Next Article in Journal
Sustainable Business Through Local Strength: A Qualitative Study of Financial, Social, and Cultural Strategies in Bandung’s Culinary Micro-Enterprises
Previous Article in Journal
Analysis of Spatiotemporal Dynamics and Driving Mechanisms of Cultural Heritage Distribution Along the Jiangnan Canal, China
Previous Article in Special Issue
Social and Economic Aspects of Sustainable Development: Intensity of Collaboration as a Key Driver of Team Work Engagement
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Impact of ESG on Earnings Quality and Real Earnings Management: The Role of Firm Size

by
Stylianos Efstratios Vatis
1,*,
George Drogalas
2,
Antonios Persakis
1 and
Evangelos Chytis
3
1
Department of Accounting and Finance, University of Thessaly, 41500 Larissa, Greece
2
Department of Business Administration, University of Macedonia, 54636 Thessaloniki, Greece
3
Department of Accounting and Finance, University of Ioannina, 48100 Preveza, Greece
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(11), 5027; https://doi.org/10.3390/su17115027
Submission received: 2 May 2025 / Revised: 22 May 2025 / Accepted: 28 May 2025 / Published: 30 May 2025

Abstract

This study examines the impact of ESG on Εarnings Quality (EQ) and Real Earnings Management (REM). Additionally, it investigates the potential role of firm size (FS) in this relationship. Using a fixed-effects multivariate regression analysis on an international sample of 32,050 firm-year observations over the period 2003–2022, we show that ESG enhances EQ and restricts REM. Further analysis confirms our main findings, indicating that the intensity of the positive relationship between ESG and EQ is more pronounced in small firms, while the negative association between ESG and REM is more intense in large firms. To the best of our knowledge, this is the first study to capture the impact of ESG on both EQ and REM using international evidence, while testing the role of FS. Our findings suggest that EQ and earnings management (EM) can be viewed as a double-edged sword of reporting quality, thus, a more flexible and proactive strategy is needed when considering the material effects of ESG.

1. Introduction

Corporate reporting has long been established as a governance mechanism for disciplining corporate executives [1]. Its latest development, ESG (Environmental, Social, Governance), has emerged as a rapidly growing topic [2,3,4,5,6,7], and it has become a critical framework guiding firms toward sustainable development [8]. Companies are increasingly incorporating ESG requirements in their operations, actively seeking ways to enhance their role as responsible corporate citizens [9]. In response, stakeholders are placing greater emphasis on evaluating firms’ ethical characteristics and sustainability potential by monitoring their environmental, social, and governance consciousness [10].
As ESG engagement substantially affects various corporate decisions [11], companies have an ethical duty to maximize shareholders’ value [12], while also being accountable to various stakeholders [13]. In this regard, organizations involved in ESG activities may leverage related disclosures to communicate their ethical goals and intentions to stakeholders [14], which can amplify the long-term value of the company [15].
However, managers engaging in earnings management (ΕΜ) can address stakeholder activism and scrutiny by resorting to sustainability practices such as corporate social responsibility (CSR) [16]. This opportunistic ΕΜ behavior represents managers’ prioritization of short-term goals over long-term benefits [17], and as part of earnings properties, it is significantly related to business strategy [18]. Healy and Wahlen [19] (p. 368) define EM as follows: “Earnings management occurs when managers use judgment in financial reporting and in structuring transactions to alter financial reports to either mislead some stakeholders about the underlying economic performance of the company, or to influence contractual outcomes that depend on reported accounting numbers”.
In the broader context of EM practices, real earnings management (REM) stands out due to its direct impact on firms’ operational decisions and long-term performance. REM modifies the timing or structure of financing, operation, or investment transactions [17], and includes activities such as sales, production, and discretionary expenses manipulation that directly affect cash flows [20,21]. In addition, compared to accrual-based EM (ACRB), which is easier to detect and may attract greater scrutiny from regulators and auditors, REM is relatively difficult to identify [22]. It is viewed as less risky, less likely to be perceived as inappropriate [23,24], more costly [25], and has real economic repercussions for a company’s long-term value [17,26]. Such attributes indicate that, while REM may serve short-term objectives, its detrimental effect on long-term firm value stands in contrast to ESG strategy, which is increasingly recognized for enhancing sustainable growth and long-term value of the company. In contrast to EM, high earnings quality (EQ) promotes transparency by accurately representing the economic reality of the company [27], thus aiding decision-making. This is crucial for effective communication between external users and corporate insiders [28]. In this context, opportunistic behavior distorts the stakeholder perception of the company and inevitably erodes firm EQ [29].
Recently, with the ongoing evolution of sustainable development, ESG has emerged as a key indicator of corporate sustainability, attracting the interest of global investors, as well as regulatory bodies [30]. This concept directly impacts the quality of reporting as reflected in the relevant literature [31,32]. The growing demand for ESG information is also grounded in established CSR theories, including inter alia, stakeholder and agency theories [33,34]. Accordingly, emphasis on external accounting communication in accounting research has yielded various communicative forms, including ESG reports, for either decision-making or accountability purposes [35]. Coupled with the relevance of EQ in reducing managerial opportunistic behavior and unethical EM [36], external accounting communication has prompted us to investigate the impact of ESG on EQ and REM. We used ESG throughout the paper to refer to ESG as a broad term. For the purposes of our study (e.g., formulation of hypotheses, methodology, results), ESG refers specifically to the ESG score from Refinitiv Eikon [37]. The ESG score is a transparent and objective measure of relative ESG performance [38,39].
In the literature, EQ and EM can be considered a double-edged sword of reporting quality, in the sense that a lower EM level exhibits a higher level of EQ [40]. This reflects both the potential for enhanced transparency through higher EQ and the risk of misleading representations due to EM behavior. Prior research has established several key proxies of EQ, including earnings smoothness, earnings persistence, accruals, timely loss recognition, restatements, target beating, and EM [41]. Since lower EM is associated with higher EQ [40], we measure both EQ (by means of the StarMine EQ score) and EM (by means of REM). To measure EQ, we use the StarMine EQ score [42] for the following reasons: it is a multi-factor metric that is a more appropriate proxy for EQ given the observed declining correlation between accruals and cash flows [43]; it is a transparent quantitative measure of the reliability and persistence of firms’ earnings because it favors earnings supported by cash flows as opposed to those driven by accruals; it is standardized (ranging from 0 to 100) and objective, allowing comparisons of firms’ performance and accounting quality [44,45]. Regarding REM, recent studies emphasize its impact on ESG [46]. Unlike ACRB, REM is harder to detect [22] and has real economic consequences on long-term firm value [17,26], often undermining it. This contrasts with ESG strategies, which are increasingly linked to sustainable growth and long-term value creation.
From this perspective, prior empirical literature examines ESG as separate components or as an aggregate score, identifying a positive relationship between ESG and EQ [31,47,48,49,50]. Concerning the impact of ESG on REM, research is inconclusive. While some studies observe a negative and significant relationship between sustainability practices and REM [14,37,51], others indicate that such practices either have a negative, insignificant relationship with REM [52,53,54,55] or may even report a positive association with REM [56]. This implies that such sustainability activities can be used to disguise EM practices [57,58]. Therefore, the association between ESG and reporting quality should be further researched.
The inconclusiveness of previous research might be due, among others, to the diverse (a) heterogeneous theoretical underpinnings (e.g., [16,32]), (b) regional, institutional and industrial settings in which the relevant research was carried out (e.g., [59]), (c) time frames and sample observations (e.g., [14,58]), (d) ESG indices derived from content analysis (e.g., [60]) or ESG scores provided by third-party data providers (e.g., [61]), and (e) research constructs used [16,50]. Given the need for increased academic attention to ESG topic [10], particularly through the inclusion of potential influencing factors that could affect these relationships [62], we believe that our research is of paramount importance for two reasons: First, both concepts, EQ and EM, are fundamental in theoretical and empirical accounting research [63], and no related topic is perhaps more provocative [64]. Second, the impact of ESG on both EQ and REM in an international context, as well as the role of firm size (FS) in these relationships, remains largely unexplored.
Against this background, this study explores these relationships empirically by drawing on a panel dataset of 32,050 firm-year observations over the period 2003–2022. The findings deliver consistent evidence that ESG enhances EQ and restricts EM, measured by REM, suggesting that companies more involved in ESG activities are less likely to engage in REM, consistent with stakeholder theory. Further analysis confirms our main findings, indicating that the intensity of the positive relationship between ESG and EQ is more pronounced in small firms, while the negative association between ESG and REM is more intense in larger firms, supporting the stakeholder and agency theories. These findings remain consistent after performing robustness checks. Finally, to complement our core findings, the additional analysis disentangles the individual effects of the E, S, and G pillars on EQ and REM, thereby offering more detailed insights.
This study makes several contributions to the literature. First, to the best of our knowledge, this is the first research to capture the impact of ESG on both EQ and EM, measured by REM, using international evidence over an extended time period that reaches up to recent years. As this issue can be viewed as a double-edged sword of reporting quality, the study represents the initial effort to address it from both sides. Second, it examines the role of FS, addressing the gap identified in the literature regarding the inclusion of possible factors in this relationship [62]. Third, it complements the study of Rahman et al. [55], which takes into account FS on the relationship between corporate sustainability practices (CSP) and EM in a single-country setting. Our study offers a broader perspective by using an international sample and by extending the analysis to include the relationship between ESG and EQ, as well as the role of FS in shaping this link. Furthermore, it expands their research by revealing a significant negative relationship between ESG and REM and showing that FS plays an important role in these relationships. Fourth, it provides useful information for academics, practitioners, and regulators. For academics, the research provides a foundation for further investigation of this complex association. For practitioners, especially those involved in financial and non-financial reporting, the findings provide useful insights into how ESG is associated with higher EQ and reduced REM. These results may encourage the integration of ESG into decision-making processes aimed at enhancing reporting quality and promoting long-term sustainability. For regulators, it delivers critical insights by highlighting ESG’s role in enhancing EQ and reducing REM, while revealing that the impact of ESG on EQ and REM varies with FS. Such an understanding can further support regulators’ endeavors to formulate new guidelines suited to the burgeoning and rapidly changing landscape.
The rest of this study is structured as follows. Section 2 describes the theoretical framework and key study variables. Section 3 develops the hypotheses to be tested. Section 4 explains the research methodology. Section 5 presents and discusses the empirical results, delivers further analysis, and provides robustness checks, and Section 6 concludes the paper.

2. Literature Review

2.1. Theoretical Background

Based on the work of Freeman [65] and Jensen [66], stakeholder theory posits that a firm’s goal is to generate shareholder value while ensuring stakeholder interests [15]. In the context of ESG, the stakeholder view advocates that companies have an ethical duty to both maximize the value of shareholders [12] as well as answer to a range of stakeholders, whether internal, external, or both [13]. Organizations committed to ESG are more likely to be externally monitored and scrutinized by stakeholders and regulators and are, thus, motivated to uphold higher levels of ethical behavior [60]. They benefit from enhanced reputation [14], and tend to have lower EM [67], or higher EQ [40] to maintain this reputation. Consequently, focusing on long-term sustainability and benefiting from favorable stakeholder treatment (e.g., lighter scrutiny, reputation) helps companies to reduce shareholder pressure for short-term profitability [14] and, thus, amplify the long-term value of the company [15].
Drawing upon stakeholder theory, firms may allocate additional resources to meet the needs and requirements of their stakeholders [68]. Since larger firms have more resources at their disposal to invest in the well-being of stakeholders [69], they are more inclined to meet social expectations by engaging more in ESG activities [70], which, in turn, may constrain EM behavior [71]. In addition, large firms may also be effective in avoiding EM due to their increased visibility and greater public scrutiny [72], as well as the risk of reputational loss from upward EM behavior [73].
Unlike stakeholder theory, agency theory argues that the fundamental issue in the nexus between owners and agents resides in conflicting interests and information asymmetry, encouraging opportunistic behaviors [74]. These agency-related issues can induce agency costs, especially EM [51] or lower EQ generating or aggravating these costs [75]. Agency problems also enable managers to act in self-interest by employing sustainability as a tool to conceal or misrepresent information [76] at the expense of shareholders, diverging from the goal of maximizing their profits [77]. They also disclose sustainability information to obscure financial reporting quality issues, aiming to distract shareholders from tracking EM activities [78]. Thus, ESG engagement serves as a corporate image management tool to offset negative effects and cover up the impact of manager misconduct [79], also increasing the likelihood of corporate misconduct [80].
In line with this theory, large entities often have extensive, more diverse, and independent boards of directors, which enhance both their monitoring and advisory roles. This helps reduce information asymmetry, managerial opportunism, and agency costs. Additionally, such boards improve the quality of corporate reporting, thereby strengthening stakeholders’ ability to monitor and scrutinize the firm effectively [55].
To summarize, we utilize prior theoretical underpinnings [34], namely stakeholder theory, to examine the impact of ESG on EQ and REM, and both stakeholder and agency theories to test the role of FS in this relationship.

