Contributing to Public Goods: Primers on Regression and Causality

If you teach a field course (e.g., international development) in a policy school or in a political science department, chances are some of your students are not quite conversant in the quantitative methods used in the social sciences.

For example, many of the undergraduates who sign up for my fall seminar on the Microeconomics of International Development Policy are incredibly bright but they are not familiar with regression analysis, and so they don’t know how to read a regression table. This makes it difficult for me to assign papers in World Development to be discussed in class, let alone papers in the Journal of Development Economics.

While I do not have the time to teach basic econometrics to students in those seminars, I have prepared two handouts for them to read in preparation for reading papers containing empirical results, which I thought I should make available to anyone who would rather not spend precious class time teaching the basics of quantitative methods. I have used both these handouts in my development seminar last fall, and my students said that they had learned quite a bit from reading them.

The first handout is a primer on linear regression, which shows analytically and graphically (and hopefully painlessly) what a regression does, and why it is such a useful tool in the social sciences. Perhaps more importantly, this handout also explains how to read a regression table.

The second handout primer on the identification of causal relationships in the social sciences, which discusses the distinction between correlation and causation and explains two ways in which social scientists go about making causal statements (i.e., randomized controlled trials and instrumental variables), with a few examples. I suggest supplementing this handout with a reading of Jim Manzi’s “What Social Science Does–and Doesn’t–Know” in City Journal as well as with Esther Duflo’s TED talk.

Of course, neither handout is a substitute for a course in econometrics or on research design, respectively, but these handouts are intended primarily for undergraduates or Masters students with little to no quantitative background.

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