, Data Scientist

    Philip Solimine is a Data Scientist with a PhD in Economics, working on the Marketing Science & Operations team at Chewy.