Marc F. Bellemare Posts

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).

It has been a while since I blogged, so I thought it would be a good idea to share this new working paper of mine.

A few years ago I was asked to pitch ideas to the editors of the Annual Review of Resource Economics (ARRE)–in case you are not familiar with the Annual Reviews, they have done a fine job of competing with the Elsevier Handbooks, especially since they feature new articles every year.

One of the ideas that I pitched, and which the editors liked enough to ask me to submit to them, was for a review of the literature on price risk–that is, unexpected departures of a price from its expected level, also known as price volatility or price uncertainty.

This is something I have done a bunch of work on, from estimating the welfare impacts of price risk in a sample of agricultural households, to looking at whether food price volatility caused food riots, to discussing the potential of experimental and behavioral economics to study price risk, to estimating the response of producers to output price risk in the lab and in the field, and to looking at whether participation in agricultural value chains can help producers insure against price risk.

Given that, and given that I think there is still a lot of work to be done on the topic, I thought it was time for a perspective of the literature, and so I teamed up with my PhD student Chris Boyd Leon, who is writing her dissertation on issues related to price risk.

Here is the paper we submitted to the ARRE, and here is its abstract:

Much of neoclassical economics is concerned with prices—more specifically with relative prices. Similarly, economists have studied behavior in the face of risk and uncertainty for at least a century, and risk and uncertainty are without a doubt a feature of economic life. It is thus puzzling that price risk—that is, price volatility, or unexpected departures from a mean price level—has received so little attention. In this review, we discuss the microeconomics of price risk. We begin by reviewing the theoretical literature, a great deal of which is concerned with the effects of unstable agricultural prices on the welfare of producers, consumers, and agricultural households. We then discuss the empirical literature on the effects of price risk on economic agents. We emphasize policy responses to agricultural price risk throughout, discussing price stabilization policies from both a theoretical as well as an empirical perspective. Perhaps most importantly, we provide several suggestions for future research in the area of price risk given increasing risk on world agricultural markets due to both policy uncertainty and climate change.