Abstract
While it is widely recognized that a country’s bureaucratic structure significantly influences economic growth, its subnational variations remain relatively unexplored. To address this gap, this paper introduces a unique model to quantify the collective career incentives of subnational leadership in China. By adopting machine-learning techniques that incorporate 250 individual features, this study derives a predicted probability of promotion as a proxy to measure an official’s career prospects. The individual career prospects are subsequently transformed into collective career incentives through an inverse-U-shaped relationship between the two. The empirical findings indicate that from 1997 to 2015, Chinese provincial governments achieved higher economic growth rates when a larger proportion of officials held mid-range rankings in terms of career prospects. This study also finds that the better economic performance stemmed from the collective career incentives of provincial leadership, rather than those of the supreme leaders of the province.
Published Version
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