Abstract
Purpose
This study investigates the influence of social trust on the attainment of corporate environmental, social and governance (ESG) objectives.
Design/methodology/approach
This study conducts panel regression analysis on a distinctive dataset for 2009–2017 on Chinese firms.
Findings
The analysis reveals a significant positive association between social trust and firm-level ESG practices. Moreover, the impact of social trust on shaping ESG outcomes is further amplified by factors such as economic growth, corporate governance standards and institutional quality. This relationship remains statistically positive when the authors employ alternative measures and methodologies, such as the instrumental variables, propensity score matching and difference-in-differences approaches. Notably, the results of heterogeneity tests indicate that the Trust–ESG nexus is more prominent for state-owned enterprises and firms with substantial market capitalization, superior profitability and higher leverage.
Originality/value
This study expands the comprehension of the determinants of ESG and underscores the influential role of social trust as an informal institution in enhancing a firm's ESG performance.
Keywords
Citation
Tang, T. and Yang, L. (2024), "Shaping corporate ESG performance: role of social trust in China's capital market", China Finance Review International, Vol. 14 No. 1, pp. 34-75. https://doi.org/10.1108/CFRI-07-2023-0187
Publisher
:Emerald Publishing Limited
Copyright © 2023, Emerald Publishing Limited
1. Introduction
A consensus has been reached regarding the imperative of attaining sustainable economic and social development while concurrently strengthening environmental preservation. Accordingly, international institutions, government agencies, nongovernmental organizations, academics and practitioners are actively exploring diverse avenues to implement sustainable development approaches – in particular, those concerning the resolution of environmental challenges and the mitigation of climate change. Consequently, it is essential to construct a contemporary economic system and establish a fundamental strategy that facilitates the achievement of sustainability. In this regard, the notion of a Green Economy, first proposed in 1989, has evolved into a multidimensional concept that encompasses not only the reduction of environmental risks but also the enhancement of human well-being and social equity. In 2006, the United Nations Principles for Responsible Investment introduced a more comprehensive framework encompassing environmental, social and corporate governance (ESG) factors.
The underlying principle of ESG is that businesses should consider the interests of society, the environment, and other pertinent stakeholders throughout their operational and investment decision-making processes in order to achieve both economic growth and sustainable development objectives (Hao and He, 2022). ESG assessment and information disclosure have garnered considerable attention from governments, nongovernmental organizations, and third-party entities worldwide, including China. Although China commenced adopting ESG practices later than other countries, it has made significant strides in keeping pace with developed markets by implementing more favorable policies aimed at encouraging public listed companies to disclose ESG information. For instance, the country compiled the Listed Companies ESG Assessment System (2019) in 2020, and its Ministry of Ecology and Environment has proposed the establishment of a comprehensive framework for mandatory disclosure of environmental information by 2025. However, notable disparities remain in ESG practices between China and developed nations.
Significantly, the extent to which firms actively embrace the ESG paradigm relies not only on formal legislative systems, policies, regulations, and enforcement mechanisms that compel them to fulfill their social responsibilities but also on informal constraints and rules, such as social norms and ethics. This aspect is particularly relevant in China, where the costs associated with violating laws are comparatively low, formal supervision is weaker, and the execution of social accountabilities is subpar (Li and Liu, 2010). Consequently, it is of considerable practical significance to examine whether informal social norms can exert a more effective influence on ESG practices in China. Social trust, as a vital component of social capital, plays a pivotal role in shaping social expectations and public belief. Studying the concept of social trust holds particular significance within the Chinese context due to its multi-dimensional implications for various aspects of socioeconomic and political dynamics. China's socio-economic transformation and rapid globalization have prompted a growing recognition of the pivotal role social trust plays in shaping relationships among corporations, institutions, and also society at large. Several academic arguments underpin the importance of studying social trust in China by demonstrating the significant impact of social trust on economic growth and development (Cui, 2017; Tao et al., 2014). Tao et al. (2014) disentangled the intricate nexus between social trust and political trust within the rural Chinese context, revealing a noteworthy discovery that underscores the positive and statistically significant influence of political trust upon social trust. The study revealed that heightened levels of political trust engendered by superior economic performance and economic modernization impart a consequential indirect elevation to social trust levels. As China's corporate landscape evolves, fostering and maintaining social trust becomes instrumental in achieving better corporate governance. Chen et al. (2011) argue that social trust reduces earnings management in China and demonstrate that social trust, as a social norm, influences corporate decisions. Moreover, social trust serves as an informal rule that fosters the cultivation of shared values and guides individuals toward adopting appropriate behaviors (Kirchler et al., 2008; Stanley et al., 2011).
As regards regions characterized by higher social trust levels, these are typically associated with a prevalence of social norms that impose greater constraints on enterprise behavior, thus encouraging businesses to engage in conduct aligned with societal morality. In turn, enterprises that effectively manage their reputation are more likely to earn long-term trust from residents, enhancing their prospects of survival amid intense market competition (Li et al., 2017; Lins et al., 2017). Consequently, firms situated in regions with high levels of trust are expected to exhibit greater adherence to social responsibility, government regulations, and environmental requirements as a means to preserve their reputation and expand their market share.
The influence of social trust on corporate finance has garnered increasing attention from researchers, who have focused on areas such as enterprise transaction costs (Wu et al., 2014), misconduct (Dong et al., 2018), and investor decision-making (Ding et al., 2015; Pevzner et al., 2015). However, to the best of our knowledge, no study has yet explored the impact of social trust on corporate ESG performance. Given the substantial heterogeneity in social trust levels across provinces in China, owing to significant variations in economic growth and education levels between regions (Zhang and Ke, 2002), it is essential to investigate the Trust–ESG relationship within this context. Furthermore, during China's period of economic transformation, there have been imperfections in its formal institutional system, leading to a greater role for informal institutions. Thus, these distinct characteristics provide an ideal setting for the current study, which aims to empirically examine the association between social trust and ESG performance for listed firms in China, thereby addressing the aforementioned research gap.
We use a province-level Trust indicator, derived from the Chinese General Social Survey (CGSS) spanning the years 2009–2017, to measure regional social trust. Further, for robustness, we incorporate firm-level social donations during the same period. In terms of firm-level ESG performance, we primarily rely on the evaluation rankings provided by Huazheng and subsequently assign ESG scores accordingly. Leveraging this panel data, we explore the relationship between social trust and ESG practices and find that enterprises situated in regions characterized by high social trust tend to obtain higher scores in ESG assessments.
To ensure the robustness of our findings, we conduct a series of additional tests. These include employing an alternative social trust indicator, employing instrumental variables, the propensity score matching (PSM) method, and the difference-in-differences (DID) method to address endogeneity concerns. The results consistently demonstrate a positive association between social trust and ESG performance. To further investigate potential heterogeneity in the relationship between social trust and ESG, we divide the sample into subgroups according to enterprise ownership, market capitalization, probability, leverage, and ESG scores. The findings indicate that the positive impact of social trust on ESG performance is amplified for state-owned enterprises (SOEs) and firms with significant market presence, higher leverage, and greater profitability.
We conduct a mechanism analysis to identify the potential channels through which economic development, corporate governance, and institutional quality operate. Specifically, we employ the regional gross domestic product per capita as a proxy for economic development, the Herfindahl Index as a measure of corporate governance, and the government's environmental governance as an indicator of institutional quality. Through panel regression analysis, we find that social trust exhibits a positive explanatory power in enhancing ESG scores.
Thus, our study contributes in several ways to the body of literature that has analyzed ESG practices. First, we provide solid empirical evidence about the impact of social trust on corporate ESG performance, thereby enriching the literature on the association between social trust and corporate governance. To the best of our knowledge, our study is the first to utilize a unique dataset from China to explore the causal effect of social trust on ESG outcomes. Second, we comprehensively examine the potential channels that underlie the relationship between social trust and ESG performance, specifically focusing on economic development, corporate governance, and institutional quality. By addressing this research gap, we offer a robust explanation for the mechanisms through which social trust influences ESG outcomes. We effectively address the endogeneity issue between social trust and ESG performance by employing a series of rigorous methods. The robustness of our results strengthens the extant literature on the driving forces behind ESG practices in China. Third, our study extends the literature on social trust by providing evidence on its environmental effects through investigating ESG performance, in contrast to prior studies that have focused on economic effects only (Ahmand and Hall, 2017; Jiang and Lim, 2018). Furthermore, this study confirms the importance of informal rules in enhancing corporate governance. Overall, our study contributes to the understanding of ESG practices by providing empirical evidence, examining potential channels, and addressing endogeneity concerns, thus enhancing the literature on the determinants of corporate ESG performance.
The remainder of the paper is structured as follows. Section 2 reviews the relevant literature and formulates hypotheses. Section 3 outlines the data used in this study and provides comprehensive descriptive statistics. Section 4 presents the results of a multivariate regression analysis conducted to examine the existence of the Trust–ESG relationship, as well as the results of robustness checks to validate this study's findings. Section 5 explores the mechanisms underlying the shaping effect of social trust. Section 6 presents the results of heterogeneity tests conducted to explore variations in the Trust–ESG relationship. Last, Section 7 concludes the paper by summarizing the key findings.
2. Literature review and hypothesis development
2.1 Related research on corporate ESG
A company's incorporation of environmental considerations into production processes (environmental), its active fulfillment of social responsibilities and establishment of strong stakeholder relationships (social), and its enhancement of corporate governance through the improvement of rules and organizational structures are all indicators of its ESG performance (governance). The ESG literature has primarily focused on identifying the determinants of corporate ESG performance and examining its economic implications. Early literature primarily explored the influencing factors of corporate social responsibility (CSR), rather than those of ESG. Numerous scholars have found that CSR fulfillment is influenced by firms' characteristics, such as size and financial status (Campbell, 2007; Chih et al., 2010; Moussu and Ohana, 2016; Orlitzky et al., 2003; Udayasankar, 2008).
Moreover, several researchers have highlighted the substantial influence of managerial characteristics on the disclosure of CSR information (Bear et al., 2010; Tang et al., 2015). Furthermore, other scholars have revealed that the external governance environment of corporations, including factors such as media coverage and analyst following, exerts a significant impact on their CSR performance (Adhikari, 2016; Akhtaruzzaman et al., 2022; Reverte, 2009; Wong and Zhang, 2022).
Extensive research has also been conducted on the relationship between internal governance features, such as board diversity (Katmon et al., 2017; McGuinness et al., 2017), and executive compensation (Cohen et al., 2023). Kaymak and Bektas (2017) demonstrated that board independence and board size are strongly positively related to several social and sustainable practices by investigating the multinational corporations that are facing increasing pressure on transparency demand and need to implement good corporate governance practices. Since managers are entrusted with the task of devising corporate strategies, including corporate sustainability efforts (Waldman et al., 2006), the incentives provided to them may impact their decisions regarding the allocation of funds for firm social responsibility and sustainability. Most recent studies, such as Cohen et al. (2023) have discussed the rapidly increasing practice of relying on ESG metrics in executive compensation contracts and they found that these compensation practices are aligned with efficient incentive contracting.
Furthermore, studies have investigated country-level factors that influence CSR, revealing that the legal system (Kolk and Perego, 2010; Liang and Renneboog, 2017; Simnett et al., 2009), the institutional environment (Campbell, 2007; Chapple and Moon, 2005; Ioannou and Serafeim, 2012; Martínez-Ferrero and García-Sánchez, 2017), and financial systems (Aguilera et al., 2007; Matten and Moon, 2008) exert significant influence.
Furthermore, certain scholars have undertaken an investigation into exogenous shocks as potential determinants of corporate ESG performance. Huang et al. (2022) have delved into the realm of natural disasters as an exogenous shock, examining its impact on corporate ESG disclosure policies and the subsequent implications for disclosure choices. Their study reveals that firms situated in regions proximate to those affected by natural disasters demonstrate an augmentation in ESG disclosure transparency during the post-disaster timeframe.
In addition, a cadre of scholars has advanced the proposition that the involvement of institutional shareholders wields an influence over managerial decisions pertaining to ESG/CSR endeavors. Dimson et al. (2015) have conducted an analysis of corporate social responsibility initiatives undertaken by institutional investors, drawing upon available data. Their findings suggest that these investors exert a stimulating effect on the intensification of ESG/CSR activities within target firms. Contrarily, Stark et al. (2019) have asserted and substantiated through empirical evidence that long-term institutional investors exhibit a predilection for firms exhibiting robust ESG profiles, rather than exerting direct influence on the strategic choices of these entities.
Regarding the economic consequences of corporate ESG performance, earlier studies did not reach a consensus on whether the impact on the economy is positive or negative. Some have provided evidence that ESG practices can have a reverse effect on firm financial performance by increasing corporate costs without yielding direct monetary benefits (Pastor et al., 2021; Lin et al., 2021) and exposing shareholders to potential risks (Hemingway and Maclagan, 2004). Moreover, executives might exploit the concept of social responsibility to justify pursuing their own interests, leading to a clash between extravagant investment practices and the dedication to sustainable development, ultimately causing a negative impact on the market value of companies (Barnea and Rubin, 2010; Nekhili et al., 2021).
However, others have found that enterprises with strong ESG performance possess better risk management abilities (Oikonomou et al., 2012) and experience fewer negative effects from extreme events (Lins et al., 2017), suggesting that ESG performance can actually enhance a firm's financial outcomes. Chen and Xie (2022) had demonstrated that favorable effect of ESG disclosure on corporate financial performance and this positive relation is more pronounced in firms with ESG investors and firms with longer inception, high media attention, and high agency cost. Numerous studies and academic literature assert that embracing social responsibility and transparent disclosure of ESG factors can greatly benefit business development (Abdi et al., 2022; Wong et al., 2021; Krueger et al., 2021; Banerjee et al., 2020). In particular, prior research has put forth compelling arguments that corporate disclosure of ESG information leads to several positive outcomes for businesses. These include reducing the cost of capital (Eichholtz et al., 2019), mitigating financing risk (Atif and Ali, 2021; Feng and Wu, 2023), and decreasing stock price volatility (Bofinger et al., 2022).
