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Provincial deficits and political centralization: evidence from the personnel management of the Chinese Communist Party

Published online by Cambridge University Press:  17 June 2021

Yang Zhang*
Affiliation:
School of Public Affairs and Law, Southwest Jiaotong University, Chengdu, China
Xi Wang
Affiliation:
School of Information, The Central University of Finance and Economics, Beijing, China
*
*Corresponding author. E-mail: yzhang_polisci@163.com
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Abstract

The political autonomy of Chinese provinces derives from their economic independence. After the 2008 economic crisis, budget deficits increased significantly in most Chinese provinces, making them more reliant on financial support from Beijing. Provinces suffering high deficits will lose their political clout in both local and national politics. Therefore, provinces with large deficits tend to be less resistant to the enforcement of the law of avoidance and underrepresented in the Central Committee of the Chinese Communist Party. We find that in provincial standing committees, the members who are native or have more birthplace ties are more likely to be ranked behind the outsiders, especially so in provinces with a high level of deficits. We also find that provincial-standing-committee members from high-deficit provinces have a low possibility to obtain seats in the party's Central Committee. These findings confirm the close relationship between economic independence and political autonomy of Chinese provinces. In addition, we find that the logic of economic independence cannot depict the whole picture and that regional pluralism is also an important concern when the party manages its provincial leadership teams.

Information

Type
Research Article
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press
Figure 0

Table 1. Ordered logistic regression analysis of rankings of provincial-standing-committee members

Figure 1

Figure 1. Estimated effect of birthplace ties (count) on rankings of provincial-standing-committee members, varying deficit: (a) ranking 1-2, (b) ranking 3–7, and (c) ranking 8+. Notes: The graphs are based on the estimates of model 1 in Table 1. The histograms indicate the distribution of provincial deficits averaged over years from 2008 to 2013. The deficit over 300% is coded 300. The six lines from the lightest to the darkest represent the number of birthplace ties changing from 0 to 5. They show how the probability of certain rankings (1-2, 3–7, or 8+) varies as the deficit increases from 0 to 300%. In estimating the probability for each ranking category, binary variables (gender, ethnicity) are set to their modes, and continuous and count variables (age, career characteristics, local–central ties) are set to their means.

Figure 2

Table 2. Multi-nominal logistic regression analysis of central-committee membership

Figure 3

Figure 2. Estimated effect of deficits on central-committee membership. Notes: The graphs are based on the estimates from the multinominal regression model in Table 2. The histograms indicate the distribution of provincial deficits averaged over years between 2008 and 2012. The deficit over 300% is coded 300. In estimating the probabilities on Central-Committee membership, the binary variables are set to their modes, and the continuous and count variables are set to their means.