Predicting Authoritarian Selections: Theoretical and Machine Learning Predictions of Politburo Promotions for the 19th Party Congress of the Chinese Communist Party Victor Shih School of Global Policy and Strategy, UC San Diego Jonghyuk Lee Department of Political Science, UC San Diego The starting argument for this paper is that elite selection and popular elections are both selection of leaders by a selectorate. Although the selectorates are small and their preference is largely hidden from public view, the Leninist institutions and established norms and rules drastically narrow the pool of potential candidates for high level offices, which can be narrowed further by observing elite social networks. Given the increasing availability of demographic, career, performance, and network data on senior Chinese officials, theoretically motivated and machine learning approaches can be used to make predictions about elite selection in China. Focusing on 19th Party Congress promotions into the Politburo, we make three sets of predictions based on theoretically motivated model specifications. We further use a variety of machine learning techniques to make multiple sets of predictions. Preliminary outcomes suggest that both the theoretically motivated models and machine learning approaches have their own pitfalls. For the theoretically motivated models, heavy reliance on informal ties variables will introduce multiple incorrect predictions if informal ties are mis‐coded for otherwise competitive candidates. Meanwhile, the accuracy of machine learning predictions may suffer from fundamental shifts in the relationship between some input variables and outcomes in between congresses. PRELIMINARY DRAFT, PLEASE DO NOT CITE OR CIRCULATE WITHOUT PERMISSION Corresponding author: Victor Shih (
[email protected]) March 8, 2017 1 The selection of authoritarian leaders is high stakes affairs.