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Article

Corporate Public Transparency on Financial Performance: The Moderating Role of Political Embeddedness

1
School of Management, Harbin Institute of Technology, Harbin 150001, China
2
School of Public Administration and Law, Northeast Agricultural University, Harbin 150030, China
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(19), 5531; https://0-doi-org.brum.beds.ac.uk/10.3390/su11195531
Submission received: 12 July 2019 / Revised: 20 September 2019 / Accepted: 30 September 2019 / Published: 8 October 2019
(This article belongs to the Section Economic and Business Aspects of Sustainability)

Abstract

:
Corporate public transparency (CPT) is instrumental for companies to establish communications and trust with the public by disclosing and communicating information concerning corporate environmental and social impacts. However, it is still in dispute whether CPT can help promote corporate financial performance (CFP). This paper studied the moderating role of political embeddedness on the relationship between CPT and CFP. We investigate multiple hypotheses about the moderating roles of the political embeddedness including bureaucratic embeddedness (political connections of a chief executive officer (CEO) who was/is a government official or member of political council) and ownership embeddedness (i.e., state-owned enterprises (SOEs)). With the data of 195 observations from top 200 Chinese enterprises ranked by revenue for the years 2014~2016, the results show the following: (1) the relationship of CPT on CFP is moderated by government official and SOE ownership; (2) a negative moderating effect of government official; and (3) a negative moderating effect of SOE ownership. The research implications are further discussed. The findings of this study have practical implications for investors, stakeholders, and regulators.

1. Introduction

The number of released corporate social responsibility (CSR) reports has experienced explosive growth [1]. Although CSR disclosure may contribute to increasing corporate financial performance [2,3], it makes the public doubtful and distrustful about the initiatives of corporate information disclosure, that is, whether the information disclosure is only “window dressing” to show a socially responsible appearance without any effective activities. Some firms that present a public image of environmental friendliness are proved to be polluters [4]. For example, Zijin Mining Corporation, a famous state-owned enterprise in China, is good at CSR disclosure, but actually creates a series of severe pollution incidents, such as the sewage leakage incident in 2010. Corporate information disclosure may mislead consumers, stakeholders, and other public organizations into making wrong decisions. Eventually, the public will lose confidence in corporate information disclosure.
Corporate public transparency (CPT) is deemed as a way to reestablish trust for companies with the public [5]. Some companies increase CPT to avoid financial risks [6,7], and to gain trust and credibility from investors [8]. Some researchers have conceptualized transparency from different perspectives such as information availability and accessibility [9,10] or visibility [11].
CPT refers to general situations about corporate information disclosure, transmission, and responding to overall information demand from the public [12], aiming at ensuring corporations to obtain public trust and create a healthy external environment for corporate development. CPT is an indicator of demonstrating CSR by making the information of corporate environmental and social impacts available to the public completely and timely. CPT is an important component of corporate sustainable competitiveness.
There is still a lack of studies on the business value of CPT. Previous studies investigated some related aspects. From the cost perspective, Gong (2018) revealed the negative relationship between corporate transparency and corporate band costs [13]. Du (2015) showed that some Chinese listed firms spent substantial advertising money to shape their green and environmental-friendly images [4]. Some analyses focused on the relationship between corporate transparency and corporate management from perspectives of customers’ pro-environmental behaviors [9,14,15], public policy [16,17], choice of CSR engagement strategy [18], and risk management [7]. Although a few studies analyzed financial performance [19,20,21,22], the results are mixed because of the differences in conceptualization of corporate transparency, measures of corporate financial performance (CFP) [2,18,19], and/or the institutional and economic background [3,19,20,21].
There are some potential variables, such as political background [22] and institutional settings [4,20], that may influence the business value of CPT. China provides a good context for the study of corporate political embeddedness and governmental political institutional influence on firms [23]. Firms may respond differently because of different types of political signals and pressures [24].

2. Research Context and Hypothesis Development

2.1. Political Context

Corporations in China have typical political embeddedness. One of the common corporate political connections is building a personal connection with the government through nominating a chief executive officer (CEO) who has working experience in political councils or government departments [25]. While the CEO in Western countries under an Anglo-American definition of capitalism and institutionalized association with enterprise is different from CEO’s role in China’s context, in which “guanxi” plays a more important influence for Chinese enterprises, and CEO as lubricant for guanxi development between the government and the enterprise has more chance to be the political contactor [26]. Therefore, focusing on CEOs allows for a consistent investigation on political embeddedness. According to Wang et al. (2018), almost 31.85% of CEOs in a sample of 3447 firms in China are former or current government bureaucrats or members of political councils, and 27.97% of the firms are state-owned [27]. There are many researchers who described the interdependence of supplier and demanders of public policy based on the political exchange view [28,29]. Referring to this view, the Chinese government has a great impact on enterprises through embedded multiple roles including acting as a regulatory shareholder [30] for the sake of controlling key resources [31]. Chinese firms have an impulsion to access to policymakers for gaining resources and realizing firms’ value via corporate political strategy [32]. Compared with inherent political connectedness of state-owned enterprises (SOEs), non-SOEs have more reasons to establish political relationships with policymakers for gaining a competitive edge and reducing uncertainty [23].
Since China’s economic reforms initiated in 1978, the Chinese government has tried to move to a market-oriented system, and government interventions to enterprises have reduced gradually. Although great progress has been made, China’s economy is still characterized by a high degree of government control [33], mainly owing to multiple important roles of the government, including as a shareholder or regulator [30,34]. There are many examples of firms bonding with government to gain more governmental support, such as access to lower taxation and greater security in property rights protection [35,36], access to preferential government bailouts [35,37], and obtaining governmental procurement contracts [38]. In China, party and government officials can hold positions in enterprises, and enterprises can also connect with governments through legal paths. The National People’s Congress (NPC) and the Chinese People’s Political Consultative Conference (CPPCC) are the two most important political councils in China, collectively known as “Lianghui”. According to the Chinese Constitution, the NPC is the national legislature and the highest authority in China, and the CPPCC’s functions are mainly in political consultation, participation in politics, and democratic supervision. Private entrepreneurs have been invited to participate in the NPC since 1990. In 1993, 23 private entrepreneurs were elected to be members of the CPPCC for the first time, and companies have been actively seeking to become deputies of the NPC or members of the CPPCC since then, in order to improve their political status.

2.2. CPT and CFP

Stakeholder theory [39] implies that corporate social activities can improve corporate relationships with various stakeholder groups, and thus will result in better firm performance [40,41]. Similarly, Jo and Harioto (2011) argue that strategic managers use CSR engagement to resolve conflicts among stakeholders, thereby maximizing the shareholders’ wealth and minimizing the shareholders’ concerns [42,43]. Corporate information disclosure as a component of CSR can significantly decrease the degree of information asymmetry among stakeholders, reducing stakeholders’ doubt, and thus avoiding conflicts and unnecessary risks. A firm with high-level CPT may signal to the market that it exhibits sound financial performance and its managers have confidence in satisfying the expectations of social responsibility. Thus, we propose the following hypothesis:
Hypothesis 1.
CPT has a positive impact on CFP.

