Can the Relative ESG Gap Reduce Managerial Myopia? A Study Based on the “Tunnel Effect”

Abstract

ESG concepts are regarded as a feasible method for enterprises to obtain a sustainable competitive advantage, but whether enterprises can spontaneously and proactively implement ESG concepts is the key to the sustainable development of ESG. Focusing on listed companies with relatively slow ESG performance and carrying out investigations from the perspective of the “tunnel effect”, this study explores whether the relative ESG gap can motivate listed companies to learn and compete in ESG and whether this behavior can alleviate managerial myopia and achieve the long-term development of the company. This study reports that there is a “tunnel effect” in the relative ESG gap in listed companies with relatively lagging ESG performance; this should motivate such listed companies to improve their ESG performance and reduce managerial myopia through voluntary disclosure. Further results show that there is a “tunnel effect” in the relative ESG gap in both state-owned and non-state-owned listed companies. The learning behavior is more obvious in listed state-owned companies, and the reduction in management myopia via ESG performance is also stronger in such companies. This study expands the research on the influence of ESG on corporate behavioral decision making in the context of sustainable development, and it also provides useful references for reducing managerial myopia from the perspective of inter-firm learning competition.

1. Introduction

Promoting sustainable development requires enterprises to uphold long-term development concepts. As the helmsmen of enterprises, managers’ business philosophy and behavior play a decisive role in the development of enterprises. Viewing the development of an enterprise from a strategic perspective and being keenly aware of market changes are essential qualities and abilities for excellent managers. The upper echelons theory states that a manager’s cognitive ability, perception ability, value orientation, and other characteristics will determine their behavior and strategic choices, thereby affecting the company’s goals, behaviors, and results [1], which are closely related to the development potential and competitive ability of the enterprise; thus, the demographic characteristics of managers, such as their age, educational background, employment experience, etc., have always been the focus of scholars. As the concept of sustainable development is deeply rooted in the hearts of the people, long-term development and sustainable growth have become important concepts of corporate management. Managers’ adherence to the long-term development concept is essential in order for enterprises to resist external environmental threats and continue to create value. However, not all managers are able to be far-sighted. Managerial myopia is a very common phenomenon that has existed for a long time in many enterprises [2], and this trait can lead to a low continuity of enterprise development strategy [3], making it difficult to achieve long-term development. ESG (environment, social, and governance) is an investment concept and corporate evaluation standard that focuses on corporate environmental, social, and governance performance rather than financial performance, and it is regarded as a new path for achieving corporate sustainable development in the current period; ESG-related issues have become a trending topic for scholars’ research. For investors, it has become a new value investment concept used to examine the enterprise’s ability to cope with risks, long-term development ability, and investable potential from the perspective of ESG; for enterprises, ESG is also a more advanced, reasonable, holistic, and comprehensive development concept and governance idea. Although expenditures on ESG in the short term reduce the profits that managers obtain every year, they help increase corporate value in the long term [4,5]. As shareholders become more and more actively involved [6], the positive role of ESG in daily corporate operations and value creation has become increasingly prominent, and this has been confirmed by the research of many scholars [7,8]. Although existing research has carried out some discussions on the two topics of ESG and managerial myopia, they are mostly independent studies of the two. ESG-related studies have mostly focused on its impact on business operations and financial status, and studies on managerial myopia have focused on analyzing its economic consequences, such as on corporate innovation [9,10], long-term investment [11], etc. There has been insufficient discussion about the ESG development concept on the trait of managerial myopia, and even fewer scholars have examined the role of ESG performance in reducing managerial myopia from the perspective of inter-firm learning competition, which is the intention and research entry point of this study.

Focusing on listed companies with relatively lagging ESG performance, our main goal is to explore whether ESG learning and the competition of such listed companies can have a significant impact on management characteristics. The research questions in this study include analyzing and testing whether the relative ESG gap can promote the voluntary disclosure of information by companies and whether the voluntary disclosure of information can achieve the purpose of improving ESG performance and thereby reduce managerial myopia.

2. The Literature Review and Research Hypotheses

2.1. The Impact of Relative ESG Gap on Corporate Information Disclosure Behavior

As the public’s awareness of sustainable development continues to increase, it has gradually become a social consensus for companies to assume social responsibilities and protect the environment. ESG has become the focus of corporate operations and development, and it has become an important part of corporate management. Zumente and Bistrova reported that companies are becoming more aware of ESG and noticing the positive role of ESG in their daily operations and value creation [12], which will help promote the companies’ competitiveness and learning behaviors when practicing ESG. According to the theory of benchmarking management, companies establish industry-leading benchmark companies for their learning and catch-up goals. By continuously comparing the gap between themselves and the benchmark enterprises in certain aspects, they can obtain information that can help and realize their own development and adopt similar behaviors. Therefore, companies with ESG awareness will continue to pay attention to and compare the ESG performance gap between themselves and benchmark companies and then take corresponding actions. Levy-Garboua and Montmarquette argue that the gap between individuals will affect their behavior, which is conducive to stimulating the learning ability of relatively disadvantaged individuals, thereby improving their situation; that is, there is a “tunnel effect” in the relative gap between individuals [13]. The concept of the “tunnel effect” was first proposed by Hirschman and Rothschild [14] and used to explain individual behavioral decisions during traffic jams via an analogy: When people in a tunnel drive in two lanes toward the same direction and encounter a traffic jam, if one lane starts to move and the other lane is still blocked, people in the blocked lane will consider the movement of the other lane as a positive signal, indicating that the lane they are in will also move soon. The meaning of the tunnel effect is that an individual’s utility and behavior are affected by both their current situation and their expectations. This concept was later introduced into the field of economics, describing how relatively disadvantaged individuals tend to predict their own development based on the rapid development of the relatively advantaged individuals; the disadvantaged individuals tend to develop in the same way. Scholars have found that the positive effects of the “tunnel effect” are particularly evident in developing countries and transition economies [15]. This is because when individuals face an uncertain future in a rapidly changing environment, they tend to seek information from other individuals around them in order to form expectations for future development [16]. According to the “tunnel effect”, companies will notice that industry-leading benchmark companies exhibit strong ESG performance and partially attribute the success of such companies to their good ESG performance, which enhances their expectations regarding the positive role of practicing ESG. This cognition stimulates them to learn, imitate, and compete, leading to the proactive disclosure of ESG-related information in order to pursue business development [17].

The “tunnel effect” states that the relative gap stimulates the motivation of enterprises to learn and compete in practicing ESG: that is, the “good” effect produced by the relative ESG gap, but the relative ESG gap may also have a “bad” effect—the “superstar effect”. Brown argues that internal competition does not necessarily motivate individual effort, and the consequences of competition depend on the magnitude of the relative gap between individuals [18]. A large gap is likely to demotivate individuals who are behind, which will result in a reduction in effort or even withdrawal from the competition, while the advantaged individuals maintain their lead effortlessly. Therefore, when there is a “superstar effect” in ESG learning and competition, the relatively disadvantaged companies may think that it is difficult for them to narrow the gap with other companies through learning and competition, and there is difficulty in highlighting the positive effect of practicing; thus, they will give up promoting their development by improving ESG performance. Contrary to the “tunnel effect”, the “superstar effect” reduces the vigor of ESG learning and competition, and the relatively disadvantaged companies do not consider that improving ESG performance is a feasible direction for corporate development; thus, they are more inclined to passively disclose ESG-related information. Accordingly, the following hypotheses are proposed:

Hypothesis 1a.