Given the largely discretionary nature of ESG disclosure of firms, particularly in oversea markets, firm managers engage in the cost/benefit analysis of disclosure to arrive at some optimal level of discretionary ESG disclosure. The impact of ESG disclosure also gains increasing interest in exploring the positive value that is associated with better ESG performance. On one hand, transparent ESG information proves that companies are actively taking ecological and social responsibility, thereby enhancing their reputation with consumers and investors, accessing capital at a lower cost, and improving their competitive advantage (Gillan et al., 2021; Bofinger et al., 2022). On the other hand, ESG disclosure is critical for reducing information asymmetry between businesses and stakeholders. Companies that share ESG data are more transparent and lower investment risks, which appeals to risk-averse investors (Frydman and Wang, 2020; Joliet and Titova, 2018; Egginton and McBrayer, 2019).
2.2 Related research on social trust
Social trust is recognized as a vital component of social capital and it also server as information rules in social networks (M'eon and Sekkat, 2015). This fosters the exchange of values (Beugelsdijk and Klasing, 2016) and directs individuals towards conforming to accepted behaviors (Stanley et al., 2011; Kirchler et al., 2008).
Most early studies have focused on examining the effects of the social trust environment on a country's macroeconomic growth. These studies have demonstrated the crucial role of social trust in fostering economic development, capital market liberalization, and international trade at the country level (Guiso et al., 2008; Knack and Keefer, 1997; Zak and Knack, 2001). Moreover, it has been observed that social trust has a greater effect on the economic growth of countries with weaker formal institutions than on the growth of countries with stronger formal institutions (Ahlerup et al., 2009).
In addition, numerous recent studies have indicated that social trust has positive effects on corporate behavior. Social trust contributes significantly to the establishment of mutual expectations regarding honest and reliable behavior within society (Bjørnskov, 2012; Kim and Li, 2014). This heightened expectation of integrity and honesty can increase the perceived costs of engaging in opportunistic behavior, and thus subsequently reduce the likelihood of dishonest activities. In regions characterized by higher levels of social trust, this mutual expectation serves as a deterrent against corporate misconduct (Dong et al., 2018) and helps mitigate the risk of stock price crashes (Li et al., 2017). Enhanced social trust is predicted to stimulate collaboration (Fehr and Gachter, 2000; Macy and Sato, 2002) and amplify funding for businesses (Huang and Shang, 2019). Moreover, social trust is inversely linked to instances of corporate deceit (Giannetti and Wang, 2016), societal violence (Depetris-Chauvin et al., 2020), and governmental corruption (Bergh and Öhrvall, 2018).
Furthermore, social trust can function as an alternative monitoring mechanism, facilitating the exchange of information (Uzzi and Dunlap, 2005). It also alleviates auditors' concerns regarding moral hazards within firms, thereby reducing the likelihood of financial manipulation (Chen et al., 2018; Jha and Chen, 2015). Moreover, firm managers hailing from regions with higher levels of social trust demonstrate greater alignment with shareholders because of shared values and norms, leading to a reduction in agency costs (Chami and Fullenkamp, 2002).
A substantial body of literature has examined the impact of social trust on corporate monitoring through information sharing (Bjørnskov and Meon, 2015; Payne et al., 2008; Putnam et al., 1994). However, the literature exploring the influence of social trust by comprehensively evaluating microenterprise performance, specifically ESG performance, is limited. On such early study, that by Paudel and Schafer (2009), sheds light on the significant role of social capital in water pollution control. The results from the study conducted by Dincer and Fredriksson (2018) validate the influence of social trust on the strictness of environmental policies. However, their research does not offer any proof regarding the impact of social trust on environmental pollution. Furthermore, Chen et al. (2021a, b) provided evidence of the pollution reduction effect of social trust. Nevertheless, to the best of our knowledge, no study has examined the impact of social trust on the overall ESG performance of corporations in the Chinese market. Given that prior studies have shown that social trust exerts a positive influence on corporate governance and environmental management, we thus state the following hypothesis:
Higher levels of social trust contribute to improved ESG performance.
2.3 Related research on social trust and ESG
One strand of literature argues that social trust positively attributes to development of regional economy. Bjørnskov (2012) has provided the evidence to show that trust affects schooling, and the rule of law directly and thereby raises economic growth rate. Moreover, information asymmetry often leads to opportunism and high transaction costs, whereas trust constrains such behavior, promoting long-term interests. Social trust acts as a lubricant for economic transactions, reducing costs, boosting investments, and enhancing returns on human capital and technology, thereby fostering economic growth (Cui, 2017). Other studies suggest that the significant positive correlation between various dimensions of economic and discretionary social responsibility in emerging economy has been demonstrated (Doshmanli and Salamzadeh, 2018). Economic development has become one of the factors influencing the foundational requirements of ESG (Rim and Dong, 2018). Hence, economic development could elucidate the Trust–ESG relation, leading us to propose the ensuing hypothesis:
Enhanced social trust fosters economic development, subsequently correlating with improved ESG performance.
Social trust may influence stakeholder engagement and activism in a positive manner, thereby fostering more effective corporate governance practices (Stuebs and Sun, 2015). This, in turn, enables enterprise managers to embrace sustainable and responsible business practices (Aguilera et al., 2006; Kocmanová et al., 2011). A positive association has been identified between social trust and the adoption of environmentally friendly practices in enterprises that exhibit good corporate governance (Chen et al., 2021a, b). Building upon these findings, we posit the subsequent hypothesis to elucidate the secondary pathway underlying the Trust–ESG relationship:
Social trust exerts a positive influence on ESG performance through the facilitation of effective corporate governance.
In the presence of elevated levels of social trust, a decrease in political corruption and crime has been observed (Bergh and Öhrvall, 2018). Subsequently, this is complemented by more stringent environmental governance (Dincer and Fredriksson, 2018). Paavola (2007) highlights that environmental governance involves creating, reasserting, or modifying institutions to address environmental issues. In this specific context, the evaluative criteria applied to the quality of environmental governance can be fundamentally situated within the overarching domain of institutional quality, as posited by Knight (1992). The interconnection between institutional quality and corporate sustainable performance has been uncovered by Rahi et al. (2023) and their results indicate that institutional quality exerts a noteworthy influence on upholding corporate sustainable performance, with the preliminary attributes of firms playing a pivotal role in shaping this association. Accordingly, we posit the subsequent hypothesis:
Social trust yields a favorable impact on institutional quality, subsequently fostering a positive effect on ESG performance.
3. Data and empirical methodology
3.1 Research samples
The sample for this study consists of firms listed on the Shanghai Stock Exchange and the Shenzhen Stock Exchange of A share over a period of nine years, from 2009 to 2017. We exclude Special Treatment and Particular Transfer companies. Companies in the financial industry and those with missing data for one or more variables are excluded from the sample. To mitigate the influence of outliers, all continuous variables are winsorized at the 1st and 99th percentiles.
The sample selection was guided by several considerations. Data on regional social trust were obtained from the CGSS, a comprehensive social survey database covering the 2003–2017 period. However, the firm-level Huazheng ESG ranking data were available only for the 2009–2022 period. To establish a connection between the two databases, we matched the social trust information with the province where each firm was located, which allowed us to generate a social trust score at the enterprise level. We further merged this information with other firm-characteristic variables obtained from the China Stock Market and Research Database. The final panel dataset used in our analysis comprises a total of 22,225 firm-year observations for 2009–2017.
3.2 Measuring ESG
We adopted the ESG rating method introduced by Lin et al. (2021), which was further developed by Sino-Securities Index Information Service (Shanghai) Co. Ltd. The Sino-Securities Index incorporates the latest ESG reporting guidelines from the Hong Kong Exchange and other international standards. It is designed to encompass China's unique characteristics, including three primary pillars (Environment, Society, and Governance), 16 secondary themes, and 44 key issues at the tertiary level. The ESG ratings provided by Sino-Securities Index follow a 9-point scale ranging from “AAA” to “C”. We assigned a score of 9 to the highest ESG ranking of “AAA” and a score of 1 to the lowest rating of “C”. Therefore, a higher score indicates a stronger ESG performance.
3.3 Measuring social trust
The independent variable in our study is social trust, which we measured in two dimensions, in line with prior empirical research. The first dimension captures the perception of trustworthiness among various social participants, including individuals, institutions, employers, employees, enterprise managers, and directors. This dimension was derived from the CGSS database, a comprehensive and continuous survey project jointly conducted by academic institutions, such as Remin University of China and Hong Kong University of Science and Technology. The CGSS aims to provide systematic information on the social structure and the evolving quality of life in urban and rural areas of China.
To measure trustworthiness at the provincial level, we used a weighted average ranking derived from the survey data. The CGSS has conducted a series of social surveys covering multiple years, including 2003, 2005, 2006, 2008, 2010–2013, 2015, and 2017. Since the data were not collected continuously, we used data from the previous year as a proxy for the current year. We assert that these measures serve as good approximations because social trust tends to exhibit stability and does not undergo significant variations over relatively short periods (Jin et al., 2016; Uslaner, 2002).
Next, we derived the measurement of provincial trustworthiness from participants' responses to specific survey questions, which aimed to gauge their perceptions of trustworthiness in society. The questions included: “In general, do you agree that most people in society are trustworthy?” and “Do you think the social environment is fair?” Respondents were provided with a range of answer choices, including “strongly agree,” “quite agree,” “neutral,” “quite disagree,” and “strongly disagree.”
To assess provincial trustworthiness, we calculated the weighted average score based on the respondents' rankings. We assigned each respondent's ranking of trustworthiness a numerical value and determined the provincial trustworthiness score by taking the average of these scores across all respondents. Then, we ranked the provinces accordingly, assigning the highest-scoring provinces the maximum score of 5, the second-placed provinces the score of 4, and so on. This scoring system allows for the comparison and ranking of provinces based on their perceived levels of trustworthiness.
We also incorporated social donation as another dimension of social trust, considering it from the perspective of altruistic behavior. We assessed this dimension by examining the total annual cash donations made by organizations at the provincial level. We sourced the data for this variable from the China Stock Market and Research Database. Social donation represents a voluntary, non-profit activity that exemplifies a community's willingness to support others and trust in society. Prior empirical studies have utilized blood donation as a measure of social trustworthiness (Ang et al., 2015; Wu et al., 2014), but we contend that social donation provides a more impartial measure of social trust. This is because individuals residing in urban areas naturally have more opportunities than those living in rural areas to participate in blood donation drives. Therefore, by considering social donation, we can obtain a more unbiased assessment of social trust levels across regions.
3.4 Control variables
We adopted an approach similar to that of other scholars (e.g. Chen et al., 2021a, b) by incorporating a set of firm-level control variables. These variables include firm age (AGE), market capitalization (SIZE), leverage (LEV), systematic risk (BETA), firm profitability (ROA), Tobin's Q (TQ), market-to-book equity ratio (MBV), cash holdings (CASH), percentage of outstanding shares held by the top five largest shareholders (OWNCON), institutional ownership (INSOWN), managerial ownership (MGROWN), independent director ratio (INDR), analyst coverage (AC) [1], and audit opinion (AUDITOP) [2]. We also included a dummy variable for state ownership (SOE), which takes the value of 1 for SOEs and 0 for non-SOEs. This variable allows us to account for the influence of state ownership on the relationship under investigation.
Furthermore, we incorporated measures of economic and institutional development at the provincial level, namely, the regional gross domestic product per capita (RGDP) and the marketization index (MKT) [3]. These variables provide insights into the economic and institutional contexts in which the firms operate. To establish the link between the trust-level information and the firm-level characteristics, we matched the data using the firms' address. This matching process yielded an unbalanced panel dataset comprising 22,225 firm-year observations for 2009–2017 across 31 provinces.
3.5 Model specification
The primary objective of our study is to examine the impact of social trust on a firm's ESG performance while considering various control variables. It is widely recognized that the government plays a pivotal role in setting social objectives and expects companies to contribute to improving societal conditions (Ward, 2004). In the context of ownership, SOEs often engage in more pronounced social interventions than other types of firms. Given the extensive policies in China, SOEs tend to implement a range of social programs to comply with legal requirements, including those related to ESG. In contrast, non-SOEs implement deliberate measures to meet social welfare standards and to foster, maintain, or enhance their relationship with the state (Zhao, 2012). Therefore, the ownership status of a firm, whether it is state-owned or not, plays a critical role in understanding the relationship between social trust and ESG performance. Following earlier studies (Fonseka et al., 2021), we employ the following specifications as our primary model:
As a contextual variable at the regional level, social trust is expected to be positively associated with the level of local market development. Therefore, we include market capitalization as a control variable. In addition, we account for firm-level characteristics that may confound the effects of our primary independent variables, such as ownership (whether the firm is an SOE), maturity (firm age), profitability (return on assets), valuation (market-to-book ratio), internal corporate governance (managerial ownership), and external corporate governance (analyst coverage). For instance, SOEs may enjoy greater trust from market participants because they are supported by state capital. Similarly, mature businesses with a long-standing track record and significant market capitalization are more likely to inspire public trust. Moreover, we introduce year fixed effects to capture time-invariant heterogeneity and industry fixed effects to mitigate the influence of other industry-specific characteristics on social trust. To account for potential heteroskedasticity and cross-sectional correlation across firms, we cluster standard errors at the firm level.
To investigate the impact of social trust on firms' ESG performance, we adopt a mechanism analysis approach that considers the influence of institutional quality, corporate governance, and economic development. Specifically, we examine the regional level of pollution control investment, the Herfindahl Index, and the regional economic development as relevant variables. A detailed discussion and analysis of these variables is presented in Section 5.