2.2. ESG

The growing impacts of environmental issues and climate change on society and the global economy have highlighted the relevance of sustainability [81]. In this context, the concept of ESG has become a central concern in recent years [2], with stakeholders and the public increasingly acknowledging its significant impact on sustainability and long-term corporate performance [82]. ESG is often used interchangeably with CSR [36], however, it is a broader term that includes governance issues explicitly rather than indirectly [83]. It comprises three pillars: E (environmental), S (social), and G (governance), each covering a broad range of issues. E includes inter alia, carbon emission control and green inputs. S deals with diverse matters such as consumer responsibility and labor standards. Finally, G covers topics such as equity structure and board independence [84].

2.3. Earnings Quality

EQ and EM are central issues in accounting research [63]. Dechow et al. [41] (p. 344) define EQ as follows: “higher quality earnings provide more information about the features of a firm’s financial performance that are relevant to a specific decision made by a specific decision-maker”. To a certain extent, higher EQ leads to greater transparency [27], which is crucial for effective communication between external users and corporate insiders [28]. In contrast, low EQ can lead to important information asymmetries, making it especially costly for the company to raise external financing [85]. Consequently, various users of financial statements require high-quality financial reports, especially EQ for efficient monitoring and contracting purposes [86].

2.4. Real Earnings Management

EM practice arises when managers exert judgment in financial reporting and transaction structuring to change financial reports, either to obscure the company’s actual performance from certain stakeholders or to affect the contractual results dependent on the reported accounting figures [19]. In this context, REM modifies the timing or structure of financing, operation, or investment transactions [17], and includes activities such as sales, production, and discretionary expenses manipulation that directly affect cash flows [20,21]. In addition, compared to ACRB, which is easier to detect and may attract greater scrutiny from regulators and auditors, REM is relatively difficult to identify [22]. It is viewed as less risky, less likely to be perceived as inappropriate [23,24], more costly [25], and has real economic repercussions for a company’s long-term value [17,26].

3. Hypotheses Development

3.1. ESG and Earnings Quality

In the scant empirical literature, findings on the relationship between ESG and EQ reveal a significant positive relationship. For example, a significant positive relationship is found between EQ and environmental and social disclosure [49], environmental disclosure quality [47], CSR (e.g., [31,48]), and non-financial disclosure score, supporting this relationship through stakeholder theory [50]. Since ESG enhances stakeholder value, stakeholder theory is the predominant theory of business sustainability [15] and the most widely used framework for explaining ESG practices [3,34]. Given that a lower EM level exhibits a higher level of EQ [40], the theoretical underpinnings underlying this relationship align with those explaining EM and are, therefore, discussed in the following section. On that basis, we extend the existing literature by exploring the relationship between ESG and EQ and formulating the following hypothesis:
H1. 
ESG significantly enhances EQ.

3.2. ESG and Real Earnings Management

Prior empirical studies examining the ESG-EM relationship report mixed findings. The extant literature addresses this nexus, adopting primarily stakeholder and agency theories, which are among the most prevalent in ESG research [34].
The corpus of literature aligned with stakeholder theory supports the long-term view [15] and asserts that firms focusing on sustainability are likely to meet stakeholder expectations (e.g., sustainability issues), viewing EM practices as unethical and socially unacceptable. This reflects that managers are incentivized to adhere to higher ethical standards and produce high-quality reports, engaging less in EM [14]. Additionally, firms with increased interest in sustainability issues are likely to have more transparency and reinforce long-term relationships with stakeholders, rather than focusing on unethical practices such as EM [60] and short-term gains [87]. The conclusions drawn from the empirical studies reveal a significant negative relationship, indicating that such sustainability practices effectively restrict EM [14,32,59,60,88,89,90].
Consistent with agency theory, empirical evidence supports the short-term view [74], managers sacrifice the long run for short-term gains, driven by their personal interests or preference for immediate results [91], and use ESG to conceal opportunistic behavior [92]. In this context, some studies find a positive association (e.g., [16,93]). Additionally, Borralho et al. [61] find a positive link between the environmental pillar and EM. Likewise, Velte [62] finds a positive link between environmental (carbon) performance and REM.
The above discussion reveals contradictory findings, with some studies reporting negative and others reporting positive relationships. Regarding the different measurements for EM, previous studies have included either one or both metrics of EM in their analyses, with mixed findings. In this study, we focus on REM and concentrate on prior studies that examine its relationship with ESG. For instance, Velte [54] examines ESG collectively and reports an insignificant negative link between ESG and REM. Houqe et al. [94] examine ESG individually (carbon emissions), identifying a significant positive link with REM. Similarly, focusing on specific components of ESG, Velte [62] shows a significant positive link with REM. Likewise, Xi and Xiao [95] find a significant negative link with REM, while Yasar and Yalçin [96] report an insignificant negative relationship with REM.
The framework adopted to study the connection between ESG and REM is stakeholder theory, the predominant theory of business sustainability [15,34]. Considering stakeholder theory, we formulate the following hypothesis:
H2. 
ESG significantly mitigates REM.

3.3. The Role of Firm Size

Previous studies examining the association between ESG and EM or EQ focus on moderators such as corporate governance mechanisms [70], corporate structure, prior-year sustainability performance [36], and firm visibility, managerial ownership, and managerial overconfidence [97]. In contrast, a few other studies with different thematic focuses use FS as a moderator in the association between ESG and firm financial performance (e.g., [98]). Moreover, Rahman et al. [55] investigate the moderation of FS in the association of CSP with EM.
Additionally, the established empirical literature considers FS as a control variable in this topic [59,61,97], but the findings are mixed. Some studies illustrate that FS is positively associated with the extent of EM [54,59]. This may stem from political pressures and costs that could lead managers in large firms to engage in EM to showcase more predictable earnings [99]. Managers in these companies are also incentivized to overstate earnings due to complex operations, making it harder for external users to identify such EM behavior [100]. As reflected in Jassim et al. [101], managers in large firms further manipulate information to misrepresent firm performance [102]. For instance, large firms in Turkey engage more in EM than small entities [103].
By contrast, other studies show that FS is negatively associated with EM [61,97]. This may be explained by the fact that large firms may be effective in avoiding EM due to the higher visibility and the public scrutiny they are subject to [72], as well as the risk of reputational loss from upward EM behavior [73]. Regarding ESG, larger firms have more resources at their disposal to invest in the well-being of stakeholders [69], they are more inclined to meet social expectations by engaging more in ESG activities [70], which in turn may constrain EM behavior [71].
Additionally, some studies have found that FS is not significantly associated with EM in Indonesia and Pakistan [104,105]. Rahman et al. [55] found that CSP does not have a significant role in constraining REM. Splitting their sample into small and large firms, they argued that this additional analysis validated their findings but only for large rather than small firms.
Following the previous literature and addressing the lack of prior research using FS as a determinant in the nexus between ESG and both EQ and REM, along with the need to examine the possible factors of this relationship [62], we formulate the following hypothesis:
H3. 
FS significantly influences the role of ESG in enhancing EQ and constraining REM.

4. Research Design

4.1. Data Collection and Sampling

Table 1 (Panel A) summarizes the sample selection process. Our sample period is 2003–2022, focusing on firms with ESG data from the Refinitiv Eikon database, recognized as one of the most comprehensive secondary data sources for ESG information [106]. The primary data for ESG, EQ, and control variables were obtained from this database, and relevant financial data were used to determine the REM proxy. This resulted in 70,311 firm-year observations. We excluded firm-year observations with missing information and further removed regulated firms (financial and utility), because firms in these industries operate under a different regulatory environment compared to those in unregulated sectors [107]. This exclusion is further justified by prior research indicating that these industries exhibit characteristics that are not directly comparable to those applied in unregulated sectors: distinct regulatory structures [108], unique accounting standards [109], and differing managerial incentives [110]. Financial firms are generally subject to stricter sector-specific disclosure and corporate governance regulation [54], and their governance systems are influenced by regulatory oversight [37], while utilities have predictable earnings growth [111] and managers may face different EM incentives [110]. This process resulted in a final data set of 32,050 firm-year observations.
We then showcased the sample distribution by industry, using Global Industry Classification Standard (GICS) sector classification [112], in Panel B of Table 1. We noticed a variation in our sample firms across different industries, with most of them belonging to Industrials (6777), Health Care (4324), Materials (3876), Consumer Discretionary (3629), and Information Technology (3378) industry sectors. The smallest number of observations is from the Consumer Staples (2059) sector.
Our final sample includes 40 countries, as ESG score data were available only for these countries in the Refinitiv Eikon database. The sample covers a diverse set of countries: the United States of America, China, and Hong Kong, Canada, Taiwan, France, Korea/Republic (S. Korea), Germany, Switzerland, Brazil, Italy, Singapore, Turkey, Spain, Japan, Sweden, Finland, Mexico, Indonesia, Belgium, the Philippines, Australia, Denmark, Russia, Malaysia, Chile, Norway, Austria, Saudi Arabia, Greece, Qatar, the United Arab Emirates, Israel, Portugal, Peru, New Zealand, Egypt, Morocco, Thailand, and Argentina.