There is a “tunnel effect” in the relative ESG gap, and companies that are relatively behind in ESG performance are more willing to disclose ESG-related information.

Hypothesis 1b.

There is a “superstar effect” in the relative ESG gap, and companies that are relatively lagging in ESG performance are less willing to disclose ESG-related information.

2.2. The Impact of Willingness to Disclose Information on Corporate ESG Performance

ESG disclosure can be divided into “hard disclosure”, which is objective and clear, verifiable, and difficult to fabricate and imitate, and “soft disclosure”, which is difficult to verify and lacking in substance. Only “hard disclosure” can send positive signals to the outside world and reduce information asymmetry. Companies with a strong willingness to disclose tend to disclose their ESG-related information through independent reports, such as sustainable development reports, social responsibility reports, and ESG development reports, which is a proactive, strategic communication-driven behavior that provides more reliable information and data, and the quality of information disclosure is higher [19]. It is undeniable that proactive information disclosure requires increased costs, which is why managers tend not to disclose or only participate in mandatory disclosures. However, when managers realize that ESG plays a positive role in a company’s daily operations and value creation, voluntary disclosure is no longer a simple resource-consuming behavior, but a necessary part of corporate operating activities. Taking environmental performance in ESG as an example, Hora and Subramanian found that companies voluntarily disclosing their efforts to protect the environment not only reflect their concern for the environment but also improve their environmental performance and demonstrate their legal advantages in complying with environmental laws and regulations [20]. It can be observed that when companies regard their ESG performance as an intangible asset that maintains relationships between stakeholders, their willingness to disclose will be stronger and they will disclose more effective information related to ESG, which helps rating agencies evaluate their ESG performance according to the established evaluation system, thereby improving ratings or scores. Although companies may have the motivation of “greenwashing” [20], ambiguous language and non-quantitative information do not enhance information quality, making it difficult to realize the purpose of “greenwashing”. Moreover, such practices require substantial resources and capabilities [21], making them impractical for most companies. Based on the above analysis, the following hypothesis is proposed:

Hypothesis 2.

A company’s willingness to disclose information has a positive effect on improving its ESG performance.

2.3. ESG Performance and Managerial Myopia

Managerial myopia originates from the time orientation theory of social psychology, which means that managers have a short decision-making horizon. Rather than focusing on the future development of the enterprise, managers are more inclined to focus on the interests that can be satisfied immediately. Managerial myopia is usually considered to be an innate, stable personal trait and subconscious process [22], which is also interfered with by external factors [23]. In particular, in the case of the separation of powers, there is serious information asymmetry between business owners and managers, causing managers’ behavior to be more affected by factors other than their characteristics. When Fama proposed the reputation model to solve the principal–agent problem, he pointed out that the automatic mechanism of the manager’s market value created the concept of “Ex Post Settling Up” [24]. He believed that in a competitive market, a manager’s market value depends on their past operating performance. In the case of the separation of two powers, professional managers, the actual controllers of enterprise operations, are constrained by the market’s competition mechanism and will focus on the short-term operating performance of the enterprise, exhibiting short-sighted behavior. The existing literature suggests that managers take advantage of information asymmetry and choose short-term investment solutions that can generate quick profits rather than making strategic decisions from a long-term perspective, even sacrificing the long-term value of the company [11], due to considerations of their own status, compensation, and reputation [25,26], as well as market pressures, short-term institutional investor preferences, analyst tracking, and the frequency of financial report disclosure [27,28,29]. It can be seen that the requirements from stakeholders for the short-term performance of enterprises can also lead to managerial myopia. When managers’ short-term performance pressure is reduced, if stakeholders can tolerate short-term failures and encourage future achievements, their myopic behavior will decrease [28,29,30]. Schuster et al. studied S&P 1500 companies and found that founder-led companies were less likely to engage in short-sighted behavior than non-founder-led companies [2]. Companies led by founders will not pursue short-term profit goals and take actions that are detrimental to the long-term value of the company. This is partly due to the fact that they have less pressure on short-term performance and can focus on the long-term development of the company. Therefore, managerial myopia results in part from conflicts of interest and principal–agent problems.

For the owners of a company, their purpose is to increase wealth, pursue maximizing shareholder wealth, and hope that the company can continue to bring them expected returns. When there is information asymmetry and the owners are unable to determine whether the goals of the managers are consistent with their own goals, a principal–agent problem occurs. A company with good ESG performance exhibits the managers’ willingness to operate steadily and pursue long-term development, comprehensively and truly reflects the development status of the company, and reduces the information asymmetry between owners and actual controllers, thereby alleviating managerial myopia caused by principal–agent problems. In addition, good ESG performance not only attracts investors [31] but also reduces the pressure of short-term corporate performance requirements from market investors. Cao et al. found in their study of the U.S. stock market that when ESG factors are taken into account, even though stocks have negative excess returns, they will still be favored by institutional investors [32]. Bai et al. also observed similar evidence in China’s capital market [7]. They reported that institutional investors have ESG investment preferences. When corporate ESG performance is good, institutional investors’ tolerance for short-term performance downturns increases significantly. Therefore, the good ESG performance of companies is an intangible asset that maintains the relationship between stakeholders. It can not only alleviate the principal–agent problem between shareholders and managers caused by information asymmetry but also help obtain financial resources from investors with ESG preferences and reduce their demand for short-term performance, thus effectively reducing managerial myopia. Based on the above analysis, the hypothesis is proposed as follows:

Hypothesis 3.

The good ESG performance of companies can reduce managerial myopia.

3. Research Design

3.1. Variable Selection and Calculation

3.1.1. Explained Variables

Willingness to disclose announcements (D): The willingness to disclose corporate social responsibility announcements in the China Stock Market Accounting Research (CSMAR) database was selected as one of the explained variables in this study. The data included in the CSMAR social responsibility database come from the annual social responsibility reports, environmental reports, sustainable development reports, corporate citizenship reports, and social responsibility contents disclosed in the annual reports of listed companies. The data sources are authoritative and stable and can present the real ESG data of listed companies completely and objectively. The specific data structure, indicator descriptions, reference regulations, etc., are described in the instruction manual of the Social Responsibility Research Database of Chinese Listed Companies in the CSMAR database (20220126).

The ESG change (I) indicates the increment of the ESG performance of listed companies from period t − 1 to period t.

Managerial myopia (myopia): Based on Brochet et al.’s study [33], we used text analysis and machine learning techniques to identify Chinese “short-term perspective” word sets in the annual reports of Chinese A-share listed companies; then, lexicographic methods are used to construct managerial myopia indicators. Hu et al. verified that this index effectively captures the inherent short-termism of managers through benchmarking, internal consistency reliability analyses, difference analyses, and economic consequence tests [11].

