Here I describe a few of the papers that I have finished. They are either about to be published or in various stages of peer review at academic journals. See my CV for details.
Democracy and the Class Struggle
A variety of scholars have argued that capitalist development incubates political democracy, but there has long been disagreement over the mechanisms by which development matters. I argue that the dominant conflict-based models of democratization misunderstand why development helps key actors win what they seek. Drawing on comparative and historical work, I introduce the concept of disruptive capacity, which I employ to better explain how development shapes the democratic transition. I use an original dataset on national employment structures over much of the modern period to examine patterns of democratization in a panel of as many as 100 countries between 1870 and the present. I find strong evidence that the disruptive capacity of non-elites drives democratic gains, and I reproduce the well-known finding that landlord capacity stymies it. In counterfactual exercises I show that almost 60% of the democracy gap between the developing and developed world can be explained by the fact that late development handicapped non-elites while prolonging the power of landlords.
The Long March to Democracy: Contentious Mobilization and Deep Democracy, with Mohammad Ali Kadivar and Benjamin H. Bradlow
Over the last several decades, dozens of authoritarian regimes have fallen and been replaced by formal democracies. These new democracies are not all of identical quality -- some have made substantially greater progress than others towards deepening democratic institutions. We make use of a new dataset which identifies five distinct dimensions of democratization in order to study this variation. We argue that prolonged unarmed contentious mobilization prior to transition drives democratic progress in each of these five dimensions. Mobilization matters because it generates a new, democratically-oriented political elite and because it furnishes non-elites with the capacity for autonomous collective action. In panel regressions spanning the 1950 to 2010 period and using original data, we show that the duration of antecedent anti-authoritarian mobilization is a significant and consistent predictor of subsequent democratic deepening. To illustrate the mechanisms, we present a historical analysis of democratic transition in Brazil. This case study shows that both political and non-elite actors, emboldened by prolonged mobilization, drove the deepening of democracy post-transition.
Humans in the Loop: Priors and Missingness on the Road to Prediction, with Anna Filippova, Connor Gilroy, Ridhi Kashyap, Antije Kirchner, Allie Morgan, Kivan Polimis, and Tong Wang
Social science datasets are challenging for prediction since they are often wider than they are long and riddled with missingness. These characteristics pose problems for traditional machine learning approaches, which are usually applied to mostly complete data with more observations than variables. We investigate techniques to improve feature extraction and imputing missing data for the Fragile Families Challenge. We use surveys to elicit priors from both experts and laypeople about the importance of different variables to different outcomes. This strategy allows the option to trim features before prediction or incorporate domain wisdom into prediction. We find that human-informed trimming reduces predictive performance, but incorporating human priors into machine learning approaches might improve it. Separately, while some form of imputation is essential, complicated approaches do not obviously outperform simple ones. All of the techniques we document are easy to implement, which we hope will encourage further testing of their relative performance.