Ron Yang

I am an Assistant Professor in the Strategy and Business Economics Division at the University of British Columbia's Sauder School of Business.

My research focuses on industrial organization and transportation economics.

CV: Link (Updated July 2023)


From Market Making to Matchmaking: Does Bank Regulation Harm Market Liquidity? Review of Financial Studies, 2023 (with Gideon Saar, Jian Sun, and Haoxiang Zhu)  [Paper, Appendix, SSRN

Media: Bloomberg

Working Papers

(Don't) Take Me Home: Home Preference and the Effect of Self-Driving Trucks on Interstate Trade [Paper] (Updated May 23, 2023)

Awards: Urban Economics Association 2021 Student Prize (Honourable Mention)

How will self-driving trucks affect U.S. interstate trade flows? I argue that human drivers’ preferences to return home generate geographic specialization in the trucking freight market, and that self-driving trucks would eliminate this "home preference." I build a model of drivers who make dynamic decisions about where to work, given that they prefer to be at home. A large home preference makes destinations that are likely to bring one home more attractive and increases the appeal of taking time off at home. Using trucking freight transactions and highway inspections data, I estimate the model parameters and find that, on average, drivers value being at home at $74 per day or about one-third of their daily wage. In a counterfactual where self-driving trucks lower per-mile costs, increase daily driving range, and eliminate home preference, overall freight prices fall by 27.4 percent. Without home preference, drivers shift from driver-rich states to driver-poor states and total driving increases as drivers spend less time off. This paper therefore shows how automation can reshape transportation costs by removing the role of worker preferences. 

Economies of Scale and Scope in Railroading (with Pedro Degiovanni) [Paper] (Pedro's Job Market Paper)

To what extent do transportation costs depend on the amount shipped, and how does infrastructure investment shape these costs? We model railroads as multiproduct firms and estimate the link between capacity utilization and costs using firm choices, the network structure of production, and publicly available routing data. We find a U-shaped relationship between marginal costs and rail utilization: As utilization increases, costs decrease by 30% to a low point at 55% utilization, before increasing by another 30%. Increased congestion in the rail network can explain a third of the 50% increase in real rail prices observed since 2004. We use our framework to study two normative and one positive policy questions: First, we estimate the network externalities of rail infrastructure investment, finding that investment in Arizona provides the highest returns, but only 3% are captured by Arizona itself. Next, we evaluate the cost efficiencies that would arise from a merger between Union Pacific and Burlington Northern Santa Fe. We find that such a merger would reduce costs by 17.1% due to reduced misallocation and process innovation. Lastly, we study the effect of the China shock on freight costs. We show that the reallocation of imports toward the West Coast led to a 3% increase in shipment costs in Los Angeles and Chicago, with heterogeneous effects across space and firms.

Work in Progress

Roadside Infrastructure, Parking, and Electric Trucks (with Frank Pinter and Sarah Armitage)

Solid(arity) Investments: Improving Unionizing Through the Stock Market (with Daniel Mark Deibler)


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Strategic Management (Instructor, University of British Columbia, Spring 2024)

Deconstructing + Reconstructing Markets (Co-Instructor, Harvard University, Spring 2020)



Ph.D. Industrial Organization II (Teaching Fellow, Harvard University, Spring 2019)

Moment Inequalities Cookbook: Practical guide to moment inequalities and inference, based on "Alternative Models for Moment Inequalities" (Pakes 2010) and "A Practical Two-Step Method for Testing Moment Inequalities" (Romano-Shaikh-Wolf 2014).

Single-Agent Dynamic Discrete Choice: Discussion of construction, identification, and estimation of single-agent DDC models, using both Rust Nested Fixed Point and Hotz-Miller CCP methods.

Dynamic Games: Discussion of dynamic games estimation using Pakes-Ostrovsky-Berry Inversion and Bajari-Benkard-Levin Simulation methods, and Markov-Perfect industry dynamics a la Ericson-Pakes.


Email Address