UC Santa Barbara

This dissertation consists of two essays studying extremism in the United States using economic tools and theory and one essay extending a well-known econometric method.

In the first chapter, I study how domestic extremist organizations in the United States attract new members. Such organizations have become a significant threat to Western democracies, with some groups attracting tens of thousands of members. I study the impact of three tactics – membership discounts, armed events, and advertisements – on national recruitment by the Oath Keepers, America’s largest paramilitary organization. Using a synthetic control framework, I find that discounts cause new member signups to increase by 144\% and armed events by 170\%; however, advertisements decrease it. Finally, I fail to find strong evidence that economic inequality drives the inflow of new members during any tactic.

In Chapter 2, I study the efficacy of social media policy designed to curb extremist activity. While governments deliberate on how to regulate, some social media companies have removed creators of offensive content -- deplatforming. I estimate the effects of deplatforming on revenue and viewership, using variations in the timing of removals across two video-streaming companies — YouTube, and its far-right competitor, Bitchute. Being deplatformed on Youtube results in a 30\% \emph{increase} in weekly Bitcoin revenue and a 50\% increase in viewership on Bitchute. This increase in Bitchute activity is less than that on YouTube, meaning that deplatforming works in decreasing a content creator's overall views and revenue.

Finally, Chapter 3 extends the synthetic control methodology to account for more scenarios. Synthetic control methods are a popular tool for measuring the effects of policy interventions on a single treated unit. In practice, researchers create a counterfactual using a linear combination of untreated units that closely mimic the treated unit. Oftentimes, creating a synthetic control is not possible due to untreated units' dynamic characteristics such as integrated processes or a time varying relationship. These are cases in which viewing the counterfactual estimation problem as a cross-sectional one fails. In this paper, I investigate a new approach to estimate the synthetic control counterfactual incorporating time varying parameters to handle such situations. This is done using a state space framework and Bayesian shrinkage. The dynamics allow for a closer pre-treatment fit leading to a more accurate counterfactual estimate. Monte Carlo simulations are performed showcasing the usefulness of the proposed model in a synthetic control setting. I then compare the proposed model to existing approaches in a classic synthetic control case study.