2.3. Moderating Effect of Political Embeddedness

2.3.1. Moderating Effect of Ownership Embeddedness

Ownerships of SOEs and private enterprises are different. Ownership embeddedness in this paper refers to ownerships of SOEs that are a form of political embeddedness. SOEs occupy a large part of the Chinese market, and usually serve as bellwether in implementing government policies. SOEs, therefore, receive more pressures from the government in investing in nonearning CSR activities and bear more social responsibilities, resulting in higher cost and lower financial efficiency. SOEs find it easier to obtain more governmental financial support [44], such as bank loans, government subsidies, and other benefits [31,45,46], which can facilitate CSR fulfilment [40,47,48] but result in financial inefficiency [40,49]. As CPT can be considered as a kind of CSR, we hypothesize the following:
Hypothesis 2.
Ownership of SOEs will negatively moderate the relationship of CPT on CFP.

2.3.2. Moderating Effect of Bureaucratic Embeddedness

In China, it is quite common for a corporation to have executives who have bureaucratic embeddednesss with the government. Corporate executives may be members of the Chinese Communist Party or hold important position in the political system [50]. A number of studies have analyzed the determinants of CSR activities [51,52,53]. One of the prominent factors is governmental regulatory requirements. Huang and Kung (2010) argued from a control-oriented perspective that corporate actions for social investment can be shaped through bureaucratic embeddednesss under regulatory pressures [54], which is consistent with the argument that bureaucratic embeddednesss can be used to promote governmental policy implementation at the corporation level [47].
The CPT–CFP relationship may vary across political contexts. Bureaucratic embeddedness may bring negative effect of rent-seeking [55] and affect CSR reporting [56]. In order to sustain bureaucratic reputation, bureaucratic CEOs may decide to invest in nonearning areas and accordingly push up corporate cost. Moreover, bureaucratic embeddedness may result in poor corporate accounting [57,58]. Overall, we thus hypothesize the following:
Hypothesis 3.
Bureaucratic embeddedness negatively moderates the relationship of CPT on CFP.
Hypothesis 3a.
CEO is/was a member of political councils negatively moderates the relationship of CPT on CFP.
Hypothesis 3b.
CEO is/was a government official negatively moderates the relationship of CPT on CFP.

2.3.3. Co-Moderating Effect of Ownership Embeddedness and Bureaucratic Embeddedness

Executives of SOEs may be members of the Chinese Communist Party and appointed by the government. Therefore, SOEs may involve dual political embeddedness, namely, ownership embeddedness and bureaucratic embeddedness. Management objectives of executives of SOEs are intervened by the government. Bureaucratic embeddedness may result in executives’ rent-seeking motivation, using corporate resources in the name of CSR to pursue private interest including personal political promotion [33,53,59]. The relationship between CPT and CFP of SOEs might imply bureaucratic intervention to corporate business. Thus, we propose Hypothesis 4:
Hypothesis 4.
The moderating effect of ownership embeddedness on the relationship of CPT on CFP is influenced by bureaucratic embeddedness.
Figure 1 depicts the conceptual model and research hypotheses of this paper.

3. Methods

3.1. Data and Samples

This paper chooses a sample of the top 200 from China’s top 500 enterprises in scales ranked by China Enterprise Union and China Entrepreneur Association. The sample enterprises are representative and have sufficient resources to devote to CSR. The CPT data are collected from China Corporate Public Transparency Report No. 1~3 compiled by Social Responsibility Professional Committee of China Enterprise Management Research Association and Beijing Rongzhi Institute of CSR (a professional institution, formally registered in Beijing Civil Affairs Bureau, specializing in research and promotion of CSR in China, and its team containing experts in research and practice of CSR in China). Thus, the authenticity and validity of the data can be guaranteed. The biographical data of the CEOs are collected from official CSR reports and corporate websites. The raw data of group enterprises are collected from CSR reports, corporate annual reports, and corporate official websites.
To test hypotheses, we use panel data from 2014 to 2016, selecting corporations that have complete required data and excluding those of missing data, and a total of 195 group enterprises meet the requirements.

3.2. Variables

Dependent variable: return on assets (ROA) is widely used as a measure of CFP [60,61]. In this paper, ROAs are calculated as net profits divided by total assets.
Independent variable: CPT has been published since 2014, aiming at promoting public understanding and trust on enterprises, reducing public doubts, and making right decisions. CPT was ranked based on three dimensions: information content, information form, and corporate response to public information demand.
The measurement of CPT in the report was based on content analysis of the publicly released information of the top 200 Chinese group enterprises. It contains indicators for evaluating the completeness of information content, the forms and channels of information disclosure, and corporate responding to public information demand, and the final score is determined through the Delphi method [12,62,63].
Moderating variables: owner is disposed as a dummy variable (1: if the government holds the majority of the corporate share; 0: otherwise) [53,59]. Bureaucratic embeddedness is disposed as another dummy variable (1: if corporate CEO was/is government official or member of NPC/CPPCC; 0: otherwise) [64]. To differentiate enterprises embedded in central, provincial, or local level, scores of 3, 2, and 1 are assigned respectively [65]. Natural logarithm (1 + score) is used in calculation.
Control variables: for control variables, we consider corporate size [27,66], corporate age [47], and CEO characteristics (i.e., CEO age, term of office, education, dual) [67]. We consider whether a corporation contains subsidiaries listed in overseas markets such as Hong Kong or New York to exclude overseas influence [27,68,69]. Finally, we add industry dummies to control the sector-specific effect, and include year dummies to control some omitted variables that may vary over time.
Table 1 summarizes the definitions of all the variables.

3.3. Model

To test the hypotheses above, we use the following multiple regression models:
CFP = α + β 1 C P T + β 2 O w n e r + β 3 B u + β 4 C P T × B u + β 5 C P T × O w n e r + β 6 C P T × B u × O w n e r + β 7 X + β 8 I n d u s t r y + β 9 Y e a r + ε
where X means a set of control variables of influencing CFP; C P T × O w n e r means interaction between CPT and state-owned ownership; C P T × B u means interaction between CPT and bureaucratic embeddedness; C P T × B u × O w n e r means three-way interaction of CPT and bureaucratic embeddedness and state-owned ownership. In addition, industry type and year are dummies as control variables.

4. Results

4.1. Descriptive Statistics and Hypotheses Testing

Table 2 presents the statistical summary. Multicollinearity is tested based on Spearman pairwise correlations and variance inflation factors (VIF). Table 3 shows the Spearman pairwise correlations of variables. All VIF values are below the limit of 10 [71], indicating no multicollinearity problem.
Table 4 presents the regression results. Model 2 implies there is no significant relationship between CPT and CFP for the overall sample, so Hypothesis 1 is not supported. Model 3 shows that for enterprises with bureaucratic embeddedness, there is a negative relationship between CPT and CFP. Model 4 shows that for enterprises without bureaucratic embeddedness, there is no significant relationship between CPT and CFP. So, the results support Hypothesis 3. Models 5 and 6 show that for private enterprises, the effect of CPT on CFP is not significant, but for SOEs, the effect of CPT on CFP is negative, so Hypothesis 2 is supported. We further analyze the combined effect of ownership type and bureaucratic embeddedness in Models 7 and 8; the results show that there is a significant relationship between CPT and CFP in SOEs with bureaucratic embeddedness. There is no significant relationship between CPT and CFP in SOEs without bureaucratic embeddedness. So, the results imply that bureaucratic embeddedness is an important factor in influencing the relationship between CPT and CFP.
Table 5 presents the results of the effect of specific bureaucratic embeddednesss on the relationship of CPT on CFP. We use the bureaucratic embeddednesss index to examine the moderating effects of different types of bureaucratic embeddednesss. The results of Model 1 and Model 3 show that the interaction coefficient between CPT and government official is negative, so Hypothesis 3b is supported. In Model 2 and Model 4, the interaction between CPT and CEO as a member of political council is not significant, so Hypothesis 3a is not supported. Therefore, we infer that the moderating effects of bureaucratic embeddednesss on the relationship of CPT and CFP may mainly come from CEOs as government officials.
Models 5–8 present the three-way interaction of CPT and bureaucratic embeddednesss and ownership type, revealing that there is no co-moderating effect. So, Hypothesis 4 is not supported.
To demonstrate the results, we use the standard approach [72] to present the interaction effects for interpretation. The interactions between political embeddedness and CPT are shown in Figure 2, Figure 3, Figure 4 and Figure 5.

