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Those of us who do applied work for a living will have at some point noticed that, depending on which variables we include in X on the right-hand side (RHS) of an equation like

(1) y = a + bX + cD + e,

the coefficient c on the treatment variable D might go from significant to insignificant or vice versa.

That this is true is the very reason why it is common practice in applied work to present several specifications of equation (1) in the same table, ranging from the most parsimonious (i.e., a regression of y on D alone) to slightly less parsimonious (i.e., a regression of y on D and ever increasing subsets of X) to the least parsimonious (i.e., a regression of y on D and all the controls in X). It is also the rationale behind the method put forth by Altonji et al. (2005) to assess the robustness of a finding.

I came across an interesting new working paper by Lenz and Sahn by way of Dave Giles’ blog, titled “Achieving Statistical Significance with Covariates,” in which the authors conduct an interesting meta-analysis of articles published in the American Journal of Political Science which reveals that in almost 40% of the observational studies analyzed, researchers obtain statistical significance of c by tinkering with the covariates included (or not, as it were) in X.

Here is the abstract of Lenz and Sahn’s paper:

That is the title of a new working paper of mine coauthored with my former doctoral students Yu Na Lee (University of Guelph Food, Agricultural & Resource Economics) and Lindsey Novak (who is joining the Department of Economics at Colby College in a few weeks). Here is the abstract:

The institution of contract farming, wherein a processing firm contracts out the production of an agricultural commodity to a grower household, has received much attention in recent years. We look at whether participation in contract farming is associated with lower levels of income variability for a sample of 1,200 households in rural Madagascar. Relying on a framed field experiment aimed at eliciting respondent marginal utility of participation in contract farming for identification in a selection-on-observables design, we find that participation in contract farming is associated with a 0.2-standard deviation decrease in income variability. Looking at the mechanism behind this finding, we find strong support for the hypothesis that fixed-price contracts explain the reduction in income variability associated with contract farming. Then, because the same assumption that makes the selection-on-observables design possible also satisfies the conditional independence assumption, we estimate propensity score matching models, the results of which show that our core results are robust and that participation in contract farming would have greater beneficial effects for those households that do not participate than for those who do, i.e., the magnitude of the average treatment effect on the untreated exceeds that of the average treatment effect on the treated. Our findings thus show that participation in contract farming can help rural households partially insure against income risk via contracts that transfer price risk from growers to processors.

My article titled “On the Measurement of Food Waste,” written with University of Minnesota colleagues Metin Çakir, Hikaru Hanawa Peterson, Lindsey Novak, and Jeta Rudi became available online a few weeks ago as an advanced access article on the American Journal of Agricultural Economics website.

Since I was traveling then, I didn’t get much of a chance to sit down and blog about it, but here goes.

In case you missed my earlier posts on the topic, here is the abstract: