Essays on Network Economics”

This dissertation focuses on the economics of networks, first investigating information transmission in networks of Bayesian agents and then studying complex networked markets where upstream and downstream markets are not easily distinguishable. 

In the first chapter, I study strategic information transmission in a hierarchy. Information is transmitted through a chain of agents to a decision maker whose action affects all agents’ payoffs. Each agent in the chain can conceal all or part of the information she receives. I prove that it is possible to focus on simple equilibria, where only the first agent ever conceals information. This allows me to formulate the hierarchical communication as a direct one between the initial sender and the decision maker subject to recursively defined incentive compatibility constraints imposed by the intermediaries. In the binaryaction case, regardless of the number of intermediaries, at most four agents determine the amount of information communicated to the decision maker. In this case, the results in this paper underscore the importance of choosing a pivotal vice president for maximizing the payoff of the decision maker, who is better off by appointing a like-minded but stubborn vice president. Moreover, I provide necessary and sufficient conditions for inefficient equilibria. 

In the second chapter (joint with Ali Shourideh and Ariel Zetlin-jones), we study the classic principal-agent model when the signal observed by the principal is chosen by the agent. We fully characterize the optimal information structure from an agent’s perspective in a general moral hazard setting with limited liability. Due to endogeneity of the contract chosen by the principal, the agent’s choice of information is non-trivial. We show that the agent’s problem can be mapped into a geometrical game between the principal and the agent in the space of likelihood ratios. We use this representation result to show that coarse contracts are sufficient: The agent can achieve her best with binary signals. Additionally, we can characterize conditions under which the agent is able to extract the entire surplus and implement the first-best efficient allocation. Finally, we show that when effort and performance are one-dimensional, under a general class of models, threshold signals are optimal. Our theory can thus provide a rationale for coarseness of contracts based on the bargaining power of the agent in negotiations. 

In the third chapter (joint with Maryam Saeedi and Ali Shourideh), we take the first steps of analyzing complex networked markets by focusing on the U.S. natural gas market. Using an extensive panel of daily data on natural gas flows through interstate pipelines, we obtain an overall view of the natural gas flows in the network by applying machine learning methods. We perform demand estimation to derive price-elasticity of demand in different sectors: residential, commercial, industrial, and electric utility. Our results suggest that while demand in all these sectors is relatively inelastic with respect to the average price, electric utility is the most elastic sector and industrial sector is the most inelastic one. We then investigate one of the largest mergers among natural gas interstate pipelines demonstrating that this merger had a significant effect on natural gas transportation prices even though the two pipelines were not in the same physical market. We also investigate the role of storage in the natural gas market by first testing the standard rational expectations competitive storage model to estimate natural gas demand and storage costs. We then quantify the effect of temperature, storage, and pipeline congestion on natural gas prices. Finally, we investigate the network effect in the natural gas market confirming that a change or shock in the temperature of one geographical region can have a significant effect on prices even in the farthest regions. This observation can be rationalized by the change of natural gas route in the pipeline network from low-demand regions to high-demand regions.