4. Empirical analysis
4.1 Descriptive statistics
Table 1 presents the variables utilized in the study. Panels A, B, and C display the mean, standard deviation, minimum, and maximum values of each variable for the entire sample, SOEs, and non-SOEs, respectively. In Panel A, the average ESG score for all firms is reported as 6.489, with a standard deviation of 1.23. Panel B reveals that the average ESG score for SOEs is 6.56, and Panel C reveals that for non-SOEs it is 5.44. The primary measures of social trust represent the social participants' beliefs about trust. The average value for social trust for the entire sample is 3.249 with a standard deviation of 0.302 for all firms. For the non-SOE firms, this value is slightly lower at 3.274 and the standard deviation is higher at 0.286.
The alternative measure of social trust, social donation, captures the altruistic behavior of firms. The natural logarithm of cash and material social donations by individual firms has an average value of 12.21, with a standard deviation of 2.31, for the entire sample. This average value slightly increases for SOEs to 13.97, whereas non-SOEs exhibit a lower average value of the natural logarithm of social donations at 11.08. These findings suggest that SOEs have made more social contributions and engaged in more CSR activities, contributing to their higher average ESG performance compared with that of non-SOEs. The descriptive statistics of social trust scores and social donations at the province-level are provided in Table A2, showcasing the variation in social trust across regions in China. Table 2 presents the correlation matrix results and shows that the correlation coefficients range from −0.2629 to 0.5436. These coefficients indicate that there is no multicollinearity problem in our dataset.
4.2 Main results: the primary measure of social trust
Table 3 presents the baseline regression results from both Model (1) and Model (2), which examine the impact of social trust, state ownership, and their joint effects on ESG performance. All continuous variables in the study are winsorized at the 1% level to mitigate potential outlier effects. For brevity, the coefficients for industry and year fixed effects are not included in the table.
In Columns 1 and 2, in which Social Trust is used as the independent variable while controlling for SOE and other variables for firm characteristics, the results show that firms located in regions with higher levels of trustworthiness are more likely to achieve higher ESG scores. Specifically, a one-unit increase in social trust is associated with a 3.88% increase in a firm's ESG score. The coefficient of Social Trust is statistically significant and aligns with our hypothesis that a positive social trust environment can incentivize firms to engage in ethical and socially responsible activities.
4.3 Robustness test: alternative measure of social trust
This section presents an analysis of the findings using alternative proxies for social trust, specifically focusing on altruistic enterprise practices as captured by social donation. The use of this variable serves as a robustness check to validate our earlier results that were based on the primary indicator of social trust. Table 4 provides evidence that higher levels of social donation are associated with improved ESG performance, with a coefficient of 0.0339 at a significance level of 1%. This finding aligns with our earlier results, indicating that firms engaging in greater social donation activities exhibit higher ESG scores. Furthermore, consistent with earlier findings, we also find that being an SOE is associated with a positive impact on ESG performance. This result reaffirms the beneficial influence of state ownership in contributing to better ESG outcomes. These findings confirm the robustness of our initial findings and reinforce the notion that both social trust, as measured by social donation, and state ownership, play important roles in influencing a firm's ESG performance.
We observe that the coefficients of firm-characteristic variables do not significantly deviate from the results presented in Table 3. This finding suggests that factors such as lower firm age, larger market capitalization, lower leverage and risk, higher return on assets (ROA), higher managerial ownership, better audit opinions, and higher analyst coverage consistently contribute to better ESG performance. In addition, the market index, which serves as a regional variable, exhibits a statistically significant increase of 3.61% in ESG at a significance level of 1% on accounting for industry and fixed effects and controlling for clustering. These findings underscore the robustness of our results and emphasize the importance of firm characteristics, as well as regional market conditions, in shaping a firm's ESG performance.
4.4 Robustness test: endogeneity test
4.4.1 Instrumental variable regression
To address potential endogeneity concerns associated with social trust and its impact on enterprises' ESG performance, we employ a method similar to that used by Ang et al. (2015) and Chen et al. (2016) in their studies on social trust in the Chinese context. By adopting this approach, we aim to mitigate any confounding factors or reverse causality that may arise from the relationship between social trust and ESG performance. This allows us to establish a more robust and credible analysis of the influence of social trust on enterprises' ESG outcomes.
We use two external instruments to address potential endogeneity concerns associated with social trust. The first instrument is the province-level number of dialects, which is used to instrument social trust. The rationale behind this instrument is that language serves as a means of communication and a repository of local culture. In particular, dialects play a crucial role in the division of ethnic groups and the construction of national identity. Within a given geographic area, individuals who speak the same dialect have a better understanding of each other's history, culture, and upbringing than those from regions with multiple dialects. This shared language creates a sense of familiarity and mutual trust, facilitating communication and establishing a foundation of confidence (Lu et al., 2019; Pendakur and Pendakur, 2002). The second instrument we utilize is the diversity of ethnic groups in a province, which serves as a measure of regional language diversity. While there is no direct evidence linking dialect diversity and ethnic groups to ESG, research suggests that regions with lower dialect diversity exhibit stronger social identification and similarity among residents, leading to higher levels of social trust (Lu et al., 2019). To quantify dialect diversity, we employ the DIAL measure, which captures the number of languages spoken in a province. In addition, we consider NMIN, which represents the number of minority ethnic groups, and PMIN, which indicates the percentage of minorities among all the residents in the province where the company is located. Higher values of NMIN and PMIN reflect greater ethnic diversity. Significantly, these instruments, dialect diversity and ethnic group diversity, indirectly capture the underlying construct of social trust by influencing the social dynamics and cohesion within a region.
The results presented in Table 5 confirm and reinforce our previous results, indicating a positive relationship between social trust and firm ESG performance, as well as the significant positive association between state ownership and the ESG score. Panel A presents the results of the two-stage least squares regression with social trust as the dependent variable. In the first-stage regression, we observe significant and positive effects of NMIN and DIAL on the social trust measure. In the second-stage regression, where we utilize the instrumented version of social trust proxies, we continue to find significant and positive associations with firm ESG performance. These results support our earlier results and provide further evidence that higher levels of social trust contribute to better ESG outcomes for enterprises. In Panel B, we examine the results using the alternative measure of social trust, which is captured by the proxies for altruistic behavior, such as social donation. Excluding the DIAL variable, all other variables display significant and positive coefficients that align with our baseline findings. This robustness check confirms that our conclusions regarding the relationship between social trust and ESG performance are not affected by potential endogeneity concerns. Overall, the results presented in Table 5 further support our hypothesis and provide additional evidence of the positive impact of social trust on firm ESG performance, while also highlighting the significant role of state ownership in driving ESG outcomes.
4.4.2 Propensity score matching approach
In order to address any remaining concerns regarding endogeneity, we conduct additional tests using PSM to match firms with control firms that have similar characteristics in terms of their propensity to engage in social donation. This approach allows us to attribute any observed effects more accurately to the firms' ESG performance itself, rather than to other firm characteristics associated with the ESG assessment. We ensure the quality of the matching by conducting balance tests (the results are presented in Table 6). These results demonstrate that after matching, the standard deviation (% bias) of variables are significantly reduced, with all variables exhibiting a standardized deviation of less than 20%. Moreover, the t-test results indicate that the differences between the treated and control samples after matching are all statistically insignificant. These findings suggest that the matching process has effectively addressed any potential biases and achieved satisfactory balance between the treated and control groups. Thus, by employing PSM, we have taken additional steps to mitigate endogeneity concerns and enhance the robustness of our findings. The satisfactory balance achieved through the matching process provides further confidence in the validity of our results.
We proceed to re-estimate Equation (1) using the matched samples in order to address endogeneity concerns. The results, presented in Table 7, reaffirm the significant and positive relationship between social trust and firm ESG performance, consistent with our baseline test results. Furthermore, we observe that ESG scores exhibit positive and significant associations with both social trust and social donation, and these relationships remain statistically significant at the 10 and 1% levels, respectively, even when accounting for the interaction term of SOE. Thus, by employing PSM to address endogeneity, we have effectively mitigated potential biases and obtained more reliable estimates of the relationships between social trust, social donation, and firm ESG performance. The consistent findings from the matched sample analysis further strengthen the evidence supporting the positive impact of social trust and altruistic behavior on ESG outcomes, while also acknowledging the moderating effect of state ownership.
4.4.3 Difference-in-differences approach
To address the potential endogeneity issue arising from the possibility of firms located in regions with higher social trust scores exhibiting higher investment efficiency (Shen et al., 2022), we adopt a quasi-experimental approach by leveraging a unique shock and conduct a series of DID tests. These tests enable us to examine the causal relationship between social trust and ESG performance. By comparing the changes in ESG performance over time between firms operating in regions with varying levels of social trust, we can isolate the specific impact of social trust on ESG outcomes. This quasi-experimental design allows us to control for time-invariant heterogeneity and factors that affect all regions uniformly, thereby providing more robust evidence of the causal relationship between social trust and ESG performance.
We exploit the exogenous shock of the state council's promulgation of a regulation in 2014 as a quasi-natural experiment to examine the causal relationship between social trust and firms' ESG performance. This regulatory change, marked by the publication of the Guidance on Social Credit System Construction Plan, represents a significant milestone in the construction of the social credit system in China. It aims to regulate and strengthen the activities related to social credit construction by providing a comprehensive legal framework and serving the modernization goals of the Chinese society. We consider this regulatory change as an exogenous shock to social trust because it is unrelated to firms' ESG performance. The establishment of the social credit system on a national level since 2014 has contributed to the enhancement of social trust across the country. We anticipate that this regulatory change will influence the development of social trust while remaining unrelated to firms' ESG outcomes. Furthermore, there is notable heterogeneity in the implementation of the regulatory change across regions, which enables us to employ a DID analysis. This approach allows us to compare the changes in social trust and ESG performance before and after the regulatory change, thereby allowing us to examine whether a substantial increase in social trust is associated with a greater improvement in firms' ESG performance. Through this quasi-natural experiment, we aim to provide empirical evidence on the causal relationship between social trust and firm's ESG.
We implement the DID analysis through the following steps. First, we create a treatment group by identifying firms that experienced the greatest increase in social trust between the year prior to the regulatory change (2013) and the year following the change (2015). Specifically, we select firms in the top three deciles of the sample on the basis of the change in social trust. These firms are assigned a treatment status of TREAT = 1. Second, we employ the PSM method to match the treatment group with a control group (TREAT = 0), using the same set of control variables as specified in Equation (1). We base the estimation model on firm characteristics at the end of 2013, the year before the regulatory change, and include all control variables and industry fixed effects. The dependent variable is equal to 1 if the firm-year belongs to the top three deciles (treatment group), and 0 otherwise. Through nearest-neighbor PSM, we match each firm in the treatment group with a firm from the remaining seven deciles that has a propensity score within 0.01 of the treatment firm [4]. Third, we construct a dummy variable, POST, to indicate the period before and after the exogenous shock. Specifically, we estimate the following model:
The variable POST takes a value of 1 for the years 2014–2017 (the post-regulatory period) and 0 for the years 2010–2013 (the pre-regulatory period). The coefficient of TREAT*POST is the key variable of interest, as it measures the change in firms' ESG score associated with the highest increase in social trust relative to firms with a low increase in social trust during the post-regulatory period. The results of this analysis are reported in column 1 of Table 9. The coefficient of TREAT*POST is found to be positive and statistically significant at the 1% level, indicating that our findings remain robust even when considering the possibility of reverse causation. To further validate the DID approach, we assess the parallel trend assumption. The results are presented in Table 8. The model used for this assessment is as follows:
5. Mechanism analysis
As outlined in Section 1, social trust can influence firms' ESG through three potential mechanisms: economic development, corporate governance, and institutional quality. In order to examine the impact of social trust on ESG improvement through these channels, we conduct additional tests in Sections 5.1–5.3 to test hypotheses 2 to 4.
5.1 Economic development
Theoretically, we posit that economic development plays a crucial role in connecting social trust and firm ESG scores. Elements such as corporate volunteering, carbon footprint reduction, and social sustainability governance are foundational requirements of ESG, and these social responsibilities are influenced by various factors, including the local history, culture, institutional quality, and economic development (Rim and Dong, 2018). The fulfillment of CSR is directly influenced by the regional economic development level, which, in turn, positively affects the provision of supportive services and infrastructure for enterprise production and operations. Moreover, social and environmental issues are significantly influenced by economic expansion (Fares et al., 2006). To denote economic development, we estimate the province-level GDP per capita (GDP) and run regressions using controls similar to those in our main model. Panel A in Table 10 presents the results of the regression using social trust as the independent variable.
Columns 1 and 4 present the loadings of all variables from the original baseline test for the comparison of the variation in the results presented in Columns 3 and 6, respectively. Columns 2 and 5 indicate that social trust and social donation have a positive effect on economic development, as expected, and lead to a significant increase of 2.46 and 2.32%, respectively, in economic achievement. Increased economic development will encourage more enterprises to accomplish sustainable goals, which contributes to increasing their ESG score. We also find that the inclusion of GDP as an additional control (see the results in Columns 3 and 6) reduces the estimated effect of social trust and social donation on ESG, compared with the results in Columns 1 and 4. Therefore, economic development mediates the loadings on social trust of ESG, which illustrates the explanatory role of economic development.
5.2 Corporate governance
Given that social trust could positively impact stakeholder engagement ultimately cultivating enhanced corporate governance practices and the proven positive association between corporate governance and social responsibility, which is mentioned in Section 2.3, we contend that social trust can play a critical role in promoting effective corporate governance as a means to enhance ESG performance.
