Tom Pepinsky

Author: tompepinsky

  • Institutions, Authoritarianism, and Field Research

    I am part of a neat collective discussion of authoritarian legislatures over at Nate Jensen‘s blog. Nate emailed me a couple of weeks ago asking if I knew any good research on legislatures and policy outputs in Malaysia or Indonesia (during the authoritarian New Order period). I responded something along the lines of “no, because that’s not who makes policy,” which generated further discussion of what these things are for, and so on. Go read the post: it’s fascinating, with contributions from Courtenay Conrad, Scott Desposato, Barbara Geddes, Victor Menaldo, Thomas Remington, Ora John Reuter, Milan Svolik, Rory Truex, and Joe Wright in addition to yours truly.

    I written a bit about institutions under authoritarianism; in particular, on why scholars of authoritarianism need to be skeptical—or at least careful—about attributing causal power to them. It is the central point in a forthcoming review essay on the institutional turn in comparative authoritarianism, and it also shows up in my book. I can sum up my general views with a quote from the review essay:

    There are few theories that can link authoritarian institutions to anything beyond regime survival and general public policies. But authoritarian regimes do many things besides grow/stagnate and survive/collapse. They decide to murder their subjects or not; to favor certain ethnic groups or not; to integrate with the global economy in various ways; to mobilize, ignore, or “reeducate” their citizens; to respond to domestic challenges with repression, concessions, or both; to insulate their bureaucracies from executive interference or not; to delegate various ruling functions to security forces, mercenaries or criminal syndicates, or subnational political units; and to structure economies in various ways that might support their rule. Authoritarian institutions will tell us little about these outcomes, and if we are to explain variation in these factors across regimes and across time, close attention to other variables will be necessary.

    Why I am so skeptical of institutional approaches to authoritarianism? I think that it comes down to my field experience in Southeast Asia, which was in the end animated around the question of where policies come from (a “dependent variable first” approach) rather than a desire to theorize institutions and estimate their causal effects (an “independent variable first” approach) in two very different authoritarian contexts. This orientation led me to look for the drivers of policy rather than the effects of institutions, so when I see various general claims about the effects of institutions, I filter them through my own country knowledge.

    (I also might just be a contrarian by disposition, but let’s leave that aside for now.)

    This issue—the role of field work in multi-method disciplines like political science—is an interesting topic for further reflection. I hope to produce at least one more post on it soon. Suffice it to say, the field experience that I just described would be considered by many to be old fashioned area studies rather than proper modern comparative politics, which recalls my always popular OMFG Exogenous Variation post from last December.

  • What Does Randomization do to Confounders?

    Lately, I have been reading a lot of descriptions of experiments and randomization by applied researchers. One kind of phrase particularly bugs me: I frequently see language like “experimental randomization controls for potential alternative explanations…” or something similar.

    Specifically, I don’t like “controls for” as a description of the function of randomization, or more generally the metaphor of the experiment as a method of controlling for confounding variables. It comes from a regression-based model of statistical inference that was dominant in most poli sci graduate programs until somewhere in the early 2000s, where you often deal with an alternative explanation by adding a “control variable” to a regression.

    I’m no expert, but here is my one-sentence description of randomization and confounders: Randomization ensures that in expectation, each confounder is independent of the joint distribution of treatments and potential outcomes in the sample. Let me be clear: this may be a very reasonable assumption. But stating it this way helps to illustrate the differences between experiments and regression-based adjustments. Careful description of what experiments do is critical for careful thinking about what experiments do, and that is undoubtedly good social science.

    Now, the dirty little secret is that while people of my generation who are trained in multiple regression always nod when they hear the words “control for,” it’s devilishly hard to explain in words what control variables actually do. (Illustrative task: explain to a smart undergraduate student—one who can talk back—exactly what a control variable does. I bet that you will find that it’s a lot harder than you think.)

    Methodologists/experimentalists: did I get my description of randomization right? If not, how can I fix it?