4.2. Models and Variables Measurement

We developed the following fixed-effect models for estimation:
E Q i t = α i + β 1 E S G i t + γ X i t + γ Y I t + c   t + δ j + ε i t
R E M i t = α i + β 1 E S G i t + γ X i t + γ Y I t + c   t + δ j + ε
where E Q i t is the earnings quality of firm i in year t. R E M i t is the real earnings management of firm i in year t. We control for several firm level variables included in vector X i t where X i t :{auditor tenure (AT), board gender (BG), auditor size (BIG4), board size (BS), CEO duality (CEOD), firm size (FS), leverage (LEV), returns on assets (ROA), liquidity (LIQ), Intangible intensity (INTINT)} and country level variables included in vector Y I t where Y I t :{origin (ORIGIN), developed (DEVELOPED)}. ε i t is the random error term. α i represents firm-specific effects. Finally, we include variables c t and δ j to control for year and sector fixed effects, respectively.
EQ measurement is obtained from StarMine, retrieved from Refinitiv Eikon. StarMine’s EQ model assesses the extent of the reliability of past earnings and their likelihood of being maintained in the future. The score is presented as a percentile rank ranging from 1 (lowest quality) to 100 (highest quality) based on the sustainability of a firm’s earnings, where 100 indicates the top rank and the highest level of EQ. The EQ score is an accurate and reliable measure of company performance and accounting quality. High EQ is indicative of the sustainability of firm performance, regardless of the income generated. Therefore, we choose the StarMine EQ score because it represents a quantitative assessment, providing more accurate and reliable measures to assess firm performance and accounting quality [45]. Furthermore, it allows us to objectively evaluate a company’s EQ in comparison to all other firms [44].
To proxy for REM, we apply the three measures proposed by Roychowdhury [21] and then aggregate them [17,23,31].
There are several ESG data providers such as Refinitiv Eikon, MSCI, KLD, and Sustainalytics [113]. ESG data for this study is retrieved from the Refinitiv Eikon database. Although ESG score providers vary in terms of methodology and results, Refinitiv stands out as one of the world’s largest providers of ESG data [114,115]. It is particularly distinguished by its transparent methodology, comprehensive data coverage, and well-defined scoring system, enabling investors to gain a deeper insight into a company’s underlying fundamentals [116]. The ESG score is a transparent and objective measure of relative ESG performance [38] and Refinitiv Eikon computes it as a weighted average of the three key pillars: E, S, and G, using percentile rank scoring that has a value range of 0–100 [117], where 100 represents the highest level of sustainability performance [118].
The study employs the following control variables. Firm size (FS) is measured as the natural logarithm of total assets [97], return on assets (ROA) is assessed as the ratio of net income divided by the total assets [60] and is expressed as a percentage, leverage (LEV) is measured as the long-term debt scaled by total assets [54] and is expressed as a percentage, auditor tenure (AT) is represented as the number of years of audit engagement [71], intangible intensity (INTINT) is the ratio of R&D expenditures to total assets [17], auditor size (Big4) is expressed as a dummy variable that takes 1 if the firm is audited by a Big4 (Ernst and Young, Deloitte and Touche, KPMG and Price Waterhouse Coopers) auditor and 0 otherwise [60], CEO duality (CEOD) is a dummy variable set to 1 if the CEO and chairman are the same person and 0 otherwise [119], board size (BS) is the total number of directors within the board [32], board gender diversity (BG) is the proportion of female directors to the total number of directors [70], liquidity (LIQ) is measured as the sum of accounts receivable and inventory to total assets [71] and is expressed as a percentage, developed (DEVELOPED) is dummy variable for (1) developed country and (0) developing country, and origin (ORIGIN) is dummy variable for (1) common law country and (0) civil law country [32]. The classification of countries into two main legal systems, common law and civil law countries, is well-established in the literature and reflects structural differences in legal enforcement, investor protection, and regulatory approaches. In this context, empirical research indicates that common law and civil law countries differ in the way they protect investors’ rights, with civil laws giving investors weaker legal rights than common laws do [120]. As reported by Koutoupis et al. [121], ESG performance tends to vary across legal systems, with civil law countries generally exhibiting a stronger dedication to sustainability. These countries tend to have more comprehensive legal frameworks that prioritize stakeholder interests and promote transparency in ESG-related disclosures, as opposed to the practices observed in common law countries [122]. Moreover, civil law countries tend to adopt more rigorous environmental regulations, which lead to improved environmental results [123].
Overall, two baseline fixed-effects regression models were constructed to evaluate the impact of ESG on EQ and REM. To examine whether FS plays a differentiating role in these relationships, a split-sample approach was used, whereby the models were re-estimated separately for small (below the mean) and large (above the mean) firms. All the estimations in this study were obtained using Stata 18.5 software.

5. Results and Discussion

5.1. Descriptive and Univariate Analysis

Table 2 provides an overview of the descriptive statistics for the variables used in the empirical analysis, based on a sample of 32,050 firm-year observations. Following similar studies (e.g., [31]), all continuous variables have been winsorized at the upper and lower 1 percent of their distribution. Considering that EQ is a percentile rank ranging from 0 to 100, with higher values reflecting higher rankings [124], the mean EQ score of 52.440 indicates a moderate to high level of EQ on average. Our REM measure has a mean close to zero (0.018) and a relatively small dispersion, suggesting that the firms in our sample engage in a relatively low level of REM on average, consistent with previous studies [22,59]. However, the minimum and maximum values indicate the presence of both downward and upward REM. Regarding ESG, the mean score in the study sample is 47.470, similar to Pathak and Gupta [32]. It also exhibits a considerable spread (standard deviation = 20.634), reflecting significant differences in firms’ ESG engagement. This corresponds to a grade of C+, “C” standing for satisfactory ESG performance compared to others and a moderate transparency level in communicating important ESG data publicly [125]. Regarding the control variables, BG averages 17.501, ranging from 0 to 50, pointing to variation in board gender diversity. BS displays a mean of 10.245 members, indicating moderately sized boards across the sample. FS has a mean of 10.403 and a lower standard deviation (SD) relative to other variables. LEV averages 18.585 with SD = 16.377. ROA records a mean of 5.942, highlighting profitability differences. LIQ shows a mean of 15.013. AT records an average of 8.897. The BIG4 variable shows that 52% of firms are audited by a BIG4 firm, while CEO duality is present in approximately 40% of the observations. Additionally, around 50% of firms originate from a specified category as captured by ORIGIN, and 79% are from developed economies. Lastly, INTINT shows a mean of 0.037, with a right-skewed distribution, indicating that while most firms have low INTINT, a few firms exhibit relatively high values.
In Table 3, Panel A and Panel B report the Pearson correlation coefficients among the variables employed in the analysis. Table 3 Panel A presents the Pearson correlation coefficients between EQ and the other explanatory variables, while Table 3 Panel B presents the Pearson correlation coefficients between REM and the other explanatory variables. The majority of the variables are significantly correlated with the dependent variables. Additionally, the findings demonstrate that the Pearson correlation values of all variables are lower than 0.8, the level generally considered indicative of serious multicollinearity [30]. In particular, the correlation coefficients are generally small to moderate in magnitude, and no evidence of problematic multicollinearity is apparent. The observed relationships support the empirical stability of regression models using these variables.

5.2. Main Analysis

Multivariate analysis was employed to examine the impact of both explanatory and control variables on the dependent variables, namely EQ and REM. To evaluate how each explanatory variable influences EQ and REM, panel data econometric techniques were applied. Table 4 presents the results of the multivariate analysis. The panel data estimation results suggest that taking into account the individual specificity of entities through fixed effects provides more robust and statistically significant outcomes compared to models using random effects [37].
Table 4 investigates the impact of ESG on EQ and REM across the full sample and FS subsamples. Across all six models, the coefficient of ESG remains statistically significant, confirming its robust influence. In particular, Table 4 (Column 1) shows the regression estimates for the association between ESG and EQ. Table 4 (Column 2) indicates the regression estimates for the association between ESG and REM. Moreover, all these models were estimated separately after splitting the sample into small (below the mean value) and large firms (above the mean value), with this information reflected in Table 4 (Columns 3–6). The core findings derived from these estimations can be interpreted as follows:
In the full sample, as can be seen from the regression results in Table 4 (Column 1), the relationship between the EQ and ESG is significantly positive (coefficient = 0.114, p < 0.001) and verifies theoretical hypothesis H1. This result aligns with those of previous researchers [31,47,48,49,50]. Given that the company’s earnings are improved by investing in ESG activities [126], the finding supporting H1 suggests that ESG enhances EQ, which is consistent with stakeholder theory.
The fixed-effects estimations presented in Table 4 (Column 2) show that ESG is significantly negatively associated with REM (coefficient = −1.24 × 10−4, p < 0.05, implying that companies more involved in ESG activities are less likely to engage in REM, consistent with stakeholder theory. This may be attributed to the fact that REM is more costly [25] and has real economic repercussions for a company’s long-term value [17,26] and future performance [127]. This finding also stems from the fact that managers are incentivized to maintain higher ethical standards and produce high-quality reports, engaging less in EM practices [14]. Consequently, the disclosure of ESG information helps firms meet stakeholders’ expectations, which in turn encourages stakeholders to support the firm’s strategy and performance with a long-term perspective and thereby reduces the need for EM through reporting practices [32]. Furthermore, organizations involved in ESG activities (e.g., carbon mitigation activities) are likely to leverage related disclosures to communicate their ethical goals and intentions to stakeholders while nurturing relationships with them, obtaining favorable treatments such as enhanced reputation [14], thus decreasing the likelihood of the firm engaging in reputation-harming EM practices [67]. Overall, this finding verifies H2 of our study and aligns with prior studies [22,71,95]. However, the outcomes lack agreement with those of Houqe et al. [94] and Velte [62], which may be attributed to various reasons as previously explained. Among the control variables, EQ is positively associated with BG, ROA, BIG4, CEOD, AT, and INTINT, and negatively associated with FS, LEV, and LIQ. For REM, BS, FS, BIG4, and LIQ are significantly positively associated with it, while ROA, INTINT, and DEVELOPED are negatively associated with it.
Additionally, this study sought to examine the role of FS on these relationships. For this reason, we classified the entities into two groups based on size: large (above the mean) and small (below the mean), similar to prior studies [55,60]. The fixed-effect estimations show that ESG engagement has a significant positive effect on the EQ of both large and small firms, with the effect being stronger in the case of smaller firms (0.138, p < 0.001) than for large firms (0.097, p < 0.001) compared to the full sample. This may be due to the fact that smaller firms often operate with fewer resources, less market access, and lower visibility compared to larger ones. Consequently, they may place greater emphasis on transparency and responsible practices through ESG, as a way to build trust with various stakeholders. Such efforts are likely to be reflected in the improved quality of their reported earnings. Likewise, estimations show that ESG played a significant positive role in restricting the REM of both large and small firms, with the effect being stronger in the case of larger firms (−2.49 × 10−4, p < 0.001) than for small firms (−1.359 × 10−4, p < 0.1). This finding somehow endorses the stakeholder and agency theories. This may be explained by the fact that large firms may be effective in avoiding EM due to the higher visibility and the public scrutiny they are subject to [72], as well as the risk of reputational loss from upward EM behavior [73]. Regarding ESG, larger firms have more resources at their disposal to invest in the well-being of stakeholders [69], they are more inclined to meet social expectations by engaging more in ESG activities [70], which in turn may constrain EM behavior [71]. Overall, these findings complement the study of Rahman et al. [55]. The authors found that CSP (here ESG) does not have a significant role in constraining REM. Splitting their sample into small and large firms, they argued that this additional analysis validated these findings, but only for large firms rather than small companies. As such, H3 of the study is confirmed.

5.3. Additional Analysis

5.3.1. The Impact of ESG Dimensions on Both EQ and REM

Further analysis presented in Table 5 reports the individual effects of the three ESG dimensions (E, S, G) on EQ and REM. In particular, the empirical findings presented in Table 5 offer meaningful insights into the distinct roles that ESG dimensions play in influencing EQ and REM. Columns 1–3 of Table 5 indicate that each of the three ESG pillars is significantly and positively related to EQ, and that the impact of S (coefficient = 0.085, p < 0.001) is stronger than the other two components E (coefficient = 0.047, p < 0.001) and G (coefficient = 0.062, p < 0.001) accordingly. This implies that firms with increased interest in sustainability issues, across all dimensions, are more likely to produce higher-quality, less manipulated earnings. They tend to have more transparency and reinforce long-term relationships with stakeholders, rather than focusing on EM and short-term gains [87].
In contrast, the relationship between ESG and REM is more nuanced (Table 5—Columns 4–6). This finding aligns with the view that ESG dimensions are not equally associated with EM practices [61]. Specifically, only the S (coefficient = −1.324 × 10−4, p < 0.05) exhibits a significant negative association with REM, consistent with Chouaibi and Zouari [37], indicating that socially responsible firms may be less inclined to engage in REM. This finding can be attributed to ethical orientation, which promotes greater transparency and discourages opportunistic behaviors such as REM. In other words, a company’s commitment to ethical and social practices contributes to the financial security of investors by reducing the likelihood of EM. Prior research supports this view, suggesting that the capital market penalizes unethical firms by lowering their valuation [128], while rewarding ethical firms by increasing their valuation [129]. Therefore, engaging in socially responsible behavior not only enhances a firm’s reputation but also aligns managerial incentives with long-term value creation, thereby limiting REM. In line with this, prior research suggests that firms with stronger social performance tend to face fewer agency problems [53], which may further contribute to reducing managerial opportunism. On the other hand, E (coefficient = 1.036 × 10−4, p < 0.05) is found to be significantly and positively associated with REM, consistent with the findings of Velte [62], albeit marginally. This suggests that E initiatives may sometimes impose cost pressures that increase the likelihood of engaging in REM. For instance, given that the E component can benefit companies, as it has been proven to ameliorate their reputation [130], companies may use such a dimension as a greenwashing method to hide EM practices [131]. Prior et al. [16] also suggest that managers strategically use, inter alia, environmental activities to conceal their EM practices, which can harm the company’s long-term value. According to Velte [54], this means that while firms with better environmental performance tend to engage less in accounting manipulations through ACRB, they may increase REM. The author suggests that this behavior aligns with greenwashing strategies, where managers improve environmental metrics to attract stakeholders and signal a genuine environmental commitment. However, since REM is harder for stakeholders to detect, managers may simultaneously increase REM activities without raising suspicion. In other words, they reduce visible accounting manipulation through ACRB but increase less obvious REM to maintain stakeholder appeal. Finally, the G component is negatively associated with REM, but the coefficient is not statistically significant, implying a limited role in constraining REM, consistent with the findings of Velte [54].