Table 11 presents the findings that underscore the role of social trust, as a socially normative force, in facilitating corporate managers' commitment to safeguard stakeholders' interests through ESG improvement. In this analysis, we introduce HINDEX as a mechanical variable representing corporate governance. Interestingly, we observe that the coefficients of both social trust and social donation lose their significance when HINDEX is included as a control variable. In addition, the results in Columns 2 and 5 demonstrate a statistically significant association between social trust and firm corporate governance, aligning with prior literature that highlights how firms are more inclined to adopt socially responsible practices when influenced by powerful social norms (Campbell, 2006).
5.3 Institutional quality
The Chinese Government has consistently prioritized enhancing institutional capacity in managing various aspects of social affairs, including environmental concerns, to achieve the vision of an ecological civilization characterized by harmonious coexistence. It is widely recognized that the quality of regional governance can significantly influence corporate environmental performance, and prior research has indicated that social trust has a positive effect on institutional quality (Guiso et al., 2008; Olson, 1971). To gauge institutional quality, we utilize the province-level pollution control investment as a proxy, which reflects the effectiveness of government-led environmental governance efforts.
Table 12 presents the findings from the channel analysis of pollution control investment, which examines the relationship between social trust and firms' ESG performance. Panel A and Panel B display the respective results on using social trust and social donation as explanatory variables in the regression.
Columns 3 and 6 present the coefficient results of social trust on firms' ESG performance, while controlling for pollution investment. These coefficients show a decrease in both magnitude and significance compared with the coefficients from the original baseline test presented in Columns 1 and 4. Specifically, we observe that an increase in social trust is expected to lead to a 9.64% increase in environmental governance. In addition, the coefficient for environmental governance is 3.76%, which is statistically significant at the 10% level. This result highlights the substantial explanatory power of institutional quality in relation to ESG performance. The results also demonstrate a similar trend when using the alternative measure of social donation. The results in Panel B of Table 12 indicate that social donation has a positive and significant influence on firm's ESG performance through the channel of pollution control investment.
6. Test for heterogeneity
We conduct subsample analyses based on the ownership structure and median values of market capitalization, leverage, profitability (ROA), ESG score, and also corporate manager's characteristics which could have an impact on the Trust–ESG relation to further examine the robustness of our results, as presented in Table 13. The coefficients of both social trust and social donation are significant for SOEs but not significantly positive for non-SOEs. This suggests that SOEs, which tend to engage in more social programs, benefit more from social trust in terms of their ESG performance (Ward, 2004).
In Panel A, we observe a positive effect of social trust on the ESG performance of firms whose market capitalization is greater than the sample average. This result indicates that large firms and firms located in regions with higher social trust levels engage in more social sustainability activities, thus highlighting that a firm's size and the region's social trust levels are directly linked to its ESG score. Further, the relationship between social trust and ESG performance is more significant for firms with higher leverage and better profitability, which aligns with prior research (Wei et al., 2023), demonstrating that social trust can substitute for formal institutional mechanisms to enhance a company's profitability. The influence of social trust becomes insignificant for companies with lower ESG scores, suggesting that the positive impact of social trust is mainly driven by firms with stronger performance in ESG aspects.
Additionally, our investigation delves deeper into the ramifications stemming from the distinctive attributes of managers concerning their influence on the dynamic interplay between social trust and performance in the realm of ESG considerations. Managerial attributes, encompassing factors such as managerial sentiment, confidence level, risk propensity, educational background, age, and the duality status of the firm, assume a pivotal role in shaping the corporate performance trajectory of an enterprise (Jiang and Lim, 2018; Goel and Thakor, 2008; Guluma, 2021). We adopt a methodology akin to that introduced by Wei (2016), wherein we calculate the Overconfidence Index grounded in manager attributes. This entails the incorporation of factors such as gender, age, education, and duality status [5]. The results indicate that the Trust–ESG relation displays positive significance only when the confidence index is lower than average. This could be explained by managers' personal attributes and behavioral biases that affect their investment decisions (Malmendier and Tate, 2005) and so as the effectiveness of corporate governance (Adams and Ferreira, 2007).
Given that executive risk preferences inevitably influence their decision-making behaviors, subsequently affecting a company's operational outcomes (Hambrick and Mason, 1984), indicators were selected from the perspective of executive impact on corporate decisions. The principal component analysis method was employed to extract principal components that reflect executive risk preference levels. This comprehensive evaluation serves to assess executive risk preference levels. Therefore, we introduce the risk preference indicator (RPI) based on managers' related attributes including asset structure, debt-paying ability, profit structure, profit distribution, and cash flow [6]. The findings indicate that social trust exerts a substantial impact on corporate ESG scores in circumstances where the collective risk preference level of firm managers is relatively elevated. However, this significant influence diminishes within the subset of firms characterized by lower risk preference levels.
In this section, CEO duality has also been included as a factor under consideration. The outcomes demonstrate that the coefficient denoting the influence of social trust on ESG performance stands at 0.163, displaying statistical significance at the 5% level. This observation aligns with the principles of stewardship theory, positing that CEO duality could potentially yield favorable outcomes for firm corporate performance by virtue of the streamlined chain of command it entails.
Similar results are observed in Panel B when using the alternative measure of social trust, that is, social donation. However, the results for most of the subsamples do not differ substantially, except for the results regarding SOEs and non-SOEs. Nevertheless, the outcomes pertaining to the diversity in attributes among firm managers do not manifest substantial variances when social donations are employed as an alternative explanatory factor.
7. Conclusion
Although numerous studies have attempted to identify the determinants of firms' ESG performance, little is known about the role of social trust in facilitating or hindering ESG practices. Therefore, in this study, we investigated whether higher levels of social trust are associated with better ESG performance. Our empirical findings provide evidence that social trust influences ESG practices through social norms, which act as informal institutions that supervise enterprise behaviors and ultimately contribute to enhanced firm-level ESG performance. Consistent with our hypotheses, our panel regression results in the context of China reveal a positive correlation between social trust and ESG scores, supporting the notion that social trust is a critical determinant in explaining ESG behavior.
We conducted several robustness checks, which further validate the relationship between trust and ESG. Moreover, we find that certain firm characteristics, including being state-owned, having a larger market size, and higher profitability and leverage, are more likely to be associated with a stronger causal effect of social trust on firm ESG. Thus, this study represents one of the earliest empirical ones to confirm that social trust serves as an external driving force behind companies' fulfillment of their ESG responsibilities – a finding that holds significant implications for enterprises, regulators, and market participants.
This paper represents a significant additional to the field of ESG research, departing from the prevalent focus on formal institutional elements like corporate governance and regulations. Instead, it advances a shift towards an informal enterprise-driven ESG paradigm. The central finding of this study underscores the substantial influence of social norms on local companies, positively affecting their ESG performance. Informal institutional factors emerge as critical determinants of ESG behavior, thus enriching neo-institutional theory.
Furthermore, this research contributes to a better comprehension of the factors influencing ESG performance. Given ESG's significant impact on stakeholder welfare, the study sheds light on the pivotal role of social trust in shaping corporate behavior and stakeholder well-being. This work not only enhances our understanding of the intricate relationship between social trust and ESG performance but also contributes to both theoretical advancements and practical applications in this domain. It fills a gap in the literature by highlighting the importance of informal institutions in ESG research and underscores the significance of social norms in influencing environment, society and governance. To date, our investigation stands as the inaugural empirical inquiry validating the premise that social trust constitutes an external impetus compelling enterprises to discharge their social obligations. This discernment carries substantial ramifications for companies, regulators, and market participants.
This study's findings offer practical insights for various stakeholders. Corporations operating in high-trust regions are encouraged to engage in positive ESG activities while mitigating negative social behaviors that could harm shareholders' interests. It's essential for corporate leaders to recognize the influence of their external social environments on ESG actions. For regulators, the study suggests that companies in low social trust regions tend to perform less effectively in terms of social responsibility. To address this, governments’ regulators should develop targets measures to incentivize companies in such regions to fulfill their social responsibilities actively. Additionally, the findings underscore the role of government in fostering favorable social trust environments, both regionally and nationally, to guide enterprises in fulfilling their social responsibilities. Market participants, including customers, suppliers, investors, and creditors, are advised to consider the social trust environments when making business decisions. A strong social trust environment correlates positively with a company's ability trust environment correlates positively with a company's ability to uphold its social responsibilities effectively. Investors and creditors should also incorporate information about the external trust environment when assessing a company's integrity for investment and lending decisions.
This study, being exploratory in nature, inherently presents certain limitations. Firstly, with respect to our data on social trust, we have exclusively utilized provincial-level data from China and have not refined the dataset to the granularity of individual cities. Subsequent research endeavors may extend this investigation to major prefecture-level cities, thereby affording a more precise exploration of the effects of social trust. Another constraint pertains to the measurement of ESG performance. The proxy variable employed to gauge ESG relies on ratings assigned by a single professional rating agency. Given that each professional agency adopts a distinct evaluation system, these ratings may not comprehensively and accurately assess the ESG performance of listed companies. Consequently, future studies could adopt diverse multidimensional approaches to measure ESG.