4.2. Robustness Tests

We use different proxies for the combinations of CPThigh & CFPlow and CPTlow & CFPhigh. Another measure of CFP is employed as an alternative variable to represent CFP for the robustness test. The measure is asset turnover ratio (ATR), which implies the operating efficiency of corporate assets (calculated as operating revenues on total assets). Table 6 shows that the results of this study are robust to alternative measures.

4.3. Endogeneity Controls

An instrument variable (IV) of the two-stage least square (2SLS) method is applied to address the potential endogeneity caused by possible omitted variables, simultaneity, and reverse causality. A construct of (CFP – the mean of CFP)*(CPT – the mean of CPT) is formulated as IV of CPT based on referring to literature [61,73].
Table 7 shows the results of 2SLS. A series of statistical results confirms the validity. The Anderson canon.corr.LM results show that there is no problem. The Cragg–Donald Wald F results also show that there is no weak instrument problem. The regression results of endogeneity controls indicate that the significances of the coefficients are similar to the results in Table 5.

5. Discussion and Conclusions

This study examined whether and how CPT influences the CFP under China’s regulatory environment. Although the main effect of CPT is not confirmed in the overall sample test results, a number of tested political factors do moderate the effect of CPT on CFP. We find that there is the moderating role of two types of political embeddedness, that is, bureaucratic embeddedness (government official in essence) and ownership embeddedness (SOE ownership in essence) on the relationship between CPT and CFP, and the moderating effect of bureaucratic embeddedness mainly comes from CEOs as governmental officials rather than CEOs as members of political councils. This implies that governmental officials have greater access to resources when information asymmetry is relatively high [74,75]. Information asymmetry may give a chance to politically connected local government officials for rent-seeking, such as prioritizing the goal of economic growth for bureaucratic personal career promotion [76], which is consistent with Xin and Pearce (1996), who argued that managers’ ties with government officials can compensate for the insufficient formal infrastructure, especially in China’s transition economy with inadequate legal framework [26]. The co-moderating effect of bureaucratic embeddedness and SOEs is not supported, which might be because the top SOEs are paid extensive attention and stringently monitored [56], which limits rent-seeking activities.
This study implies that CPT is more likely to be negatively related to CFP for the enterprises with embeddedness of governmental official or SOE ownership. Accordingly, the results indicate that, in the context of politically embedded enterprises, effectiveness methods should be taken into account for comprehensive value and quality of government induced corporate information disclosure and the diffusion of CSR activities. From a business cost perspective, the results imply that government interventions involve opportunity cost. Different types of political embeddedness play different roles in responding to the government signals, such as the paradox effects of CSR activities and the implementation of information disclosure policies.
This study has interesting implications for investors regarding different types of political embededdness. As investors aiming at increasing financial performance, they tend to invest to corporations with high-transparency to avoid information asymmetry and uncertainty that may cause loss of investment. Meanwhile, this study shows that the transparency may not be as simple as it looks. The political embededdness behind the transparency should be considered for investment decisions.
This study contributes to the literature in several aspects. First, prior corporate transparency studies were largely market-oriented. This study enriches the literature by introducing political embeddedness by linking the macroscopic institutional level and microscopic corporation level, demonstrating the importance of political embeddedness in affecting the relationship of CPT on CFP. Second, this study sheds light on the interactive relationship between corporate political resources and their market-based abilities [27]. We find that although political embeddedness is a kind of important social capital in emerging countries, its effects on CFP may not be positive because of inefficiency from structural lock-in [77].
The limited data source of CPT makes it hard to employ more data for study. The data constraints in our analysis may result in weaker findings. So, future studies in this direction are encouraged to use other indicators that are free of similar constraints. Future studies are also encouraged to adopt various methods and evidences to triangulate the findings.

Author Contributions

Writing-original draft preparation and data curation, Y.L.; conceptualization and supervision, X.M.; methodology, D.Z.; writing-review and editing, Y.T.