5.3.2. Quantile Regression Analysis (QRA)

In this section, we perform QRA to examine the possibly heterogeneous impacts of ESG on EQ and REM across different quantiles. This allows us to analyze whether these associations hold consistently across the entire range of values or whether their effects vary between the lower and upper ranges of the dependent variables. This analysis has increasingly been adopted as a complementary method to the conventional mean-based estimation techniques, offering a more thorough and nuanced understanding of the nexus among variables [132].
In Table 6, Panels A and B display the results of the QRA at the 10th to 90th percentiles of the dependent variables. The results of Table 6 Panel A reveal that ESG is positively and significantly associated with EQ across all quantiles consistently at the 1% level, but not uniformly across the distribution. The magnitude of the ESG coefficient increases from 0.079 at the 10th percentile to a peak of 0.145 at the 40th percentile, before gradually declining to 0.063 at the 90th percentile. This pattern suggests that ESG has the strongest positive impact on firms with medium-to-high levels of EQ, while the effect is still positive but weaker for firms at the highest end of the EQ distribution. This implies that firms with medium levels of EQ may benefit from the adoption of ESG, as such initiatives can enhance transparency, reduce opportunistic behavior, improve the quality of information, and make companies more transparent [37].
Regarding REM (Table 6 Panel B), the ESG coefficient displays a different dynamic across quantiles. ESG is positively and significantly associated with REM at the 10th and 20th percentiles (p < 0.001 and p < 0.05, respectively), but the magnitude is very small. From the 40th percentile onward, the ESG coefficient becomes negative, reaching statistical significance from the 50th percentile upward (p < 0.001 across most quantiles). The negative effect intensifies towards the higher quantiles, reaching −4.45 × 104 at the 90th percentile. These results suggest that ESG is particularly effective in constraining REM among firms with initially higher levels of REM.

5.4. Robustness Checks

To examine the robustness of our findings, we conduct the following analyses: (a) Alternative measure of the dependent variable (EQ), (b) alternative sampling, and (c) potential endogeneity concerns. Table 7 presents robustness tests examining the relationship between ESG and both EQ and REM, using different model specifications, as described below. Across all specifications, ESG remains statistically significant and maintains the expected direction of its effects.

5.4.1. Alternative Measure of EQ

Consistent with previous studies (e.g., [44]), we test the robustness of our findings by applying a logarithmic transformation to the EQ measure (Ln(EQ)), in order to account for potential skewness in its original measure. The results are presented in Table 7 (Column 1). In the fixed effects model, where LNEQ is the dependent variable, ESG is significantly and positively associated with EQ (coefficient = 0.003, p < 0.001), albeit with a small magnitude due to the log transformation. This suggests that the robustness of this result is confirmed.

5.4.2. Alternative Sampling

In this sub-section, we test the robustness of our results by modifying our sample. Specifically, to address concerns about the overrepresentation of certain countries, we exclude companies headquartered in the United States, China, and Hong Kong. Repeating our analyses with this adjusted sample (see Table 7—Columns 2–3) does not change our main findings, as the coefficient of ESG remains positive and statistically significant in its relationship with EQ (coefficient = 0.109, p < 0.001), and negative and statistically significant in its relationship with REM (coefficient = −2.254 × 10−4, p < 0.05). This suggests that the robustness of these results is confirmed.

5.4.3. Endogeneity Concerns

As noted by Choi et al. [133] and referenced in the study by Chouaibi and Zouari [37], CSR engagement (represented here by ESG) and EM may be jointly determined, with each exerting influence on the other. This interdependence introduces endogeneity issues when estimating regressions involving either variable. The endogeneity concern may arise due to reverse causality—firms with higher EQ or lower REM may also be more likely to engage in ESG initiatives. To control for any endogeneity bias arising from reverse causality and thus mitigate potential endogeneity concerns regarding the relationship between ESG and both EQ and REM, we employ a two-stage least squares (2SLS) instrumental variable (IV) approach.
Following similar studies (e.g., [37,134]), we re-estimate our baseline regressions using 2SLS, employing the initial ESG score (ESG_init_filled) as an instrumental variable for current ESG. This IV is assumed to be exogenous to the contemporaneous ESG score. We describe the procedure as follows: Initially, we regress the ESG engagement level on the IV (ESG_init_filled) and all control variables included in our main regression models. This constitutes the first stage of our 2SLS analysis. The results indicate that the instrument is strongly and significantly associated with the current ESG score, confirming its relevance. Then, we save the predicted values from the first-stage regression and substitute them for the actual ESG score in the second-stage model, where the dependent variables are EQ and REM, respectively. The 2SLS regression results are reported in Table 7 (Columns 4–5). These findings provide support for a causal interpretation of the relationship between ESG and financial reporting outcomes, demonstrating that ESG remains positively and significantly associated with EQ (coefficient = 0.093, p < 0.001) and negatively and significantly associated with REM (coefficient = −1.376 × 10−4, p < 0.05) even after controlling for endogeneity. While the exclusion restriction assumption cannot be directly tested, we strengthen its plausibility through several factors: (a) the temporal precedence and predetermined nature of the instrument (ESG_init-filled), (b) the strategic persistence of ESG orientation over time, (c) the inclusion of rich firm-level and institutional controls, and (d) alignment with prior literature using similar instruments (e.g., 37,136). Collectively, these factors reduce concerns about alternative channels. Together with the strong first-stage results (F-statistic = 22,215.6 > 10), these elements provide conceptual and empirical support for the assumption’s validity in our context. Overall, these estimations further enhance our research outcomes, ensuring their robustness and reliability, mitigating potential biases, strengthening their validity, and suggesting that endogeneity does not drive our main findings.

6. Conclusions

This study examines the impact of ESG on EQ and REM using an international sample of 32,050 firm-year observations over the period of 2003–2022. The findings reveal that ESG enhances EQ and constrains REM, suggesting that companies more involved in ESG activities are less likely to engage in REM opportunistic behavior. Further analysis confirms our main findings, indicating that the intensity of the positive relationship between ESG and EQ is more pronounced in small firms, while the negative association between ESG and REM is more intense in large firms. These findings extend the existing literature by offering new insights. We complemented the study of Rahman et al. [55], who found that CSP (here ESG) does not play a significant role in constraining REM. Splitting their sample into small and large firms, they argued that their analysis validated these findings, but only for large firms rather than small companies. Using international evidence, our study identifies a significant negative relationship between ESG and REM, with the impact of ESG on EQ and REM varying with FS. The results still hold after several robustness checks.
These findings carry significant research, theoretical, and practical implications. From a research standpoint, they address the gap suggested by Velte [62], investigating determinants such as FS in this nexus in an international context. From a theoretical standpoint, stakeholder theory is the dominant theory in ESG studies [34] used to explain ESG. Agency theory is also relevant in studies examining EM and EQ with ESG. While the starting points of the two theories are different, their findings converge in the case of large firms, possibly due to the greater diversity and experience of large company boards in mitigating managerial opportunism. From a practical standpoint, the study’s findings have important implications for both regulators and managers. Regulators are encouraged to strengthen ESG-related regulations and enforcement, as ESG engagement reflects not only a more ethical approach to REM and stronger responsiveness to diverse stakeholder expectations but also enhances investor protection since it leads to better financial reporting. Moreover, regulators should develop stronger, firm-size-adjusted regulations to further reinforce ESG’s impact on EQ and restrict REM. In this context, for large firms, regulators should require the ESG score to be explicitly linked to key financial ratios, enabling the detection of potential opportunistic behavior such as REM concealed through ESG activities. Independent audits should also be conducted to uncover potential REM practices. For small firms, where ESG more strongly enhances EQ, regulators might further encourage the participation of smaller firms in ESG adoption. However, small firms often avoid ESG practices due to limited available resources. Therefore, a practical implication would be to facilitate their engagement by introducing simplified ESG requirements, such as standardized reporting templates with fewer disclosure items, automated digital filing options, or even offering tax incentives for ESG-compliant firms. These measures could promote wider ESG adoption without creating disproportionate administrative burdens. Managers should exploit these insights by adjusting their ESG strategy to FS and adopting stricter internal procedures to further ameliorate corporate transparency, enhance EQ, and constrain EM practices.
Finally, this study is subject to some limitations that offer opportunities for future research. First, while this study uses the ESG score, it is important to acknowledge potential biases associated with ESG scoring methodologies. As noted by Hajek et al. [135], though widely adopted, ESG scores are not free from methodological concerns. ESG scoring methodologies often rely on subjective assessments, vary widely across providers, and many of them overlook important qualitative aspects of a firm’s ESG efforts. Additionally, because they typically rely on annual reports, they may not capture the most up-to-date strategies and information that shape a company’s current ESG performance [136,137]. Future studies could use contemporary tools such as machine learning or natural language processing (NLP) that can read and analyze sustainability narratives or earnings calls in order to obtain a better understanding of the actual ESG behavior of the company. Second, although this study uses FS, future studies may explore additional determinants in an international context. Third, it relies on a quantitative approach; combining it with a qualitative approach could provide useful insights.

Author Contributions

Conceptualization, S.E.V.; Methodology, A.P.; Formal analysis, A.P.; Data curation, A.P.; Writing—original draft preparation, S.E.V.; Writing—review and editing, S.E.V.; Visualization, S.E.V.; Supervision, G.D. and E.C.; Project administration, S.E.V.; Funding acquisition, S.E.V. All authors have read and agreed to the published version of the manuscript.

Funding

The research is conducted in the operating framework of the University of Thessaly Innovation, Technology Transfer Unit and Entrepreneurship Center “One Planet Thessaly”, under the “Scholarship Grants to University of Thessaly Doctoral Candidates” and was funded by the Special Account of Research Grants of the University of Thessaly.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data that support the findings of this study were obtained from the Refinitiv Eikon database and are not publicly available. Access to the data is subject to restrictions, as they were used under license for this study.

