UC San Diego

This paper addresses the question of how bureaucratic incentives affect economic performance in China at the provincial level. This paper develops a novel model to measure promotion incentives of the Chinese provincial standing committee members from 1995 to 2015. Using machine learning to incorporate over 200 variables of individual features, this paper derives a predicted probability of political advancement (i.e. prior likelihood of promotion) as a proxy to evaluate officials’ career prospects. The empirical results show that Chinese provincial governments are likely to generate higher economic growth rates when composed of a greater proportion of officials facing intense competition in the promotion tournament. On the other hand, a higher proportion of provincial leaders whose careers are at a dead-end or on a fast track has no effect on the local economic development. Contrary to popular belief, better economic performance stem mainly from the career incentives of the rank-and-file standing committee members, not from those of the supreme leaders of the province, such as the party secretary and the governor.