Adam Harris

Job Market Candidate

Research Fields

Industrial Organization

Contact Information

Job Market Paper

Human Decision-Making with Machine Prediction: Evidence from Predictive Maintenance in Trucking (with Maggie Yellen)

In this paper, we use observational data to study how a predictive algorithm changes human decision-making.  Using a rich decision-level data set from the maintenance of heavy-duty trucks, we document how skilled technicians' decision-making is changed by the introduction of an algorithm designed to predict the risk of truck breakdowns.  We develop and estimate a dynamic discrete choice model of technician decision-making. The results show that technicians with the algorithm exhibit a substantially better ability to predict breakdown risk.  This results in better outcomes: The algorithm reduces the total costs technicians incurred by $245-$490 per truck per year; equivalently, it narrows the gap between the costs incurred under actual versus optimal decision-making by about 45%.  This gain comes in large part from a reduction in unnecessary repairs.  While the algorithm enables technicians to achieve better outcomes, their use of the algorithm is not optimal; slightly larger cost reductions could be achieved if technicians were more responsive to algorithmic alerts.


Working Papers

Long-Term Relationships and the Spot Market: Evidence from US Trucking (with Thi Mai Anh Nguyen)

Long-term informal relationships play an important role in the economy, capitalizing on match-specific efficiency gains and mitigating incentive problems. However, 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 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.


Long-Term Relationships in the US Truckload Freight Industry (with Thi Mai Anh Nguyen) 

Revise and Resubmit at AEJ: Microeconomics

This paper provides evidence on relational contracting in the US truckload freight industry. In this setting, shippers and carriers engage in repeated interactions under contracts that fix prices but leave scope for inefficient opportunism. We describe empirically the strategies of shippers and the responses of carriers. 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 show that separate relational contracts appear to govern transactions on each lane.


2023 - 2024
NBER Postdoctoral Fellowship (supported by the US Department of Transportation)
2018 - 2023
NSF Graduate Research Fellowship
George and Obie Shultz Fund