Benefit Timing, Income Fungibility, and Food Purchases among SNAP Households

A new paper in the American Journal of Agricultural Economics by Minnesota applied economics alum Travis Smith and some of his University of Georgia colleagues:

The Supplemental Nutrition Assistance Program (SNAP) is the largest nutritional safety net in the United States. Prior research has found that participants have higher consumption shortly after receiving their benefits, followed by lower consumption towards the end of the benefit month. Known as the “SNAP benefit cycle,” this consumption pattern has been found to have negative effects on beneficiaries. We hypothesize two behavioral responses of SNAP participants may work in tandem to drive much of the cycle: (1) short-run impatience—a higher preference to consume today, and (2) fungibility of income—the degree of substitutability between a SNAP dollar and a cash dollar. Using data from the National Food Acquisition and Purchase Survey (FoodAPS), a newly developed nationally representative survey of daily food acquisitions by SNAP households, we find evidence of both behavioral responses. However, the degree of short-run impatience and fungibility of income is found to differ significantly across poverty levels and use of grocery lists to plan food purchases. SNAP households could gain from food purchase planning education.

Should More Academics Blog?

Last week, Michigan State agricultural economics graduate student Jeff Bloem had a nice post about why he thinks more applied economists should be blogging. And seeing as to how blogging has seemingly done very good things for the careers of those academics who do blog, I can see why Jeff might have chosen the title “Why (More) Applied Economists Should Blog.” In a recent article in Economic Development and Cultural Change, David McKenzie shows that economics blogs play an important role in the dissemination of knowledge, they raise the profile of bloggers and their institution, and they improve the knowledge of the blog’s subject matter for the average reader.

But in a Twitter exchange with Jeff (whom you can follow here), I crystallized my thoughts on the could-should-would of blogging. Though I used to think along the same lines as Jeff–“More economists should be blogging!”–I am now a bit more skeptical. Continue reading

‘Metrics Monday: Statistical vs. Economic Significance

I had to address the topic of significance–broadly defined–at some point in this series of posts. With that said, this post is not about the seeming arbitrariness of the conventional levels of significance, i.e., of the 10, 5, and 1 percent levels of significance. First, because like Ellickson (1994), I believe that social norms emerge and evolve to minimize transactions costs, and the conventional levels of significance do just that by minimizing the amount of explanation producers of empirical findings have to provide for their findings and by minimizing the amount of thinking the consumers of empirical findings have to do in order to figure out just how (statistically) significant those findings are. In other words, the conventional levels of significance provide something for everyone to hang their hat on, and they are easy to explain to the general public.

And second, because as Noah Smith noted last summer, “[d]issing p-values in 2015 is a little like dissing macroeconomics in 2011–something that gives you a free pass to sound smart in certain circles … But like all hipster fads, I expect this one to fade.”

Rather, this post is about statistical vs. economic significance. Every so often, you run into a paper in which the authors have a good story, a good identification strategy, and robust, statistically significant findings, but in which there is little to no discussion of the findings’ economic significance. Continue reading