3.1.2. Explanatory Variables

Relative ESG gap (Gap): This study refers to Li’s measurement method of relative income distance [34], and it measures the relative distance gap of ESG performance within the industry according to the classification of enterprises via industry sector in the Guidelines for Industry Classification of Listed Companies (revised 2012) issued by the China Securities Regulatory Commission. Formula (1) describes the calculation method:

$${\mathrm{g}\mathrm{a}\mathrm{p}}_{\mathrm{i},\mathrm{t}}={\mathrm{E}\mathrm{S}\mathrm{G}}_{\mathrm{i},\mathrm{t}}-\frac{1}{\mathrm{n}-1}{\sum }_{\mathrm{j}\ne \mathrm{i}}^{\mathrm{n}}{\mathrm{E}\mathrm{S}\mathrm{G}}_{\mathrm{j},\mathrm{t}}$$

(1)

where n is the total number of companies in the industry sector to which company i belongs. There are two reasons for this measurement: first, individuals usually do not include themselves in the calculation of the average level when compared with other individuals and only consider the gap between themselves and the average level of other individuals; second, when excluding the ESG performance of target enterprises from the calculation of the reference value of the same industry, different target enterprises’ reference values are different, which better reflects the target enterprises’ subjective perception of their ESG.

The meaning of the specific value of the gap varies among companies. For companies with relatively leading ESG performance, the gap is positive; the larger the value—which indicates that the ESG performance of the company surpasses the subjective industry reference value—the larger the relative gap. For companies that are behind in ESG performance, the gap is negative; the smaller the value—which indicates that the ESG performance of the company is behind its subjective industry reference value—the larger the relative gap. In order to unify the expression, the absolute value of the gap is taken to obtain the Gap, and the larger the value, the larger the relative gap.

ESG performance (lnESG): This study selected the “social responsibility score” issued by Hexun, a Chinese ESG rating agency, and used the natural logarithm of the score. According to its professional evaluation system for social responsibility reports, Hexun evaluates the ESG performance of listed companies in Shanghai and Shenzhen exchanges with respect to the environmental, social, and corporate governance dimensions.

3.1.3. Control Variables

In terms of company characteristics, this study uses company age (lnAge), company size (lnSize), management size (lnEx), two positions in one (CEO), and equity balance to control for differences in company establishment, company size, management characteristics, and ownership structure. It is widely recognized that long-established and large-scale companies have accumulated extensive experience, held a certain market share, and often exhibited higher enterprise value compared with new and small-scale enterprises. The size of the management team influences decision-making efficiency and stability, thereby impacting the enterprise’s performance. The separation of the CEO and board chair positions is generally considered a more effective corporate governance structure, and proper equity balance is also conducive to corporate development; thus, it is also necessary to control for this. In terms of financial information, this study uses the asset–liability ratio (LEV), equity multiplier (EM), and return on assets (ROA) as control variables because the company’s debt level, financial leverage, and performance will have an important impact on the company. In addition, the long-term growth capability of a company is one of the important aspects that concern investors in the market; thus, this study uses the sustainable growth rate (Growth) to control the difference in the long-term growth capability of a company. Institutional investors usually pursue the concept of value investment and have stronger motivation to pay attention to the long-term value of the company; moreover, as the capital market’s investment wind vane, an increase or decrease in shareholding also has an important impact on the company. Therefore, this study selects institutional investors’ shareholding ratio (Hold) as one of the control variables.

The managerial myopia indicator in this study was obtained via the textual analysis of MD&A sections in the annual reports of listed companies. When managers disclose information in annual reports, they are motivated to report information strategically; that is, they will hide or obscure adverse news through lengthy report content. In order to eliminate the impact of cheap talk in the text on the managerial myopia indicator, we referred to the research of Hu et al. and further used a text readability indicator (Readability) as a control variable [11]. We used the text readability index construction method adopted by Shin et al. [35] under the assumption of naive Bayes: That is, it is assumed that the sentences in the text are independent of each other, and the order of the words in the sentence is considered. The mean value of the log likelihood of the product of the probability of generating each sentence in the text is used as the readability measure of this text. This indicator is constructed using a deep learning algorithm, and the process is as follows.

First, each word is represented as a vector using the word embedding method, and words with similar semantics are represented as the same vector in the vector space. Secondly, the optimization ideas of Hierarchical SoftMax and Negative Sampling are used to calculate the generation probability of sentences. Finally, the logarithmic mean of the product of individual sentence generation probabilities is used as the readability measure of the document. Formula (2) describes the calculation:

$$\mathrm{R}\mathrm{e}\mathrm{a}\mathrm{d}\mathrm{a}\mathrm{b}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{y}=\frac{1}{\mathrm{N}}{\sum }_{\mathrm{s}=1}^{\mathrm{N}}\mathrm{l}\mathrm{o}\mathrm{g}{\mathrm{P}}_{\mathrm{s}}$$

(2)

Among them, ${\mathrm{P}}_{\mathrm{s}}$ represents the probability of generating sentence s, and N represents the number of sentences that constitute the text. The higher the value, the higher the frequency of the collocation sequence of word pairs in the text in the corpus. On the contrary, this means that the text is more difficult to understand, and the readability of the text is worse.

Table 1. Variable definition.