Acknowledgments

The authors thank the anonymous reviewers for their insightful and constructive comments.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Bushman, R.M.; Smith, A.J. Financial accounting information and corporate governance. J. Account. Econ. 2001, 32, 237–333. [Google Scholar] [CrossRef]
  2. Edmans, A. Applying economics—Not gut feel—To ESG. Financ. Anal. J. 2023, 79, 16–29. [Google Scholar] [CrossRef]
  3. Tsang, A.; Frost, T.; Cao, H. Environmental, social, and governance (ESG) disclosure: A literature review. Br. Account. Rev. 2023, 55, 101149. [Google Scholar] [CrossRef]
  4. Chen, S.; Cheng, M.; Luo, Y.; Tsang, A. ESG performance and analyst recommendations: Evidence from sustainability analysts in the Chinese market. J. Account. Lit. 2024, ahead of print. [Google Scholar] [CrossRef]
  5. Guest, D.E. Strengthening links between HRM theories, HR practices and outcomes: A proposal to advance research on HRM and outcomes. Hum. Resour. Manag. J. 2024, 35, 319–335. [Google Scholar] [CrossRef]
  6. Simpson, S.N.Y.; Aboagye-Otchere, F.; Ahadzie, R. Assurance of environmental, social and governance disclosures in a developing country: Perspectives of regulators and quasi-regulators. Account. Forum 2022, 46, 109–133. [Google Scholar] [CrossRef]
  7. Tian, T.; Chen, N. How does entrepreneurship promote corporate ESG performance? Int. Rev. Financ. Anal. 2024, 96, 103557. [Google Scholar] [CrossRef]
  8. Luo, R.; Ye, Y. Do firms listen to the ESG voices of minority investors? Evidence from China. Int. Rev. Financ. Anal. 2024, 96, 103562. [Google Scholar] [CrossRef]
  9. George, G.; Haas, M.R.; McGahan, A.M.; Schillebeeckx, S.J.; Tracey, P. Purpose in the for-profit firm: A review and framework for management research. J. Manag. 2023, 49, 1841–1869. [Google Scholar] [CrossRef]
  10. Zhu, S.; Du, J.; Lu, J.; Zheng, Q. How Does Foreign Acquirers’ ESG Misbehaviour Exposure Affect the Completion of Cross-Border Acquisitions? Br. J. Manag. 2024, 35, 1348–1366. [Google Scholar] [CrossRef]
  11. Zhao, X.; Zhang, H. How does ESG performance determine the level of specific financing in capital structure? New insights from China. Int. Rev. Financ. Anal. 2024, 95, 103508. [Google Scholar] [CrossRef]
  12. Azmi, W.; Hassan, M.K.; Houston, R.; Karim, M.S. ESG activities and banking performance: International evidence from emerging economies. J. Int. Financ. Mark. Inst. Money 2021, 70, 101277. [Google Scholar] [CrossRef]
  13. Lozano, R.; Carpenter, A.; Huisingh, D. A review of ‘theories of the firm’ and their contributions to Corporate Sustainability. J. Clean. Prod. 2015, 106, 430–442. [Google Scholar] [CrossRef]
  14. Bui, B.; Houqe, M.N.; Zaman, M. Climate change mitigation: Carbon assurance and reporting integrity. Bus. Strategy Environ. 2021, 30, 3839–3853. [Google Scholar] [CrossRef]
  15. Ng, A.C.; Rezaee, Z. Business sustainability performance and cost of equity capital. J. Corp. Financ. 2015, 34, 128–149. [Google Scholar] [CrossRef]
  16. Prior, D.; Surroca, J.; Tribó, J.A. Are socially responsible managers really ethical? Exploring the relationship between earnings management and corporate social responsibility. Corp. Gov. Int. Rev. 2008, 16, 160–177. [Google Scholar] [CrossRef]
  17. Wu, Y.; Zhou, S. Do firms practicing integrated reporting engage in less myopic behavior? International evidence on opportunistic earnings management. Corp. Gov. Int. Rev. 2022, 30, 290–310. [Google Scholar] [CrossRef]
  18. Karampinis, N.; Vlismas, O.; Ballas, A. Business strategy, earnings properties, and earnings quality. J. Int. Account. Audit. Tax. 2024, 56, 100632. [Google Scholar] [CrossRef]
  19. Healy, P.M.; Wahlen, J.M. A review of the earnings management literature and its implications for standard setting. Account. Horiz. 1999, 13, 365–383. [Google Scholar] [CrossRef]
  20. Davis, F.; Khadivar, H. Accrual and real earnings management by rumored takeover targets. Int. Rev. Financ. Anal. 2024, 92, 103105. [Google Scholar] [CrossRef]
  21. Roychowdhury, S. Earnings management through real activities manipulation. J. Account. Econ. 2006, 42, 335–370. [Google Scholar] [CrossRef]
  22. Wu, K.; Kong, D.; Yang, W. Does environmental, social, and governance rating affect firms’ real earnings management? Financ. Res. Lett. 2024, 67, 105764. [Google Scholar] [CrossRef]
  23. Cohen, D.A.; Dey, A.; Lys, T.Z. Real and accrual-based earnings management in the pre-and post-Sarbanes-Oxley periods. Account. Rev. 2008, 83, 757–787. [Google Scholar] [CrossRef]
  24. Graham, J.R.; Harvey, C.R.; Rajgopal, S. The economic implications of corporate financial reporting. J. Account. Econ. 2005, 40, 3–73. [Google Scholar] [CrossRef]
  25. Liu, S.; Wu, X.; Hu, N. Does CEO agreeableness personality mitigate real earnings management? Int. Rev. Financ. Anal. 2024, 95, 103458. [Google Scholar] [CrossRef]
  26. Laksmana, I.; Yang, Y.W. Product market competition and earnings management: Evidence from discretionary accruals and real activity manipulation. Adv. Account. 2014, 30, 263–275. [Google Scholar] [CrossRef]
  27. Wang, K.; Zhao, J.; Zhou, J. Online sales and stock price synchronicity: Evidence from China. Int. Rev. Financ. Anal. 2024, 95, 103356. [Google Scholar] [CrossRef]
  28. Li, Z.; Liu, X.; Wang, B. Military-experienced senior executives, corporate earnings quality and firm value. J. Account. Lit. 2024, 46, 401–445. [Google Scholar] [CrossRef]
  29. Belot, F.; Serve, S. Earnings quality in private SMEs: Do CEO demographics matter? J. Small Bus. Manag. 2018, 56, 323–344. [Google Scholar] [CrossRef]
  30. Zhou, G.; Liu, L.; Luo, S. Sustainable development, ESG performance and company market value: Mediating effect of financial performance. Bus. Strategy Environ. 2022, 31, 3371–3387. [Google Scholar] [CrossRef]
  31. Kim, Y.; Park, M.S.; Wier, B. Is earnings quality associated with corporate social responsibility? Account. Rev. 2012, 87, 761–796. [Google Scholar] [CrossRef]
  32. Pathak, R.; Gupta, R.D. Environmental, social and governance performance and earnings management–The moderating role of law code and creditor’s rights. Financ. Res. Lett. 2022, 47, 102849. [Google Scholar] [CrossRef]
  33. Bae, K.H.; El Ghoul, S.; Gong, Z.J.; Guedhami, O. Does CSR matter in times of crisis? Evidence from the COVID-19 pandemic. J. Corp. Financ. 2021, 67, 101876. [Google Scholar] [CrossRef]
  34. Del Gesso, C.; Lodhi, R.N. Theories underlying environmental, social and governance (ESG) disclosure: A systematic review of accounting studies. J. Account. Lit. 2024, 47, 433–461. [Google Scholar] [CrossRef]
  35. Merkl-Davies, D.M.; Brennan, N.M. A theoretical framework of external accounting communication: Research perspectives, traditions, and theories. Account. Audit. Account. J. 2017, 30, 433–469. [Google Scholar] [CrossRef]
  36. Rezaee, Z.; Tuo, L. Are the quantity and quality of sustainability disclosures associated with the innate and discretionary earnings quality? J. Bus. Ethics 2019, 155, 763–786. [Google Scholar] [CrossRef]
  37. Chouaibi, Y.; Zouari, G. The effect of corporate social responsibility practices on real earnings management: Evidence from a European ESG data. Int. J. Discl. Gov. 2022, 19, 11–30. [Google Scholar] [CrossRef]
  38. Lee, M.T.; Raschke, R.L.; Krishen, A.S. Understanding ESG scores and firm performance: Are high-performing firms E, S, and G-balanced? Technol. Forecast. Soc. Change 2023, 195, 122779. [Google Scholar] [CrossRef]
  39. Refinitiv. Environmental, Social and Governance Scores from Refinitiv. 2022. Available online: https://blogs.cranfield.ac.uk/wp-content/uploads/2021/05/refinitiv-esg-scores-methodology-May22-1.pdf. (accessed on 20 May 2025).
  40. Prencipe, A.; Viarengo, L. Should I trust you? Bidder’s earnings quality as an indicator of trustworthiness in earnout agreements. Int. J. Account. 2022, 57, 2250002. [Google Scholar] [CrossRef]
  41. Dechow, P.; Ge, W.; Schrand, C. Understanding earnings quality: A review of the proxies, their determinants and their consequences. J. Account. Econ. 2010, 50, 344–401. [Google Scholar] [CrossRef]
  42. Fassas, A.; Nerantzidis, M.; Tsakalos, I.; Asimakopoulos, I. Earnings quality and firm valuation: Evidence from several European countries. Corp. Gov. Int. J. Bus. Soc. 2023, 23, 1298–1313. [Google Scholar] [CrossRef]
  43. Bushman, R.M.; Lerman, A.; Zhang, X.F. The changing landscape of accrual accounting. J. Account. Res. 2016, 54, 41–78. [Google Scholar] [CrossRef]
  44. Abdelsalam, O.; Chantziaras, A.; Ibrahim, M.; Omoteso, K. The impact of religiosity on earnings quality: International evidence from the banking sector. Br. Account. Rev. 2021, 53, 100957. [Google Scholar] [CrossRef]
  45. Bakar, N.A.; Abdelsalam, O.; Taamouti, A.; Elmasry, A. The market uncertainty of ethically compliant equity: An integrated screening approach. J. Int. Financ. Mark. Inst. Money 2023, 86, 101759. [Google Scholar] [CrossRef]
  46. Liu, T.; Abdelbaky, A.; Elamer, A.A.; Elmahgoub, M. Real earnings management and ESG disclosure in emerging markets: The moderating effect of managerial ownership from a social norm perspective. Heliyon 2023, 9, e22832. [Google Scholar] [CrossRef]
  47. Alipour, M.; Ghanbari, M.; Jamshidinavid, B.; Taherabadi, A. The relationship between environmental disclosure quality and earnings quality: A panel study of an emerging market. J. Asia Bus. Stud. 2019, 13, 326–347. [Google Scholar] [CrossRef]
  48. Bose, S.; Yu, C. Does earnings quality influence corporate social responsibility performance? Empirical evidence of the causal link. Abacus 2023, 59, 493–540. [Google Scholar] [CrossRef]
  49. Mahjoub, L.B.; Khamoussi, H. Environmental and social policy and earning persistence. Bus. Strategy Environ. 2013, 22, 159–172. [Google Scholar] [CrossRef]