Funding

This research for this paper was funded by the National Natural Science Foundation of China (No. 71471047), and the Humanities and Social Science Foundation of Ministry of Education (No. 15YJC630116).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Wang, K.T.; Li, D. Market reactions to the first-time disclosure of corporate social responsibility reports: Evidence from China. J. Bus. Ethics 2016, 138, 661–682. [Google Scholar] [CrossRef]
  2. Orlitzky, M.; Schmidt, F.L.; Rynes, S.L. Corporate social and financial performance: A meta-analysis. Organ. Stud. 2003, 24, 403–441. [Google Scholar] [CrossRef]
  3. Lu, W.; Chau, K.W.; Wang, H.; Pan, W. A decade’s debate on the nexus between corporate social and corporate financial performance: A critical review of empirical studies 2002–2011. J. Clean. Prod. 2014, 79, 195–206. [Google Scholar] [CrossRef]
  4. Du, X. How the market values greenwashing? Evidence from China. J. Bus. Ethics 2015, 128, 547–574. [Google Scholar] [CrossRef]
  5. Khosroshahi, H.; Rasti-Barzoki, M.; Hejazi, S.R. A game theoretic approach for pricing decisions considering CSR and a new consumer satisfaction index using transparency-dependent demand in sustainable supply chains. J. Clean. Prod. 2019, 208, 1065–1080. [Google Scholar] [CrossRef]
  6. Jo, H.J.; Na, H. Does CSR reduce firm risk? Evidence from controversial industry sectors. J. Bus. Ethics 2012, 110, 441–456. [Google Scholar] [CrossRef]
  7. Benlemlih, M.; Shaukat, A.; Qiu, Y.; Trojanowski, G. Environmental and social disclosures and firm risk. J. Bus. Ethics 2018, 152, 613–626. [Google Scholar] [CrossRef]
  8. Healy, P.M.; Hutton, A.P.; Palepu, K.G. Stock performance and intermediation changes surrounding sustained increases in disclosure. Contemp. Account. Res. 1999, 16, 485–520. [Google Scholar] [CrossRef]
  9. Granados, N.; Gupta, A.; Kauffman, R.J. Research commentary-information transparency in business-to-consumer markets: Concepts, framework, and research agenda. Inf. Syst. Res. 2010, 21, 207–226. [Google Scholar] [CrossRef]
  10. Nicolaou, A.I.; McKnight, D.H. Perceived information quality in data exchanges: Effects on risk, trust, and intention to use. Inf. Syst. Res. 2006, 17, 332–351. [Google Scholar] [CrossRef]
  11. Kaptein, M. Developing and testing a measure for the ethical culture of organizations: The corporate ethical virtues model. J. Organ. Behav. 2008, 29, 923–947. [Google Scholar] [CrossRef]
  12. Huang, S.J.; Xiong, M.; Wang, X.G.; Xiao, H. Annual Report on China’s Companies Public Transparency (2016~2017) (No.3); Social Sciences Academic Press: Beijing, China, 2017. [Google Scholar]
  13. Gong, G.; Xu, S.; Gong, X. On the value of corporate social responsibility disclosure: An empirical investigation of corporate bond issues in China. J. Bus. Ethics 2018, 150, 227–258. [Google Scholar] [CrossRef]
  14. Vaccaro, A.; Echeverri, D.P. Corporate transparency and green management. J. Bus. Ethics 2010, 95, 487–506. [Google Scholar] [CrossRef]
  15. Clark, C.F.; Kotchen, M.J.; Moore, M.R. Internal and external influences on pro-environmental behavior: Participation in a green electricity program. J. Environ. Psychol. 2003, 23, 237–246. [Google Scholar] [CrossRef]
  16. Shimshack, J.P. Full disclosure: The perils and promise of transparency. Polit. Sci. Q. 2007, 122, 705–706. [Google Scholar] [CrossRef]
  17. Bushman, R.M.; Piotroski, J.D.; Smith, A.J. What determines corporate transparency? J. Account. Res. 2004, 42, 207–252. [Google Scholar] [CrossRef]
  18. Tang, Z.; Hull, C.E.; Rothenberg, S. How corporate social responsibility engagement strategy moderates the CSR–financial performance relationship. J. Manag. Stud. 2012, 49, 1274–1303. [Google Scholar] [CrossRef]
  19. Iwata, H.; Okada, K. How does environmental performance affect financial performance? Evidence from Japanese manufacturing firms. Ecol. Econ. 2011, 70, 1691–1700. [Google Scholar] [CrossRef] [Green Version]
  20. Wei, Z.; Shen, H.; Zhou, K.Z.; Li, J.J. How does environmental corporate social responsibility matter in a dysfunctional institutional environment? Evidence from China. J. Bus. Ethics 2017, 140, 209–223. [Google Scholar] [CrossRef]
  21. Wang, Q.; Dou, J.S.; Jia, S.H. A meta-analytic review of corporate social responsibility and corporate financial performance: The moderating effect of contextual factors. Bus. Soc. 2016, 55, 1083–1121. [Google Scholar] [CrossRef]
  22. Yang, H.H.; Craig, R.; Farley, A. A review of Chinese and English language studies on corporate environmental reporting in China. Crit. Perspect. Account. 2015, 28, 30–48. [Google Scholar] [CrossRef] [Green Version]
  23. Haveman, H.A.; Jia, N.; Shi, J.; Wang, Y.X. The dynamics of political embeddedness in China. Adm. Sci. Q. 2017, 62, 67–104. [Google Scholar] [CrossRef]
  24. Wang, Z.; Reimsbach, D.; Braam, G. Political embeddedness and the diffusion of corporate social responsibility practices in China: A trade-off between financial and CSR performance? J. Clean. Prod. 2018, 198, 1185–1197. [Google Scholar] [CrossRef]
  25. Yan, J.Z.; Chang, S.J. The contingent effects of political strategies on firm performance: A political network perspective. Strateg. Manag. J. 2018, 39, 2152–2177. [Google Scholar] [CrossRef]
  26. Xin, K.K.; Pearce, J.L. Guanxi: Connections as substitutes for formal institutional support. Acad. Manag. J. 1996, 39, 1641–1658. [Google Scholar] [CrossRef]
  27. Wang, F.J.; Xu, L.Y.; Zhang, J.R.; Shu, W. Political connections, internal control and firm value: Evidence from China’s anti-corruption campaign. J. Bus. Res. 2018, 86, 53–67. [Google Scholar] [CrossRef]
  28. Hillman, A.J.; Hitt, M.A. Corporate political strategy formulation: A model of approach, participation, and strategy decisions. Acad. Manag. Rev. 1999, 24, 825–842. [Google Scholar] [CrossRef]
  29. Hillman, A.J.; Keim, G.D.; Schuler, D. Corporate political activity: A review and research agenda. J. Manag. 2004, 30, 837–857. [Google Scholar] [CrossRef]
  30. Liu, Q. Corporate governance in China: Current practices, economic effects and institutional determinants. Cesifo Econ. Stud. 2006, 52, 415–453. [Google Scholar] [CrossRef]