Variable Type	Variable Symbol	Variable Definitions
Explained variable	${\mathrm{D}}_{\mathrm{i},\mathrm{t}}$	Willingness to disclose announcements, which is a binary logic value: take the value of 1 when listed companies voluntarily disclose social responsibility-related information and take 0 when disclosure is required by regulations.
	${\mathrm{I}}_{\mathrm{i},\mathrm{t}}$	ESG change, which is a binary logic value: when the increment of the listed company’s ESG performance from period t − 1 to period t is positive, it takes 1; otherwise, it takes 0.
	${\mathrm{m}\mathrm{y}\mathrm{o}\mathrm{p}\mathrm{i}\mathrm{a}}_{\mathrm{i},\mathrm{t}}$	Managerial myopia.
Explanatory variables	${\mathrm{l}\mathrm{n}\mathrm{E}\mathrm{S}\mathrm{G}}_{\mathrm{i},\mathrm{t}}$	ESG performance: select the social responsibility score of Hexun and take the natural logarithm.
	${\mathrm{G}\mathrm{a}\mathrm{p}}_{\mathrm{i},\mathrm{t}}$	Relative ESG gap: that is, the absolute value of the relative distance between the ESG performance of listed companies in the industry.
Control variables	$\mathrm{l}\mathrm{n}{\mathrm{A}\mathrm{g}\mathrm{e}}_{\mathrm{i},\mathrm{t}}$	Company age: that is, the natural logarithm of the difference between the statistical ending year and the company’s founding year.
	$\mathrm{l}\mathrm{n}{\mathrm{S}\mathrm{i}\mathrm{z}\mathrm{e}}_{\mathrm{i},\mathrm{t}}$	Company size: that is, the natural logarithm of the total assets of the company at the end of the year.
	${\mathrm{R}\mathrm{O}\mathrm{A}}_{\mathrm{i},\mathrm{t}}$	Return on assets: that is, net profit/average total assets, average total assets = total assets ending balance
	${\mathrm{L}\mathrm{E}\mathrm{V}}_{\mathrm{i},\mathrm{t}}$	Asset–liability ratio: that is, total liabilities/total assets.
	${\mathrm{E}\mathrm{M}}_{\mathrm{i},\mathrm{t}}$	Equity multiplier: that is, total assets/total owner’s equity.
	$\mathrm{l}\mathrm{n}{\mathrm{E}\mathrm{x}}_{\mathrm{i},\mathrm{t}}$	The natural logarithm of the number of directors, supervisors, and senior managers.
	${\mathrm{C}\mathrm{E}\mathrm{O}}_{\mathrm{i},\mathrm{t}}$	Two positions in one, which is a binary logic value: when the CEO is also the chairman, the value is 1; otherwise, it is 0.
	${\mathrm{H}\mathrm{o}\mathrm{l}\mathrm{d}}_{\mathrm{i},\mathrm{t}}$	Institutional investor shareholding ratio.
	${\mathrm{Growth}}_{\mathrm{i},\mathrm{t}}$	Sustainable growth rate: that is, return on net assets * income retention rate/(1 − return on net assets * income retention rate).
	${\mathrm{R}\mathrm{e}\mathrm{a}\mathrm{d}\mathrm{a}\mathrm{b}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{y}}_{\mathrm{i},\mathrm{t}}$	Text readability: a measure of how easily MD&A text can be understood.
Other variables	${\mathrm{g}\mathrm{a}\mathrm{p}}_{\mathrm{i},\mathrm{t}}$	Relative ESG distance: relative gap = listed company i score − industry score mean (excluding company i)
	$\mathrm{C}\mathrm{H}\_{\mathrm{E}\mathrm{S}\mathrm{G}}_{\mathrm{i},\mathrm{t}}$	ESG increment, which is the increment of ESG score from period t − 1 to period t
	${\mathrm{O}\mathrm{S}}_{\mathrm{i},\mathrm{t}}$	Ownership nature: take 1 for state-owned and state-controlled; otherwise, take 0.
	${\mathrm{I}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}\mathrm{C}\mathrm{o}\mathrm{d}\mathrm{e}}_{\mathrm{i},\mathrm{t}}$	Industry classification code.
	${\mathrm{P}\mathrm{r}\mathrm{o}\mathrm{v}\mathrm{i}\mathrm{n}\mathrm{c}\mathrm{e}}_{\mathrm{i},\mathrm{t}}$	The province to which the listed company’s registered address belongs.

provides the definition of each variable.

3.2. Model Design

First, a panel logit model was constructed to examine the impact of the relative ESG gap on the willingness to disclose in order to test whether there is a “tunnel effect” or a “superstar effect” with respect to the relative ESG gap. Model (3) was constructed as follows:

$${\mathrm{D}}_{\mathrm{i},\mathrm{t}}={\mathsf{\alpha }}_1+{\mathsf{\beta }}_1{\mathrm{G}\mathrm{a}\mathrm{p}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\delta }}_1{\mathrm{C}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\eta }}_{\mathrm{i}}+{\mathsf{\sigma }}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

$${\mathrm{D}}_{\mathrm{i},\mathrm{t}}=\left\{\begin{array}{c}1, \mathrm{Voluntary} \mathrm{disclosure}\\ 0, \mathrm{regulatory} \mathrm{disclosure}\end{array}\right.$$

(3)

where ${\mathrm{D}}_{\mathrm{i},\mathrm{t}}$ is the announcement disclosure willingness, with 1 denoting voluntary disclosure and 0 denoting regulatory disclosure. ${\mathrm{C}}_{\mathrm{i},\mathrm{t}}$ is the set of control variables, and the detailed variable definitions are shown in Table 1.

We divided the sample into two categories, relatively leading and relatively lagging, according to whether or not the relative distance gap was greater than zero, respectively, and introduced them into model (3). According to the previous analysis, both the “tunnel effect” and “superstar effect” describe the learning activity of the companies with relatively lagging ESG performance. For leading companies, regardless of their motivation to disclose information related to corporate social responsibility, it is not related to these two effects. Therefore, in the test results of the model (3) on the samples of lagging companies in ESG performance, the “tunnel effect” requires the regression coefficient ${\mathsf{\beta }}_1$ of the samples of relatively lagging companies in order to be significantly positive, while the “superstar effect” requires the regression coefficient ${\mathsf{\beta }}_1$ of relatively lagging companies to be significantly negative. If ${\mathsf{\beta }}_1$ is statistically insignificant, it indicates that neither effect exists.

Secondly, model (4) was constructed to explore whether a company’s willingness to disclose can enhance its ESG performance, and it was combined with model (3) to determine whether the relative ESG gap can be improved by stimulating the company’s willingness to disclose. We constructed a panel logit model to test whether a company’s willingness to disclose announcements can increase the likelihood of positive changes in its ESG performance. Model (4) was set as follows:

$${\mathrm{I}}_{\mathrm{i},\mathrm{t}}={\mathsf{\alpha }}_2+{\mathsf{\beta }}_2{\mathrm{D}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\delta }}_2{\mathrm{C}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\eta }}_{\mathrm{i}}+{\mathsf{\sigma }}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

$${\mathrm{I}}_{\mathrm{i},\mathrm{t}}=\left\{\begin{array}{c}1,\mathrm{CH}\_{\mathrm{E}\mathrm{S}\mathrm{G}}_{\mathrm{i},\mathrm{t}}>0\\ 0,\mathrm{CH}\_{\mathrm{E}\mathrm{S}\mathrm{G}}_{\mathrm{i},\mathrm{t}}\le 0\end{array}\right.$$

(4)

Here, ${\mathrm{I}}_{\mathrm{i},\mathrm{t}}$ is the change in ESG, with a value of 1 indicating that the company’s ESG performance has improved and vice versa; $\mathrm{C}\mathrm{H}\_{\mathrm{E}\mathrm{S}\mathrm{G}}_{\mathrm{i},\mathrm{t}}$ is the increment of ESG performance from period t − 1 to period t. The previous analysis points out that companies tend to voluntarily disclose relevant information when they wish to improve their ESG performance through rating agencies. Therefore, whether they are leading companies or lagging companies, the willingness to disclose should have a positive impact on the improvement in ESG performance, and no heterogeneity is expected.

Finally, model (5) was constructed to examine the impact of ESG performance on managerial myopia in the sample of listed companies with relatively lagging ESG performance:

$${\mathrm{m}\mathrm{y}\mathrm{o}\mathrm{p}\mathrm{i}\mathrm{a}}_{\mathrm{i},\mathrm{t}}={\mathsf{\alpha }}_1+{\mathsf{\beta }}_1{\mathrm{l}\mathrm{n}\mathrm{E}\mathrm{S}\mathrm{G}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\delta }}_1{\mathrm{C}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\eta }}_{\mathrm{i}}+{\mathsf{\sigma }}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

(5)

where ${\mathrm{m}\mathrm{y}\mathrm{o}\mathrm{p}\mathrm{i}\mathrm{a}}_{\mathrm{i},\mathrm{t}}$ represents managerial myopia. $\mathrm{l}\mathrm{n}{\mathrm{E}\mathrm{S}\mathrm{G}}_{\mathrm{i},\mathrm{t}}$ represents the company’s ESG performance.