Furthermore, it is noteworthy that the landscape of ESG research is undergoing a transition, shifting from a focus on formal to informal institutional factors. This paper represents an initial step in this transition, centering its attention on social trust, a pivotal component of informal institutions. Subsequent investigations may delve more profoundly into informal institutions from perspectives such as cultural traditions and social relations. Additionally, a comprehensive inquiry into the correlation between social trust and corporate governance holds the potential to enhance our comprehension of the shifts in ESG considerations. Our upcoming research endeavors are geared toward a meticulous examination of the managerial implications of social trust on corporate executives and boards. Ultimately, we intend to investigate corporate ESG performance within the purview of the intersecting influences of governmental regulations, corporate governance structures, and social oversight.
Descriptive statistics for the entire sample, state-owned firms and non-state-owned firms
| PANEL A: Entire sample | PANEL B: Non-SOEs | PANEL C: SOEs | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Variable | N | Mean | SD | Minimum | Maximum | N | Mean | SD | Minimum | Maximum | N | Mean | SD | Minimum | Maximum |
| ESG | 20,510 | 6.489 | 1.230 | 1.000 | 9.000 | 12,185 | 6.560 | 0.978 | 1.000 | 9.000 | 8,325 | 5.440 | 1.680 | 1.000 | 9.000 |
| TRUST | 22,225 | 3.249 | 0.302 | 0.000 | 4.707 | 13,495 | 3.232 | 0.310 | 0.000 | 4.707 | 8,730 | 3.274 | 0.286 | 0.000 | 4.707 |
| DONATION | 22,225 | 12.210 | 2.310 | 6.280 | 15.310 | 13,495 | 11.080 | 3.140 | 7.520 | 15.040 | 8,730 | 13.970 | 2.010 | 10.790 | 15.430 |
| AGE | 22,225 | 2.655 | 0.450 | 0.000 | 3.466 | 13,495 | 2.588 | 0.484 | 0.000 | 3.466 | 8,730 | 2.758 | 0.370 | 0.000 | 3.466 |
| SIZE | 22,225 | 22.158 | 3.134 | 0.000 | 26.166 | 13,495 | 21.691 | 3.586 | 0.000 | 26.166 | 8,730 | 22.880 | 2.067 | 0.000 | 26.166 |
| LEV | 22,225 | 0.257 | 0.292 | 0.000 | 1.000 | 13,495 | 0.251 | 0.288 | 0.000 | 1.000 | 8,730 | 0.266 | 0.299 | 0.000 | 1.000 |
| BETA | 21,897 | 1.039 | 0.458 | −0.837 | 1.927 | 13,242 | 1.019 | 0.441 | −0.758 | 1.843 | 8,655 | 1.151 | 0.479 | −0.811 | 1.904 |
| ROA | 22,225 | 0.035 | 0.089 | −0.650 | 0.361 | 13,495 | 0.040 | 0.084 | −0.650 | 0.361 | 8,730 | 0.027 | 0.095 | −0.650 | 0.299 |
| TQ | 22,225 | 2.174 | 1.408 | 0.000 | 8.190 | 13,495 | 2.330 | 1.509 | 0.000 | 8.190 | 8,730 | 1.934 | 1.197 | 0.000 | 8.190 |
| MBV | 22,225 | 2.110 | 1.303 | 0.000 | 7.584 | 13,495 | 2.255 | 1.393 | 0.000 | 7.584 | 8,730 | 1.885 | 1.113 | 0.000 | 7.584 |
| CASH | 22,225 | 0.180 | 0.144 | 0.006 | 0.630 | 13,495 | 0.202 | 0.158 | 0.006 | 0.630 | 8,730 | 0.145 | 0.112 | 0.006 | 0.630 |
| INSOWN | 22,225 | 0.400 | 0.231 | 0.000 | 0.907 | 13,495 | 0.332 | 0.232 | 0.000 | 0.907 | 8,730 | 0.505 | 0.185 | 0.000 | 0.907 |
| MGROWN | 22,225 | 0.116 | 0.193 | 0.000 | 0.461 | 13,495 | 0.177 | 0.217 | 0.000 | 0.461 | 8,730 | 0.020 | 0.084 | 0.000 | 0.461 |
| OWNCON | 22,225 | 0.539 | 0.158 | 0.171 | 0.886 | 13,495 | 0.541 | 0.156 | 0.171 | 0.886 | 8,730 | 0.535 | 0.160 | 0.171 | 0.886 |
| INDR | 22,225 | 0.285 | 0.165 | 0.000 | 0.600 | 13,495 | 0.307 | 0.152 | 0.000 | 0.600 | 8,730 | 0.250 | 0.178 | 0.000 | 0.600 |
| AUDITOP | 22,225 | 2.928 | 0.582 | 0.000 | 6.000 | 13,495 | 2.937 | 0.577 | 0.000 | 6.000 | 8,730 | 2.915 | 0.589 | 0.000 | 6.000 |
| AC | 22,225 | 0.152 | 0.560 | 0.000 | 4.000 | 13,495 | 0.132 | 0.522 | 0.000 | 3.000 | 8,730 | 0.184 | 0.613 | 0.000 | 4.000 |
| MKT | 22,225 | 7.978 | 1.944 | 0.000 | 11.530 | 13,495 | 8.292 | 1.872 | 0.000 | 11.530 | 8,730 | 7.492 | 1.955 | 0.000 | 11.530 |
| RGDP | 22,225 | 3.160 | 2.844 | 0.000 | 9.250 | 13,495 | 3.661 | 2.951 | 0.000 | 9.250 | 8,730 | 2.385 | 2.479 | 0.000 | 9.250 |
| POLLVEST | 22,167 | 4.901 | 0.649 | 2.277 | 6.128 | 13,445 | 4.999 | 0.635 | 2.2773 | 6.1278 | 8,722 | 4.750 | 0.642 | 2.277 | 6.128 |
Note(s): This table shows the descriptive statistics (mean, maximum, minimum and standard deviation) for variables applied in our analysis. NON-SOEs and SOEs denote non-state-owned and state-owned firms, respectively. TRUST is the primary social trust variable, and DONATION is the alternative social trust measure, calculated as the natural logarithm of the total value of social donations at the firm level. SOE is a dummy variable that takes the value of 1 for state-owned firms and 0 otherwise. The control variables are as follows: AGE is the natural logarithm of the age of the firm, SIZE is the natural logarithm of the market capitalization of the firm, LEV is financial leverage, BETA is systematic risk, ROA is return on assets, TQ is the Tobin's Q ratio, MBV is the market-to-book equity ratio, CASH is the cash holding ratio, INSOWN is the institutional ownership, MGROWN is the managerial ownership, OWNCON is the ownership concentration of the five largest shareholders, INDR is the independent director ratio, AUDITOP is the audit opinion, AC is the number of analysts covering the firm, MKT is the marketization index, RGDP is the regional gross domestic product per capita and POLLVEST is the natural logarithm of the total amount of pollution control investment (100 million yuan) at the province level
Source(s): Authors’ own creation/work
Correlation matrix
| Variable | ESG | TRUST | DONATION | AGE | SIZE | LEV | BETA | ROA | TQ |
|---|---|---|---|---|---|---|---|---|---|
| ESG | 1 | ||||||||
| TRUST | 0.0242*** | 1 | |||||||
| DONATION | 0.2306*** | 0.0159 | 1 | ||||||
| AGE | −0.0828*** | −0.00864 | 0.0229*** | 1 | |||||
| SIZE | 0.1776*** | 0.09275 | 0.1461*** | 0.2235*** | 1 | ||||
| LEV | −0.0457*** | −0.14307 | 0.0105*** | 0.1137*** | 0.0491*** | 1 | |||
| BETA | 0.0394*** | −0.03683 | −0.0107*** | −0.0616*** | −0.1104*** | −0.1062*** | 1 | ||
| ROA | 0.2598*** | 0.04522 | 0.0968*** | −0.0832*** | 0.0182*** | 0.0439*** | 0.04498*** | 1 | |
| TQ | −0.2158*** | −0.1093*** | −0.0835*** | 0.1296*** | 0.1481*** | 0.0683*** | −0.0099*** | −0.0888*** | 1 |
| MBV | −0.2056*** | −0.1084*** | −0.0817*** | 0.1300*** | 0.1577*** | 0.0711*** | −0.0135*** | −0.0826*** | 0.1900*** |
| CASH | 0.1398*** | 0.0520*** | −0.0514*** | −0.2712*** | −0.1533*** | −0.1696*** | 0.1027*** | 0.1596*** | 0.0411*** |
| INSOWN | 0.0553*** | −0.0148* | 0.1335*** | 0.1324*** | 0.1975*** | 0.0392*** | −0.0518*** | 0.0743*** | −0.0671*** |
| MGROWN | 0.0812*** | −0.0664*** | −0.0407*** | −0.2403*** | −0.1189*** | −0.0294*** | 0.0724*** | 0.1100*** | −0.0068*** |
| OWNCON | 0.1598*** | −0.0185*** | 0.0809*** | −0.2629*** | −0.0352*** | −0.036*** | 0.0315*** | 0.1839*** | −0.2255*** |
| INDR | 0.0677*** | −0.0512*** | 0.0686*** | −0.0422*** | 0.0538*** | 0.0035*** | 0.0191*** | 0.0475*** | −0.0158*** |
| AUDITOP | 0.1512*** | −0.0097*** | 0.0347*** | −0.0449*** | 0.0757*** | 0.0246*** | 0.0139*** | 0.2017*** | −0.12464 |
| AC | 0.0488*** | −0.0436*** | 0.0973*** | 0.0646*** | 0.1187*** | 0.0444*** | −0.0287*** | 0.0069 | −0.11255 |
| MKT | 0.0955*** | −0.1737*** | 0.0191*** | 0.1339*** | 0.0932*** | 0.1355*** | 0.00389 | 0.0892*** | −0.0625*** |
| RGDP | 0.013 | −0.1427*** | −0.0184*** | 0.0834*** | 0.0142*** | 0.1135*** | 0.0063 | 0.0295*** | 0.0177*** |
| POLLVEST | 0.0284*** | −0.2336*** | 0.0187*** | 0.1112*** | 0.06987*** | 0.1800*** | 0.00605 | 0.0432*** | 0.0666*** |
| Variable | MBV | CASH | INSOWN | MGROWN | OWNCON | INDR | AUDITOP | AC | MKT | RGDP | POLLVEST |
|---|---|---|---|---|---|---|---|---|---|---|---|
| MBV | 1 | ||||||||||
| CASH | 0.0402*** | 1 | |||||||||
| INSOWN | −0.0684*** | −0.1397*** | 1 | ||||||||
| MGROWN | −0.0036 | 0.20503*** | −0.3536*** | 1 | |||||||
| OWNCON | −0.23011 | 0.1819*** | 0.4013*** | 0.0777*** | 1 | ||||||
| INDR | −0.0178** | 0.0146** | −0.0046 | 0.3215*** | 0.0349*** | 1 | |||||
| AUDITOP | −0.1233*** | 0.0321*** | 0.0341*** | 0.0576*** | 0.0951*** | 0.0343*** | 1 | ||||
| AC | −0.1121*** | −0.1222*** | 0.0481*** | −0.0358*** | −0.0136*** | 0.0489*** | 0.0257*** | 1 | |||
| MKT | 0.0686*** | 0.0206*** | −0.0372*** | 0.1958*** | 0.0899*** | 0.1691*** | 0.0596*** | 0.0440*** | 1 | ||
| RGDP | 0.0120*** | −0.0645*** | −0.0557*** | 0.1263*** | 0.0336*** | 0.0376*** | 0.0302*** | 0.0471*** | 0.3115*** | 1 | |
| POLLVEST | 0.0722*** | −0.0166** | −0.0554*** | 0.1554*** | 0.0688*** | 0.0835*** | 0.0459*** | 0.0984*** | 0.5436*** | 0.2941*** | 1 |
Note(s): TRUST is the primary social trust variable, and DONATION is the alternative social trust measure, calculated as the natural logarithm of the total value of social donations at the firm level. SOE is a dummy variable that takes the value of 1 for state-owned firms and 0 otherwise. The control variables are as follows: AGE is the natural logarithm of the age of the firm, SIZE is the natural logarithm of the market capitalization of the firm, LEV is financial leverage, BETA is systematic risk, ROA is return on assets, TQ is the Tobin's Q ratio, MBV is the market-to-book equity ratio, CASH is the cash holding ratio, INSOWN is the institutional ownership, MGROWN is the managerial ownership, OWNCON is the ownership concentration of the five largest shareholders, INDR is the independent director ratio, AUDITOP is the audit opinion, AC is the number of analysts covering the firm, MKT is the marketization index, RGDP is the regional gross domestic product per capita and POLLVEST is the natural logarithm of the total amount of pollution control investment (100 million yuan) at the province level. RGDP is the regional gross domestic product per capita, and POLLVEST is the natural logarithm of the total amount of pollution control investment (100 million yuan) at the province level
Source(s): Authors’ own creation/work
Main regression results: baseline test
| (1) | (2) | |
|---|---|---|
| Variable | ESG | ESG |
| TRUST | 0.0388 | 0.0917* |
| (0.0309) | (0.0515) | |
| SOE | 0.124*** | 0.162*** |
| (0.0307) | (0.0299) | |
| AGE | −0.0379 | −0.106*** |
| (0.0305) | (0.0304) | |
| SIZE | 0.0972*** | 0.0987*** |
| (0.00879) | (0.00924) | |
| LEV | −0.137*** | −0.0793* |
| (0.0241) | (0.0470) | |
| BETA | 0.0629*** | 0.0218* |
| (0.0153) | (0.0119) | |
| ROA | 1.963*** | 1.969*** |
| (0.129) | (0.127) | |
| TQ | −0.339*** | −0.344*** |
| (0.0358) | (0.0349) | |
| MBV | 0.230*** | 0.234*** |
| (0.0396) | (0.0386) | |
| CASH | 0.930*** | 0.805*** |
| (0.0783) | (0.0756) | |
| INSOWN | −0.117* | −0.109* |
| (0.0614) | (0.0600) | |
| MGROWN | 0.176*** | 0.187*** |
| (0.0657) | (0.0633) | |
| OWNCON | 0.292*** | 0.381*** |
| (0.0905) | (0.0867) | |
| INDR | 0.0937 | 0.065 |
| (0.0841) | (0.0794) | |
| AUDITOP | 0.0949*** | 0.0954*** |
| (0.0179) | (0.0177) | |
| AC | 0.0214 | 0.0316 |
| (0.0225) | (0.0204) | |
| MKT | 0.0383*** | 0.0361*** |
| (0.00701) | (0.00751) | |
| Constant | 1.112*** | 1.039*** |
| (0.244) | (0.288) | |
| Industry fixed effects | No | Yes |
| Year fixed effects | No | Yes |
| Cluster | Firm | Firm |
| Observations | 20,195 | 20,192 |
| R-squared | 0.169 | 0.236 |
Note(s): This table examines independently the explanatory power of social trust for the firm's environmental, social and governance score (ESG) and also investigates the Trust–ESG relationship by taking SOE*Trust as an interactive variable. TRUST is the primary social trust variable. SOE is a dummy variable that is coded 1 for state-owned firms, and 0 otherwise. AGE is the natural logarithm of the age of the firm, SIZE is the natural logarithm of the market capitalization of the firm, LEV is the financial leverage, BETA is the systematic risk, ROA is the return on assets, TQ is the Tobin's Q ratio, MBV is the market-to-book equity ratio, CASH is the cash holding ratio, INSOWN is the institutional ownership, MGROWN is the managerial ownership, OWNCON is the ownership concentration of the five largest shareholders, INDR is the independent director ratio, AUDITOP is the audit opinion, AC is the number of analysts covering the firm and MKT is the marketization index. All variables are explained in Section 3 and also summarized in Appendix 2. ***, **, and * indicate statistical significance at the 1%, 5% and 10% levels, respectively
Source(s): Authors’ own creation/work
Robustness test results: alternative measure of social trust
| Variable | ESG | ESG |
|---|---|---|
| DONATION | 0.0339*** | 0.0346*** |
| (0.00204) | (0.00188) | |
| SOE | 0.117*** | 0.154*** |
| (0.0295) | (0.0285) | |
| AGE | −0.041 | −0.107*** |
| (0.0299) | (0.0294) | |
| SIZE | 0.0778*** | 0.0803*** |
| (0.00759) | (0.00797) | |
| LEV | −0.134*** | −0.0762* |
| (0.0237) | (0.046) | |
| BETA | 0.0589*** | 0.0207* |
| (0.0149) | (0.0119) | |
| ROA | 1.829*** | 1.842*** |
| (0.126) | (0.124) | |
| TQ | −0.330*** | −0.337*** |
| (0.0359) | (0.0353) | |
| MBV | 0.227*** | 0.233*** |
| (0.0394) | (0.0386) | |
| CASH | 0.931*** | 0.801*** |
| (0.0757) | (0.0728) | |
| INSOWN | −0.142** | −0.128** |
| (0.0586) | (0.0575) | |
| MGROWN | 0.194*** | 0.202*** |
| (0.0638) | (0.0613) | |
| OWNCON | 0.258*** | 0.353*** |
| (0.0868) | (0.0825) | |
