Abstract
Factionalism and performance are the dominant explanations of elite dynamics in China. While recent studies focus on the interaction between the two, this article introduces a crucial mediating factor—fiscal transfers—which has largely been overlooked. At the provincial level, leaders have incentives to obtain more transfers from the center and invest to boost GDP growth. Simultaneously decreasing their reliance on transfers is another performance indicator. The resulting balance is political, as leaders may receive more support based on their political connections. Based on two datasets of leaders and provincial finances from 1997 to 2015 and the introduction of instrumental variables, this article finds that while political ties can increase fiscal transfers, they also provide crucial information for leaders to achieve the optimal balance between transfers and growth. The political nature of transfers is also much more pronounced for provincial secretaries than governors. This study has implications for the literature on elite politics and links this research field with the literature on fiscal decentralization.
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Notes
Although such decisions must be approved by the provincial people’s congress, this is basically a rubber-stamping process [11].
For example, the average tenure of governors dropped from over 3 years in 1990 to under 2.5 years in the early 2000s [19 , p. 90].
A higher growth rate would allow provinces to spend and transfer more in the future, but the tradeoff still exists once the size of the economy is accounted for.
The Prais-Winsten AR(1) model returns similar results (see the Appendix).
As pointed out by an anonymous reviewer, the “Shanghai clique” is called as such but not “Shanghai-and-surrounding-provinces clique.” It is less likely that cliques would be formed external to provinces, as they are sizable units. To clarify, I am not necessarily suggesting that a patron would always place clients in neighboring provinces to expand his base. The instrument would be valid as long as there is a correlation between political ties and geographical proximity of provinces, which can be attributed to regional cliques, the similar characteristics of neighboring provinces, and the nature of political appointment (discussed below). I thank the reviewer for raising this point.
For example, the instrument for the secretary ties of Shanxi in 1998 is the average secretary ties/promotion outcomes of Inner Mongolia, Hebei, Henan, and Shaanxi in 1997.
For example, Choi [6] classified Jiang Zemin’s faction as those who had connections with him when he served in the First Ministry of Machine Building Industry and the “Shanghai clique,” i.e., those who advanced their careers in Shanghai under Jiang. Yet, those who had relationships with Zhu Rongji were excluded.
Shih et al. [35] defined work ties as the connection between an official and a leader working in the same work unit within two administrative steps for over a year.
While some scholars have focused on revenue-collection ability, fiscal transfer also takes into account the size of the public sector (such as spending) and captures the extent to which the province depends on/contributes to the central government, which should be the most relevant consideration for central leaders.
Other variables, such as whether the official is female or an ethnic minority are also tested but are insignificant across all models. They are excluded for model parsimony.
The raw figures of population and GDP per capita are used in the main models. Using their log-transformations does not affect the main results, as reported in the Appendix.
Detailed correlation figures for this set of variables are reported in the Appendix.
Although GDP is a key predictor of transfer gap, the effect of secretary ties is significant in both sub-samples of high/low GDP provinces (results are presented in the Appendix).
In addition, following Sovey and Green [38], the t-ratio of the first stage must be around 3 for a single instrumental variable. This is also fulfilled in the current analysis.
In other words, as province fixed effects eliminate all variations across units and focus only on within-unit variations, we are comparing the effect between those who were promoted and not promoted in the same province, thus minimizing potential endogeneity.
This does not mean that the level of transfers is irrelevant to promotion. First, the difference is merely less pronounced among those with strong ties. Second, the predictions account for the average level of transfers of the province.
I would like to thank an anonymous reviewer for this insightful comment.
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The author would like to acknowledge the support of the Strategic Research Fund, Department of Social Sciences, Education University of Hong Kong.
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Appendix to “Performance, Factions, and Promotion in China: The Role of Provincial Transfers”
Appendix to “Performance, Factions, and Promotion in China: The Role of Provincial Transfers”
A1. Summary of Datasets and Details of Variables Used
A2. Descriptive Statistics of Main Variables
A3. Correlation Figures for Provincial Leader Dataset
A4. Correlation Figures for Provincial Transfer Dataset
A5. Correlation Figures for Instrumental Variables
B1. Note on the Coding of Factional Ties
B2. Note on the Coding of Promotion
C1. Robustness Tests
A6. Probit Model of Provincial Leader Promotion
A7. Robustness Tests for Models on Provincial Transfer
A8. Estimations for Leaders Based on Career Outcome
A9. Provincial GDP and Transfer Gap
C2. Instrumental Variables Regressions
A10. First Stage Instrumental Variables Regressions
D. References Used
B1. Note on the Coding of Factional Ties
The coding followed the methodology developed by Shih and colleagues (Shih 2004; 2008; Shih et al. 2010, 2012). Three types of ties, namely birth, education, and work (xitong), are developed to capture factional ties between provincial secretaries/governors and any member of the Standing Committee of the Politburo (PSC) with dummy variables (1 = existence of ties; 0 = otherwise; missing values are dropped).
Birth ties capture whether the provincial leader has the same birth place (native province) as any PSC member. Education ties measure if the leader has a shared institution of higher education as any PSC member during the same period of time. Work ties capture whether the leader had previously worked in the same "xitong" as any member of the PSC. Xitong, or bureaucratic grouping, is defined as a ministry or a province, but not a group of ministries (e.g., First Ministry of Machine Building is different from Second Ministry of Machine Building). The work tie between the two also has to be 1) within two administrative steps from each other; 2) during the same period of time, and 3) lasting for at least half a year (Shih 2004). Codings are done from scratch for 1990-2015 (although the study only uses 1997-2015) based on the new CCP Elite Database (Shih et al. 2016), which is the most updated and systematic database of all previous contributions.