  31. Li, H.B.; Meng, L.S.; Wang, Q.; Zhou, L.A. Political connections, financing and firm performance: Evidence from Chinese private firms. J. Dev. Econ. 2008, 87, 283–299. [Google Scholar] [CrossRef]
  32. Schuler, D.A.; Rehbein, K.; Cramer, R.D. Pursuing strategic advantage through political means: A multivariate approach. Acad. Manag. Rev. 2002, 45, 659–672. [Google Scholar] [CrossRef]
  33. Xu, B.X.; Zeng, T. Profitability, state ownership, tax reporting and corporate social responsibility: Evidence from Chinese listed firms. Soc. Responsib. J. 2016, 12, 23–31. [Google Scholar] [CrossRef]
  34. Guthrie, D. China and Globalization: The Social, Economic and Political Transformation of Chinese Society; Routledge: New York, NY, USA, 2012. [Google Scholar] [CrossRef]
  35. Faccio, M. Politically connected firms. J. Financ. 2006, 96, 369–386. [Google Scholar] [CrossRef]
  36. Zhou, W. Political connections and entrepreneurial investment: Evidence from China’s transition economy. J. Bus. Ventur. 2013, 28, 299–315. [Google Scholar] [CrossRef]
  37. Johnson, S.; Mitton, T. Cronyism and capital controls: Evidence from Malaysia. J. Financ. Econ. 2003, 67, 351–382. [Google Scholar] [CrossRef]
  38. Goldman, E.; Rocholl, J.; So, J. Politically connected boards of directors and the allocation of procurement contracts. Rev. Financ. 2013, 17, 1617–1648. [Google Scholar] [CrossRef]
  39. Freeman, R.E. The politics of stakeholder theory: Some future directions. Bus. Ethics Q. 1994, 4, 409–421. [Google Scholar] [CrossRef]
  40. Waddock, S.A.; Graves, S.B. The corporate social performance–financial performance link. Strateg. Manag. J. 1997, 18, 303–319. [Google Scholar] [CrossRef]
  41. Preston, L.E.; O’Bannon, D.P. The corporate social-financial performance relationship: A typology and analysis. Bus. Soc. 1997, 36, 419–429. [Google Scholar] [CrossRef]
  42. Zhao, X.P.; Murrell, A.J. Revisiting the corporate social performance-financial performance link: A replication of waddock and graves. Strateg. Manag. J. 2016, 37, 2378–2388. [Google Scholar] [CrossRef]
  43. Jo, H.; Harjoto, M.A. Corporate governance and firm value: The impact of corporate social responsibility. J. Bus. Ethics 2011, 103, 351–383. [Google Scholar] [CrossRef]
  44. Zeng, S.X.; Xu, X.D.; Yin, H.T.; Tam, C.M. Factors that drive Chinese listed companies in voluntary disclosure of environmental information. J. Bus. Ethics 2012, 109, 309–321. [Google Scholar] [CrossRef]
  45. Chen, C.J.P.; Li, Z.; Su, X.; Sun, Z. Rent-seeking incentives, corporate political connections, and the control structure of private firms: Chinese evidence. J. Corp. Financ. 2011, 17, 229–243. [Google Scholar] [CrossRef]
  46. Agrawal, A.; Knoeber, C.R. Do some outside directors play a political role? J. Law Econ. 2001, 44, 179–198. [Google Scholar] [CrossRef]
  47. Tan, J.; Peng, M.W. Organizational slack and firm performance during economic transitions: Two studies from an emerging economy. Strateg. Manag. J. 2004, 25, 307. [Google Scholar] [CrossRef]
  48. Seifert, B.; Morris, S.A.; Bartkus, B.R. Having, giving, and getting: Slack resources, corporate philanthropy, and firm financial performance. Bus. Soc. 2004, 43, 135–161. [Google Scholar] [CrossRef]
  49. Chang, L.; Li, W.; Lu, X. Government engagement, environmental policy, and environmental performance: Evidence from the most polluting Chinese listed firms. Bus. Strateg. Environ. 2015, 24, 1–19. [Google Scholar] [CrossRef]
  50. Ma, D.L.; Parish, W.L. Tocquevillian moments: Charitable contributions by Chinese private entrepreneurs. Soc. Forces 2006, 85, 943–964. [Google Scholar] [CrossRef]
  51. Aerts, W.; Cormier, D.; Magnan, M. Corporate environmental disclosure, financial markets and the media: An international perspective. Ecol. Econ. 2008, 64, 643–659. [Google Scholar] [CrossRef]
  52. Kansal, M.; Joshi, M.; Batra, G.S. Determinants of corporate social responsibility disclosures: Evidence from India. Adv. Account. 2014, 30, 217–229. [Google Scholar] [CrossRef]
  53. Cheng, Z.H.; Wang, F.; Keung, C.; Bai, Y.X. Will corporate political connection influence the environmental information disclosure level? Based on the panel data of A-shares from listed companies in shanghai stock market. J. Bus. Ethics 2017, 143, 209–221. [Google Scholar] [CrossRef]
  54. Huang, C.-L.; Kung, F.H. Drivers of environmental disclosure and stakeholder expectation: Evidence from Taiwan. J. Bus. Ethics 2010, 96, 435–451. [Google Scholar] [CrossRef]
  55. Krueger, A.O. The political economy of the rent-seeking society. Am. Econ. Rev. 1974, 64, 291–303. [Google Scholar]
  56. Marquis, C.; Qian, C.L. Corporate social responsibility reporting in China: Symbol or substance? Organ. Sic. 2014, 25, 127–148. [Google Scholar] [CrossRef]
  57. Khwaja, A.I.; Mian, A. Do lenders favor politically connected firms? Rent provision in an emerging financial market. Q. J. Econ. 2005, 120, 1371–1411. [Google Scholar] [CrossRef]
  58. Boubakri, N.; Cosset, J.C.; Saffar, W. Political connections of newly privatized firms. J. Corp. Financ. 2008, 14, 654–673. [Google Scholar] [CrossRef]
  59. Li, S.H.; Song, X.Z.; Wu, H.Y. Political connection, ownership structure, and corporate philanthropy in China: A strategic-political perspective. J. Bus. Ethics 2015, 129, 399–411. [Google Scholar] [CrossRef]
  60. Clarkson, P.M.; Fang, X.; Li, Y.; Richardson, G. The relevance of environmental disclosures: Are such disclosures incrementally informative? J. Account. Public Policy 2013, 32, 410–431. [Google Scholar] [CrossRef]
  61. Li, D.Y.; Cao, C.C.; Zhang, L.; Chen, X.H.; Ren, S.G.; Zhao, Y.N. Effects of corporate environmental responsibility on financial performance: The moderating role of government regulation and organizational slack. J. Clean. Prod. 2017, 166, 1323–1334. [Google Scholar] [CrossRef]
  62. Huang, S.J.; Wang, X.G.; Xiao, H.J. Annual Report on China’s Companies Public Transparency (2014~2015) (No.1); Social Sciences Academic Press: Beijing, China, 2015; pp. 365–371. [Google Scholar]
  63. Huang, S.J.; Xiong, M.; Wang, X.G.; Xiao, H.J. Annual Report on China’s Companies Public Transparency (2015~2016) (No.2); Social Sciences Academic Press: Beijing, China, 2016; pp. 364–370. [Google Scholar]