| INDR | 0.0397 | 0.00607 |
| (0.0816) | (0.0766) | |
| AUDITOP | 0.101*** | 0.101*** |
| (0.0176) | (0.0172) | |
| AC | 0.00498 | 0.0147 |
| (0.021) | (0.019) | |
| MKT | 0.0371*** | 0.0322*** |
| (0.0065) | (0.00711) | |
| Constant | 1.652*** | 1.740*** |
| (0.178) | (0.204) | |
| Industry fixed effects | No | Yes |
| Year fixed effects | No | Yes |
| Cluster | Firm | Firm |
| Observations | 20,195 | 20,192 |
| R-squared | 0.195 | 0.262 |
Note(s): This table investigates the impact of social donation, as the alternative explanatory variable of social trust, on a firm's environmental, social and governance score (ESG). It also investigates the Donation–ESG relationship. DONATION is the natural logarithm of the value of the total social donation by the firm. SOE is a dummy variable that is coded 1 for state-owned firms, and 0 otherwise. AGE is the natural logarithm of the age of the firm, SIZE is the natural logarithm of the market capitalization of the firm, LEV is the financial leverage, BETA is the systematic risk, ROA is the return on assets, TQ is the Tobin's Q ratio, MBV is the market-to-book equity ratio, CASH is the cash holding ratio, INSOWN is the institutional ownership, MGROWN is the managerial ownership, OWNCON is the ownership concentration of the five largest shareholders, INDR is the independent director ratio, AUDITOP is the audit opinion, AC is the number of analysts covering the firm, MKT is the marketization index. All the variables are explained in Section 3 and also summarized in Appendix 2. ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively
Source(s): Authors' own creation/work
Robustness test of endogeneity: two-stage least squares regression
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | |
|---|---|---|---|---|---|---|---|---|
| 1st TRUST | 2nd ESG | 1st TRUST | 2nd ESG | 1st TRUST | 2nd ESG | 1st TRUST | 2nd ESG | |
| Panel A: social trust as explanatory variable | ||||||||
| TRUST | 0.11452*** | 0.3349** | 0.39557*** | 0.3612*** | ||||
| (0.021) | (0.164) | (0.0824) | (0.0782) | |||||
| MIN | 0.002** | 0.013*** | ||||||
| (0.001) | (0.0011) | |||||||
| PMIN | −0.032*** | −0.0026*** | ||||||
| (0.002) | (0.001) | |||||||
| DIAL | 0.0036*** | 0.054*** | ||||||
| (0.0012) | (0.014) | |||||||
| Constant | 4.1371*** | −3.0093 | 4.3575*** | 0.3505 | 4.0661*** | −14.9404*** | 4.2461*** | −1.1848* |
| (0.043) | (2.995) | (0.043) | (0.697) | (0.031) | (3.381) | (0.044) | (0.656) | |
| Controls | YES | YES | YES | YES | YES | YES | YES | YES |
| Industry fixed effects | YES | YES | YES | YES | YES | YES | YES | YES |
| Year fixed effects | YES | YES | YES | YES | YES | YES | YES | YES |
| Cluster | Firm | Firm | Firm | Firm | Firm | Firm | Firm | Firm |
| N | 10,612 | 10,612 | 10,672 | 10,672 | 20,102 | 20,102 | 10,562 | 10,562 |
| Adj. R-squared | 0.165 | 0.182 | 0.191 | 0.173 | 0.154 | 0.147 | 0.198 | 0.145 |
| (9) | (10) | (11) | (12) | (13) | (14) | (15) | (16) | |
|---|---|---|---|---|---|---|---|---|
| 1st DONATION | 2nd ESG | 1st DONATION | 2nd ESG | 1st DONATION | 2nd ESG | 1st DONATION | 2nd ESG | |
| Panel B: social donation as explanatory variable | ||||||||
| DONATION | 0.2694 | 0.0961* | −0.6336** | 0.0972* | ||||
| (0.243) | (0.056) | (0.319) | (0.053) | |||||
| MIN | 0.017*** | 0.0711*** | ||||||
| (0.0037) | (0.011) | |||||||
| PMIN | 0.0111*** | 0.0110*** | ||||||
| (0.003) | (0.003) | |||||||
| DIAL | −0.0102** | −0.0045 | ||||||
| (0.005) | (0.006) | |||||||
| Constant | −8.4460*** | 4.0037* | −9.0470*** | 0.9406* | −11.0397*** | −5.8509 | −9.0215*** | 0.9361** |
| (0.676) | (2.049) | (0.69) | (0.496) | (0.562) | (3.606) | (0.706) | −0.468 | |
| Controls | YES | YES | YES | YES | YES | YES | YES | YES |
| Industry fixed effects | YES | YES | YES | YES | YES | YES | YES | YES |
| Year fixed effects | YES | YES | YES | YES | YES | YES | YES | YES |
| Cluster | Firm | Firm | Firm | Firm | Firm | Firm | Firm | Firm |
| N | 10,612 | 10,612 | 10,672 | 10,672 | 20,102 | 20,102 | 10,562 | 10,562 |
| Adj. R-squared | 0.167 | 0.112 | 0.169 | 0.157 | 0.18 | 0.149 | 0.169 | 0.121 |
Note(s): DIAL is the number of languages spoken in a province. NMIN is the number of ethnic groups. PMIN is the proportion of the minority population in the total population of the province. TRUST is the primary social trust variable. DONATION is the natural logarithm of the value of the total social donation by the firm. SOE is a dummy variable that is coded 1 for state-owned firms, and 0 otherwise. AGE is the natural logarithm of the age of the firm, SIZE is the natural logarithm of the market capitalization of the firm, LEV is the financial leverage, BETA is the systematic risk, ROA is the return on assets, TQ is the Tobin's Q ratio, MBV is the market-to-book equity ratio, CASH is the cash holding ratio, INSOWN is the institutional ownership, MGROWN is the managerial ownership, OWNCON is the ownership concentration of the five largest shareholders, INDR is the independent director ratio, AUDITOP is the audit opinion, AC is the number of analysts covering the firm and MKT is the marketization index. All the variables are explained in Section 3 and also summarized in Appendix 2. ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively
Source(s): Authors' own creation/work
Test of effectiveness: propensity score matching by firm characteristics
| Unmatched | Mean | %Reduct | t-test | V(T)/ | ||||
|---|---|---|---|---|---|---|---|---|
| Variable | Matched | Treated | Control | %Bias | |bias| | t | p > |t| | V(C) |
| TRUST | U | 3.263 | 3.2555 | 2.9 | 1.35 | 0.176 | 0.88* | |
| M | 3.263 | 3.2597 | 1.3 | 55.4 | 0.48 | 0.629 | 0.98 | |
| SOE | U | 0.51447 | 0.39249 | 24.7 | 11.97 | 0 | ||
| M | 0.51447 | 0.52424 | −2 | 92 | −0.71 | 0.476 | ||
| AGE | U | 2.6972 | 2.6638 | 7.9 | 3.77 | 0 | 0.95 | |
| M | 2.6972 | 2.714 | −4 | 49.5 | −1.52 | 0.128 | 1.12* | |
| SIZE | U | 23.433 | 22.373 | 72.2 | 32.03 | 0 | 0.62* | |
| M | 23.433 | 22.763 | 45.6 | 36.8 | 1.34 | 0 | 0.58* | |
| LEV | U | 0.26583 | 0.25223 | 4.6 | 2.23 | 0.026 | 1.05 | |
| M | 0.26583 | 0.26631 | −0.2 | 96.4 | −0.06 | 0.954 | 0.97 | |
| BETA | U | −0.35856 | −0.35177 | −1.4 | −0.71 | 0.477 | 1.14* | |
| M | −0.35856 | −0.35411 | −0.9 | 34.5 | −0.33 | 0.741 | 0.97 | |
| ROA | U | 0.05607 | 0.03142 | 31.9 | 14.39 | 0 | 0.69* | |
| M | 0.05607 | 0.05334 | 3.5 | 88.9 | 1.47 | 0.14 | 1.15* | |
| TQ | U | 1.8442 | 2.1679 | −27.8 | −12.42 | 0 | 0.64* | |
| M | 1.8442 | 1.8533 | −0.8 | 97.2 | −0.33 | 0.742 | 1.10* | |
| MBV | U | 0.1586 | 0.17834 | −15.1 | −6.81 | 0 | 0.69* | |
| M | 0.1586 | 0.15628 | 1.8 | 88.3 | 0.71 | 0.48 | 0.95 | |
| CASH | U | 0.48033 | 0.39245 | 37.7 | 18.61 | 0 | 1.15* | |
| M | 0.48033 | 0.48747 | −3.1 | 91.9 | −1.12 | 0.264 | 1.16* | |
| INSOWN | U | 0.09219 | 0.11564 | −12.7 | −5.91 | 0 | 0.82* | |
| M | 0.09219 | 0.08566 | 3.5 | 72.1 | 1.37 | 0.17 | 1.03 | |
| MGROWN | U | 0.56868 | 0.53253 | 22.5 | 11.11 | 0 | 1.16* | |
| M | 0.56868 | 0.57186 | −2 | 91.2 | −0.72 | 0.47 | 1.16* | |
| OWNCON | U | 0.31138 | 0.28006 | 19.7 | 9.11 | 0 | 0.81* | |
| M | 0.31138 | 0.30889 | 1.6 | 92.1 | 0.6 | 0.546 | 1 | |
| INDR | U | 2.9823 | 2.9257 | 12.5 | 4.92 | 0 | 0.20* | |
| M | 2.9823 | 2.9827 | −0.1 | 99.3 | −0.04 | 0.97 | 0.33* | |
| AUDITOP | U | 0.29838 | 0.13636 | 24.5 | 13.77 | 0 | 2.13* | |
| M | 0.29838 | 0.30815 | −1.5 | 94 | −0.46 | 0.645 | 0.98 | |
| AC | U | 8.0936 | 7.9788 | 5.9 | 2.87 | 0.004 | 1.04 | |
| M | 8.0936 | 8.0955 | −0.1 | 98.3 | −0.04 | 0.97 | 1.15* | |
| MKT | U | 8.0936 | 7.9788 | 5.9 | 2.87 | 0.004 | 1.04 | |
| M | 8.0936 | 8.0955 | −0.1 | 98.3 | −0.04 | 0.97 | 1.15* | |
Note(s): We conducted a balance test to ensure that the PSM matching is satisfactory. The test results show that the standardized deviation (% bias) of variables after matching significantly reduced. Moreover, the t-test results show that the differences between the treated and the control samples after matching are all insignificant. All these results indicate that the matching effect is effective. TRUST is the primary social trust variable. SOE is a dummy variable that is coded 1 for state-owned firms, and 0 otherwise. AGE is the natural logarithm of the age of the firm, SIZE is the natural logarithm of the market capitalization of the firm, LEV is the financial leverage, BETA is the systematic risk, ROA is the return on assets, TQ is the Tobin's Q ratio, MBV is the market-to-book equity ratio, CASH is the cash holding ratio, INSOWN is the institutional ownership, MGROWN is the managerial ownership, OWNCON is the ownership concentration of the five largest shareholders, INDR is the independent director ratio, AUDITOP is the audit opinion, AC is the number of analysts covering the firm and MKT is the marketization index. All variables are explained in Section 3 and also summarized in Appendix 2. ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively
Source(s): Authors' own creation/work
Robustness test of endogeneity: propensity score matching
| Variable | ESG | ESG |
|---|---|---|
| TRUST | 0.0918* | |
| (0.0516) | ||
| DONATION | 0.0331*** | |
| (0.0019) | ||
| SOE | 0.164*** | 0.168*** |
| (0.0300) | (0.0290) | |
| AGE | −0.111*** | −0.0902*** |
| (0.0305) | (0.0296) | |
| SIZE | 0.0996*** | 0.0811*** |
| (0.00923) | (0.0082) | |
| LEV | −0.0807* | −0.0621 |
| (0.0471) | (0.0455) | |
| BETA | 0.016 | 0.0215* |
| (0.012) | (0.0118) | |
| ROA | 2.002*** | 1.826*** |
| (0.128) | (0.1240) | |
| TQ | −0.135*** | −0.336*** |
| (0.0098) | (0.0343) | |
| MBV | 0.794*** | 0.235*** |
| (0.0757) | (0.0377) | |
| CASH | −0.106* | 0.838*** |
| (0.0604) | (0.0728) | |
| INSOWN | 0.195*** | −0.156*** |
| (0.0636) | (0.0569) | |
| MGROWN | 0.365*** | 0.173*** |
| (0.087) | (0.0602) | |
| OWNCON | 0.0527 | 0.348*** |
| (0.08) | (0.0817) | |
| INDR | 0.0948*** | 0.0178 |
| (0.0176) | −0.0764 | |
| AUDITOP | 0.0317 | 0.102*** |
| (0.0204) | (0.0173) | |
| AC | 0.0372*** | 0.011 |
| (0.00756) | (0.0185) | |
| MKT | 1.067*** | −0.014 |
| (0.2880) | (0.0010) | |
| Constant | 1.067*** | 2.041*** |
| (0.2880) | −0.218 | |
| Industry fixed effects | YES | YES |
| Year fixed effects | YES | YES |
| Cluster | Firm | Firm |
| Observations | 20,191 | 20,191 |
| R-squared | 0.231 | 0.27 |
Note(s): This table reports the propensity score matching (PSM) regression results of firm ESG score on its social trust score and social donation amount. In PSM, we calculate a propensity score for each firm, which is the conditional probability that the firm offers the social donation, given all the observable data. Then, we match each firm with a social donation to one control firm with the closest propensity score. Next, we repeat the baseline tests by using data on the donating firms and the matched controls. We perform the balance test to confirm the effectiveness of the matching, as shown in Table 7
Source(s): Authors' own creation/work
Parallel test: difference-in-differences (DID) approach
| Variable | Robust coefficient | SE | t | p > |t| | [95% conf | Interval] |
|---|---|---|---|---|---|---|
| Y2010 | 0.244 | 0.160 | 1.52 | 0.127 | 0.559 | 0.070 |
| Y2011 | 0.078 | 0.033 | 2.36 | 0.018 | 0.144 | 0.013 |
| Y2012 | 0.015 | 0.087 | 0.17 | 0.867 | 0.156 | 0.185 |
| Y2013 | 0.007 | 0.034 | 0.21 | 0.83 | 0.059 | 0.073 |
| Y2014 | 0.042 | 0.032 | 1.31 | 0.189 | 0.106 | 0.021 |
| Y2015 | 0.419 | 0.057 | 7.35 | 0 | 0.308 | 0.531 |
| Y2016 | 0.517 | 0.051 | 10.17 | 0 | 0.418 | 0.617 |
| Y2017 | 0.372 | 0.046 | 8.04 | 0 | 0.281 | 0.463 |
| SOE | 0.168 | 0.029 | 5.77 | 0 | 0.111 | 0.225 |
| AGE | −0.092 | 0.030 | −3.11 | 0.002 | −0.150 | −0.034 |
| SIZE | 0.084 | 0.008 | 10.09 | 0 | 0.068 | 0.100 |
| LEV | −0.059 | 0.046 | −1.29 | 0.198 | −0.148 | 0.031 |
| BETA | 0.021 | 0.012 | 1.73 | 0.084 | −0.003 | 0.044 |
| ROA | 1.848 | 0.124 | 14.89 | 0 | 1.604 | 2.091 |
| TQ | −0.338 | 0.035 | −9.75 | 0 | −0.406 | −0.270 |
| MBV | 0.238 | 0.038 | 6.25 | 0 | 0.163 | 0.312 |
| CASH | 0.843 | 0.073 | 11.52 | 0 | 0.699 | 0.986 |
| INSOWN | −0.152 | 0.057 | −2.67 | 0.008 | −0.264 | −0.040 |
| MGROWN | 0.174 | 0.060 | 2.88 | 0.004 | 0.056 | 0.292 |
| OWNCON | 0.350 | 0.082 | 4.27 | 0 | 0.189 | 0.510 |
| INDR | 0.026 | 0.077 | 0.34 | 0.733 | −0.124 | 0.176 |
| AUDITOP | 0.101 | 0.017 | 5.84 | 0 | 0.067 | 0.135 |
| AC | 0.013 | 0.019 | 0.68 | 0.495 | −0.024 | 0.049 |
| MKT | −0.014 | 0.010 | −1.38 | 0.167 | −0.033 | 0.006 |
| Constant | 1.980 | 0.221 | 8.96 | 0 | 1.547 | 2.413 |
Note(s): This table presents the results of the parallel test for the standard DID test regarding the effects of social trust on firm ESG. The results indicate the robust coefficient after the implementation of the social credit system becomes statistically significant, that is, in 2014. This finding illustrates the feasibility of the DID approach. The results of the regression approach reported in Table 9 show the dynamic impact of policy implementation over time.
