Graduate Students

George Derpanopoulos

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Essay 1: Elites, Financial Networks, and Constraints on Dictators: Evidence from the Panama Papers

Abstract: A large literature argues that dictatorships can achieve high levels of economic growth if dictators can commit to not expropriate elites. Extant research has focused on the role of formal institutions—legislatures and parties—in helping elites constrain dictators’ predation. I complement this literature by documenting the role of an informal institution, financial networks, in constraining the dictator. I argue that dense financial ties among elites serve to diffuse private information on the economy, hence facilitating elites’ monitoring—if the dictator reneges on his commitment, informed elites are able to infer and punish his defection. This credible threat deters the dictator from predation and commits him to sharing rents with elites. Accordingly, I hypothesize that dictatorships where elites’ financial network allows for greater diffusion of private information enjoy stronger property rights. To test my claims, I uncover networks of elites’ co-ownership of offshore companies—a strong type of financial tie—using the largest leak of financial information to date, the Panama Papers. A statistical analysis of all cases during 1990-2015 supports my hypothesis.

Essay 2: Predicting Foreign Fighter Flows to Syria Using Machine Learning

Some countries have counted hundreds of their citizens fleeing to fight in Syria, while other countries’ citizens have remained bystanders? There are three methodological challenges to answering this question. First, there may be two groups of countries: one at no risk of ``supplying” foreign fighters and another supplying some positive amount. Second, there is no clear theory to specify the functional forms linking features to foreign fighter supply. Third, existing models perform poorly out of sample or yield output that is not amenable to social-scientific interpretations. To solve these challenges, we augment a hurdle negative binomial model with two machine learning tools. Namely, we allow our features to affect the response non-parametrically by using kernel functions that represent expansions of the data. Furthermore, we add regularization terms that penalize complexity to mitigate overfitting. Our approach combines the strengths of predictive and confirmatory models: it performs similarly to state-of-the-art machine learning algorithms in prediction while providing substantively interpretable output. Applying our model to data on 163 countries, we find that populous, developed countries, with a large Sunni population and proximity to Syria supply more fighters. These results lend themselves to viewing foreign fighter supply as largely driven by structural forces.

Essay 3: Modeling Inter-Rebel Group Violence Using Social Network Analysis: The Case of Lebanon's Civil War

When do rebel groups fight each other? How is conflict between rebel groups structured? The literature on civil war has recently shifted its attention from state-rebel violence to rebel-rebel violence. I build on this work by adopting an empirical, exploratory approach. Namely, I apply tools from Social Network Analysis to predict conflict between 22 rebel groups in Lebanon’s Civil War, specifically in the period 1980-1991. My best-performing Exponential Random Graph Models predict that groups that command support from the ethnic group they belong to, control valuable natural resources and territory, and use terrorist tactics are more likely to attack other rebels. On the other hand, my analysis finds that groups that are able to reach an agreement with the state are less likely to attack other rebels. My findings are relevant to policy-makers deciding which rebel groups to support, particularly in conflicts where opposition to the state is fragmented.


MS Statistics, UCLA (expected 2017); MPhil Economics, University of Cambridge (2011); BSc Economics and Politics, University of Bristol (2010)

Fields of Study

Comparative Politics: Authoritarian Regimes, Political Economy | Methodology: Social Network Analysis, Machine Learning


I study comparative politics and political methodology. In the former subfield, my research interest revolve around authoritarian politics, particularly the determinants of regime stability, leader turnover, and coups. In my dissertation, I use a network approach to model authoritarian regimes, and test the implications of my theory using originally collected cross-national data. In political methodology, I am interested in network analysis and machine learning, with a focus on network games and regularization, respectively.

Grants and Awards

  • Humane Studies Fellowship, Institute for Humane Studies (2017-2018, 2016-2017)
  • Adam Smith Fellowship (Research Track), Mercatus Center (2016-2017)
  • PhD Scholarship, Institute for Humane Studies (2015-2016)
  • Adam Smith Fellowship, Mercatus Center (2015-2016
  • Dissertation Year Fellowship, UCLA Graduate Division (2017-2018)
  • Summer Fellowship, UCLA Dept. Political Science (2017, 2016, 2015)
  • Graduate Research Mentorship, UCLA Graduate Division (2014-2015)
  • Graduate Summer Research Mentorship, UCLA Graduate Division (2014, 2013)
  • Graduate Fellowship, UCLA Graduate Division (2012-2013)


  • American Political Science Association Annual Conference (2017)
  • Midwestern Political Science Association Annual Conference (2017, 2016)
  • UCLA Graduate Student Conference in Comparative Politics (2017, 2015


Barbara Geddes