  64. Shi, H.N.; Xu, H.P.; Zhang, X. Do politically connected independent directors create or destroy value? J. Bus. Res. 2018, 83, 82–96. [Google Scholar] [CrossRef]
  65. Liu, L. Multiplex political connections of entrepreneurs and performance of firms: Super-additivity, sub-additivity or no-additivity. J. Syst. Manag. 2018, 27, 433–451. (In Chinese) [Google Scholar]
  66. Zhao, M. CSR-based political legitimacy strategy: Managing the state by doing good in China and Russia. J. Bus. Ethics 2012, 111, 439–460. [Google Scholar] [CrossRef]
  67. Zhang, C. Political connections and corporate environmental responsibility: Adopting or escaping? Energy Econ. 2017, 68, 539–547. [Google Scholar] [CrossRef]
  68. Wong, C.W.Y.; Miao, X.; Cui, S.; Tang, Y.H. Impact of corporate environmental responsibility on operating income: Moderating role of regional disparities in China. J. Bus. Ethics 2018, 149, 363–382. [Google Scholar] [CrossRef]
  69. McGuinness, P.B.; Vieito, J.P.; Wang, M. The role of board gender and foreign ownership in the CSR performance of Chinese listed firms. J. Corp. Financ. 2017, 42, 75–99. [Google Scholar] [CrossRef] [Green Version]
  70. Chen, C.R.; Li, Y.Q.; Luo, D.L.; Zhang, T. Helping hands or grabbing hands? An analysis of political connections and firm value. J. Bank. Financ. 2017, 80, 71–89. [Google Scholar] [CrossRef]
  71. Ryan, T.P. Modern Regression Methods; John Wiley & Sons: New York, NY, USA, 1997. [Google Scholar]
  72. Aiken, L.S.; West, S.G. Multiple Regression: Testing and Interpreting Interactions; Sage: Newbury Park, CA, USA, 1991. [Google Scholar]
  73. Lewbel, A. Constructing instruments for regressions with measurement error when no additional data are available, with an application to patents and R&D. Econometrica 1997, 65, 1201–1213. [Google Scholar] [CrossRef]
  74. Peng, M.W.; Luo, Y.D. Managerial ties and firm performance in a transition economy: The nature of a micro-macro link. Acad. Manag. J. 2000, 43, 486–501. [Google Scholar] [CrossRef]
  75. Li, H.; Zhang, Y. The role of managers’ political networking and functional experience in new venture performance: Evidence from China’s transition economy. Strateg. Manag. J. 2007, 28, 791–804. [Google Scholar] [CrossRef]
  76. Hong, T.; Yu, N.; Mao, Z. Does environment centralization prevent local governments from racing to the bottom?—Evidence from China. J. Clean. Prod. 2019, 231, 649–659. [Google Scholar] [CrossRef]
  77. Sun, P.; Mellahi, K.; Thun, E. The dynamic value of MNE political embeddedness: The case of the Chinese automobile industry. J. Int. Bus. Stud. 2010, 41, 1161–1182. [Google Scholar] [CrossRef]
Figure 1. The conceptual model and research hypotheses. CPT, corporate public transparency; CFP, corporate financial performance.
Figure 1. The conceptual model and research hypotheses. CPT, corporate public transparency; CFP, corporate financial performance.
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Figure 2. Interaction of CPT and ownership type on CFP.
Figure 2. Interaction of CPT and ownership type on CFP.
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Figure 3. Interaction of CPT and bureaucratic embeddedness on CFP.
Figure 3. Interaction of CPT and bureaucratic embeddedness on CFP.
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Figure 4. Interaction of CPT and bureaucratic embeddedness (government official) on CFP.
Figure 4. Interaction of CPT and bureaucratic embeddedness (government official) on CFP.
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Figure 5. Interaction of CPT and bureaucratic embeddedness (official index) on CFP.
Figure 5. Interaction of CPT and bureaucratic embeddedness (official index) on CFP.
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Table 1. Variable definitions. CPT, corporate public transparency; CFP, corporate financial performance; CEO, chief executive officer; NPC, National People’s Congress; CPPCC, Chinese People’s Political Consultative Conference.
Table 1. Variable definitions. CPT, corporate public transparency; CFP, corporate financial performance; CEO, chief executive officer; NPC, National People’s Congress; CPPCC, Chinese People’s Political Consultative Conference.
VariablesDefinitions
CFPReturn on assets (ROA) is calculated as net profit dividing by total assets [33].
CPTRating score of composite measures on CPT [12,62,63].
Enterprise ageEnterprise age is measured by age starting from the year of corporate establishment.
CEO ageCEO age is measured in years.
Term of officeCEO’s term of office is measured by the years in the position [67].
EducationCEO’s education is a dummy that is equal to 1 if the CEO attained a master degree or above; 0 means otherwise.
DualCEO duality is measured using a dummy variable that equal to 1 when the CEO also serves as the chairman; 0 means otherwise [67].
OwnerOwner is a dummy variable that is equal to 1 if the owner of the enterprise is the government, i.e., the government accounts for the majority in the corporate share structure; 0 means otherwise [53].
BuBureaucratic embeddedness is a dummy variable that is equal to 1 if the CEO was/is government official or member of NPC or CPPCC; 0 means otherwise.
OfficialOfficial is a dummy variable that is equal to 1 if enterprise’s CEO was/is a government official; 0 means otherwise.
PCPolitical council is a dummy variable that is equal to 1 if enterprise’s CEO was/is a member of political councils; 0 means otherwise.
Official-indexOfficial index means CEO’s political rank in the government. 3 for central government level; 2 for provincial level; 1 for municipal level; 0 for other lower level. Official index = natural logarithm (1 + score) [70].
PC-indexPolitical council index means CEO’s political rank in political council. 3 for political council at central level; 2 for political council at provincial level; 1 for political council at municipal level; 0 for other lower level. PC index = natural logarithm (1 + score).
CPThigh & CFPlowA combination dummy of CPT and CFP. The value is 1 if CPT value is higher than the industrial median and CFP value is lower than the industrial median; 0 for otherwise.
CPTlow & CFPhighA combination dummy of CPT and CFP. The value is 1 if CPT value is lower than the industrial median and CFP value is higher than the industrial median; 0 for otherwise.