Source(s): Authors' own creation/work
Robustness test of endogeneity: difference-in-differences (DID) approach
| ESG | ESG | |
|---|---|---|
| Variable | (1) | (2) |
| Treatment*Post2014 | 0.423*** | |
| (0.0324) | ||
| Treatment*D2010 | 0.244 | |
| (0.16) | ||
| Treatment*D2011 | 0.12 | |
| (0.0755) | ||
| Treatment*D2012 | 0.0146 | |
| (0.0869) | ||
| Treatment*D2013 | 0.00724 | |
| (0.0338) | ||
| Treatment*D2014 | 0.0424 | |
| (0.0323) | ||
| Treatment*D2015 | 0.419*** | |
| (0.057) | ||
| Treatment*D2016 | 0.517*** | |
| (0.0509) | ||
| Treatment*D2017 | 0.372*** | |
| (0.0463) | ||
| SOE | 0.169*** | 0.168*** |
| (0.0297) | (0.0291) | |
| AGE | −0.0924*** | −0.0922*** |
| (0.0302) | (0.0297) | |
| SIZE | 0.0928*** | 0.0839*** |
| (0.00891) | (0.00831) | |
| LEV | −0.0699 | −0.0588 |
| (0.0458) | (0.0457) | |
| BETA | 0.0256** | 0.0205* |
| (0.0119) | (0.0118) | |
| ROA | 1.918*** | 1.848*** |
| (0.126) | (0.124) | |
| TQ | −0.342*** | −0.338*** |
| −0.0346 | (0.0346) | |
| MBV | 0.241*** | 0.238*** |
| −0.038 | (0.038) | |
| CASH | 0.819*** | 0.843*** |
| −0.0745 | (0.0732) | |
| INSOWN | −0.135** | −0.152*** |
| (0.0583) | −0.057 | |
| MGROWN | 0.167*** | 0.174*** |
| (0.061) | (0.0602) | |
| OWNCON | 0.355*** | 0.350*** |
| (0.0841) | (0.0819) | |
| INDR | 0.0512 | 0.0262 |
| (0.0777) | (0.0765) | |
| AUDITOP | 0.0988*** | 0.101*** |
| (0.0175) | (0.0173) | |
| AC | 0.0164 | 0.0128 |
| (0.0193) | (0.0187) | |
| MKT | −0.0172* | −0.0138 |
| (0.00993) | −0.00997 | |
| Constant | 1.833*** | 1.980*** |
| (0.234) | (0.221) | |
| Industry FE | YES | YES |
| Year FE | YES | YES |
| Cluster | Firm | Firm |
| Observation | 20,191 | 20,191 |
| Adj. R-squared | 0.262 | 0.273 |
Note(s): This table reports the effect of social donation on social trust based on the DID approach. On June 14, 2014, the state council issued a circular that outlined the plan to construct a social credit system (2014–2020). This plan required the credit legislation to be completed as a crucial component of the national legislative system. This policy also mandated the establishment of a social credit blacklist system and a market exit mechanism. After the promulgation of the regulation, social trust improved rapidly throughout the nation. The implementation of this policy change has significantly affected social trust, but it has not substantially affected share pledging. The treatment group (Treat = 1) consists of firms whose social trust score increased the most from 2013 to 2015 (the top three deciles of the sample) after the regulatory change. D2010, D2011, D2012, D 2013, D2014, D2015, D2016 and D2017 are dummy variables equal to 1 if in the year 2010, 2011, 2012, 2013, 2014, 2015, 2016 and 2017, respectively. Column 1 reports the DID estimation. Column 2 presents the dynamic effects of the policy shock on social trust. Variable definitions are presented in the Appendix. We control for the year and industry fixed effects. Robust p-values, based on standard errors clustered at the firm level, are reported in parentheses. ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively
Source(s): Authors' own creation/work
Analysis of economic development as a potential mechanism through which social trust affects ESG
| (1) | (2) | (3) | |
|---|---|---|---|
| Variable | ESG | RGDP | ESG |
| Panel A: social trust as explanatory variable | |||
| TRUST | 0.0917* | 0.02463*** | 0.0726 |
| (0.0515) | (0.218) | (0.0515) | |
| RGDP | 0.00765*** | ||
| (0.00259) | |||
| Constant | 1.039*** | 10.05*** | 0.960*** |
| (0.288) | (1.152) | (0.292) | |
| Controls | YES | YES | YES |
| Industry fixed effects | YES | YES | YES |
| Year fixed effects | YES | YES | YES |
| Cluster | Firm | Firm | Firm |
| Observations | 20,192 | 21,837 | 20,192 |
| R-squared | 0.236 | 0.178 | 0.237 |
| (4) | (5) | (6) | |
|---|---|---|---|
| Variable | ESG | RGDP | ESG |
| Panel B: Social donation as explanatory variable | |||
| DONATION | 0.0346*** | 0.0346*** | 0.0511 |
| (0.00188) | (0.00188) | −0.0127 | |
| RGDP | 0.00819*** | ||
| (0.00245) | |||
| Constant | 1.740*** | 18.36*** | 1.587*** |
| (0.204) | (0.883) | (0.21) | |
| Controls | YES | YES | YES |
| Industry fixed effects | YES | YES | YES |
| Year fixed effects | YES | YES | YES |
| Cluster | Firm | Firm | Firm |
| Observations | 20,192 | 21,837 | 20,192 |
| R-squared | 0.262 | 0.069 | 0.264 |
Note(s): The dependent variable RGDP in Columns (2) and (5) represents the GDP per capita at the province level. TRUST is an indicator representing social trust at the province level. Columns (1) and (4) report our baseline findings from Table 3. Columns (7) and (10) report our findings from Table 4. Robust standard error clustered by firm and year are in parentheses. ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively
Source(s): Authors' own creation/work
Analysis of corporate governance as a potential mechanism through which social trust affects ESG
| (1) | (2) | (3) | |
|---|---|---|---|
| Variable | ESG | HINDEX | ESG |
| Panel A: social trust as explanatory variable | |||
| TRUST | 0.0917* | 0.1228** | 0.0931 |
| (0.0515) | (0.0577) | (0.05715) | |
| HINDEX | 0.211* | ||
| (0.108) | |||
| Constant | 1.039*** | −0.145*** | 1.078*** |
| (0.288) | (0.0249) | (0.289) | |
| Controls | YES | YES | YES |
| Industry fixed effects | YES | YES | YES |
| Year fixed effects | YES | YES | YES |
| Cluster | Firm | Firm | Firm |
| Observations | 20,192 | 21,581 | 20,189 |
| R-squared | 0.236 | 0.362 | 0.236 |
| (4) | (5) | (6) | |
|---|---|---|---|
| Variable | ESG | HINDEX | ESG |
| Panel B: social donation as explanatory variable | |||
| TRUST | 0.0346*** | 0.0345*** | 0.00476* |
| (0.00188) | (0.0018) | (0.0402) | |
| HINDEX | 0.181* | ||
| (0.1030) | |||
| Constant | 1.740*** | −0.164*** | 1.777*** |
| (0.204) | (0.0181) | (0.2050) | |
| Controls | YES | YES | YES |
| Industry fixed effects | YES | YES | YES |
| Year fixed effects | YES | YES | YES |
| Cluster | YES | YES | YES |
| Observations | YES | YES | YES |
| R-squared | 0.262 | 0.362 | 0.263 |
Note(s): The dependent variable HINDEX in Columns (2) and (5) represents the Herfindahl index which is calculated as the sum of the squares of shareholding percentage of several top shareholders. TRUST is an indicator representing social trust at the province level. Columns (1) and (4) report our baseline findings from Table 3. Columns (7) and (10) report our findings from Table 4. Robust standard errors clustered by firm and year are in parentheses. ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively
Source(s): Authors' own creation/work
Analysis of institutional quality as a potential mechanism through which social trust affects ESG
| (1) | (2) | (3) | |
|---|---|---|---|
| Variable | ESG | POLLVEST | ESG |
| TRUST | 0.0917* | 0.122*** | 0.0964* |
| (0.0515) | (0.0305) | (0.0515) | |
| POLLVEST | 0.0376* | ||
| (0.0227) | |||
| Constant | 1.039*** | 3.564*** | 1.173*** |
| (0.288) | (0.131) | (0.304) | |
| Controls | YES | YES | YES |
| Industry fixed effects | YES | YES | YES |
| Year fixed effects | YES | YES | YES |
| Cluster | Firm | Firm | Firm |
| Observations | 20,192 | 21,836 | 20,191 |
| R-squared | 0.236 | 0.45 | 0.236 |
| (4) | (5) | (6) | |
|---|---|---|---|
| Variable | ESG | POLLVEST | ESG |
| DONATION | 0.0346*** | −0.0819*** | 0.0142*** |
| (0.0019)) | (0.0013) | (0.0019) | |
| POLLVEST | 0.034*** | ||
| (0.0022) | |||
| Constant | 1.740*** | 3.973*** | 1.876*** |
| (0.2040) | (0.09320) | (0.2260) | |
| Controls | YES | YES | YES |
| Industry fixed effects | YES | YES | YES |
| Year fixed effects | YES | YES | YES |
| Cluster | Firm | Firm | Firm |
| Observations | 20,192 | 21,836 | 20,191 |
| R-squared | 0.262 | 0.449 | 0.263 |
Note(s): The dependent variable POLLVEST in Columns (2) and (5) represents the pollution control investment (100 million yuan) at province level. TRUST is an indicator representing the social trust at the province level. Columns (1) and (4) report our baseline findings from Table 3. Columns (7) and (10) report our findings from Table 4. Robust standard errors clustered by firm and year are in parentheses. ***, ** and *indicate statistical significance at the 1%, 5% and 10% levels, respectively
Heterogeneity test and subsample analysis
| Variable | SOE = 0 | SOE = 1 | SIZE > mean | SIZE < mean | LEV> mean | LEV < mean | ROA > mean | ROA > mean |
|---|---|---|---|---|---|---|---|---|
| Panel A: social trust as explanatory variable | ||||||||
| TRUST | 0.0747 | 0.109*** | 0.112* | 0.0601 | 0.176*** | 0.0917* | 0.0458*** | 0.0127* |
| (0.0702) | (0.0201) | (0.0634) | (0.0704) | (0.0523) | (0.0515) | (0.0036) | (0.069) | |
| Constant | 0.827* | 1.455*** | −2.132*** | 2.282*** | 0.999*** | 1.039*** | 0.463 | 1.366*** |
| (0.466) | (0.356) | (0.484) | (0.323) | (0.317) | (0.288) | (0.455) | (0.352) | |
| Controls | YES | YES | YES | YES | YES | YES | YES | YES |
| Industry fixed effects | YES | YES | YES | YES | YES | YES | YES | YES |
| Year fixed effects | YES | YES | YES | YES | YES | YES | YES | YES |
| Cluster | Firm | Firm | Firm | Firm | Firm | Firm | Firm | Firm |
| Observations | 11,941 | 8,250 | 11,372 | 8,851 | 10,924 | 9,271 | 11,377 | 8,847 |
| R-squared | 0.232 | 0.301 | 0.238 | 0.276 | 0.234 | 0.236 | 0.194 | 0.246 |
| Coefficient difference | ||||||||
| p-value | 0.032 | 0.002 | 0.012 | 0.023 | ||||
| Variable | ESG > Mean | ESG < Mean | OC>Mean | OC<Mean | RPI>Mean | RPI < Mean | Duality = 1 | Duality = 0 |
|---|---|---|---|---|---|---|---|---|
| TRUST | 0.0304*** | 0.016 | 0.0869 | 0.0343*** | 0.156** | 0.00217 | 0.163** | 0.0500 |
| (0.0021) | (0.0449) | (0.0748) | (0.00292) | (0.0634) | (0.0660) | (0.0700) | (0.0948) | |
| Constant | 2.775*** | 2.111*** | 1.424*** | 2.161*** | 0.627 | 1.330*** | 0.820** | 1.592*** |
| (0.314) | (0.214) | (0.506) | (0.374) | (0.533) | (0.355) | (0.346) | (0.509) | |
| Controls | YES | YES | YES | YES | YES | YES | YES | YES |
| Industry fixed effects | YES | YES | YES | YES | YES | YES | YES | YES |
| Year fixed effects | YES | YES | YES | YES | YES | YES | YES | YES |
| Cluster | Firm | Firm | Firm | Firm | Firm | Firm | Firm | Firm |
| Observations | 10,533 | 9,771 | 7,172 | 13,050 | 9,634 | 10,591 | 8,897 | 4,425 |
| R-squared | 0.3 | 0.28 | 0.227 | 0.251 | 0.185 | 0.278 | 0.253 | 0.332 |
| Coefficient difference | ||||||||
| p-value | 0.0037 | 0.014 | 0.036 | 0.047 | ||||
| Variable | SOE = 0 | SOE = 1 | SIZE > Mean | SIZE < Mean | LEV > Mean | LEV < Mean | ROA > Mean | ROA > Mean |
|---|---|---|---|---|---|---|---|---|
| Panel B: social donation as explanatory variable | ||||||||
| DONATION | 0.0338 | 0.0337*** | 0.0305*** | 0.0284*** | 0.0331*** | 0.0346*** | 0.0328*** | 0.0354*** |
| (0.70263) | (0.00268) | (0.00216) | (0.00322) | (0.00215) | (0.00188) | (0.00207) | (0.00317) | |
| Constant | 2.062*** | 1.602*** | −0.409 | 2.500*** | 1.933*** | 1.740*** | 1.346*** | 1.990*** |
| (0.247) | (0.34) | (0.428) | (0.229) | (0.247) | (0.204) | (0.336) | (0.246) | |
| Controls | YES | YES | YES | YES | YES | YES | YES | YES |
