An Interview with Wally Thurman on his Retirement

Walter “Wally” Thurman, William Neal Reynolds Scholar, is now an emeritus professor after 38 years at NC State. Thurman taught undergraduate and graduate classes such as agricultural markets, microeconomics and agricultural production and supply. He also advised many Economics graduate students and conducted important research, which is discussed more below.

What does retirement as an emeritus look like for you? Will you still carry on your work in Montana?

Fortunately, retirement is a variable thing for an academic. I still have papers I want to work on (though it’s remarkable what happens to one’s incentives when one is no longer paid to do something)!  I also have consulting work to keep me engaged in interesting economics. Rita [Wally’s wife] and I plan to continue spending academic years in Raleigh and summers in Bozeman. In the non-economics sphere, I’m sure that more time will be spent on music and bike riding and visiting our daughters and (born October 2020) our new grand daughter.

What are your fondest memories of ARE/NC State?

Wow – a tough question. Broadly, my fond memories are all about the people – my colleagues and students. NC State has been a great place to practice economics. Students bring a new crop of people to get to know and work with every year. Conversations with colleagues and learning about their areas of expertise were pretty great. Especially poignant are memories of the ever-cheerful Jon Brandt – sticking my head in his office and asking if he had time to talk. He always said “of course, come on in.”

How did your research evolve over time? What are you interested in now?

I started out at NC State in 1983, with a dissertation on refined copper storage that was almost but not quite done.  At the time, I was pretty innocent of any real knowledge of agriculture. (Some may say this condition persists). The senior faculty at NC State were kind enough to accept my promise to learn something about the poultry industry and to try to understand its economics. It turned out to be an incredible opportunity for me. Over time, my professional identity became more that of an agricultural economist. Multiple research projects taught me that there was no substitute for actually knowing something about something – understanding the relevant  institutional detail.

A research hobby horse of mine for a while now has been the economics of pollination – honey bees, in particular. It has been a terrific subject to study as it involves biology and disease, logistics and transportation, and geographically far flung but connected markets. The deeper I got into it the more interesting the economic questions became. Recently I’ve become more interested in economic history, which I feel I should have paid more attention to all along. Currently, I’m working on the introduction of the honey bee to California during the mid-19th century gold rush and, separately, the quite remarkable tile draining of America’s mid-continent farm land during the 19^th century, which created the corn belt. The introduction of bees was almost entirely a private entrepreneurial venture; the draining of the midwest involved substantial collective action, both private and governmental.

What advice do you have for economics students and/or junior faculty?

Well, this is a pretty dangerous question to address. Trends in the economic profession come and go, but the importance of deep subject matter knowledge in research is a constant. No one wants to listen to the clever prognostications of an economist about an industry or policy area that the economist knows only from 30,000 feet.

What field of economics do you see growing in the next 5-10 years?

My crystal ball is cloudy here. I used to think that the incredible decline in the cost of computing over recent decades would make Bayesian analysis dominant – cheap computing makes the calculation of Bayesian posteriors feasible and economic theory provides a foundation for theory-based priors. But the Bayesian combination of theory with data has been overtaken by applied statistics, and theory these days seems largely assigned to exercises in pure theory – structural Industrial Organization is a notable exception to this trend.  From here on out, maybe it’s all machine learning!