Enterprise sizeEnterprise size is measured by logarithm of the total assets at the end of the year [53].
CrosslistingCross-listing is a dummy that is equal to 1 if an enterprise contains subsidiaries listed overseas; 0 for otherwise.
IndustryIndustry dummy is a control variable for sector-specific effects.
YearYear dummy is a control variable for time effects, and for omitted variables that vary over time, but have nearly no difference among enterprises.
Table 2. Statistical summary.
Table 2. Statistical summary.
Panel A Total Sample
VariableNMeanMedianStd.DevMinMax
CFP1950.0220.0120.031−0.0380.186
CPT19549.67753.50017.3349.85077.800
Enterprise age19531.12325.00021.2561.000106.000
Enterprise size19513.07612.7331.6929.87516.999
Crosslisting1950.5851.0000.4940.0001.000
CEO age19554.14453.0004.56345.00075.000
Term of office1954.4623.0004.4290.10024.000
Education1950.7081.0000.4560.0001.000
Dual1950.1900.0000.3930.0001.000
Bu1950.4210.0000.4950.0001.000
Owner1950.8151.0000.3890.0001.000
Panel B Sample of state-owned enterprises
VariableNMeanMedianStd.DevMinMax
CFP1590.0200.0120.031−0.0380.186
CPT15948.19051.41017.7779.85077.800
Enterprise age15931.64224.00023.2121.000106.000
Enterprise size15913.01412.7331.6299.87516.999
Crosslisting1590.5661.0000.4970.0001.000
CEO age15954.22054.0000.49745.00063.000
Term of office1594.1533.0004.3560.10024.000
Education1590.7111.0000.4550.0001.000
Dual1590.1890.0000.3920.0001.000
Bu1590.4400.0000.4980.0001.000
Panel C Sample of enterprises with bureaucratic embeddedness
VariableNMeanMedianStd.DevMinMax
CFP820.0170.0100.026−0.0380.138
CPT8247.19352.33018.1539.85077.800
Enterprise age8227.58522.00018.4141.00069.000
Enterprise size8212.97412.5341.57910.29916.795
Crosslisting820.5121.0000.5030.0001.000
CEO age8254.75654.0005.09346.00075.000
Term of office826.0514.0005.4700.10024.000
Education820.6711.0000.4730.0001.000
Dual820.2320.0000.4250.0001.000
Owner820.8541.0000.3560.0001.000
Panel D Sample of state-owned enterprise with bureaucratic embeddedness
VariableNMeanMedianStd.DevMinMax
CFP700.0160.0100.027−0.0380.138
CPT7044.53148.41518.1679.85077.800
Enterprise age7027.51419.50019.8901.00069.000
Enterprise size7012.72212.4461.37510.29916.795
Crosslisting700.5571.0000.5000.0001.000
CEO age7053.88653.0003.45846.00063.000
Term of office705.7344.0005.5470.10024.000
Education700.7291.0000.4480.0001.000
Dual700.2710.0000.4480.0001.000
Table 3. Correlation coefficients.
Table 3. Correlation coefficients.
12345678910
1. CPT0.017
2. Bu−0.145 **−0.122 *
3. Owner−0.117−0.181 **0.084
4. Enterprise age0.1030.181 **−0.142 **0.051
5. Firm size−0.1070.598 ***−0.051−0.0770.287 ***
6. CEO age−0.0480.0810.1150.0350.0870.199 ***
7. Term of office−0.025−0.0000.306 ***−0.147 **−0.031−0.137 *0.372 ***
8. Education−0.0580.105−0.0690.0140.145 **0.074−0.445 ***−0.400 ***
9. Dual−0.059−0.0290.091−0.0060.028−0.0240.0390.318 ***−0.005
10. Crosslisting0.202 ***0.220 ***−0.125 *−0.0790.146 **0.082−0.010−0.064−0.038−0.043
Note: * p < 0.10; ** p < 0.05; *** p < 0.01.
Table 4. Regression results. SOE, state-owned enterprise.
Table 4. Regression results. SOE, state-owned enterprise.
Samples:Total SampleSub Samples: Politically Embedded Enterprises vs. Non-Politically Embedded Enterprises
Enterprises with Bureaucratic Embeddedness vs. Enterprises without Bureaucratic EmbeddednessSOEs vs. Non-SOEsSOEs with Political Connecttions vs. SOEs without Bureaucratic Embeddedness
Bu = 1Bu = 0Owner = 1Owner = 0Owner = 1 & Bu = 1Owner = 1 & Bu = 0
Model 1Model 2Model 3Model 4Model 5Model 6Model 7Model 8
CPT−0.0001
(0.0001)
−0.0001
(0.0001)
−0.0003 *
(0.0002)
0.0001
(0.0001)
−0.0002 *
(0.0001)
0.0003 (0.0003)−0.0004 *
(0.0002)
−0.0000
(0.0001)
Bu 0.0053
(0.0039)
0.0067
(0.0046)
−0.0377
(0.0404)
Owner −0.0048
(0.0116)
−0.0069
(0.0070)
−0.0185
(0.0146)
Enterprise age0.0002
(0.0002)
0.0003
(0.0002)
0.0005 *
(0.0003)
0.0005
(0.0003)
0.0001
(0.0002)
−0.0005
(0.0007)
0.0004
(0.0003)
0.0003
(0.0003)
Enterprise size−0.0022
(0.0053)
−0.0022
(0.0058)
−0.0164 ***
(0.0062)
−0.0023
(0.0078)
−0.0031
(0.0065)
0.0066
(0.0231)
−0.0139 *
(0.0076)
−0.0063
(0.0081)
CEO age−0.0004
(0.0003)
−0.0004
(0.0003)
−0.0019 ***
(0.0007)
−0.0000
(0.0003)
−0.0001
(0.0002)
0.0012
(0.0021)
−0.0024 *
(0.0013)
0.0001
(0.0002)
Term of office0.0004
(0.0005)
0.0003
(0.0005)
−0.0009
(0.0009)
0.0010 *
(0.0006)
−0.0002
(0.0003)
0.0014
(0.0034)
−0.0004
(0.0012)
−0.0001
(0.0003)
Education−0.0011
(0.0026)
−0.0009
(0.0027)
−0.0231 **
(0.0095)
0.0010
(0.0042)
−0.0024
(0.0021)
0.0211
(0.0199)
−0.0208 *
(0.0110)
−0.0019
(0.0025)
Dual−0.0193 ***
(0.0072)
−0.0194 ***
(0.0074)
−0.0103
(0.0076)
−0.0195 ***
(0.0073)
−0.0143 **
(0.0066)
−0.0059
(0.0370)
−0.0133
(0.0093)
−0.0069 ***
(0.0023)
Crosslisting0.0064
(0.0066)
0.0067
(0.0067)
0.0293 ***
(0.0084)
−0.0087
(0.0090)
0.0080
(0.0069)
−0.0363
(0.0359)
0.0286 **
(0.0125)
−0.0035
(0.0098)
IndustryYYYYYYYY
YearYYYYYYYY
Constant0.0468
(0.0577)
0.0463
(0.0589)
0.3093 ***
(0.0798)
0.0443
(0.0899)
0.0476
(0.0708)
−0.1415
(0.3050)
0.3021 ***
(0.0854)
0.0772
(0.0936)
Note: * p < 0.10; ** p < 0.05; *** p < 0.01.
Table 5. Regression analysis results for effect of the bureaucratic embeddednesss on the relation between CPT and CFP.
Table 5. Regression analysis results for effect of the bureaucratic embeddednesss on the relation between CPT and CFP.
Model 1Model 2Model 3Model 4Model 5Model 6Model 7Model 8
CPT0.0001
(0.0001)
−0.0001
(0.0001)
0.0001
(0.0001)
−0.0001
(0.0001)
0.0003 *
(0.0002)
0.0003 *
(0.0002)
0.0003 *
(0.0002)
0.0003 *
(0.0002)
Owner−0.0050
(0.0118)
−0.0046
(0.0117)
−0.0050
(0.0118)
−0.0046
(0.0117)
0.0112
(0.0155)
0.0186
(0.0158)
0.0112
(0.0155)
0.0186
(0.0158)
CPT*Owner −0.0003 *
(0.0002)
−0.0005 **
(0.0002)
−0.0003 *
(0.0002)
−0.0005 **
(0.0002)
Official0.0228 **
(0.0095)
0.0190 **
(0.0085)
PC 0.0074
(0.0095)
0.0040
(0.0099)
CPT*Official−0.0004 **
(0.0001)
−0.0004 ***
(0.0001)
CPT*PC 0.0000
(0.0002)
−0.0001
(0.0002)
CPT*Official*Owner 0.0001
(0.0001)
CPT*PC*Owner 0.0002
(0.0002)
Officialindex 0.0756 **
(0.0314)
0.0633 **
(0.0284)
PCindex 0.0247
(0.0315)
0.0131
(0.0330)
CPT*Officialindex −0.0012 **
(0.0005)
−0.0012 ***
(0.0005)
CPT*PCindex 0.0000
(0.0007)
−0.0003
