Adam Harris

I am an Assistant Professor of Economics at Cornell.

I apply industrial organization tools to questions at the intersection of technology and transportation. My current research studies artificial intelligence and the US trucking industry.

Office hours: By appointment using Calendly.

CV (Updated June 2025)

Published papers

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

AEJ: Microeconomics, Feb 2025

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.

Working papers

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

Best Paper by a Junior Researcher—International Transportation Economics Association 2025 Annual Conference

In this paper, we study the role of predictive artificial intelligence (AI) in human decision-making. Using a rich decision-level data set from the maintenance of heavy-duty trucks, we document how the repair decision-making of expert technicians changes with the introduction of an AI tool designed to predict the risk of truck breakdowns. We develop and estimate a dynamic discrete choice model of technician decision-making. The resulting estimates show that technicians with the AI tool exhibit a substantially better ability to predict breakdown risk than those without the tool. This improvement in predictive ability translates into better outcomes: The AI tool reduces the total costs that technicians incur by $240-$480 per truck per year. This brings the technician close to the efficient frontier; only 15% more cost savings could be achieved by further improvements in the quality of decision-making. The AI tool enables these cost savings by helping technicians avoid costly, unnecessary repairs.

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

Revise & Resubmit at American Economic Review

Long-term 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 relationship and spot performance, and we apply this framework to the US truckload freight industry, one in which relationships with fixed-rate contracts predominate. We find that while the intrinsic benefits of relationships outweigh their negative externalities, social optimality requires a balance between relationship and spot transactions. The current institution comes reasonably close to achieving this balance, as the friction generated by the incompleteness of the current fixed-rate contracts acts as a partial corrective tax on relationship transactions. Overall, the current institution achieves 92% of the market-level first-best surplus, despite achieving only 64% of the relationship-level first-best surplus

Work in progress

Which Workers Benefit From AI? Estimating Heterogeneous Effects on Productivity

This extension of my job market paper aims to explore heterogeneity in how technicians utilize a predictive AI tool in making engine repair decisions for heavy-duty trucks. By combining data on technician characteristics with rich data on repair decisions, this study seeks to address two pivotal questions: First, how might the quality of technicians’ decision-making vary with experience? Second, how does the introduction of a predictive AI tool differentially affect the quality of decision-making for technicians with different experience levels? The first question speaks to the returns to experience in this context. The second speaks to whether predictive AI tools act as complements to or substitutes for such experience. The findings aim to offer insights into the distributional impacts of predictive AI on professional human decision-makers, as well as potential effects on incentives for these decision-makers to invest in experience (i.e., human capital).

Long-term Relationships and Supply Chain Resilience (with Thi Mai Anh Nguyen)

Recent supply chain disruptions have highlighted the vulnerability of the goods economy to upheaval in freight transportation markets.  In the US, the trucking industry may represent a particular susceptibility, both because of its singularly central role (72% of all domestic shipments are transported by truck) and because of its peculiar market institutions.  As described in our first two papers, long-term relationships, rather than a centralized spot market, are the key means of arranging trucking transactions.  This likely affects the ability of the industry—and thus, the US goods economy as a whole—to adjust to shocks.  If transactions in this industry were arranged through a spot market, we would expect price signals to effect a rapid adjustment to shocks.  However, in a world where transactions are actually arranged through a decentralized network of informal long-term relationships with prices that are (at least in the short-run) fixed, this may not be true.  With this motivation in mind, this study analyzes—at the micro level—how shocks affect relationship stability and—at the macro level—how such shocks are transmitted through relationship networks.