The validity of my codings is verified by cross-checking with the data provided by Shih (2004). First, the measure provided by Shih (2004) aggregates the ties for both provincial secretaries and governors for a given province-year, i.e., it would assign a 1 for birth tie if either the secretary or the governor has the relevant tie. In my version the data for provincial secretaries and governors are separately coded. Second, Shih’s (2004) coding only covers up to 2004, and I extend the dataset to 2015. Noting these differences, while most of my data are the same as Shih’s (2004), there are two other minor discrepancies. First, regarding the coding of birth ties, it appears that Shih (2004) did not clearly distinguish between the actual place of birth (chusheng di 出生地) from the native place (jiguan籍貫) of some leaders (*although Shih has a separate variable, NATIVE, recording the native province of PSC members). Which this is not an issue for most leaders, some cases become ambiguous. For example, while both Li Peng and Qiao Shi were born in Shanghai, their native places were used in the coding (Sichuan and Zhejiang respectively). In my codings I have focused exclusively on native place for better consistency. Second, there are some codings in Shih’s (2004) data that are believed to be typographical errors. For example, in Tianjin’s birth tie and education tie are coded as 1 in 1995, whereas both ties are 0 in the years before and after (1994 and 1996). Tianjin’s governor and party secretary did not change during the period to warrant such a shift; there was also no change in the composition of the 14th PSC (1992-1997; excluding ordinary Politburo members). The reason for the same shift in Hebei 1995 was also unclear. These are corrected in my dataset.
B2. Note on the Coding of Promotion
In this study, promotion is coded as a dummy variable to capture upward promotion (PROMOTION = 1) as opposed to other career changes (= 0; i.e., transfer, dismissal, demotion, retirement, and death). The coding methodology mainly adopts Landry et al.’s (2017). The codes apply to the official’s post within 12 months of exiting his/her provincial office; therefore, if a promotion occurred within 12 months after a lateral transfer, the outcome would still be coded as a promotion. Promotion for provincial secretaries include a position in a higher level party/state organ such as in the Politburo Such appointments can also be concurrent to their provincial position. Governors are usually promoted to the post of provincial secretary or minister of a central bureau. CCP Elite Database (Shih et al. 2016), the China Vitae, and miscellaneous self-collected biographies.
The results of this study are not sensitive to the specific coding of PROMOTION. As discussed in the manuscript (footnote 6), the results are robust to excluding promotion to national offices (e.g., vice-chairman of the National People’s Congress and the Political Consultative Conference). Alternatively, the dropping of cases of retirement and death would also not affect the findings (i.e., only compare promotion against transfer, dismissal, and demotion).
C1. Robustness Tests
As discussed in the manuscript, BUDGET SURPLUS is the alternative measure of transfer/fiscal dependence and will be used as in robustness tests. Results are reported in Table 11 here. Although there are some variations in the level and significance of some variables, the main findings are identical to the models using TRANSFER GAP reported in the manuscript. Robustness test using BUDGET SURPLUS in the Provincial Transfer Dataset (second set of tests) is reported in Table 4 in the manuscript.
Next, robustness tests for the Provincial Transfer Dataset are shown in Table 12. Besides the inclusion of year fixed effects (in the manuscript), temporal dynamics are further assessed by including variables counting the year of the leaders’ tenure (Model M8) and dummies for each session of the CCP National Congress (M9). Model 10 includes Han people as a share of total population to control for the impact of ethnic composition. As mentioned in the manuscript, this greatly reduces the coverage of the dataset as this variable is not available after 2010. Models 12 and 13 repeat the baseline model by replacing population and GDP by their logarithmic transformation respectively. The main findings across all of these robustness test hold, in that secretary ties significantly increase TRANSFER GAP, whereas the effect of governor ties is in the same direction, but smaller in size and at times loses significance.
Finally, in Table 13, Models 14-17 repeat the analysis by dividing the sample based on leaders’ career outcome, i.e., whether they are promoted. Models 14 and 15 divide by secretary while 16 and 17 by governor. Predicted effects plotted in Fig. 3 in the manuscript are based the estimations returned by these four models.
Another robustness test is done by re-testing the effect of political ties in determining transfer gap. All provinces are divided into high/low GDP by mean. Here, data availability prevents a similar estimation using the PCSE model (which requires all provinces to have the same year in the model). Therefore, a simple OLS is used here. The result shows that the effect of secretary ties is significant in both high/low GDP provinces.
C2. Instrumental Variables Regressions
Next the first-stage regression results from instrumental variable regression models (2SLS fixed effects) are presented (second-stage results in Tables 5 in the manuscript). In these first-stage regressions, the independent variables are the lagged average of neighboring provincial secretaries’ ties and promotion outcomes introduced in the article alongside all other covariates in the second-stage regressions; the dependent variable is secretary/governor ties. As shown in Table 14, these two instruments have a significant relationship with secretary ties, although it is not always significant for that of the governors. F-tests of excluded instruments, suggested by Angrist and Pischke (2009), can be used to assess whether an endogenous regressor is weakly identified. Again, whereas this measure is significant for secretary ties, it is not consistent for governors. In sum, the instruments should be considered strong and valid for the effect of secretary’s political ties, and slightly less for governors (Table 15).
D. References Used
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Jia, Ruixue, Masayuki Kudamatsu, and David Seim. "Political selection in China: The complementary roles of connections and performance." Journal of the European Economic Association 13.4 (2015): 631-668.
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Wong, .Y.H. Performance, Factions, and Promotion in China: The Role of Provincial Transfers. J OF CHIN POLIT SCI 27, 41–75 (2022). https://doi.org/10.1007/s11366-021-09764-1
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DOI: https://doi.org/10.1007/s11366-021-09764-1