  50. Rezaee, Z.; Tuo, L. Voluntary disclosure of non-financial information and its association with sustainability performance. Adv. Account. 2017, 39, 47–59. [Google Scholar] [CrossRef]
  51. Zhang, Z.; Yap, T.L.; Park, J. Does voluntary CSR disclosure and CSR performance influence earnings management? Empirical evidence from China. Int. J. Discl. Gov. 2021, 18, 161–178. [Google Scholar] [CrossRef]
  52. Grimaldi, F.; Caragnano, A.; Zito, M.; Mariani, M. Sustainability engagement and earnings management: The Italian context. Sustainability 2020, 12, 4881. [Google Scholar] [CrossRef]
  53. Habib, A.M. Does real earnings management affect a firm’s environmental, social, and governance (ESG), financial performance, and total value? A moderated mediation analysis. Environ. Dev. Sustain. 2024, 26, 28239–28268. [Google Scholar] [CrossRef]
  54. Velte, P. The bidirectional relationship between ESG performance and earnings management–empirical evidence from Germany. J. Glob. Responsib. 2019, 10, 322–338. [Google Scholar] [CrossRef]
  55. Rahman, H.U.; Zahid, M.; Khan, P.A.; Al-Faryan, M.A.S.; Hussainey, K. Do corporate sustainability practices mitigate earnings management? The moderating role of firm size. Bus. Strategy Environ. 2024, 33, 5423–5444. [Google Scholar] [CrossRef]
  56. Khuong, N.V.; Ly, H.T.N.; Anh, L.H.T. Accrual-based, real activities earnings management and corporate social responsibility: A virtuous circle? emerging market evidence. Cogent Econ. Financ. 2023, 11, 2209955. [Google Scholar] [CrossRef]
  57. Amarna, K.; Garde Sánchez, R.; López-Pérez, M.V.; Marzouk, M. The effect of environmental, social, and governance disclosure and real earning management on the cost of financing. Corp. Soc. Responsib. Environ. Manag. 2024, 31, 3181–3193. [Google Scholar] [CrossRef]
  58. Martínez-Ferrero, J.; Banerjee, S.; García-Sánchez, I.M. Corporate social responsibility as a strategic shield against costs of earnings management practices. J. Bus. Ethics 2016, 133, 305–324. [Google Scholar] [CrossRef]
  59. Masmoudi, S.; Ben Salem, M. Reconnecting sustainability reporting with earnings management: Empirical evidence from Kuwait. Account. Res. J. 2024, 37, 270–287. [Google Scholar] [CrossRef]
  60. Alodat, A.Y.; Al Amosh, H.; Alorayni, O.; Khatib, S.F. Does corporate sustainability disclosure mitigate earnings management: Empirical evidence from Jordan. Int. J. Discl. Gov. 2024, 21, 165–174. [Google Scholar] [CrossRef]
  61. Borralho, J.M.; Hernández-Linares, R.; Gallardo-Vázquez, D.; de Sousa Paiva, I.C. Environmental, social and governance disclosure’s impacts on earnings management: Family versus non-family firms. J. Clean. Prod. 2022, 379, 134603. [Google Scholar] [CrossRef]
  62. Velte, P. Environmental performance, carbon performance and earnings management: Empirical evidence for the European capital market. Corp. Soc. Responsib. Environ. Manag. 2021, 28, 42–53. [Google Scholar] [CrossRef]
  63. Beyer, A.; Guttman, I.; Marinovic, I. Earnings management and earnings quality: Theory and evidence. Account. Rev. 2019, 94, 77–101. [Google Scholar] [CrossRef]
  64. Lo, K. Earnings management and earnings quality. J. Account. Econ. 2008, 45, 350–357. [Google Scholar] [CrossRef]
  65. Freeman, R.E. Stakeholder Management: A Strategic Approach; Pitman: New York, NY, USA, 1984; p. 46. [Google Scholar]
  66. Jensen, M.C. Value maximization, stakeholder theory, and the corporate objective function. J. Appl. Corp. Financ. 2001, 14, 8–21. [Google Scholar] [CrossRef]
  67. Choi, B.B.; Lee, D.; Park, Y. Corporate social responsibility, corporate governance and earnings quality: Evidence from Korea. Corp. Gov. Int. Rev. 2013, 21, 447–467. [Google Scholar] [CrossRef]
  68. Harrison, J.S.; Bosse, D.A.; Phillips, R.A. Managing for stakeholders, stakeholder utility functions, and competitive advantage. Strateg. Manag. J. 2010, 31, 58–74. [Google Scholar] [CrossRef]
  69. Bhaskaran, R.K.; Ting, I.W.K.; Sukumaran, S.K.; Sumod, S.D. Environmental, social and governance initiatives and wealth creation for firms: An empirical examination. Manag. Decis. Econ. 2020, 41, 710–729. [Google Scholar] [CrossRef]
  70. Adeneye, Y.B.; Fasihi, S.; Kammoun, I.; Albitar, K. Does earnings management constrain ESG performance? The role of corporate governance. Int. J. Discl. Gov. 2024, 21, 69–92. [Google Scholar] [CrossRef]
  71. Al-Shaer, H. Sustainability reporting quality and post-audit financial reporting quality: Empirical evidence from the UK. Bus. Strategy Environ. 2020, 29, 2355–2373. [Google Scholar] [CrossRef]
  72. Siregar, S.V.; Utama, S. Type of earnings management and the effect of ownership structure, firm size, and corporate-governance practices: Evidence from Indonesia. Int. J. Account. 2008, 43, 1–27. [Google Scholar] [CrossRef]
  73. Chi, J.D.; Gupta, M. Overvaluation and earnings management. J. Bank. Financ. 2009, 33, 1652–1663. [Google Scholar] [CrossRef]
  74. Jensen, M.C.; Meckling, W.H. Theory of the firm: Managerial behavior, agency costs and ownership structure. J. Financ. Econ. 1976, 3, 305–360. [Google Scholar] [CrossRef]
  75. Davidson III, W.N.; Jiraporn, P.; Kim, Y.S.; Nemec, C. Earnings management following duality-creating successions: Ethnostatistics, impression management, and agency theory. Acad. Manag. J. 2004, 47, 267–275. [Google Scholar] [CrossRef]
  76. Burke, Q.L.; Chen, P.C.; Lobo, G.J. Is corporate social responsibility performance related to conditional accounting conservatism? Account. Horiz. 2020, 34, 19–40. [Google Scholar] [CrossRef]
  77. Lee, J.; Koh, K. ESG performance and firm risk in the US financial firms. Rev. Financ. Econ. 2024, 42, 328–344. [Google Scholar] [CrossRef]
  78. Lemma, T.T.; Shabestari, M.A.; Freedman, M.; Mlilo, M. Corporate carbon risk exposure, voluntary disclosure, and financial reporting quality. Bus. Strategy Environ. 2020, 29, 2130–2143. [Google Scholar] [CrossRef]
  79. Zhang, T.; Zhang, Z.; Yang, J. When does corporate social responsibility backfire in acquisitions? Signal incongruence and acquirer returns. J. Bus. Ethics 2022, 175, 45–58. [Google Scholar] [CrossRef]
  80. He, F.; Du, H.; Yu, B. Corporate ESG performance and manager misconduct: Evidence from China. Int. Rev. Financ. Anal. 2022, 82, 102201. [Google Scholar] [CrossRef]
  81. Liu, X.; Dai, J.; Dong, X.; Liu, J. ESG rating disagreement and analyst forecast quality. Int. Rev. Financ. Anal. 2024, 95, 103446. [Google Scholar] [CrossRef]
  82. Liu, M.; Luo, X.; Lu, W.Z. Public perceptions of environmental, social, and governance (ESG) based on social media data: Evidence from China. J. Clean. Prod. 2023, 387, 135840. [Google Scholar] [CrossRef]
  83. Gillan, S.L.; Koch, A.; Starks, L.T. Firms and social responsibility: A review of ESG and CSR research in corporate finance. J. Corp. Financ. 2021, 66, 101889. [Google Scholar] [CrossRef]
  84. Zhou, M.; Huang, Z.; Jiang, K. Environmental, social, and governance performance and corporate debt maturity in China. Int. Rev. Financ. Anal. 2024, 95, 103349. [Google Scholar] [CrossRef]
  85. Farinha, J.; Mateus, C.; Soares, N. Cash holdings and earnings quality: Evidence from the Main and Alternative UK markets. Int. Rev. Financ. Anal. 2018, 56, 238–252. [Google Scholar] [CrossRef]
  86. Wang, D. Founding family ownership and earnings quality. J. Account. Res. 2006, 44, 619–656. [Google Scholar] [CrossRef]
  87. Palacios-Manzano, M.; Gras-Gil, E.; Santos-Jaen, J.M. Corporate social responsibility and its effect on earnings management: An empirical research on Spanish firms. Total Qual. Manag. Bus. Excell. 2021, 32, 921–937. [Google Scholar] [CrossRef]
  88. Mao, Z.; Wang, S.; Lin, Y.E. ESG, ESG rating divergence and earnings management: Evidence from China. Corp. Soc. Responsib. Environ. Manag. 2024, 31, 3328–3347. [Google Scholar] [CrossRef]
  89. Salem, R.I.A.; Ghazwani, M.; Gerged, A.M.; Whittington, M. Anti-corruption disclosure quality and earnings management in the United Kingdom: The role of audit quality. Int. J. Account. Inf. Manag. 2023, 31, 528–563. [Google Scholar] [CrossRef]
  90. Yuan, X.; Li, Z.; Xu, J.; Shang, L. ESG disclosure and corporate financial irregularities–Evidence from Chinese listed firms. J. Clean. Prod. 2022, 332, 129992. [Google Scholar] [CrossRef]
  91. Walsh, J.P.; Seward, J.K. On the efficiency of internal and external corporate control mechanisms. Acad. Manag. Rev. 1990, 15, 421–458. [Google Scholar] [CrossRef]
  92. Velayutham, E. Sustainability disclosure and earnings management. In Research Handbook of Finance and Sustainability; Boubaker, S., Cumming, D., Nguyen, D.K., Eds.; Elgar: New York, NY, USA, 2018; pp. 347–367. [Google Scholar]
  93. Habbash, M.; Haddad, L. The impact of corporate social responsibility on earnings management practices: Evidence from Saudi Arabia. Soc. Responsib. J. 2020, 16, 1073–1085. [Google Scholar] [CrossRef]
  94. Houqe, M.N.; Opare, S.; Zahir-Ul-Hassan, M.K. Carbon emissions, female CEOs and earnings management. Int. J. Account. Inf. Manag. 2024, 32, 593–621. [Google Scholar] [CrossRef]
  95. Xi, J.; Xiao, H. Relation among corporate environmental disclosure, earnings management and accounting conservatism: Evidence from Chinese listed firms. Manag. Audit. J. 2022, 37, 565–593. [Google Scholar] [CrossRef]
  96. Yasar, A.; Yalçın, N. Voluntary disclosure of scope 3 greenhouse gas emissions and earnings management: Evidence from UK companies. Cogent Bus. Manag. 2023, 10, 2275849. [Google Scholar] [CrossRef]
  97. Sun, W.; Chen, S.; Jiao, Y.; Feng, X. How does ESG constrain corporate earnings management? Evidence from China. Financ. Res. Lett. 2024, 61, 104983. [Google Scholar] [CrossRef]
