Category: Research

My sociology colleague Kim Weeden recently shared her paper in Sociological Science (coauthored with Sarah Thébaud and Dafna Gelbgiser) entitled “Degrees of Difference: Gender Segregation of U.S. Doctorates by Field and Program Prestige.” It studies the relative degree of gender segregation across academic fields among earned U.S. doctoral degress, using statistical models that allow the authors to identify differences in segregation by relative program “prestige” (basically, by 1995 NRC rankings).

The paper is full of data, but one piece of information that might be of interest to political scientists is the result for U.S. political science doctorates, which appears only in a supplemental appendix as a line in a table. I’ve turned it into a figure, displayed below.

What we learn here is that among 2003-2014 doctoral degrees holders, men are slightly overrepresented at the top 10% of programs, more so at the top 20%, but especially so at the lowest ranked programs. We also learn that women are overrepresented at “unranked” political science PhD programs, which are the three categories at the right end of this figure.

The other piece of information that might be of interest is their estimate of the field-specific measure of prestige segregation in political science (which corresponds to Φ in Equation 3 in this article). The value for political science is .31, which is relatively low compared to most hard sciences and also many humanities fields like Comparative Literature, German, and Religion, in which men are much more overrepresented in the highest ranked programs. For results that are more easily interpretable to compare gendered prestige segregation across fields, you can look at the Panel B on Table S3 here (PDF).

These results, to reiterate, only look at who has completed a Ph.D. The authors have a very nice discussion of the possible mechanisms that might generate these results:

(1) sorting based on gender differences in readily observed indicators of ability (and their unobserved correlates), (2) sorting based on gender-biased self-assessments of ability, (3) self-selection based on gender-differentiated preferences for different program attributes that are correlated with prestige, (4) prestige-linked organizational strategies surrounding admissions, and (5) gender-specific attrition from graduate programs.

The fourth and fifth of these seem particularly interesting for current discussions within the field political science.

I remember exactly where I was and what I was doing the first time I saw Gerber, Green, and Kaplan‘s “The Illusion of Learning from Observational Research” (PDF): eating some peanut butter cookies in the back of a seminar room filled with august political scientists discussing methodology and the study of politics. I remember the reaction being pretty stark: “OK, here it is, the argument that we should all do experiments.” Like many pieces of this sort, I suspect that the Gerber et al. piece has been cited more than it has been read. Also like many pieces of this sort, the title does not help. The essay considers the problem of learning when confronted with an experimental result and an observational result subject to bias, and also asks how one would optimally allocate finite resources between research of these two types. The paper was subsequently published as part of the volume Problems and Methods in the Study of Politics.

I recently finished an essay with Andrew Little that argues that learning from biased research designs is not an illusion. We argue instead that we can reformulate this challenge as a Bayesian learning problem, analogous to many formal theories of learning in the social sciences. The key to our argument is insisting that researchers do have (informative and often non-neutral) prior beliefs about both causal effects and the bias in observational research designs. One provocative implication of our argument is to suppose otherwise, that you are unwilling to specify prior beliefs about causal effects or bias. If that’s the case, as we note, then it follows that

no result – hugely positive, hugely negative, or zero – would be more or less surprising to you.

We clearly don’t live in a world where researchers have no prior beliefs. Our paper shows us how to think through the problem of learning from observational research when we recognize that we do have those beliefs.