Marc F. Bellemare Posts

Since 2015, I have served as co-editor of leading field journals in agricultural economics almost continuously, first at Food Policy (2015-2019), and now at the American Journal of Agricultural Economics (officially since January 1, but I started handling manuscripts at the end of July), where I am slated to serve until the end of 2023.

Both those experiences have been hugely rewarding.

Both those experiences, however, also come with their fair share of frustrations.

Perhaps the worst aspect of editing a journal is dealing with would-be reviewers who are at once always-takers and never-givers (ATNGs)–that is, those academics who just love to submit to a journal but never have time to review for it.

The behavior of ATNGs is all the more annoying when I see bright young academics ask #EconTwitter how many reviews is too many for an assistant professor, because they have done one per week since the start of the calendar year and they feel like they aren’t really in a position to say “No” to journal editors–who may eventually get asked to write external review letters for those same young academics.

The behavior of ATNGs imposes a huge externality on those who (feel like they) cannot say no. Speaking with colleagues who also serve as editors at the AJAE and elsewhere, this behavior is much more common than most people suspect. And lest you think that there is some kind of editor fixed effect at play, the same ATNGs keep coming up when comparing notes with other editors in my area of research.

Recently, I discussed this with one of my mentors, who has extensive experience editing journals. His solution has been to directly tell ATNGs that for the peer-review system to work, people have to review in addition to submit, and that he will not be sending out any of their papers for review until they start reviewing for him. After all, for the system to work, for every paper of yours that gets sent out for review at a journal, you should be prepared to do two or three reviews for that journal.

As such, and because I believe in an equitable division of labor when it comes to peer review, I am seriously thinking of adopting the same policy for those manuscript I handle at the AJAE.

One concern that arose when discussing that policy with others was “Doesn’t it unfairly penalize the coauthors of ATNGs?” There is no doubt it does, but the hope here is that those same coauthors will put pressure on their ATNG coauthors so that those ATNGs become better citizens of the profession.*

All of my time away from home is spent in an academic department or in volunteer organizations, so I am painfully aware of the fact that in the absence of incentives, it’s always the same people who show up to do the work. But as an economist, I also know that when they are available, incentives work: Going back to my mentor, he told me that after he’d initially adopted the aforementioned policy toward the two ATNGs he had to deploy it with, one of them stopped submitting to the journal he edited altogether, and the other started reviewing ipso facto. From a social-welfare perspective, either outcome is better than having to deal with ATNGs.

* Alternatively, “Won’t we be missing out on good manuscripts if we do that?” Again, no doubt, but there is a considerable supply of excellent manuscripts–there are way many more of them than we can ever hope to publish–and I am happy accepting those manuscripts that are sitting right at the margin.

A few weeks ago, I received the galley proofs for my forthcoming paper in the American Journal of Agricultural Economics (AJAE) on price risk. Because the AJAE is just now transitioning from one publisher (Oxford University Press) to another (Wiley), and because I am one of four co-editors of the journal, this was a good occasion to go over some of the journal’s house rules for how papers look like in the journal.

One of the things that struck me as weird in the initial set of galley proofs that I received was that, fit those tables where all three of the usual symbols of statistical significance (i.e., *, **, and *** to denote statistical significance at less than the 1, 5, and 10 percent levels) were not used, the journal’s production team had seen fit to only list those symbols that were actually used in the table.

So for example, if a table reported findings that were significant at the 1 and 5 percent level, but did not report findings that were significant at the 10 percent level, the symbols ** and *** were defined in the table’s notes, but not the symbol *. Similarly, if a table reported a finding that was significant at the 5 percent level, but did not report findings that were significant at the 1 or 10 percent levels, the symbol ** was defined in the table’s notes, but not the symbols *** and *.

Presumably, the journal’s production team did that to save space–however infinitesimally little of it–on each page where a table appeared.

This struck me as counter to good statistical reporting practice: When looking at a table, we are no less interested in the dogs that didn’t bark than we are interested in the dogs that did bark. With table notes that define symbols in the usual way (i.e., defining *, **, and *** for coefficients significant at the 10, 5, and 1 percent levels), a coefficient without any stars next to it is understood not to be significant at any of those levels.