3.3. Data Sources and Descriptive Statistics

This study selected A-share companies listed in the Shanghai and Shenzhen Stock Exchanges from 2011 to 2021, excluding listed companies with substantial missing financial data and ESG scores, finally obtaining 3400 companies. The ESG scores of listed companies are the social responsibility scores of listed companies published by Hexun (https://www.hexun.com/), and the relative ESG gaps were obtained by the author according to the previous calculation method. The basic information, financial data, and announcement disclosure willingness of listed companies were obtained from the CSMAR database (all variable data sources are listed in

Table A1. Variable data source.

Variable Symbol	Variable Date Source
${\mathrm{m}\mathrm{y}\mathrm{o}\mathrm{p}\mathrm{i}\mathrm{a}}_{\mathrm{i},\mathrm{t}}$	WinGo financial text data platform. http://www.wingodata.com (accessed on 2 February 2024)
${\mathrm{R}\mathrm{e}\mathrm{a}\mathrm{d}\mathrm{a}\mathrm{b}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{y}}_{\mathrm{i},\mathrm{t}}$
${\mathrm{l}\mathrm{n}\mathrm{E}\mathrm{S}\mathrm{G}}_{\mathrm{i},\mathrm{t}}$	Hexun. https://www.hexun.com/ (accessed on 30 June 2022)
${\mathrm{D}}_{\mathrm{i},\mathrm{t}}$	China Stock Market Accounting Research database. https://data.csmar.com/ (accessed on 2 February 2024)
$\mathrm{l}\mathrm{n}{\mathrm{A}\mathrm{g}\mathrm{e}}_{\mathrm{i},\mathrm{t}}$
$\mathrm{l}\mathrm{n}{\mathrm{S}\mathrm{i}\mathrm{z}\mathrm{e}}_{\mathrm{i},\mathrm{t}}$
${\mathrm{R}\mathrm{O}\mathrm{A}}_{\mathrm{i},\mathrm{t}}$
${\mathrm{L}\mathrm{E}\mathrm{V}}_{\mathrm{i},\mathrm{t}}$
${\mathrm{E}\mathrm{M}}_{\mathrm{i},\mathrm{t}}$
$\mathrm{l}\mathrm{n}{\mathrm{E}\mathrm{x}}_{\mathrm{i},\mathrm{t}}$
${\mathrm{C}\mathrm{E}\mathrm{O}}_{\mathrm{i},\mathrm{t}}$
${\mathrm{H}\mathrm{o}\mathrm{l}\mathrm{d}}_{\mathrm{i},\mathrm{t}}$
${\mathrm{Growth}}_{\mathrm{i},\mathrm{t}}$
${\mathrm{O}\mathrm{S}}_{\mathrm{i},\mathrm{t}}$
${\mathrm{I}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}\mathrm{C}\mathrm{o}\mathrm{d}\mathrm{e}}_{\mathrm{i},\mathrm{t}}$
${\mathrm{P}\mathrm{r}\mathrm{o}\mathrm{v}\mathrm{i}\mathrm{n}\mathrm{c}\mathrm{e}}_{\mathrm{i},\mathrm{t}}$
$\mathrm{C}\mathrm{H}\_{\mathrm{E}\mathrm{S}\mathrm{G}}_{\mathrm{i},\mathrm{t}}$	Obtained according to the calculation method in the text.
${\mathrm{I}}_{\mathrm{i},\mathrm{t}}$
${\mathrm{g}\mathrm{a}\mathrm{p}}_{\mathrm{i},\mathrm{t}}$
${\mathrm{G}\mathrm{a}\mathrm{p}}_{\mathrm{i},\mathrm{t}}$

, Appendix A). In order to avoid data instability and differences in variable dimensions, we took the natural logarithm for all variables in the original data except the ratio variables and binary logistic values. The descriptive statistics of each variable are presented in

Table 2. Descriptive statistics.

Variable	Definition	Observations	Mean	Standard Deviation	Minimum Value	Maximum Value
myopia	Managerial myopia	22,106	0.0834	0.0752	0	0.8550
D	Willingness to disclose	22,106	0.8725	0.3335	0	1
I	ESG changes	22,106	0.5045	0.5000	0	1
ESG	ESG performance	22,106	23.2468	13.1455	0.0100	90.8700
lnESG	ESG performance	22,106	2.9616	0.7422	−4.6052	4.5094
Gap	Relative ESG distance absolute value	22,106	8.4666	9.3967	0.0001	67.6211
Age	Company age	22,106	18.2025	5.8529	3	120
lnAge	Company age	22,106	2.8470	0.3427	1.0986	4.7875
Size	Company size	22,106	160.6213	707.3416	0.0308	27,329.9994
lnSize	Company size	22,106	3.7952	1.3400	−3.4790	10.2157
ROA	Return on assets	22,106	0.0771	0.3039	−31.0623	17.1989
LEV	Asset–liability ratio	22,106	0.4196	0.2041	0.0084	1.5454
EM	Equity multiplier	22,106	2.1739	5.0260	−339.1706	417.2532
Ex	Management size	22,106	16.0982	3.5840	7	40
lnEx	Management size	22,106	2.7556	0.2124	1.9459	3.6889
CEO	Two positions in one	22,106	0.2791	0.4486	0	1
Hold	Institutional investor holdings	22,106	42.9702	24.6143	0.0001	101.1401
Growth	Sustainable growth rate	22,106	0.0602	0.6583	−14.7790	91.8506
gap	Relative ESG distance	22,106	1.6681	12.5380	−44.1241	67.6211
CH_ESG	ESG incremental	22,106	−0.7154	13.4699	−69.9800	69.1900
OS	Nature of ownership	22,106	0.3847	0.4865	0	1
Readability	Text readability	22,106	−24.2800	5.1754	−147.4657	−9.0835
MI_lnESG	The average ESG performance of listed companies in the same industry	22,106	2.9606	0.2189	1.1631	4.1701
MR_lnESG	The average ESG performance of listed companies in the same registration location	22,106	2.9610	0.3089	−3.9120	4.5025

The reason why the shareholding ratio of institutional investors is greater than 100% is because the number of non-tradable shares held exceeds the number of tradable shares.

, from which it can be observed that the mean value of lnAge is 2.8470 and the mean value of Size is 3.7952; that is, the average duration of listed companies is approximately 18 years, and the average asset size is approximately CNY 16 billion, indicating that the listed companies studied in this study are relatively mature and representative. The variables LEV, ROA, equity multiplier (EM), and sustainable growth rate (Growth), which are related to the financial status of listed companies, differ greatly from the mean value in both the maximum and minimum values, which indicates that the operating conditions vary greatly between different companies, and it is necessary to control the differences in these aspects during the research process.

4. Empirical Results and Discussion

4.1. The Impact of the Relative ESG Gap on Willingness to Disclose

Table 3. The impact of the relative ESG gap on willingness to disclose.