  98. Ahmad, N.; Mobarek, A.; Roni, N.N. Revisiting the impact of ESG on financial performance of FTSE350 UK firms: Static and dynamic panel data analysis. Cogent Bus. Manag. 2021, 8, 1900500. [Google Scholar] [CrossRef]
  99. Pincus, M.; Rajgopal, S. The interaction between accrual management and hedging: Evidence from oil and gas firms. Account. Rev. 2002, 77, 127–160. [Google Scholar] [CrossRef]
  100. Lobo, G.J.; Zhou, J. Did conservatism in financial reporting increase after the Sarbanes-Oxley Act? Initial evidence. Account. Horiz. 2006, 20, 57–73. [Google Scholar] [CrossRef]
  101. Jassim, A.; Dexter, C.R.; Sidhu, A. Agency theory: Implications for financial management. Manag. Financ. 1988, 14, 1–5. [Google Scholar] [CrossRef]
  102. Salamon, G.L.; Smith, E.D. Corporate control and managerial misrepresentation of firm performance. Bell J. Econ. 1979, 10, 319–328. [Google Scholar] [CrossRef]
  103. Türegün, N. Effects of borrowing costs, firm size, and characteristics of board of directors on earnings management types: A study at Borsa Istanbul. Asia-Pac. J. Account. Econ. 2018, 25, 42–56. [Google Scholar] [CrossRef]
  104. Setyoputri, L.S.; Mardijuwono, A.W. The impact of firm attributes on earnings management. Pol. J. Manag. Stud. 2020, 22, 502–512. [Google Scholar] [CrossRef]
  105. Naz, I.; Bhatti, K.; Ghafoor, A.; Khan, H.H. Impact of firm size and capital structure on earnings management: Evidence from Pakistan. Int. J. Contemp. Bus. Stud. 2011, 2, 22–31. [Google Scholar]
  106. He, Y.; Chittoor, R. When does it (not) pay to be good? Interplay between stakeholder and competitive strategies. J. Manag. 2023, 49, 2490–2522. [Google Scholar] [CrossRef]
  107. Bayar, O.; Floros, I.V.; Liu, Y.; Mao, J. Litigation and information effects on private sales of securities. J. Corp. Financ. 2024, 88, 102628. [Google Scholar] [CrossRef]
  108. Azmi, W.; Anwer, Z.; Mohamad, S.; Shah, M.E. The substitution hypothesis of agency conflicts: Evidence on Shariah compliant equities. Glob. Financ. J. 2019, 41, 90–103. [Google Scholar] [CrossRef]
  109. Abdelbaky, A.; Liu, T.; Mingyang, X.; Shahzad, M.F.; Hassanein, A. Real Earnings Management and ESG Performance in China: The Mediating Role of Corporate Innovations. Int. J. Financ. Econ. 2024, ahead of print. [Google Scholar] [CrossRef]
  110. Anderson, M.; Hyun, S.; Warsame, H. Corporate social responsibility, earnings management and firm performance: Evidence from panel VAR estimation. Rev. Quant. Financ. Account. 2024, 62, 341–364. [Google Scholar] [CrossRef]
  111. Bansal, M.; Kumar, V. Forcing responsibility? Examining earnings management induced by mandatory corporate social responsibility: Evidence from India. Rev. Account. Financ. 2021, 20, 194–216. [Google Scholar] [CrossRef]
  112. Choi, B.; Lee, D.; Luo, L. Carbon Disclosure and Common Ownership. J. Account. Audit. Financ. 2024, ahead of print. [Google Scholar] [CrossRef]
  113. Berg, F.; Kölbel, J.F.; Rigobon, R. Aggregate confusion: The divergence of ESG ratings. Rev. Financ. 2022, 26, 1315–1344. [Google Scholar] [CrossRef]
  114. Haque, F. The effects of board characteristics and sustainable compensation policy on carbon performance of UK firms. Br. Account. Rev. 2017, 49, 347–364. [Google Scholar] [CrossRef]
  115. Seok, J.; Kim, Y.; Oh, Y.K. How ESG shapes firm value: The mediating role of customer satisfaction. Technol. Forecast. Soc. Change 2024, 208, 123714. [Google Scholar] [CrossRef]
  116. Benuzzi, M.; Sahin, Ö.; Paterlini, S. From KPIs to ESG: Addressing Redundancy and Distortions in ESG Scores. 2025. Available online: https://ssrn.com/abstract=5136069 (accessed on 19 May 2025).
  117. Candio, P. The effect of ESG and CSR attitude on financial performance in Europe: A quantitative re-examination. J. Environ. Manag. 2024, 354, 120390. [Google Scholar] [CrossRef]
  118. Rahman, A.F.; Bintoro, N.S.; Dewi, A.A.; Kholilah, K. The effect of ESG and earnings quality on the value relevance of earnings and book value. Australas. Account. Bus. Financ. J. 2024, 8, 133–157. [Google Scholar] [CrossRef]
  119. Zhang, J.; Li, Y.; Xu, H.; Ding, Y. Can ESG ratings mitigate managerial myopia? Evidence from Chinese listed companies. Int. Rev. Financ. Anal. 2023, 90, 102878. [Google Scholar] [CrossRef]
  120. Porta, R.L.; Lopez-de-Silanes, F.; Shleifer, A.; Vishny, R.W. Law and finance. J. Political Econ. 1998, 106, 1113–1155. [Google Scholar] [CrossRef]
  121. Koutoupis, A.; Fassas, A.; Nerantzidis, M.; Persakis, A.; Tzeremes, P. ESG and cost of capital components: Does the legal system matter? J. Account. Organ. Change 2025, ahead of print. [Google Scholar] [CrossRef]
  122. Amor-Esteban, V.; García-Sánchez, I.M.; Galindo-Villardón, M.P. Analysing the effect of legal system on corporate social responsibility (CSR) at the country level, from a multivariate perspective. Soc. Indic. Res. 2018, 140, 435–452. [Google Scholar] [CrossRef]
  123. DasGupta, R.; Roy, A. Firm environmental, social, governance and financial performance relationship contradictions: Insights from institutional environment mediation. Technol. Forecast. Soc. Change 2023, 189, 122341. [Google Scholar] [CrossRef]
  124. Sikalidis, A.; Bozos, K.; Chantziaras, A.; Grose, C. Influences of family ownership on dividend policy under mandatory dividend rules. Rev. Quant. Financ. Account. 2022, 59, 939–967. [Google Scholar] [CrossRef]
  125. Pozzoli, M.; Pagani, A.; Paolone, F. The impact of audit committee characteristics on ESG performance in the European Union member states: Empirical evidence before and during the COVID-19 pandemic. J. Clean. Prod. 2022, 371, 133411. [Google Scholar] [CrossRef]
  126. Chen, S.; Song, Y.; Gao, P. Environmental, social, and governance (ESG) performance and financial outcomes: Analyzing the impact of ESG on financial performance. J. Environ. Manag. 2023, 345, 118829. [Google Scholar] [CrossRef]
  127. Cohen, D.A.; Zarowin, P. Accrual-based and real earnings management activities around seasoned equity offerings. J. Account. Econ. 2010, 50, 2–19. [Google Scholar] [CrossRef]
  128. Orlitzky, M.; Schmidt, F.L.; Rynes, S.L. Corporate social and financial performance: A meta-analysis. Organ. Stud. 2003, 24, 403–441. [Google Scholar] [CrossRef]
  129. Verschoor, C.C. Corporate performance is closely linked to a strong ethical commitment. Bus. Soc. Rev. 1999, 104, 407–415. [Google Scholar] [CrossRef]
  130. Bae, S.C.; Chang, K.; Yi, H.C. Corporate social responsibility, credit rating, and private debt contracting: New evidence from syndicated loan market. Rev. Quant. Financ. Account. 2018, 50, 261–299. [Google Scholar] [CrossRef]
  131. Gerged, A.M.; Al-Haddad, L.M.; Al-Hajri, M.O. Is earnings management associated with corporate environmental disclosure? Evidence from Kuwaiti listed firms. Account. Res. J. 2020, 33, 167–185. [Google Scholar] [CrossRef]
  132. Reber, B. Does mispricing, liquidity or third-party certification contribute to IPO downside risk? Int. Rev. Financ. Anal. 2017, 51, 25–53. [Google Scholar] [CrossRef]
  133. Choi, H.; Choi, B.; Byun, J. The relationship between corporate social responsibility and earnings management: Accounting for endogeneity. Invest. Manag. Financ. Innov. 2018, 15, 69. [Google Scholar] [CrossRef]
  134. Attig, N.; El Ghoul, S.; Guedhami, O.; Suh, J. Corporate social responsibility and credit ratings. J. Bus. Ethics 2013, 117, 679–694. [Google Scholar] [CrossRef]
  135. Hajek, P.; Sahut, J.M.; Myskova, R. Predicting corporate credit ratings using the content of ESG reports. Ann. Oper. Res. 2024, ahead of print. [Google Scholar] [CrossRef]
  136. Christensen, D.M.; Serafeim, G.; Sikochi, A. Why is corporate virtue in the eye of the beholder? The case of ESG ratings. Account. Rev. 2022, 97, 147–175. [Google Scholar] [CrossRef]
  137. Erdiaw-Kwasie, M.O.; Abunyewah, M.; Baah, C. Corporate social responsibility (CSR) and cognitive bias: A systematic review and research direction. Resour. Policy 2023, 86, 104201. [Google Scholar] [CrossRef]
Table 1. Process of sample selection and distribution across industries.
Table 1. Process of sample selection and distribution across industries.
Panel A: Process of Sample Selection
70,311We focus on firms with data for ESG performance from Refinitiv Eikon (Initial sample)
(20,974)We exclude firm-year observations with missing values
(17,287)We exclude firm-year observations of regulated sectors (financial services and utilities)
32,050 Final sample [t = 2003, 2022]
Panel B: Sample distribution by Industry (GICS Sector Name)
Communication Services2341Industrials6777
Consumer Discretionary3629Information Technology3378
Consumer Staples2059Materials3876
Energy2604Real Estate3062
Health Care4324
Total32,050
Table 2. Summary statistics.
Table 2. Summary statistics.
Variables ObsMean Std. DevMinMax
EQ32,05052.44027.155399
REM32,0500.0180.107−0.4070.280
ESG32,05047.47020.6346.24088.451
BG32,05017.50113.484050
BS32,05010.2453.247421
FS32,05010.4031.1258.40313.913
LEV32,05018.58516.377068.985
ROA32,0505.9425.6800.15930.085
BIG432,0500.5200.50001
CEOD32,0500.4000.49001
LIQ32,05015.01350.430.005396.287
AT32,0508.8977.009127
INTINT32,0500.0370.0831.06 × 10−50.557
ORIGIN32,0500.4970.50001
DEVELOPED32,0500.7890.40801
Table 3. Pearson correlations matrix.
Table 3. Pearson correlations matrix.
Panel A. EQ
Variables EQESGBGBSFSLEVROABIG4CEODLIQATINTINTORIGINDEVELOPED
EQ1.000
ESG0.043 ***1.000
BG0.039 ***0.305 ***1.000
BS−0.044 ***0.223 ***0.079 ***1.000
FS−0.122 ***0.239 ***−0.221 ***0.205 ***1.000
LEV−0.134 ***0.097 ***0.109 ***−0.023 ***−0.118 ***1.000
ROA0.383 ***−0.047 ***−0.014 **−0.178 ***−0.189 ***−0.106 ***1.000