(0.0006)
CPT*Officialindex*Owner 0.0003
(0.0003)
CPT*PCindex*Owner 0.0007
(0.0005)
IndustryYYYYYYYY
YearYYYYYYYY
Constant0.0390
(0.0588)
0.0475
(0.0591)
0.0390
(0.0588)
0.0475
(0.0591)
0.0246
(0.0638)
0.0215
(0.0640)
0.0246
(0.0638)
0.0215
(0.0640)
N195195195195195195195195
Wald X259.9356.6359.9356.6360.7466.9260.7466.92
Notes: Robust standard errors are in parentheses. The control variables are included, but not reported here for brevity. * p < 0.10; ** p < 0.05; *** p < 0.01.
Table 6. Robustness tests.
Table 6. Robustness tests.
Variables:CPThigh & CFPlowCPTlow & CFPhigh
CPThighROAlowCPThighATRlowCPTlowROAhighCPTlowATRhigh
Model 1Model 2Model 3Model 4Model 1Model 2Model 3Model 4
Official2.157 **
(1.053)
0.894
(2.704)
0.819 **
(0.403)
0.465
(1.085)
−1.016 **
(0.464)
−1.506
(1.143)
−0.883 *
(0.453)
−1.638
(1.141)
PC1.037
(1.528)
1.028
(3.451)
0.219
(0.689)
2.599
(1.776)
0.550
(0.772)
0.033
(1.762)
0.446
(0.765)
0.146
(1.752)
Owner1.705
(1.382)
1.168
(1.659)
0.965 *
(0.532)
1.314 *
(0.709)
−1.090 **
(0.502)
−1.408 **
(0.642)
−1.005 **
(0.496)
−1.393 **
(0.640)
Official*Owner 1.474
(2.940)
0.382
(1.207)
0.655
(1.290)
0.979
(1.282)
PC*Owner −0.151
(4.092)
−2.991
(2.030)
0.671
(2.039)
0.389
(2.028)
Enterprise age0.008
(0.025)
0.009
(0.025)
−0.007
(0.010)
−0.010
(0.010)
−0.012
(0.011)
−0.011
(0.011)
−0.010
(0.010)
−0.010
(0.010)
Enterprise size0.375
(0.463)
0.379
(0.456)
0.213
(0.188)
0.261
(0.194)
−0.048
(0.197)
−0.060
(0.199)
−0.003
(0.195)
−0.017
(0.194)
CEO age−0.082
(0.108)
−0.073
(0.114)
−0.099 **
(0.047)
−0.145 **
(0.057)
−0.050
(0.048)
−0.029
(0.057)
−0.051
(0.047)
−0.030
(0.050)
Term of office0.132
(0.116)
0.132
(0.121)
0.056
(0.060)
0.081
(0.062)
0.017
(0.066)
0.008
(0.068)
0.028
(0.065)
0.030
(0.064)
Education0.860
(1.114)
0.761
(1.128)
−0.648
(0.458)
−0.751
(0.471)
0.172
(0.492)
0.158
(0.499)
0.250
(0.487)
0.239
(0.485)
Dual1.345
(1.180)
1.181
(1.187)
−1.005 **
(0.508)
−0.918 *
(0.516)
−0.959 *
(0.525)
−1.076 *
(0.550)
−0.980 *
(0.517)
−1.077 **
(0.538)
Crosslisting2.240 **
(1.087)
1.991 *
(1.204)
−0.014
(0.370)
−0.176
(0.422)
0.204
(0.392)
0.149
(0.437)
0.259
(0.387)
0.100
(0.407)
IndustryYYYYYYYY
YearYYYYYYYY
Constant−7.591
(7.785)
−7.525
(7.876)
2.751
(2.894)
4.404
(3.090)
4.457
(3.079)
3.743
(3.317)
3.589
(3.034)
2.880
(3.076)
N186186192192192192192192
Wald X217.4318.1724.1025.1322.6022.9621.1121.59
* p < 0.10; ** p < 0.05; *** p < 0.01.
Table 7. Two-stage least square (2SLS) results for the bureaucratic embeddednesss on CPT–CFP.
Table 7. Two-stage least square (2SLS) results for the bureaucratic embeddednesss on CPT–CFP.
VariablesFirst Stage CPTSecond Stage CFPFirst Stage CPT Second Stage CFPFirst Stage CPT Second Stage CFPFirst Stage CPT Second Stage CFP
Model 1Model 2Model 3Model 4Model 5Model 6Model 7Model 8
CPTed (instrumented) 0.0006
(0.0004)
0.0005
(0.0004)
0.0006
(0.0004)
0.0006
(0.0004)
CPTted*Owner −0.0007 **
(0.0003)
−0.0006 *
(0.0003)
−0.0009 **
(0.0004)
−0.0009 **
(0.0004)
CPTted*Official −0.0004 **
(0.0002)
−0.0006 ***
(0.0002)
CPTted*PC −0.0000
(0.0005)
−0.0001
(0.0006)
CPTted*Official*Owner 0.0003
(0.0002)
CPTted*PC*Owner 0.0002
(0.0003)
Owner−19.5448 ***
(4.1076)
0.0194
(0.0135)
−20.8326 ***
(4.3514)
0.0135
(0.0148)
−21.9766 ***
(4.2066)
0.0253 *
(0.0144)
−21.73***
(4.2842)
0.0253 *
(0.0144)
Official−8.2500 ***
(2.7599)
0.0233 **
(0.0093)
−9.4036 **
(2.8400)
0.0188 *
(0.0101)
PC 5.5691
(7.6671)
0.0017
(0.0302)
6.9747
(8.0741)
−0.0026
(0.0313)
Enterprise age0.0703 **
(0.0277)
0.0002
(0.0001)
0.0688 **
(0.0268)
0.0002
(0.0001)
0.0840 ***
(0.0284)
0.0002
(0.0001)
0.0832 **
(0.0287)
0.0002
(0.0001)
Enterprise size−0.2082
(0.5753)
−0.0038 **
(0.0017)
−0.3448
(0.5754)
−0.0042 **
(0.0017)
−0.6691
(0.5335)
−0.0030 *
(0.0016)
−0.0029 *
(0.0016)
CEO age0.1374
(0.1467)
−0.0002
(0.0005)
0.1291
(0.1433)
−0.0002
(0.0005)
0.1535
(0.1475)
−0.0002
(0.0005)
0.0975
(0.1770)
−0.0000
(0.0006)
Term of office0.1051
(0.1714)
−0.0009
(0.0006)
0.0811
(0.1667)
−0.0010
(0.0006)
−0.1067
(0.1929)
−0.0009
(0.0006)
−0.0801
(0.1970)
−0.0010
(0.0007)
Education−0.4803
(1.4655)
0.0011
(0.0053)
−0.8925
(1.4497)
−0.0002
(0.0054)
−0.4669
(1.4435)
0.0021
(0.0053)
−0.5739
(1.4706)
0.0024
(0.0053)
Dual0.6009
(1.4743)
−0.0099 **
(0.0049)
0.1436
(1.4313)
−0.0113 **
(0.0050)
−0.5987
(1.4998)
−0.0076
(0.0050)
−0.4248
(1.5325)
−0.0081
(0.0050)
Crosslisting2.6111 **
(1.1808)
0.0020
(0.0042)
1.6008
(1.2318)
−0.0010
(0.0046)
2.2177 *
(1.2155)
0.0048
(0.0042)
2.0958 *
(1.2487)
0.0051
(0.0042)
CPT_iv (instruments)22.3129 ***
(4.5987)
21.6627 ***
(4.6328)
22.6967 ***
(4.8069)
22.4710 ***
(4.8573)
CPT_iv*Owner0.4830 ***
(0.0937)
0.4903 ***
(0.0950)
0.5388 ***
(0.0934)
0.5462 ***
(0.0940)
CPT_iv*Official0.0979 *
(0.0532)
0.0502 *
(0.0611)
CPT_iv*PC −0.0391
(0.1364)
−0.0216
(0.1402)
CPT_iv*Official*Owner 0.0903 *
(0.0538)
CPT_iv*PC*Owner −0.0577
(0.0827)
IndustryYYYYYYYY
YearYYYYYYYY
Constant21.3891 **
(9.8192)
0.0466
(0.0322)
25.8104 **
(10.2419)
0.0627 *
(0.0348)
24.1285 **
(9.5448)
0.0398
(0.0333)
26.6925 **
(10.5658)
0.0316
(0.0373)
Anderson canon.corr.LM statistic 57.456 *** 54.792 *** 55.742 *** 54.332 ***
Cragg-Donald Wald F statistic 71.432 66.434 68.448 65.661
F Value 4.05 *** 3.99 *** 3.71 *** 3.55 ***
N195195195195195195195195
Notes: CPTed is the predicted values of CPT from the first stage regression. The instrument variable (IV) is noted as CPT_iv. * p < 0.10; ** p < 0.05; *** p < 0.01.

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MDPI and ACS Style

Li, Y.; Miao, X.; Zheng, D.; Tang, Y. Corporate Public Transparency on Financial Performance: The Moderating Role of Political Embeddedness. Sustainability 2019, 11, 5531. https://0-doi-org.brum.beds.ac.uk/10.3390/su11195531

AMA Style

Li Y, Miao X, Zheng D, Tang Y. Corporate Public Transparency on Financial Performance: The Moderating Role of Political Embeddedness. Sustainability. 2019; 11(19):5531. https://0-doi-org.brum.beds.ac.uk/10.3390/su11195531

Chicago/Turabian Style

Li, Yuxuan, Xin Miao, Dequan Zheng, and Yanhong Tang. 2019. "Corporate Public Transparency on Financial Performance: The Moderating Role of Political Embeddedness" Sustainability 11, no. 19: 5531. https://0-doi-org.brum.beds.ac.uk/10.3390/su11195531

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