With a table only defines * and **, a busy reader (or a reader who is not as well-verse in statistics as most of the readers of this blog; say, a policy maker) will have no idea whether any of the coefficients significant at the 5 percent level are significant at the 1 percent level. In practice, the difference between a coefficient that is significant at the 5 percent level or at the 1 percent level can translate into decisions in which a policy maker or manager is respectively “pretty sure” or “almost certain,” and we should strive to be as clear as possible in how we define the results we report.

We have the social norms we have for good reasons. No matter how some people want to get rid of any talk of statistical significance,* the social norm scholars have settled on when reporting statistical results is to talk of the three usual levels of statistical significance. Defining only those symbols that appear in a table to save a small amount of journal page space can be misleading regarding what the authors chose to report, and it should be opposed whenever possible.

* I encourage those readers to read Ellickson’s Order without Law or his 1989 JLEO article for a good explanation of why we have the social norms that we have–and why the majority of those norms are not going away.

I have been working on contract farming for 15 years. The first I came into contact with the institution, in the principal contracts the production of an agricultural commodity to the agent, was in 2004, while doing my dissertation fieldwork in Madagascar.

At the time, I was doing research on agrarian contracts. Consequently, my dissertation’s third essay (a much-improved version of which became this article) was on contract farming.

Many things have changed about my research agenda since then, but this has been the one problem I have consistently worked on (often contre vents et marées) and in the in the intervening years, I have written seven additional articles on contract farming. So after publishing a review of the literature on the topic in World Development with Jeff Bloem in 2018, I thought I was done working on contract farming.

Little did I know that I would get pulled right back in, and to answer one of the big important questions in the literature on contract farming.

In in our 2018 article, Jeff and I had bemoaned the lack of external validity in the literature looking at the welfare impacts of contract farming on the participating households:

In Meemken and Bellemare (2019), just published in Proceedings of the National Academy of Sciences, we substantially improve on both the external and internal validity of the typical contract farming study. On the external validity front, we use comparable survey data from six developing countries; on the internal validity front, several individuals per household were surveyed, which allows incorporating household fixed effects to control for unobserved heterogeneity between households.*

Here is the abstract of this new paper:

Poverty is prevalent in the small-farm sector of many developing countries. A large literature suggests that contract farming—a preharvest agreement between farmers and buyers—can facilitate smallholder market participation, improve household welfare, and promote rural development. These findings have influenced the development policy debate, but the external validity of the extant evidence is limited. Available studies typically focus on a single contract scheme or on a small geographical area in one country. We generate evidence that is generalizable beyond a particular contract scheme, crop, or country, using nationally representative survey data from 6 countries. We focus on the implications of contract farming for household income and labor demand, finding that contract farmers obtain higher incomes than their counterparts without contracts only in some countries. Contract farmers in most countries exhibit increased demand for hired labor, which suggests that contract farming stimulates employment, yet we do not find evidence of spillover effects at the community level. Our results challenge the notion that contract farming unambiguously improves welfare. We discuss why our results may diverge from previous findings and propose research designs that yield greater internal and external validity. Implications for policy and research are relevant beyond contract farming.

And here is the paper’s significance statement:

Achieving the United Nations’ Sustainable Development Goals remains a challenge in many developing countries, and especially in rural areas. Smallholder farmers are often trapped in a vicious cycle of low-intensity farming, low yields, limited market access, and insufficient profits, all of which prevents beneficial investments. Contract farming is commonly seen as a suitable means of linking poor farmers to markets, improving household welfare, and promoting the modernization of the agricultural sector. The available evidence supports the notion that contract farming increases welfare, but external validity is limited. We address this gap using data from 6 developing countries and discuss implications for policy and research.

* Though we improve on internal validity relative to the typical contract farming study, this still falls short of the gold standard–a randomized controlled trial (RCT)–when it comes to internal validity. The only RCT I know of contract farming I know of is this wonderful paper by Arouna et al. (2019).