The 2016 MidDev conference, which my colleague Paul Glewwe organizes here at the University of Minnesota every other year, is almost upon us.
One of the papers I am most looking forward to seeing presented at the conference is a new paper on the inverse farm size-productivity relationship by Leah Bevis and Chris Barrett, which Leah will be presenting on Saturday morning in an organized session on agriculture in developing countries.
Briefly, for those of you who may not be familiar with it, the inverse farm size-productivity relationship is the empirical regularity whereby smaller farms are on average more productive than larger farms in developing countries. (Here, note that “productivity” refers to the amount produced per unit of land–kilograms of rice per hectare, for example–and not the total amount harvested.)
The existence of that inverse relationship has preoccupied economists for almost 100 years now, and many have tried to explain how and why it arises. If there truly is an inverse relationship, then this is at odds with neoclassical economic theory, according to which we would expect low-productivity producers to sell or lease their plots to high-productivity producers.
Moreover, if there truly is an inverse relationship, then the obvious policy recommendation for anyone interested in improving food security is to break up larger farms into smaller ones. Continue reading
Last week I discussed U-shaped relationships, and how to test for them. This week, I would like to discuss higher-order nonlinear relationship, or relationships that are “more nonlinear” than U-shaped relationships.
There are many ways one can approach the estimation of nonlinear relationships. I will focus only on a handful of them in this post, from least to most nonlinear, and from semiparametric to nonparametric.
A good first step beyond the estimation of a U-shaped relationship would be to estimate the equation Continue reading