Variable	Relatively Lagging Listed Companies (Gap < 0)		Relatively Leading Listed Companies (Gap > 0)
	D	D	D	D
Gap	0.0687 ***	0.0687 ***	−0.0381 ***	−0.0381 ***
	[0.0227]	(0.0247)	[0.0063]	(0.0076)
lnAge	−4.4683	−4.4683	−2.5114 *	−2.5114
	[3.2071]	(5.4893)	[1.5240]	(2.9365)
lnSize	−4.3924 ***	−4.3924 ***	−4.5332 ***	−4.5332 ***
	[0.6473]	(0.9507)	[0.4383]	(0.8535)
LEV	0.4115	0.4115	2.4007	2.4007
	[2.1828]	(2.3518)	[1.4659]	(2.2568)
ROA	6.5555	6.5555	4.3225 **	4.3225
	[4.3002]	(5.2096)	[2.1185]	(3.2677)
EM	0.0428	0.0428	0.1058	0.1058
	[0.2218]	(0.2017)	[0.1862]	(0.3175)
lnEx	2.1184 *	2.1184	−1.2394	−1.2394
	[1.2116]	(1.4867)	[0.8577]	(0.9444)
Hold	−0.0257 *	−0.0257	0.0198 *	0.0198
	[0.0150]	(0.0185)	[0.0116]	(0.0174)
CEO	0.4965	0.4965	−0.0992	−0.0992
	[0.4771]	(0.6567)	[0.3229]	(0.4031)
Growth	−3.6311	−3.6311	−5.5600 **	−5.5600
	[4.9910]	(5.3918)	[2.1970]	(4.1112)
Readability	−0.0505	−0.0505	−0.0103	−0.0103
	[0.0377]	(0.0345)	[0.0235]	(0.0320)
Individual effects	Yes	Yes	Yes	Yes
Time effects	Yes	Yes	Yes	Yes
Observations	10,344	10,344	11,762	11,762
Pseudo.R2	0.3102	0.3102	0.2828	0.2828

***, **, and * indicate significance at 1%, 5%, and 10%, respectively. In parentheses, the robust estimated standard errors using the Bootstrap method are listed, and the number of sampling times is 50. When using the Bootstrap method for robust estimation, the randomness of sampling results in differences in the standard errors of each estimate, so the non-robust estimation standard errors are given in brackets.

shows the estimation results of the relative ESG gap of listed companies on their willingness to disclose. Columns (1) and (2) are the estimation results of listed companies with relatively lagging ESG performance. It can be observed that the estimated coefficient of variable Gap is significantly positive among listed companies with relatively lagging ESG performance, indicating that in such listed companies, the greater the ESG performance and its subjective industry reference value, the stronger their willingness to disclose and the more inclined they are to voluntarily disclose ESG-related information; Hypothesis 1a holds in these cases. On the contrary, the estimated coefficient of variable Gap is significantly negative at the 1% level for listed companies with relatively leading ESG performance; this indicates that among such listed companies, the more their ESG performance is in the lead within the industry, the weaker their willingness to disclose, and the more likely they will disclose information according to regulations. The completely different estimation results of relatively leading listed companies further confirm Hypothesis 1a: that is, the relative ESG gap can only stimulate the learning and imitation behavior of listed companies with relatively lagging ESG performance, and there is a “tunnel effect” in the relative ESG gap.

Our findings are similar to the research conclusion of Cao et al. [36], which examined the reaction of companies to the CSR practices of other companies in the same industry, and they reported that companies in the same industry adopt similar actions: that is, there is a “peer effect”. The difference is that we only find that listed companies with relatively low ESG performance show a stronger willingness to disclose because of the gap between themselves and the relatively leading listed companies: that is, only listed companies with relatively lagging ESG performance exhibit the “learning from the leaders” behavior, and they do not exhibit the “peer effect” behavior of the two companies imitating each other.

The “tunnel effect” of the relative ESG gap stimulates the internal motivation of companies to practice the ESG framework, which is conducive to the sustainable development of listed Chinese companies. Due to the ESG framework, listed companies are no longer a simple “profit machine”, but they transform into entities that enhance social welfare, promote environmental protection, and take into account the interests of stakeholders.

4.2. The Impact of Willingness to Disclose on Enhancing ESG Performance

Table 4. The impact of willingness to disclose on enhancing ESG performance.

Variable	Relatively Backward Listed Companies (Gap < 0)	Relatively Leading Listed Companies (Gap > 0)
	I	I
D	0.6872 ***	0.3432 ***
	(0.2572)	(0.1310)
lnAge	−0.184	−0.9905 **
	(0.8327)	(0.4277)
lnSize	−0.4950 ***	−0.2814 ***
	(0.1107)	(0.1034)
LEV	0.5906	−0.0964
	(0.3935)	(0.5627)
ROA	5.8692 **	13.5543 ***
	(2.3977)	(1.0514)
EM	0.0774 *	−0.054
	(0.0463)	(0.0894)
lnEx	0.4304	−0.5010
	(0.2651)	(0.3144)
Hold	0.0069 *	0.0041
	(0.0039)	(0.0044)
CEO	−0.1163	0.1456
	(0.0897)	(0.1111)
Growth	0.0291	−0.4107 ***
	(0.0703)	(0.0631)
Readability	−0.0133	0.0221 **
	(0.0087)	(0.0087)
Individual effects	Yes	Yes
Time effects	Yes	Yes
Observations	10,344	11,762
Pseudo.R2	0.0618	0.0963

***, **, and * indicate significance at 1%, 5%, and 10%, respectively. In parentheses, the robust estimated standard errors using the Bootstrap method are listed, and the number of sampling times is 50.

shows the impact of corporate disclosure willingness on ESG performance. As observed from the estimation results, the estimated coefficient of variable D is significantly positive at the 1% level for both the sample of listed companies with relatively leading and relatively lagging ESG performance, indicating that the stronger the willingness of listed companies to disclose ESG-related information, the greater the possibility of their ESG performance improvement; moreover, Hypothesis 2 holds. Our conclusion is consistent with the current research: that is, proactive disclosure of information can improve its performance, and the level of information disclosure is positively related to its performance [20]. Different from the perspective of our research, Liu found that greater quantitative ESG disclosure, especially disclosure on environmental and social pillars, results in a greater divergence of ESG ratings [37], which also shows that information disclosure is related to the ESG performance of listed companies.

The results in Table 4 show that for listed companies with relatively leading ESG performance, the strategy of actively disclosing ESG-related information to improve ESG performance is also effective for such listed companies. Combined with the results in Table 3, it can further prove that the “tunnel effect” of the relative ESG gap only exists in the sample of relatively lagging listed companies. Because improving ESG performance by disclosing information is an optional but unimplemented strategy for companies with leading ESG performance, they will not learn from lagging companies; that is, there is no “tunnel effect” in this type of sample.

4.3. The Impact of ESG Performance on Managerial Myopia

Table 5. The impact of ESG performance on managerial myopia.