| Industry fixed effects | YES | YES | YES | YES | YES | YES | YES | YES |
| Year fixed effects | YES | YES | YES | YES | YES | YES | YES | YES |
| Cluster | Firm | Firm | Firm | Firm | Firm | Firm | Firm | Firm |
| Observations | 11,941 | 8,250 | 11,372 | 8,851 | 10,924 | 9,271 | 11,377 | 8,847 |
| R-squared | 0.253 | 0.329 | 0.264 | 0.286 | 0.257 | 0.262 | 0.226 | 0.263 |
| Coefficient difference | ||||||||
| p-value | 0.0023 | 0.0002 | 0.038 | 0.075 | ||||
| Variable | ESG > Mean | ESG < Mean | OC>Mean | OC<Mean | RPI>Mean | RPI < Mean | Duality = 1 | Duality = 0 |
|---|---|---|---|---|---|---|---|---|
| DONATION | 0.0169*** | 0.00523** | 0.0914 | 0.0350*** | 0.0321*** | 0.0356*** | 0.0407*** | 0.0358*** |
| (0.00131) | (0.00246) | (0.0611) | (0.00234) | (0.00252) | (0.00241) | (0.00384) | (0.00208) | |
| Constant | 2.161*** | 1.591*** | 2.124*** | 1.603*** | 2.058*** | 1.720*** | ||
| (0.374) | (0.221) | (0.436) | (0.251) | (0.387) | (0.239) | |||
| Controls | YES | YES | YES | YES | YES | YES | YES | YES |
| Industry fixed effects | YES | YES | YES | YES | YES | YES | YES | YES |
| Year fixed effects | YES | YES | YES | YES | YES | YES | YES | YES |
| Cluster | Firm | Firm | Firm | Firm | Firm | Firm | Firm | Firm |
| Observations | 10,533 | 9,771 | 7,172 | 13,050 | 9,634 | 10,591 | 4,425 | 15,721 |
| R-squared | 0.322 | 0.28 | 0.253 | 0.278 | 0.211 | 0.303 | 0.363 | 0.282 |
| Coefficient difference | ||||||||
| p-value | 0.025 | 0.018 | 0.024 | 0.036 | ||||
Note(s): This table reports the regression results for the impact of social trust on firms' ESG. We perform subsample analyses by incorporating additional controls and using alternative measures of social donation. The p-value of coefficient difference is derived through the test of seemingly unrelated regression. The Overconfidence index (OC) is a metric that encompasses factors such as gender, age, education and duality status in its computation. Executive's risk preference (RPI) which is assessed via six indicators, including asset proportions, debt equity, profitability, earnings, financing and capital spending ratios. Duality presence results in a score of 1 for the firm, while its absence is assigned a score of 0. Robust standard errors clustered by firm and year are in parentheses. ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively
Source(s): Authors’ own creation/work
Description of variables
| Variable | Description |
|---|---|
| ESG | Environmental, social and governance (ESG) rating from Huazheng |
| AAA rating corresponds to 9 for ESG score | |
| AA rating corresponds to 8 for ESG score | |
| A rating corresponds to 7 for ESG score | |
| BBB rating corresponds to 6 for ESG score | |
| BB rating corresponds to 5 for ESG score | |
| B rating corresponds to 4 for ESG score | |
| CCC rating corresponds to 3 for ESG score | |
| CC rating corresponds to 2 for ESG score | |
| C rating corresponds to 1 for ESG score | |
| TRUST | Social trust measured based on a survey conducted by Peking University and government statistical institutions |
| DONATION | Natural logarithm of total value of social donations by individual organization |
| SOE | Nature of equity; a dummy variable that takes the value of 1 for state-owned firms and 0 otherwise |
| AGE | Firm's age, measured as the natural logarithm of the age of the firm since its inception year |
| SIZE | Firm size, measured as the natural logarithm of the market value of the firm at the beginning of the year |
| LEV | Leverage, calculated as the ratio of interest-bearing liabilities to total assets of the firm at the beginning of the year |
| BETA | Systematic risk (market risk) measured by the beta at the beginning of the year |
| ROA | Return on assets at the beginning of the year |
| TQ | Tobin's Q, calculated as the market value of tradable shares to total assets at the beginning of the year |
| MBV | Ratio of the market value to the book value of equity at the beginning of the year |
| CASH | Cash holding ratio, calculated as the sum of cash and cash equivalents to total assets at the beginning of the year |
| INSOWN | Institutional ownership, calculated as the ratio of total institutionally owned shares to total shares outstanding at the beginning of the year |
| MGROWN | Ratio of shares owned by managers to total tradable shares at the beginning of the year |
| OWNCON | Ownership concentration of five largest shareholders at the beginning of the year |
| INDR | Number of independent directors divided by the total number of directors of the firm at the beginning of the year |
| RGDP (10,000RMB) | Regional GDP per capita at the beginning of the year |
| AC | Number of analysts covering the firm every year |
| AUDITOP | Type of audit opinion |
| if audit opinion = “Unqualified Opinion with Emp”, then AUDITOP = 1 if audit opinion = “Adverse Opinion”, then AUDITOP = 2 if audit opinion = “Standard Unqualified Opinion”, then AUDITOP = 3 if audit opinion = “Disclaimer of Opinion”, then AUDITOP = 4 if audit opinion = “Qualified Opinion with Empha”, then AUDITOP = 5 if audit opinion = “Qualified Opinion”, then AUDITOP = 6 | |
| MKT | Marketization index |
| POLLVEST | Pollution control investment (100 million yuan) at province-level |
| HINDEX | Herfindahl index; sum of the squares of shareholding percentage of several top shareholders |
Source(s): Authors' own creation/work
Descriptive statistics of social trust variable at provincial level
| Social trust | Social donation | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Province | N | Mean | SD | Minimum | Maximum | Mean | SD | Minimum | Maximum |
| Beijing | 558 | 3.799 | 1.209 | 0.000 | 3.211 | 1.635 | 2.746 | 0 | 13.122 |
| Tianjin | 1894 | 3.281 | 0.168 | 3.064 | 3.515 | 2.510 | 5.472 | 0 | 16.933 |
| Shanghai | 336 | 3.113 | 0.383 | 2.510 | 3.571 | 1.674 | 4.612 | 0 | 16.933 |
| Chongqing | 1739 | 3.205 | 0.239 | 2.812 | 3.513 | 1.868 | 4.736 | 0 | 16.933 |
| Hebei | 465 | 3.315 | 0.289 | 2.821 | 3.610 | 2.027 | 5.075 | 0 | 16.933 |
| Shanxi | 274 | 3.228 | 0.172 | 2.995 | 3.498 | 1.965 | 4.945 | 0 | 16.836 |
| Liaoning | 608 | 3.171 | 0.268 | 2.783 | 3.484 | 0.902 | 3.429 | 0 | 16.933 |
| Jilin | 352 | 3.210 | 0.513 | 2.033 | 3.931 | 0.669 | 3.002 | 0 | 15.996 |
| Heilongjiang | 266 | 3.175 | 0.308 | 2.677 | 3.534 | 0.490 | 2.398 | 0 | 15.856 |
| Jiangsu | 2108 | 3.257 | 0.172 | 2.997 | 3.455 | 1.565 | 4.391 | 0 | 16.793 |
| Zhejiang | 2289 | 3.273 | 0.234 | 2.890 | 3.497 | 1.462 | 4.230 | 0 | 16.933 |
| Anhui | 647 | 3.406 | 0.218 | 3.086 | 3.714 | 1.242 | 3.912 | 0 | 16.530 |
| Fujian | 801 | 3.206 | 0.120 | 3.023 | 3.357 | 3.258 | 5.864 | 0 | 16.933 |
| Jiangxi | 304 | 3.244 | 0.188 | 2.968 | 3.455 | 2.127 | 5.060 | 0 | 16.836 |
| Shandong | 1290 | 3.443 | 0.144 | 3.246 | 3.633 | 1.330 | 4.131 | 0 | 16.933 |
| Henan | 552 | 3.394 | 0.183 | 3.187 | 3.765 | 2.374 | 5.089 | 0 | 16.933 |
| Hubei | 758 | 3.345 | 0.363 | 2.697 | 3.706 | 1.463 | 4.291 | 0 | 16.933 |
| Hunan | 645 | 3.212 | 0.215 | 2.884 | 3.625 | 1.368 | 4.194 | 0 | 16.660 |
| Guangdong | 3375 | 3.097 | 0.244 | 2.723 | 3.445 | 1.792 | 4.691 | 0 | 16.933 |
| Hainan | 230 | 3.365 | 0.070 | 3.246 | 3.410 | 1.309 | 4.055 | 0 | 15.425 |
| Sichuan | 1129 | 3.381 | 0.216 | 3.037 | 3.700 | 1.496 | 4.363 | 0 | 16.933 |
| Guizhou | 228 | 2.062 | 0.086 | 1.944 | 2.175 | 0.796 | 2.474 | 0 | 10.139 |
| Yunan | 248 | 3.368 | 0.266 | 3.004 | 3.699 | 4.132 | 6.543 | 0 | 16.836 |
| Shaanxi | 335 | 3.269 | 0.287 | 2.785 | 3.578 | 0.623 | 2.825 | 0 | 15.607 |
| Gansu | 225 | 3.547 | 0.262 | 3.194 | 3.960 | 1.561 | 4.447 | 0 | 15.463 |
| Qinghai | 93 | 3.300 | 0.298 | 2.851 | 3.680 | 3.225 | 6.057 | 0 | 16.933 |
| Neimenggu | 202 | 3.437 | 0.239 | 3.033 | 3.765 | 1.385 | 4.141 | 0 | 16.933 |
| Guangxi | 455 | 3.209 | 0.256 | 2.777 | 3.492 | 1.137 | 3.773 | 0 | 16.836 |
| Xizang | 95 | 3.064 | 0.045 | 3.051 | 3.211 | 2.616 | 5.525 | 0 | 16.836 |
| Ningxia | 102 | 3.400 | 0.161 | 3.223 | 3.590 | 0.733 | 2.967 | 0 | 14.080 |
| Xinjiang | 350 | 3.104 | 0.647 | 2.730 | 4.707 | 1.862 | 4.720 | 0 | 16.933 |
Note(s): This table presents the descriptive of social trust and social donations at the provincial level. Social trust measures how participants in society evaluate the trustworthiness of their counterparts. Social donation is the natural logarithm of the total value of social donations by a firm
Source(s): Authors' own creation/work
Notes
External corporate governance is operationalized by the level of analyst coverage. Analysts play a crucial role in shaping management decisions, and a higher degree of analyst coverage indicates more effective monitoring and oversight of management practices (Hou and Liu, 2020).
The audit opinion serves as a proxy for assessing the quality of financial reporting and capturing the level of information asymmetry. According to Biddle et al. (2009), a higher level of financial reporting quality reduces information asymmetry between the firm and external sources of capital.
These indices are original from “The report of provincial marketization index in China” (Wang et al., 2017) and it has been widely used in the literature (Hu et al., 2021; Huang and Lei, 2021).
The differences in the means of all control variables are not significant after the PSM, although most of them exhibit significant differences before the authors perform the PSM procedure. The authors do not report the results of the effectiveness check of PSM for brevity.
The scoring criteria based on manager attributes are as follows:
Gender: female managers are assigned a score of 0, while male managers are assigned a score of 1.
Age: the age score is calculated by taking the difference between the maximum age and the average age, divided by the age range. That is:
age score = (maximum age - average age)/age range
Education: managers with academic attainment below the threshold of a bachelor's degree are assigned a score of 1, otherwise, they are assigned a score of 0.
Duality status: If duality exists in the managerial team, the firm is assigned a score of 1. If not, the firm is assigned a score of 0.
The authors take the average of the scores from the four aspects mentioned above as the final aggregate score. That is:
aggregate score = (gender score + age score + education score + duality score)/4
The risk preference level of executives was evaluated using six indicators: the proportion of risk assets to total assets, the debt-to-equity ratio, the core profitability ratio, the retained earnings ratio, the self-financing ratio and the capital expenditure ratio. Among these, the core profitability ratio, retained earnings ratio and self-financing ratio were utilized as reverse indicators. In this study, a positive transformation was applied to reverse indicators by adding a negative sign to their data.
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Acknowledgements
The authors thank the support provided by the National Natural Science Foundation of China (NSFC) [Grant No. 72173072] and National Social Science Foundation (NSSF) [Grant No. 21BJL034].