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My coauthor and doctoral student Lindsey Novak and I have recently revised our paper titled “Contract Farming and Food Security.” Here is the abstract:

Contract farming has often been associated with an increase in the income of participating households. It is unclear, however, whether contract farming increases other aspects of household welfare. We use data from six regions of Madagascar and a selection-on-observables design in which we control for a household’s marginal utility of participating in contract farming, which we elicited via a contingent valuation experiment, to show that participating in contract farming reduces the duration of a household’s hungry season by about eight days on average. Further, participation in contract farming makes participating households about 18 percent more likely to see their hungry season end at any point in time. Further, we find that these effects are more pronounced for households with a larger number of children, and for households with a larger number of girls. This is an important result as children—especially girls—often bear the burden of food insecurity.

The study of contract farming has been the gift that keeps on giving. Every time I think I am done with contract farming, I find the topic pulling me back in. I thought this was going to be the last paper I would write on the topic, but it turns out I have at least two more ideas for papers on contract farming, which I hope to get around to writing sometime over the next few years.

My coauthor and doctoral student Yu Na Lee and I have finally revised (and, perhaps more importantly, resubmitted) our paper titled “Attitudes to Price Risk and Uncertainty: The Earnest Search for Identification and Policy Relevance.” Here is the abstract:

After several decades of neglect, the food crises of 2007-2008 and 2010-2011 have brought food price volatility back on the policy agenda. The study of price volatility, however, is really the study of price risk and uncertainty as they relate to individuals, households, and firms. Because the study of behavior in the face of risk and uncertainty has mostly focused on behavior in the face of income risk and uncertainty, we first review the theoretical and empirical literatures on behavior in the face of price risk and uncertainty. Then, because policy recommendations are only as good as the empirical findings on which they are based, and because market-level phenomena such as price risk do not lend themselves well to randomization, we discuss the ways in which experimental economics can inform our understanding of price risk. Finally, because expected utility—the workhorse model used to study behavior in the face of risk and uncertainty—fails to account for a number of behaviors, we discuss how insights from behavioral economics could be incorporated into the study of price risk, with the ultimate goal of generating more policy-relevant findings.

If you are a doctoral student or a young researcher interested in behavioral and/or experimental methods as well as behavior in the face of risk and uncertainty, our paper provides a number of potentially interesting research questions.

Rob writes:

I am not an econometrician–I spend my time playing with CGE models–but have to know something about econometrics. Recently I have been reviewing draft papers on a project using detailed tax data in my country–firm-level, matched with individual returns of employees, valued-added tax, import duties, etc.–for the period 2009-2014. A massive and rather unusual database.

It is all good work, but I have two concerns. (Note: I will get to Rob’s second concern at next week’s installment of ‘Metrics Mondays. — MFB.) One is about big data. Many of the researchers report t-statistics and other statistics as if this does not matter. In fact some say they are dealing with the population of firms, in which case my sense is that standard errors say nothing about statistical fit, but maybe about economic significance of relations between means. Even if it is a sample, as n/N becomes closer to 1, sample statistics become problematic.

That is a very interesting question. Let me just rephrase it a bit more broadly to this: What do you do when you are dealing with the population itself instead of dealing with a sample that is representative of a population?