Variable	Myopia	Myopia
lnESG	−0.0060 ***	−0.0045 ***
	(0.0012)	(0.0015)
L.myopia		0.0309
		(0.0198)
lnAge	0.0159	0.0463 **
	(0.0166)	(0.0233)
lnSize	−0.0068 **	−0.0053
	(0.0029)	(0.0035)
LEV	0.0257 **	0.0239 *
	(0.0108)	(0.0131)
ROA	−0.0051 **	−0.0124 *
	(0.0024)	(0.0074)
EM	0.0001	0.0000
	(0.0002)	(0.0002)
lnEx	−0.0108	−0.0118
	(0.0084)	(0.0095)
Hold	−0.0002 *	−0.0002
	(0.0001)	(0.0001)
CEO	0.0010	0.0038
	(0.0029)	(0.0034)
Growth	0.0018 ***	−0.0028
	(0.0004)	(0.0034)
Readability	0.0003	0.0002
	(0.0002)	(0.0003)
Individual effects	Yes	Yes
Time effects	Yes	Yes
Observations	10,344	9372
Adj.R2	4.70%	4.79%

***, **, and * indicate significance at the 1%, 5%, and 10%, respectively. Robust estimated standard errors are shown in parentheses. Although we controlled individual and time effects in model (5) and some variables that may affect managerial myopia, this reduced the endogeneity problem caused by omitted variables. However, there may also be endogeneity problems caused by mutual causation between the explained variables and the explanatory variables. To solve this problem, we used instrumental variable (IV) estimations.

reports the impact of ESG performance on managerial myopia for relatively lagging listed companies. As observed in column (1), the estimated coefficient of the lnESG is significantly negative at the 1% level, indicating that the better the ESG performance, the lower the managerial myopia. In order to avoid the interference of the possible trend of the explained variable on the results, we add the lag term of the explained variable to the model. The results are shown in column (2). The estimated coefficient of lnESG is still significantly negative, indicating that the conclusion of this study is not affected by the possible trend of the explained variable. The above results all indicate that the good ESG performance of listed companies can reduce managerial myopia, and Hypothesis 3 holds. Our conclusion is consistent with Zhang et al. [38]. In addition, although there are differences in our research on the causal relationship between ESG performance and managerial myopia, Lu et al. and Fan et al. also believe that the two are negatively correlated [39,40].

Combining the results in Table 5 with Table 3 and Table 4, it can be concluded that among listed companies with relatively lagging ESG performance, there is a “tunnel effect” in the relative ESG gap, which can stimulate the willingness of listed companies to disclose, and voluntary disclosure can improve the ESG performance of such companies, thereby reducing managerial myopia.

Lin et al. pointed out that if the endogeneity problem exists only at the firm level, the industry/regional mean can be used as an instrumental variable for firm-level explanatory variables [41]. Referring to the research of Bai et al. [7], we used the average ESG performance of listed companies in the same industry (MI_lnESG) and the average ESG performance of listed companies in the same registration place (MR_lnESG) as instrumental variables for the explanatory variables, respectively. The average ESG performance of listed companies is affected by common factors such as the economic environment and the degree of marketization, and specifying the influence that managerial myopia exhibited in the MD&A of a single company’s annual report is difficult; this satisfies the requirements of correlations and the exogeneity of instrumental variables.

Table 6. Two-stage least-squares estimation results.

Variable	Same Industry		Same Place of Registration
	The First Stage	The Second Stage	The First Stage	The Second Stage
	lnESG	Myopia	lnESG	Myopia
lnESG		−0.0175 **		−0.0109 ***
		(0.0074)		(0.0039)
MI_lnESG	0.8702 ***
	(0.0833)
MR_lnESG			0.7638 ***
			(0.0425)
Control variables	Control	Control	Control	Control
Individual effects	Yes	Yes	Yes	Yes
Time effects	Yes	Yes	Yes	Yes
Observations	9878	9878	9878	9878
Kleibergen–Paap rk LM statistic		90.569		77.552
		p = 0.000		p = 0.000
Cragg–Donald Wald F statistic		109.229		323.346
		p = 0.000		p = 0.000

*** and ** indicate significance at 1% and 5%, respectively. The robust estimated standard errors are in parentheses.

shows the results of the two-stage least-squares estimation of model (5). In the first stage of the regression results, instrumental variables MI_lnESG and MR_lnESG and explanatory variable lnESG are both significantly positive, which indicates that the instrumental variables meet the correlation requirements. In the second stage of regression, the estimated coefficients of the variable lnESG are all significantly negative, and the model passes the under-identification and weak identification tests (LM statistic and Wald F statistic are both greater than the critical value), which indicates that this part of the conclusion still holds after the endogeneity problem is alleviated.

4.4. Robustness Test

4.4.1. The Tunnel Effect of the Relative ESG Gap

Model (3) was designed to test whether there is a “tunnel effect” in the relative ESG gap by examining the impact of the relative ESG gap on their willingness to disclose among listed companies with relatively lagging ESG performance. To ensure the robustness of the above findings, we further designed the test strategy to exclude alternative hypotheses. When there is a tunnel effect in the relative ESG gap, after the third-party organization discloses the ESG evaluation of listed companies in the current period, the listed companies with relatively lagging performance will be more willing to disclose information, and their positive disclosure behavior will be reflected in the ESG performance during the next period. Hence, for the listed companies with relatively lagging ESG performance in the industry, the relative ESG gap in the current period should have a positive impact on their ESG performance during the next period. Based on this analysis, after dividing the sample by L.gap, which indicates the one-period lag of the relative ESG gap, we examine whether L.Gap can significantly enhance the ESG performance of listed companies in the current period.

If the previous conclusions are reliable, in the sample of listed companies with relatively lagging ESG performance in the previous period, the coefficient of L.Gap should be significantly positive, and in the sample of listed companies with relatively leading ESG performance in the previous period, the coefficient of L.Gap should show heterogeneity.

Table 7. Robustness test of the tunnel effect.

Sample Division Criteria	L.gap < 0	L.gap > 0
Variable	lnESG	lnESG
L.Gap	0.0065 ***	−0.0005
	(0.0019)	(0.0006)
Control variables	Control	Control
Observations	8423	9421
Individual effects	Yes	Yes
Time effects	Yes	Yes
Adj.R2	0.2727	0.2256

*** indicate significance at 1%. The robust estimated standard errors are in parentheses.

shows the test results of the above empirical strategy. It can be observed that the estimated coefficient of the variable L.Gap is significantly positive at the 1% level in the sample of listed companies with relatively lagging ESG performance in the previous period, while the estimated coefficient of the variable L.Gap is insignificant in the sample of listed companies with relatively leading ESG performance in the previous period, indicating that only listed companies with relatively lagging ESG performance in the industry have the motivation to improve their ESG performance; moreover, there is a “tunnel effect” in the relative ESG gap. The previous conclusions remain robust.

4.4.2. The Impact of Willingness to Disclose on Enhancing ESG Performance

Model (4), using the panel logit model, shows that willingness to disclose can increase the likelihood of ESG performance improvements in listed companies and accordingly concludes that there is a promotion effect of willingness to disclose on ESG performance. To ensure the robustness of this conclusion, we now change the test method and use a linear regression model to test whether D is positively related to CH_ESG.

Table 8. Robustness test of the impact of willingness to disclose on enhancing ESG performance.

Sample Division Criteria	Gap < 0	Gap > 0
Variable	CH_ESG	CH_ESG
D	9.5853 ***	1.8245 **
	(1.4068)	(0.8836)
Control variables	Control	Control
Observations	8423	9421
Individual effects	Yes	Yes
Time effects	Yes	Yes
Adj.R2	7.17%	17.05%

*** and ** indicate significance at 1% and 5%, respectively. The robust estimated standard errors are shown in parentheses.

presents the estimation results, from which it can be observed that the stronger the willingness to disclose, the greater the positive change in ESG performance for both relatively leading and relatively lagging ESG performers, which is consistent with previous findings.

