A few months ago, I came across a reference to a review of the literature on contract farming by Otsuka et al. (2016) in a paper I was handling at Food Policy. Given how much work I have done on contract farming so far, I made sure to make time to read Otsuka et al.’s review.
One of the things that grabbed me in their review was the part where Otsuka and his coauthors write:
[i]t is less clear … how far [contract farming] improves farmers’ welfare. Although many empirical studies found positive effects of [contract farming] on the income from contracted crops, such evidence is not conclusive, because crops and products under [contract farming] are usually labor-intensive so that income from other crop production or nonfarm activities might be sacrificed … [I]ncome from other sources should be analyzed along with income from contracted production to identify the net income gain and the degree to which [contract farming] sacrifices other income … To our knowledge, such a study is lacking … (p.369)
When I read that, I realized that I could actually answer that question using the data I used in my 2012 World Development article and in my 2017 American Journal of Agricultural Economics article with Lindsey Novak, and that I could do so rather quickly. Continue reading
This week’s edition of ‘Metrics Monday will be a slight departure from the usual post in that I won’t be making any specific point about applied work. Rather, this will be more of a meta-post on econometrics focusing on how econometrics is taught.
The way I see it, econometrics has two general objectives:
- Causal inference, and
- Forecasting or properly modeling the data-generating process (DGP).
According to a new working paper by Angrist and Pischke (2017), the way econometrics is taught needs to be rethought, because although many of the problems economists are currently studying involve causal inference, a lot of the tools and the language that is used to teach econometrics to undergraduates (some of whom will go on to learn nothing else in econometrics after that one introductory course) is a holdover from the days when econometrics was all about forecasting or properly modeling the data-generating process.
Among other things, here is what Angrist and Pischke recommend: Continue reading
My colleagues Metin Çakır, Hikaru Hanawa Peterson and I, along with our doctoral students Lindsey Novak (who will be joining the faculty of Colby College this summer) and Jeta Rudi (who has joined the faculty at Cal Poly San Luis Obispo since we started working on this) have a new working paper on food waste.
In that paper, we propose a new definition of food waste–one that avoids value judgments and that has the merit of not counting the productive uses of food (e.g., composting, feeding animals, etc.) as “waste.” On that basis, we argue that most food waste definitions vastly overestimate the extent of the problem.
Moreover, because most food waste is valued at retail prices when, in fact, food often gets wasted well before the retail stage, we also argue that most definitions of food waste overestimate the price per unit of the food that is wasted. Since the value of food waste multiplies those two overstated quantities, it is obvious that reported values are even more overstated.
Here is the abstract of our new paper: Continue reading