Machine-learning analysis of leadership formation in China to parse the roles of loyalty and institutional norms

Abstract

A thriving cottage industry has long tried to predict the selection outcomes of the Chinese leadership using qualitative judgments based on historical trends and elite interviews. This study contributes to the discourse by adopting machine-learning techniques to quantitatively and systematically evaluate the promotion prospects of Chinese high-ranking officials. By incorporating over 250 individual features of approximately 20,000 high-ranking positions from 1982 to 2020, this paper calculated predicted probabilities of promotion for the 19th Politburo members of the Communist Party of China. The rankings of the promotion probabilities can be used not only to identify candidates who would have traditionally advanced within the party’s promotion norms but also to gauge Xi Jinping’s personal favoritism toward specific individuals. Based on different specifications for positions and periods, we developed measurements to quantify candidates’ levels of perceived loyalty and promotion eligibility. The empirical results demonstrated that the newly formed 20th Politburo Standing Committee was predominantly composed of loyalists who would not have risen to such positions under conventional promotion standards. We further found that, even within his circle of known allies, Xi Jinping did not opt for candidates with strong credentials. The findings of this study underscore the increasing emphasis on loyalty and the diminishing role of institutional norms in China’s high-ranking selections.