I admit this story is insider stuff for those with experience of academic seminars for economists. But it made me laugh, so I pass it along. It’s how Jesse Tack, Jisang Yu, and Roderick M. Rejesus introduce their review essay “Recent approaches in agricultural production economics: Where the heck are the prices?” (Food Policy, May 2026).
A scene is unfolding. It’s 2005 and an excited young PhD candidate applied for a job and has been invited to give a job market seminar at a prominent department. They are nervous, but well prepared. Nice new outfit, a couple practice presentations under their belt. Their voice cracks just a smidge as they introduce themselves to the audience and share some personal background on why they are so excited about this job opportunity. By the time they get to the title of the paper they have recovered and are settling in. As the final word of the title rolls off their tongue, they are about to click to the introduction slide when a hand shoots up in the audience…
“I read the paper linking the new technology to crop production, but…[dramatic pause]…where the heck are the prices? Those would seem to be really important variables to be included in the analysis.”
The speaker is rattled. The questioner seems genuinely upset, and this was not expected so early in the presentation. They stammer a bit but aren’t panicking yet. They decide to go with a succinct reply in hopes of moving on. Remember, be polite…
“Great point, thank you. We agree that prices are important drivers of production but we do not need them in order to estimate the effect of interest for reasons that I will discuss later.”
The questioner doesn’t like this answer, they are a little offended…
“Well you want a job here and I asked you a question. I would appreciate an answer now.”
The speaker is again surprised and fidgets a bit. They had everything perfectly planned out. Number of slides. Length of presentation. Leave enough room for questions along the way, sure, but this soon in the presentation? Not sure what exactly to do, they summarize their strategy succinctly in the hopes that they can move on…
“One of the really cool aspects of this project is that we spent a large amount of time setting up the experimental design in which the technology was randomly assigned to different producers and thus plausibly exogeneous to both input and output prices.”
Experimental design??? Sensing shenanigans, they dig back in…
“Okay…hmmm…I don’t know about all that but I do know that you’re trying to explain production outcomes and prices are really important in that context. We are economists after all, not agronomists”.
It’s clear the questioner is well practiced in this slight against agronomists, putting just the perfect amount of snark into the comment to elicit a chuckle from the audience. Damn! After an internal sigh, the speaker wades back into the madness….
“I appreciate your concern and we have thought a lot about it, I suppose there might be some incidental in-sample correlation of prices and technology adoption so to check that this isn’t indeed a major concern we did consider a robustness check where we controlled for prices with annual fixed effects and the estimates remained stable.”
“A what effect?”
“A fixed effect. Like a dummy variable for each year in the sample.”
“You controlled for prices with a dummy variable?”
“Yes, a different one for each year.”
“Like a zero-one variable? Prices are continuous variables how can that possibly work?”
“I know right, its super cool. As long as the producers all face the same prices it is observationally equivalent to putting prices directly in the model.”
“Aha! But the economic environment is different by regions so that can’t possibly work.”
“Agreed! That’s why we also used region-by-year fixed effects!”
“What the what???”
“Yeah, our results actually seem to be pretty robust to a wide range of possible confounders.”
Baffled, and sensing they might be a bit behind on current empirical approaches as some of their colleagues in the audience seem to be nodding along in agreement with the speaker, they make one last attempt….
“But prices are a really important drivers of production and I want to see what their effect is in this study”
“That’s not our focus here”
“Well…it should be!”
“Ummmm…okay…”
OK, to appreciate the humor, perhaps you have to have been in this kind of seminar room. For those who were not, it’s probably useful background to know that for a long time, agricultural economists had a heavy focus on “production functions”–that is, a function where the inputs would be land, labor, seeds, irrigation, fertilizer, farm machinery and the like, and the question was how changing various inputs would affect output.
The goal seems straightforward, but as has been well-recognized for a long time, drawing inferences in this approach can be messy. For example, if one observes a bunch of farmers using more farm equipment, and producing higher output, is the farm equipment causing the higher output? Or is the greater use of farm equipment a reaction to some factor not included in the analysis, but also affecting output? For example, perhaps some land is just much better-suited to farm equipment, and if you don’t have that kind of land, buying farm equipment won’t benefit you as much. What if certain inputs are not well-measured in the analysis? For exmaple, perhaps some farmers may be more entrepreneurial, risk-taking, and knowledgeable than others, but you can’t just take the inputs those farmers are using, apply them to farmers with different managerial characteristics, and expect the same result.
The question of how to get more plausible causal estimates is at the center of lots of economic empirical work in the last few decades, including agricultural economics. As a straightforward example, imagine a study of several hundred or several thousand farms. Half of the farms are randomly chosen to receive a certain intervention: perhaps seeds that produce more if given particular care, or loans to buy additional fertilizer before planting, or insurance against future fluctuations in crop prices, or conservation payments, or a sophisticated weather app. Later, one can then compare output of farms that got randomly got the technology and those that didn’t.
A researcher can also look for events that introduce a random component for agricultural production, so that out of a group of farmers who are similarly situated and growing similar crops, some of thosse farmers randomly face a situation that others do not. Variations in weather conditions can offer such randomization: more specifically, it’s now becoming possible to look at detailed variation in humidity, wind speed, solar radiation, evaporation, and so on. If you want to know, for example, how global warming might affect agricultural output, these sorts of studies offer a place to start. Farmer may also vary in what technologies are available to them, or by variations in the policy environment.
And yes, it turns out to be fairly straightforward to add effects of prices into these models, as our pestering seminar participant in the story above wanted back in 2005–although the methods for doing so weren’t yet well-developed 20 years ago. The essay is a good example of a pattern that has happened fairly often over the years, in which agricultural economics raises problems that lead to new econometric techniques: for example, the birth of instrumental variable estimation and fixed-effects methods.
