| Summary: | This thesis comprises three chapters on long-term relationships and networks. The first and second chapters study long-term relationships between shippers and carriers in the US truckload freight industry. The third chapter studies properties of learning and information aggregation on social networks.
The first chapter, joint with Adam Harris, provides evidence on the scope and incentive mechanisms of long-term relationships in the US truckload freight industry. In this setting, shippers and carriers engage in repeated interactions under fixed-rate contracts that leave scope for inefficient opportunism. We show that shippers use the threat of relationship termination to deter carriers from short-term opportunism. Carriers respond to the resultant dynamic incentives, behaving more cooperatively when their potential future rents are higher. While shippers and carriers often interact on multiple lanes, we find evidence that shippers' incentive schemes do not take advantage of this multi-lane scope for certain classes of carriers.
The second chapter, joint with Adam Harris, builds on the first, exploring a market-level tradeoff that informal long-term relationships present. On the one hand, relationships capitalize on match-specific efficiency gains and mitigating incentive problems. On the other hand, the prevalence of long-term relationships can also lead to thinner, less efficient spot markets. We develop an empirical framework to quantify the market-level tradeoff between long-term relationships and the spot market. We apply this framework to an economically important setting—the US truckload freight industry—exploiting detailed transaction-level data for estimation. At the relationship level, we find that long-term relationships have large intrinsic benefits over spot transactions. At the market level, we find a strong link between the thickness and the efficiency of the spot market. Overall, the current institution performs fairly well against our first-best benchmarks, achieving 44% of the relationship-level first-best surplus and even more of the market-level first-best surplus. The findings motivate two counterfactuals: (i) a centralized spot market for optimal spot market efficiency and (ii) index pricing for optimal gains from individual long-term relationships. The former results in substantial welfare loss, and the latter leads to welfare gains during periods of high demand.
The third chapter proposes a novel learning model on social networks that captures settings where individuals interact frequently on multiple, relatively short-lived topics. In this model, each period features a new draw of nature and multiple rounds in which information arrives, gets aggregated, and diffuses through network links. The repetitive nature of interactions across periods allows for a separation between learning about the environment and aggregating information about the current state. A class of empiricist learning rules achieve convergence of learning on all networks. On clique trees, these learning rules further achieve strong efficiency in information aggregation. The paper also presents a converse to the positive efficiency result and identifies distinct reasons why efficiency is hard to obtain in general circumstances, even though convergence of learning holds generally.
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