Marc F. Bellemare Posts

Since I joined Minnesota in 2013, I have had the privilege of teaching the second-year paper seminar, which all of our PhD students are required to take, and in which they get to write an entire research paper from start to finish.

Every fall, I go over Keith Head’s tremendously useful Introduction Formula, which has the double benefit of (i) minimizing the amount of uncertainty you face when writing the introduction for your research papers, and (ii) ensuring that you follow the social norm(s) surrounding how an introduction should be written for an economics paper. Then, because there isn’t much more to the introduction formula than Hook-Research Question-Antecedents-Value Added-Roadmap, I show students examples of introductions written using that formula, to show them that the formula does indeed work.

When I taught the introduction formula last week, someone asked: “But how should we write the conclusion?” Beyond what I had learned in high school, I didn’t really have a good answer, so I figured I should look around and see if there are any obvious social norms surrounding how conclusions are written for economics papers; I found nothing. Even William Thomson’s otherwise wonderful Guide for the Young Economist has nothing about how to write conclusions.

I have some good news to share. My paper with Tom Pepinsky and Taka Masaki titled “Lagged Explanatory Variables and the Estimation of Causal Effects” has been accepted for publication and is forthcoming in the Journal of Politics.

Because an image is worth a thousand words, here is one of the key figures in the paper, which illustrates in panel (a) what the problem is with using the lag of an explanatory variable to “exogenize” it, and in panel (b) the hard-to-swallow assumption the needs to be made in order for this trick to work:

Here is a link to the latest version of the paper, and here is the abstract:

Lagged explanatory variables are commonly used in political science in response to endogeneity concerns in observational data. There exist surprisingly few formal analyses or theoretical results, however, that establish whether lagged explanatory variables are effective in surmounting endogeneity concerns and, if so, under what conditions. We show that lagging explanatory variables as a response to endogeneity moves the channel through which endogeneity biases parameter estimates, supplementing a “selection on observables” assumption with an equally untestable “no dynamics among unobservables” assumption. We build our argument intuitively using directed acyclic graphs and then provide analytical results on the bias of lag identification in a simple linear regression framework. We then use Monte Carlo simulations to show how, even under favorable conditions, lag identification leads to incorrect inferences. We conclude by specifying the conditions under which lagged explanatory variables are appropriate responses to endogeneity concerns.

I spent part of last week in Rome at a conference organized by the Food and Agriculture Organization (FAO) of the United Nations. While there, I sat down with Jeremy Cherfas, who is based in Rome, from where he produces and hosts the Eat This podcast.

Over the course an hour, and my jet-lagged state notwithstanding, we chatted about a number of topics I have been working on these past few years: food prices spikes and food riots, commodity speculators and food crises, quinoa consumption in rich countries and rural livelihoods in the Andes, etc. More broadly, the conversation was about agricultural economics, and what agricultural economists do.

You can listen to (part of) our conversation here (the link opens an .mp3 file).