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Explaining China’s Budget Punctuations: Empirical Evidence Based on Cadre Management System

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Abstract

Recent applications of Punctuated Equilibrium Theory have linked institutional design to budgetary changes, yet much more work is needed to unravel the mechanism through which bureaucracy moderates the odds of budget punctuations. Three important systems of China’s bureaucracy receive close attention: cadre promotion, exchange, concurrent system and their impact on budgetary dynamics. This research investigated the impact of cadre management systems in China’s bureaucracy on budgetary changes by leveraging a unique dataset of 31 provinces budget punctuations and provincial personnel deployments during 1996–2016 and employing multidimensional panel fixed effect regression. Empirical evidence shows that secretaries of provincial Party Committee with stronger promotion incentives and career experience in relevant institution reduce budget punctuations. In addition, provinces with concurrent secretaries of Party Committee experience less punctuated budgetary changes. Findings of the study are conducive to improving the understanding of the impact of China's bureaucracy on policy changes and governance performance, and expanding the application of punctuated equilibrium budget theory in the Chinese context.

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Data Availability

The data that support the findings of this study are openly available from the China Fiscal Statistics Yearbook and the China Statistical Yearbook in the Economy Prediction System at https://www.epsnet.com.cn/index.html#/Home. The control variables data is obtained from the National Bureau of Statistics, Wind database and the Statistical Yearbook of China's Population and Employment, Financial Statistical Yearbook and China's Labor Statistical Yearbook. The data of Chinese officials that support the findings of this study are available with the permission of the National School of Development at Peking University. Or can be collected from the database of local leaders of China Economic Network at http://district.ce.cn/zt/rwk/default_18967.shtml?_t_t_t=0.022879348674783084.

Notes

  1. The reform of the classification of budget expenditure accounts in 2007 in China has led to changes in the statistical caliber of budget expenditure data. Therefore, local budgets are divided into two periods: 1995–2006 and 2007–2017. Before the reform in 2007, there were a total of 27 local fiscal expenditures. On the basis of excluding defense expenditures, this article also excluded expenditure with discontinuous data to avoid abnormal fluctuations, such as Additional Appropriation for Enterprises' Circulating Capital、Expenditure for Supporting Rural Production, Expenditure for External Assistance, Expenditure for Supporting Undeveloped Areas, Operating Expenses in Other Department, Central Government Reserve, Local Government Reserve, Expenditure on Education Surcharge, Expenditure for Retired Persons in Administrative Department, Expenditure by Using the Vehicle Purchase Tax, Expenditure for the Off-budget administrative reform and Interest Payment for the Domestic and Foreign Debts. After the reform in 2007, there were a total of 14 local fiscal expenditures. This article excluded defense expenditures, and the expenditure for industry, commerce, and banking.

  2. Due to the rotation of Chinese officials between party and government agencies, organs and agencies here include party and government agencies at the same level.

  3. Local Leaders Database: http://district.ce.cn/zt/rwk/default_18967.shtml?_t_t_t=0.022879348674783084 (access date:2023-07-19).

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Acknowledgements

I have benefited from comments provided by Chenggen Zhao, Yongjun Li, Yang Yao, Rundong Ji, Zhaoguang Wang, Tony Saich and Saul Wilson. All errors belong to the author.

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  1. Department of Public Administration, National Academy of Governance, Beijing, 100089, China

    Mengyao Li

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Appendices

Appendix

Measurement of Linear Kurtosis

Based on the method proposed by Breunig, Koski (2009), the Linear Kurtosis score is calculated as followed. Kurtosis is usually defined as the fourth moment around the mean, and can be expressed as\(k=\frac{{E(X-\mu )}^{4}}{{{(E\left(X-\mu \right)}^{2})}^{2}}=\frac{{\mu }_{4}}{{\sigma }^{4}}\), where \(E\) is the expectation operator, \(\mu\) is the mean, \({\mu }_{4}\) is the fourth moment of the mean, and \(\sigma\) the standard deviation.

L-moments (Hosking, 1990,1998)—which are the expected values of linear combinations of order statistics \({X}_{i:n}\) multiplied by scalar constants—provide more efficient statistics for distributions. If \(F(x)\) is a distribution function of a random variable \(X and {X}_{1:n} \le {X}_{2:n}\le . . . \le {X}_{n:n}\) are the order statistics associated to the distribution \(F\) (or the ordered values of a single sample of size\(n)\), then L-moments\({L}_{r}(F)\), \(r = 1, 2, . . .\) are defined as\({L}_{r}\left(F\right)=\frac{1}{r}\sum_{j=0}^{r-1}{\left(-1\right)}^{j}\left(\genfrac{}{}{0pt}{}{r-1}{j}\right)E({X}_{r-j:r})\). The first L-moment \({L}_{1}(F)\) is a measure of location. \({L}_{2}(F)\) is a scale measure for dispersion. The fourth moment (L-kurtosis) is obtained by normalizing \({L}_{4}(F)\) by\({L}_{2}(F)\). Thus, the L-moment ratio \({\tau }_{4}=\frac{{L}_{4}(F)}{{L}_{2}(F)}\) measures the kurtosis of a distribution, named as L-kurtosis in this study.

L-kurtosis score of 31 provinces from 1996–2016

Table 5

Table 5 L-kurtosis score of 31 provinces from 1996–2016
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Robustness Check

I replace the measurement of independent variable Promotion incentive here, which is measured by age gap which equals to 67 minus actual age in given year. Thanks for the anonymous reviewer’s comments.

Table 6

Table 6 Multidimensional panel fixed effect regressions
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Li, M. Explaining China’s Budget Punctuations: Empirical Evidence Based on Cadre Management System. J OF CHIN POLIT SCI (2023). https://doi.org/10.1007/s11366-023-09867-x

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