Faculty Affiliate Spotlight: James Bushnell
August 6, 2024
In this edition, we shine the spotlight on Professor James Bushnell. Jim is a Professor in the Department of Economics at the University of California, Davis, a Faculty Affiliate at the Energy Institute at Haas, and a Research Associate of the National Bureau of Economic Research. Prior to joining UC Davis, he spent 15 years as the Research Director of the University of California Energy Institute in Berkeley, and two years as the Cargill Chair in Energy Economics at Iowa State University. Jim received a Ph.D. in Operations Research from U.C. Berkeley in 1993. He has written extensively on the regulation, organization, and competitiveness of energy markets. His research on restructured electricity markets has appeared in leading economics journals such as the American Economic Review and RAND Journal of Economics.
What led you to become an energy and environmental economist?
When I was an undergraduate at the University of Wisconsin studying industrial engineering I worked as an intern at Madison Gas and Electric, which is a 600 megawatt utility that serves the city of Madison. I worked on long run capacity expansion modeling, which is a crossover between simulation and economics. That’s how I got into energy. My undergraduate field of industrial engineering was basically mathematical modeling, and I had the chance to take lots of economics classes. When I looked for graduate programs, I wanted to work on economic questions in the energy space. I didn’t think of it as energy economics, but that’s what I ended up doing.
What brought you to the Energy Institute?
As a graduate student in industrial engineering my PhD thesis was on auction theory, and I had become an EINO (Engineer in Name Only). There wasn’t really much of a job market for academic industrial engineers that didn’t do a lot of actual engineering. Fortunately, the Energy Institute had just created a job for a professional researcher. Ed Kahn, who was at the Energy Institute at that time, described the job as prolonged adolescence. That sounded strangely appealing. Since then I moved first to Iowa State and then to UC Davis, but always kept an affiliation with the Energy Institute.
How would you describe your overall research focus?
Right now it’s a combination of energy markets and market based environmental regulation, like cap and trade. Because of my background I’ve always been interested in electricity. Electricity can be an isolated area where there’s a lot of acronyms, institutions and things that are really unique to the industry. But now, it’s more of a safe space for academic economists than it used to be.
What is one research project you are most proud of?
Severin Borenstein, Frank Wolak and I were all working on competition questions during the electricity crisis. It was a very stressful but exciting time. I was only four or five years out of graduate school, so it was quite a deep end to be dropped into. Our research arguably predicted the California electricity crisis. After the crisis, my papers with Frank and Severin laid the groundwork for major legal settlements that recouped billions of dollars for the state of California. So whenever I’m asked about the public impact of my research I can say we developed the framework that got something like $14 billion in refunds to the state of California. It’s always a fun stat to share, but there’s a lot of qualifications there. Our paper was building on work by Catherine Wolfram and others. Other people took our work and came up with even more accurate measures of market power. It’s called competitive benchmarking. But through a combination of the right place and right time and taking the right ideas to the right data, it had a huge impact.
Finally, what’s an ongoing project that you’re excited about?
Increasingly I’ve seen in both the energy sector and in climate policy that policymakers are relying on models that are super complicated, hard to digest and don’t deal with uncertainty very well. I have an agenda to think about how to bring measures of uncertainty from statistics into these kinds of models to put statistically grounded confidence intervals on the results. I’m looking into whether simple models with six or seven variables can get to the crux of what these super complicated models are doing. We’re looking at how to blend simple forecasting with more complicated systems modeling to see if there’s a middle ground that can be much more useful for making policy decisions.