6. Conclusion, limitations and future research
6.2 Limitations
Our study is subject to limitations that we openly acknowledge. Firstly, since there is very little publicly available data on corporate governance of non-listed SOEs in China, this study was limited to analyzing 742 listed SOEs, which constitutes only 0.8% of the 89,548 local-SOEs registered within the state-owned assets system (SASAC, 2016, p. 689). This creates a selection bias, since listed companies are not a representative sample of all Chinese local-SOEs.
Much of the “real” governance of listed state-controlled companies takes place through their non-listed holding group companies, which possess decision-making authority over the entire enterprise group. Yet, information on the corporate governance of these holding group companies is very sparse (Rosen, Lautert, & Guo, 2018). For this reason, many studies are limited to their listed subsidiaries, which are required to disclose detailed information on their corporate governance.
Secondly, our study measures corporate governance in a two-dimensional way by considering concentration of ownership and level of shareholding by MBMs. However, assessing quality of corporate governance is a complex process that involves a broader set of evaluations of
184 manifold operational aspects of a company. It would therefore be beneficial to test the results against a broader scoring system for Chinese SOE corporate governance, such as those used to evaluate enterprises in other emerging economies (Sarkar et al., 2012; Warganegara et al., 2013).
Thirdly, our paper only addresses formal corporate governance structures and not the political layer of corporate governance in terms of the Party groups (党组) and Party committees (党委 员会), which exist in all Chinese listed enterprises (regardless of ownership) and are known to exert influence on SOE’s corporate governance (A. H. Zhang & Liu, 2019; Y. Zhang, 2012).
Future studies should address variations within the role and structure of various Party groups in SOEs and non-state-controlled companies. One might expect that the reinforcement of the Party inside various types of enterprises significantly increase the potential political risk associated with Chinese enterprises. To assess such risks, future studies should expand the limited literature on personnel appointments, decision-making and supervision functions of enterprise Party groups/committees (E. C. Chang & Wong, 2004; Cho & Huang, 2012; W. Yu, 2013).
Finally, we have no access to appropriate measurements for institutional diversity at city-level and below (within provinces). Therefore, we categorized SOEs owned by city governments under their respective province or autonomous region in order to assign corresponding indicators at the provincial level. We need to acknowledge that this is an approximation and that there might be within-province institutional variations, which we do not account for in our approach42.
42 For example, institutional setups in Suzhou in southern Jiangsu (Sunan region), and Xuzhou in northern Jiangsu (Subei region) have been shown to differ (Y. D. Wei, 2004).
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194 Appendix 1: computation of variables
Independent variables Assistance from industry associations
Extent of assistance to firms from local industry associations/guild (survey data). The original data of this index come from enterprise questionnaire surveys, where enterprise operators evaluate conditions and development of associations. Higher scores equal more extensive assistance.
Credit allocation to local-SOEs
In the data period 2008-2014, the proportion of loans granted to local state-owned enterprises out of all loans are applied, In 2015 and 2016, the proportion of local state-owned enterprises’ liabilities as proportion of total liabilities are used as a substitute. The proportion of loans and the proportion of liabilities are highly correlated. Higher score = larger proportion of all credit is allocated to state-owned enterprises.
Local administrative level
Dummy variable. 0 = city-level or below, 1 = province level Category of state
capitalism
Mode of yearly categorization based on:
Interdependent = “Credit allocation to local-SOEs” > yearly mean &
“Assistance from industry associations” > yearly mean
Classic = “Credit allocation to local-SOEs” > yearly mean & “Assistance from industry associations” < yearly mean
Networked = “Credit allocation to local-SOEs” < yearly mean &
“Assistance from industry associations” > yearly mean
Atomistic = “Credit allocation to local-SOEs” < yearly mean &
“Assistance from industry associations” < yearly mean Dependent variables
Largest shareholder (%) (Number of shares held by the first largest shareholder/total number of shares)*100
Top 5 largest shareholders (%)*
(Number of shares held by the five largest shareholders/total number of shares)*100
Shareholding by MBMs (Shares held by executives + board of directors / total number of shares)*100
Assets owned by MBMs* (million RMB)
(Shares held by executives + board of directors / total number of shares)*total assets
Control variables
GDP per capita (RMB) Provincial gross domestic product per capita Total assets Ln(total assets)
Return on assets Net profit/total assets Leverage Total liabilities/total assets Listing date Listing year
Industry Dummy variables
Year 1 if the company is in year Y, 0 otherwise
Province Dummy variables for each province (only in test of province–city SOE differences)
Notes: *=only used for robustness check in Appendix 10.
195
Appendix 2: correlations table
Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)
(1) Largest shareholder (%) 1.000
(2) Top 5 largest shareholders (%) 0.765* 1.000 (0.000)
(3) Shareholding by MBMs (%) -0.146* -0.066* 1.000
(0.000) (0.000)
(4) Assets owned by MBMs (million RMB) -0.069* -0.044* 0.142* 1.000 (0.000) (0.001) (0.000)
(5) State-ownership level (1=province-level) 0.179* 0.160* -0.054* -0.047* 1.000 (0.000) (0.000) (0.000) (0.000)
(6) Assistance from industry associations 0.055* 0.051* 0.049* 0.021 0.101* 1.000 (0.000) (0.000) (0.000) (0.122) (0.000)
(7) Credit allocation to local-SOEs 0.023 0.014 -0.062* -0.038* 0.173* 0.023 1.000 (0.089) (0.302) (0.000) (0.005) (0.000) (0.081)
(8) SOE holding group or SASAC as largest shareholder
0.134* 0.056* -0.007 0.016 0.051* -0.015 0.001 1.000 (0.000) (0.000) (0.588) (0.242) (0.000) (0.265) (0.930)
(9) GDP per capita (RMB) 0.065* 0.049* 0.057* 0.014 0.131* 0.664* 0.021 0.005 1.000
(0.000) (0.000) (0.000) (0.292) (0.000) (0.000) (0.115) (0.696)
(10) Log of total assets -0.031* -0.048* -0.027 0.177* -0.006 0.020 0.031* 0.005 0.013 1.000
(0.019) (0.000) (0.051) (0.000) (0.676) (0.137) (0.020) (0.713) (0.341)
(11) leverage -0.076* -0.090* -0.060* 0.002 -0.019 -0.047* 0.013 -0.037* -0.044* 0.040* 1.000
(0.000) (0.000) (0.000) (0.910) (0.147) (0.000) (0.326) (0.006) (0.001) (0.002)
(12) ROA 0.021 0.029* 0.009 0.003 0.015 -0.014 -0.019 0.029* -0.004 -0.004 -0.565* 1.000
(0.122) (0.031) (0.507) (0.810) (0.244) (0.290) (0.155) (0.028) (0.789) (0.743) (0.000)
(13) Listing year 0.164* 0.295* 0.303* -0.039* -0.011 -0.096* 0.037* 0.051* -0.126* -0.118* -0.125* 0.020 1.000
(0.000) (0.000) (0.000) (0.004) (0.403) (0.000) (0.006) (0.000) (0.000) (0.000) (0.000) (0.130)
* p<0.05
196 Appendix 3: provincial scores in 2008, 2012 and 2016
Notes: State direction of the economy is proxies by allocation of credit to the state-owned sector. Business coordination of socioeconomic activities is proxies by degree of assistance to firm from local business associations.
197 Appendix 4: Provincial average scores on credit allocation to local-SOEs, 2008–2016
Note: calculated as average yearly provincial score on the (reversed) NERI inducator for credit allocation.