4.4.3. The Impact of ESG Performance on Managerial Myopia

The results of model (5) show that, among listed companies with relatively lagging ESG performance, ESG performance can reduce managerial myopia. If this conclusion is robust, the following results can be considered to be true: For listed companies with relatively lagging ESG performance, improving their ESG performance can narrow the gap between their position and the industry reference values. Therefore, Gap and myopia should show a positive correlation.

Table 9. Robustness test of the impact of ESG performance on managerial myopia.

Relatively Lagging Listed Companies (Gap < 0)
Variable	Myopia
Gap	0.0010 ***
	(0.0002)
Control variables	Control
Observations	10,344
Individual effects	Yes
Time effects	Yes
Adj.R2	0.0483

*** indicate significance at 1%. The robust estimated standard errors are shown in parentheses.

shows the results of the above empirical strategy. It can be observed that in the sample of listed companies with relatively lagging ESG performances, the coefficient of Gap is significantly positive at the 1% level, indicating that listed companies with relatively lagging ESG performance are shrinking when compared with the industry reference value; this can reduce managerial short-termism. This conclusion is consistent with the previous study, and the conclusion of this study is robust.

5. Further Research

Listed Chinese enterprises can be classified into state-owned enterprises (SOEs) and non-state-owned enterprises (non-SOEs) according to the nature of their ownership. These two types of companies differ greatly in terms of their business objectives, management style, and organizational structure—in addition to environmental protection, social responsibility, and corporate governance aspects—and the public expects different ESG performances from them. Taking these factors into consideration, we classify listed companies with relatively lagging ESG performance according to the nature of their ownership and investigate whether there is heterogeneity among such companies.

As observed in

Table 10. Property rights heterogeneity test.

Variable	SOEs			Non-SOEs
	Model (3)	Model (4)	Model (5)	Model (3)	Model (4)	Model (5)
	D	CH_ESG	Myopia	D	CH_ESG	Myopia
Gap	0.0977 **			0.0658 *
	[0.0427]			[0.0375]
D		10.3771 ***			6.0104 ***
		(1.9485)			(2.1461)
lnESG			−0.0069 ***			−0.0049 ***
			(0.0020)			(0.0016)
Control variables	Control	Control	Control	Control	Control	Control
Observations	4158	4158	4158	6186	6186	6186
Individual effects	Yes	Yes	Yes	Yes	Yes	Yes
Time effects	Yes	Yes	Yes	Yes	Yes	Yes
Adj.R2/Pseudo.R2	0.4019	0.1544	0.0636	0.3274	0.0658	0.0356

***, **, and * indicate significance at 1%, 5%, and 10%, respectively. Robust estimated standard errors are shown in parentheses. The robust estimated standard errors obtained using the Bootstrap method are shown in brackets, and the number of sampling times is 500.

, regardless of whether it is a listed state-owned or non-state-owned company, the estimated coefficients of Gap and D in model (3) and model (4) are significantly positive: that is, there is no difference between SOEs and non-SOEs. The reason for this is that companies seek their own sustainable development operations. For lagging companies, compared with promoting their own development through complete innovation and new methods, learning from the successful experience of benchmark companies is undoubtedly less costly, yielding faster results with lower risks, and they may even be able to overtake others by having the advantage of latecomers. Therefore, as long as there is a favorable opportunity for the development of the company, the enterprise will be motivated to learn and imitate leaders; thus, there is a “tunnel effect” among companies that are lagging behind in terms of ESG performance. In addition, as SOEs pursue the maximization of economic and social benefits, their ESG awareness is stronger, and the “tunnel effect” of the relative ESG gap is also stronger (0.0977 > 0.0658).

The estimated coefficients of the variable lnESG in model (5) are all significantly negative, and the difference is only reflected in the size of the coefficient values. This is mainly due to the special status of the managers of SOEs. The chairmen of SOEs are generally appointed and serve for a relatively short period of time. Their promotion is not only related to the business performance of the company but also related to non-performance factors, such as undertaking social responsibility and accomplishing political tasks, which gives the executives of SOEs the dual identities of “officials” and “professional managers”. The “professional manager” status tends to direct managers of SOEs toward short-term achievements, and myopia caused by the principal–agent problem is serious; in contrast, the “official” status renders managers of SOEs subordinate to national strategies. Therefore, under the government’s sustainable development strategy, the management style and business philosophy of SOEs change more rapidly than those of non-SOEs, and ESG performance has a greater impact on managerial myopia (0.0069 > 0.0049).

6. Conclusions

The core of the ESG concept is to explore a sustainable development path that enables enterprises to strike a balance between business value and social responsibility. Through the lens of the relative ESG gap, this study examined whether listed companies with relatively lagging ESG performance will try to improve their ESG performance by learning from and imitating the “leaders” in the industry when they are aware of their own shortcomings and whether this behavior will have a positive impact on managerial myopia. The empirical results confirm that there is a “tunnel effect” in the relative ESG gap, which can stimulate the disclosure willingness of listed companies with relatively lagging ESG performance; moreover, voluntary disclosure can improve the ESG performance of such companies, thus reducing managerial myopia. For listed companies with relatively leading ESG performance, although voluntary disclosure is also an effective method for improving their ESG performance, they are more inclined to comply with regulatory disclosures. In such listed companies, there is no “tunnel effect” in the relative ESG gap. Further research found that among the listed companies with relatively lagging ESG performance, the “tunnel effect”, the promotion effect of the willingness to disclose information on ESG performance, and the reduction effect of ESG performance on managerial myopia are all higher in SOEs than in non-SOEs.

The findings suggest that to “Learn from the leading companies and strive to be like them” is a good way for Chinese listed companies to continue to practice the ESG development concept. Compared with external pressures such as rigid regulation, social pressure, and media attention, a company’s correct understanding and clear planning of ESG development are more conducive to its ESG practice. “Whipping the slow bull” is not a good strategy to motivate lagging enterprises to improve their ESG performance; thus, we should give full play to the initiative of companies themselves to stimulate the vitality of such enterprises in ESG learning. Accordingly, the following suggestions are provided: (1) The government should formulate corresponding measures to reward companies with outstanding ESG performance. On the one hand, the government can promote companies with outstanding ESG performance through its official media to enhance their visibility and corporate image. On the other hand, the government can provide direct economic incentives such as tax exemptions or financial subsidies to enhance companies’ positive expectations for implementing ESG development concepts. Through these measures, enterprises are encouraged to spontaneously and actively carry out ESG practices, thereby alleviating the short-sighted behavior of managers and achieving sustainable development of enterprises. (2) The benchmarking role of SOEs should be given complete flexibility. The government should encourage and support SOEs to become pioneers in the ESG field by setting clear guidelines and evaluation standards. At the same time, an incentive mechanism can be established to provide more resource support and policy preferences to SOEs that have made remarkable achievements in ESG performance, thereby stimulating more enterprises to follow up. In addition, the experience accumulated by SOEs in the process of implementing ESG practices should be widely shared to provide references for other enterprises and industry-standard demonstrations.