Tom Pepinsky

Author: tompepinsky

  • Syllabus Ideas: The Politics of Violence in Southeast Asia

    I am in the process of putting together a syllabus for a graduate-level half-semester course on the politics of violence in Southeast Asia. The goal of such a course would be to make sense of the micro-dynamics of conflict and violence—type of violence, its spatial distribution and incidence, its causes and consequences—but also to retain a macrostructural perspective as well, how war and conflict shape state development and vice versa.

    Although I recognize that conflict and violence is a subset of the broader conceptual category of contentious politics, in the interest of keeping this short class manageable I am holding off on including important works on mobilization, protest, and “weapons of the weak.” (Although someday it would be nice to create such a dedicated Southeast Asia-focused course too.)

    In building this syllabus, I recognize that my own knowledge is biased towards Indonesia. So, I’m hoping that readers might be able to identify some foundational texts that I have missed. I am especially interested in books, but good edited collections like Violence and the State in Suharto’s Indonesia could be useful too. It is generally easier for me to identify interesting and important journal articles, so that is not a major concern at this stage.

    Here is my working list of books and monographs.

    • Edward Aspinall, Islam and Nation: Separatist Rebellion in Aceh, Indonesia
    • Jacques Bertrand, Nationalism and Ethnic Conflict in Indonesia
    • Mary Callahan, Making Enemies: War and State Building in Burma
    • Christopher Duncan, Violence and Vengeance: Religious Conflict and Its Aftermath in Eastern Indonesia
    • Benedict Kerkvliet, The Huk Rebellion: A Study of Peasant Revolt in the Philippines
    • Ben Kiernan, The Pol Pot Regime: Race, Power, and Genocide in Cambodia under the Khmer Rouge, 1975-79
    • Duncan McCargo, Tearing Apart the Land: Islam and Legitimacy in Southern Thailand
    • Geoffrey Robinson, The Dark Side of Paradise: Political Violence in Bali
    • James Scott, The Moral Economy of the Peasant: Rebellion and Subsistence in Southeast Asia
    • John Sidel, Riots, Pogroms, Jihad: Religious Violence in Indonesia
    • Richard Stubbs, Hearts and Minds in Guerrilla Warfare: The Malayan Emergency 1948-1960
    • Yuhki Tajima, The Institutional Origins of Communal Violence: Indonesia’s Transition from Authoritarian Rule

    Amazingly, I have no entry yet on the Vietnam conflicts (Scott is something of an exception).

    And in case this is not obvious, I am completely uninterested in the academic discipline of the author(s). If the subject is politics, or political, it counts.

  • Plug-and-Play Statistical Models and Treatment Effects

    The “credibility revolution” has transformed how social scientists think about the relationship between causal inference and statistical estimation. An active research agenda has developed over the past twenty years that seeks to reground or reformulate existing statistical models as treatment effects estimators, and Angrist and Pischke’s Mostly Harmless Econometrics is an early progress report.

    Nevertheless, there are many more statistical models than there are derivations of treatment effects estimators associated with them. Treating a standard statistical model as a plug-and-play extension of a related model that does have treatment effects interpretations can be particularly dangerous. This post discusses an example.

    My use of “plug-and-play” comes from my experience using standard statistical software. Once you realize that you have a dependent variable that requires a nonlinear model, nothing stops you from replacing (in Stata) reg Y X ... with (say) logit Y X .... Interpreting the results requires a bit more work, but the regression output looks about the same, and all too rarely do we actually interpret the substantive results (or care about the specificities of that interpretation). So for better or for worse, it is common to treat these as plug-and-play models. Just plug in the nonlinear model that fits your dependent variable and off you go.

    The context of my example is the linear difference-in-differences model, where one compares the changes in a group that experienced a change between t1 and t2 with a group that did not experience such a change. The standard linear regression-based formulation of the model is
    Y = \alpha + \beta G + \gamma T + \delta G\cdot T + \theta X + \epsilon
    with G = group (treated or not), T = time (before and after the treated group got the treatment), and X are controls. The model relies on the assumption that the trend between periods in the treated group is the same as that in the control group. The model below (from Wikipedia) shows why such an assumption is commonly called “parallel trends.”

    One convenient feature of the regression-based implementation of the diff-in-diff model is that the coefficient \delta, the interaction term between G and T, represents the causal quantity of interest.

    Now, interaction terms in general have proven troublesome for applied researchers basically forever (cavepeople were omitting constituent terms back in the Neolithic, which is why it took so long to discover that fire required fuel and heat). But in nonlinear models they are especially challenging. Most notably, in 2003 Ai and Norton (PDF) reminded applied researchers that in nonlinear models like logit and probit, the marginal effect of an interaction term is not the same as the interaction effect. They can have different magnitudes, different levels of statistical significance, even different signs. Interaction terms in the nonlinear models cannot be interpreted as they can be as in linear models.

    So what if one is interested in estimating treatment effect using a nonlinear diff-in-diff model? Say, the effect of a policy change on whether individuals attend college or not. The plug-and-play extension of the above linear regression specification will require an interaction term, just as the standard diff-in-diff model does.
    Y = \Phi \{\alpha + \beta G + \gamma T + \delta G\cdot T + \theta X + \epsilon\}
    It would seem that the Ai and Norton conclusions ought to extend perfectly: if the same statistical model is being used, then shouldn’t the same interpretations of interaction terms follow? However, Puhani (PDF) demonstrates that this is not true.

    How? Why? Puhani and also Lechner (PDF) provide extended discussions, but the core reason is that causal effects must be defined differently when bounding the dependent variable because potential outcomes too must be bounded. One consequence, as Puhani discusses, is that \delta will necessarily have the same sign as the causal quantity of interest. But the causal quantity of interest itself is now something different than the average treatment effect as justified by a common trend assumption. Lechner’s review discusses further the challenges of invoking parallel trend assumptions in nonlinear diff-in-diff models, and suggests ways that more parametric identification assumptions may provide plausible foundations in particular applications. (The related literature is large and technical; Athey and Imbens (PDF) is one notable contribution.)

    The takeaway from this discussion is that the very same statistical model must be interpreted differently when used to identify treatment effects versus summarizing partial correlations. Plug-and-play statistical models are generally dangerous in a treatment effects world. This is perhaps a further reason that many are skeptical of logit or probit as alternatives to OLS—not because the differences don’t usually matter, but because treatment effects interpretations of nonlinear models might not be obvious. The real work is in determining the conditions under which plug-and-play models are appropriate, something which new research (which uses the terminology of “plug-in estimators”) promises to do.