BIG40.054 ***−0.036 ***−0.0030.049 ***−0.125 ***0.151 ***0.022 ***1.000
CEOD0.049 ***−0.032 ***0.034 ***0.027 ***−0.099 ***0.067 ***0.030 ***0.182 ***1.000
LIQ0.004−0.148 ***−0.027 ***−0.115 ***−0.112 ***−0.126 ***0.114 ***−0.110 ***−0.060 ***1.000
AT0.071 ***0.036 ***0.134 ***0.012 **−0.148 ***0.104 ***0.043 ***0.503 ***0.191 ***−0.075 ***1.000
INTINT0.067 ***−0.129 ***0.028 ***−0.144 ***−0.298 ***−0.033 ***0.106 ***0.0070.025 ***0.065 ***0.030 ***1.000
ORIGIN0.053 ***−0.132 ***0.097 ***−0.044 ***−0.375 ***0.186 ***0.0050.476 ***0.260 ***−0.135 ***0.440 ***0.091 ***1.000
DEVELOPED0.038 ***0.086 ***0.203 ***0.030 ***−0.273 ***0.172 ***−0.045 ***0.128 ***0.146 ***−0.288 ***0.111 ***0.095 ***0.514 ***1.000
Panel B. REM
Variables REMESGBGBSFSLEVROABIG4CEODLIQATINTINTORIGINDEVELOPED
REM1.000
ESG0.057 ***1.000
BG−0.050 ***0.305 ***1.000
BS0.133 ***0.223 ***0.079 ***1.000
FS0.329 ***0.239 ***−0.221 ***0.205 ***1.000
LEV0.001 *0.097 ***0.109 ***−0.023 ***−0.118 ***1.000
ROA−0.460 ***−0.047 ***−0.014 **−0.178 ***−0.189 ***−0.106 *** 1.000
BIG4−0.032 ***−0.036 ***−0.003 0.049 ***−0.125 ***0.151 *** 0.022 ***1.000
CEOD−0.048 ***−0.032 ***0.034 *** 0.027 ***−0.099 ***0.067 *** 0.030 ***0.182 ***1.000
LIQ0.017 **−0.148 ***−0.027 ***−0.115 ***−0.112 ***−0.126 *** 0.114 ***−0.110 ***−0.060 *** 1.000
AT−0.044 ***0.036 ***0.134 *** 0.012 **−0.148 ***0.104 *** 0.043 ***0.503 ***0.191 ***−0.075 ***1.000
INTINT−0.248 ***−0.129 ***0.028 ***−0.144 ***−0.298 ***−0.033 *** 0.106 ***0.0070.025 *** 0.065 ***0.030 ***1.000
ORIGIN−0.111 ***−0.132 ***0.097 ***−0.044 ***−0.375 ***0.186 *** 0.0050.476 ***0.260 ***−0.135 ***0.440 ***0.091 ***1.000
DEVELOPED−0.130 ***0.086 ***0.203 *** 0.030 ***−0.273 ***0.172 ***−0.045 ***0.128 ***0.146 ***−0.288 ***0.111 ***0.095 ***0.514 ***1.000
*** Significant at p < 0.001; ** p < 0.05; * p < 0.1.
Table 4. ESG: impact on EQ and REM.
Table 4. ESG: impact on EQ and REM.
VariablesModel FE: EQModel FE: REMModel FE: EQ Large Firms (Above the Mean)Model FE: EQ Small Firms (Below the Mean)Model FE: REM Large Firms (Above the Mean)Model FE: REM Small Firms (Below the Mean)
ESG0.114 ***
(0.012)
−1.24 × 10−4 **
(5.34 × 10−5)
0.097 ***
(0.017)
0.138 ***
(0.016)
−2.49 × 10−4 ***
(6.37 × 10−5)
−1.359 × 10−4 *
(7.570 × 10−5)
BG0.030 *
(0.018)
−2.81 × 10−5
(7.67 × 10−5)
−0.011
(0.027)
0.061 **
(0.022)
−1.62 × 10−4 *
(9.71 × 10−5)
−1.892 × 10−4 *
(1.009 × 10−4)
BS0.018
(0.072)
6.20 × 10−4 **
(3.04 × 10−4)
−0.051
(0.098)
0.261 **
(0.110)
−4.66 × 10−4
(3.30 × 10−4)
−1.342 × 10−3 **
(5.065 × 10−4)
FS−1.758 ***
(0.243)
0.020 ***
(0.001)
−1.480 ***
(0.424)
−2.436 ***
(0.667)
2.68 × 10−3 *
(1.55 × 10−3)
0.069 ***
(0.004)
LEV−0.219 ***
(0.015)
1.87 × 10−5
(6.24 × 10−5)
−0.316 ***
(0.028)
−0.186 ***
(0.018)
−4.87 × 10−4 ***
(9.25 × 10−5)
5.660 × 10−5
(7.740 × 10−5)
ROA1.743 ***
(0.038)
−7.90 × 10−3 ***
(2.31 × 10−4)
2.088 ***
(0.073)
1.550 ***
(0.043)
−8.11 × 10−3 ***
(3.41 × 10−4)
−7.324 × 10−3 ***
(2.531 × 10−4)
BIG41.420 **
(0.511)
0.004 *
(0.002)
1.904 **
(0.739)
1.121 *
(0.687)
−2.68 × 10−3
(2.48 × 10−3)
0.010 **
(0.003)
CEOD1.148 **
(0.443)
−5.37 × 10−4
(0.002)
0.869
(0.672)
1.440 **
(0.568)
−2.86 × 10−3
(2.43 × 10−3)
2.950 × 10−6
(0.003)
LIQ−0.017 ***
(0.005)
1.65 × 10−4 ***
(2.48 × 10−5)
−0.075 ***
(0.020)
−8.48 × 10−3 **
(3.87 × 10−3)
4.29 × 10−4 ***
(7.19 × 10−5)
8.170 × 10−5 ***
(1.880 × 10−5)
AT0.107 **
(0.037)
1.12 × 10−4
(1.57 × 10−4)
0.072
(0.057)
0.123 **
(0.047)
−3.27 × 10−4
(2.05 × 10−4)
1.764 × 10−4
(2.111 × 10−4)
INTINT4.550 *
(2.700)
−0.188 ***
(0.017)
0.181
(15.256)
4.966 *
(2.688)
−0.248 ***
(0.053)
−0.117 ***
(0.017)
ORIGIN0.528
(0.667)
0.003
(0.003)
0.547
(1.091)
1.140
(0.817)
0.013 ***
(0.004)
−0.011 **
(0.004)
DEVELOPED1.048
(0.705)
−0.014 ***
(0.003)
1.248
(0.941)
−0.665
(1.157)
−0.010 **
(0.003)
0.009 *
(0.005)
CONSTANT55.153 ***
(2.788)
−0.135 ***
(0.014)
54.346 ***
(5.411)
59.373 ***
(6.407)
0.098 ***
(0.019)
−0.609 ***
(0.035)
Adj R-squared0.1780.3170.1860.1650.2950.319
Note: *** Significant at p < 0.001; ** p < 0.05; * p < 0.1. Standard errors in parentheses.
Table 5. ESG dimensions: impact on EQ and REM.
Table 5. ESG dimensions: impact on EQ and REM.
VariablesModel FE: EQ (E)Model FE: EQ (S)Model FE: EQ (G)Model FE: REM (E)Model FE: REM (S)Model FE: REM (G)
E0.047 ***
(0.009)
1.036 × 10−4 **
(3.870 × 10−5)
S 0.085 ***
(0.010)
−1.324 × 10−4 **
(4.320 × 10−5)
G 0.062 ***
(0.010)
−7.030 × 10−5
(4.410 × 10−5)
BG0.059 ***
(0.017)
0.043 **
(0.017)
0.052 **
(0.018)
−1.295 × 10−4 *
(7.520 × 10−5)
−2.530 × 10−5
(7.450 × 10−5)
−5.050 × 10−5
(7.730 × 10−5)
BS0.054
(0.073)
0.028
(0.073)
0.131 *
(0.071)
3.320 × 10−4
(3.034 × 10−4)
6.572 × 10−4 **
(3.050 × 10−4)
4.980 × 10−4 *
(2.992 × 10−4)
FS−1.524 ***
(0.246)
−1.586 ***
(0.240)
−1.431 ***
(0.240)
0.019 ***
(0.001)
0.020 ***
(1.232 × 10−3)
0.020 ***
(0.001)
LEV−0.214 ***
(0.015)
−0.219 ***
(0.015)
−0.208 ***
(0.015)
−1.540 × 10−5
(6.260 × 10−5)
2.560 × 10−5
(6.240 × 10−5)
7.130 × 10−6
(6.190 × 10−5)
ROA1.753 ***
(0.039)
1.741 ***
(0.038)
1.766 ***
(0.038)
−7.962 × 10−3 ***
(2.331 × 10−4)
−7.887 × 10−3 ***
(2.302 × 10−4
−0.008 ***
(0.000)
BIG41.500 **
(0.515)
1.507 **
(0.511)
1.486 **
(0.511)
0.003
(0.002)
3.770 × 10−3
(2.302 × 10−3)
0.004
(0.002)
CEOD1.048 **
(0.445)
0.943 **
(0.442)
1.432 ***
(0.450)
−6.481 × 10−4
(0.002)
−2.357 × 10−4
(1.943 × 10−3)
−8.622 × 10−4
(0.002)
LIQ−0.019 ***
(0.005)
−0.016 ***
(0.005)
−0.020 ***
(0.005)
1.715 × 10−4 ***
(2.480 × 10−5)
1.626 × 10−4 ***
(2.480 × 10−5)
1.683 × 10−4 ***
(2.470 × 10−5)
AT0.112 **
(0.038)
0.102 **
(0.037)
0.133 ***
(0.037)
5.960 × 10−5
(1.567 × 10−4)
1.288 × 10−4
(1.577 × 10−4)
8.430 × 10−5
(1.574 × 10−4)
INTINT4.249
(2.705)
4.081
(2.696)
3.756
(2.705)
−0.184 ***
(0.017)
−0.188 ***
(0.017)
−0.187 ***
(0.017)
ORIGIN0.431
(0.693)
0.593
(0.669)
−0.646
(0.665)
0.006 *
(0.003)
2.671 × 10−3
(3.240 × 10−3)
0.004
(0.003)
DEVELOPED1.510 **
(0.713)
0.991
(0.710)
2.006 **
(0.702)
−0.016 ***
(0.003)
−0.013 ***
(3.190 × 10−3)
−0.015 ***
(0.003)
CONSTANT54.878 ***
(2.852)
54.484 ***
(2.790)
51.582 ***
(2.792)
−0.124 ***
(0.014)
−0.136 ***
(0.014)
−0.131 ***
(0.014)
Adj R-squared0.1740.1770.1740.3170.3170.317
Note: *** Significant at p < 0.001; ** p < 0.05; * p < 0.1. Standard errors in parentheses.
Table 6. Quantile regression analysis.
Table 6. Quantile regression analysis.
Panel A. EQ and ESG
EQ
q10q20q30q40q50q60q70q80q90
ESG0.079 ***
(0.012)
0.112 ***
(0.011)
0.136 ***
(0.012)
0.145 ***
(0.011)
0.142 ***
(0.012)
0.133 ***
(0.012)
0.120 ***
(0.012)
0.103 ***
(0.011)
0.063 ***
(0.010)
Constant−1.208
(2.672)
13.935 ***
(2.477)
32.414 ***
(2.743)
45.949 ***
(2.644)
58.469 ***
(2.697)
70.484 ***
(2.727)
84.167 ***
(2.754)
95.209 ***
(2.562)
104.157 ***
(2.266)
ControlsIncludedIncludedIncludedIncludedIncludedIncludedIncludedIncludedIncluded
Pseudo R20.0650.0950.1090.1150.1160.1130.1060.0940.072
Panel B. REM and ESG
REM
q10q20q30q40q50q60q70q80q90
ESG2.687 × 10−4 ***
(5.89 × 10−5)
1.227 × 10−4 **
(3.75 × 10−5)
3.16 × 10−5
(3.03 × 10−5)
−3.23 × 10−5
(2.67 × 10−5)
−1.03 × 10−4 ***
(2.59 × 10−5)
−1.61 × 10−4 ***
(2.72 × 10−5)
−2.09 × 10−4 ***
(2.79 × 10−5)
−2.92 × 10−4 ***
(2.94 × 10−5)
−4.45 × 10−4 ***
(4.11 × 10−5)
Constant−0.281 ***
(0.014)
−0.166 ***
(0.009)
−0.119 ***
(0.007)
−0.089 ***
(0.006)
−0.068 ***
(0.006)
−0.050 ***
(0.006)
−0.033 ***
(0.006)
−0.009
(0.007)
0.032 **
(0.009)
ControlsIncludedIncludedIncludedIncludedIncludedIncludedIncludedIncludedIncluded
Pseudo R20.3230.2780.2320.1840.1430.1240.1170.1040.085
Note: *** Significant at p < 0.001; ** p < 0.05; Standard errors in parentheses.
Table 7. Robust checks.
Table 7. Robust checks.
VariablesLNEQEQ: Excluding United States, China, and Hong KongREM: Excluding United States, China, and Hong Kong2SLS EQ2SLS REM
ESG0.003 ***
(3.29 × 10−4)
0.109 ***
(0.016)
−2.254 × 10−4 **
(7.150 × 10−5)
0.093 ***
(0.012)
−1.376 × 10−4 **
(4.110 × 10−5)
BG0.001 **
(5.06 × 10−4)
0.016
(0.022)
7.250 × 10−7
(9.920 × 10−5)
0.021 *
(0.013)
1.151 × 10−4 **
(4.510 × 10−5)
BS0.003
(0.002)
−0.077
(0.092)
8.170 × 10−4 **
(3.811 × 10−4)
0.084 *
(0.045)
3.502 × 10−4 **
(1.563 × 10−4)
FS−0.039 ***
(0.007)
−1.936 ***
(0.268)
0.015 ***
(0.001)
−1.593 ***
(0.159)
0.020 ***
(0.001)
LEV−0.006 ***
(4.27 × 10−4)
−0.248 ***
(0.024)
−2.696 × 10−4 **
(9.870 × 10−5)
−0.214 ***
(0.009)
2.280 × 10−5
(3.360 × 10−5)
ROA0.040 ***
(0.001)
1.678 ***
(0.055)
−0.008 ***
(3.460 × 10−4)
1.738 ***
(0.027)
−0.008 ***
(1.398 × 10−4)
BIG40.035 **
(0.015)
−0.494
(0.719)
0.006 **
(0.003)
1.721 ***
(0.348)
0.001
(0.001)
CEOD0.032 **
(0.012)
1.272 **
(0.633)
0.003
(0.003)
1.294 ***
(0.292)
−0.001
(0.001)
LIQ−6.40 × 10−4 ***
(1.45 × 10−4)
−0.006
(0.027)
6.904 × 10−4 ***
(1.543 × 10−4)
−0.019 ***
(0.003)
1.715 × 10−4 ***
(1.430 × 10−5)
AT0.004 ***
(0.001)
−0.023
(0.061)
5.768 × 10−4 **
(2.551 × 10−4)
0.094 ***
(0.024)
1.899 × 10−4 **
(8.830 × 10−5)
INTINT0.013
(0.079)
−4.062
(4.525)
−0.188 ***
(0.025)
4.026 **
(1.898)
−0.185 ***
(0.010)
ORIGIN0.033 *
(0.019)
−1.934 *
(1.079)
−0.007
(0.005)
0.543
(0.416)
0.004 **
(0.002)
DEVELOPED0.031
(0.020)
−2.016 **
(0.842)
−0.015 ***
(0.004)
1.547 ***
(0.444)
−0.017 ***
(0.002)
CONSTANT3.727 ***
(0.080)
63.670 ***
(3.362)
−0.074 ***
(0.017)
53.425 ***
(1.800)
−0.134 ***
(0.007)
Adj R-squared0.1280.1690.3140.1750.304
*** Significant at p < 0.001; ** p < 0.05; * p < 0.1. Standard errors in parentheses. In the first stage of the 2SLS estimation, the F-statistic is 22,215.6 (well above the threshold of 10), indicating strong instrument relevance, and the adjusted R2 is 0.5633.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Vatis, S.E.; Drogalas, G.; Persakis, A.; Chytis, E. The Impact of ESG on Earnings Quality and Real Earnings Management: The Role of Firm Size. Sustainability 2025, 17, 5027. https://doi.org/10.3390/su17115027

AMA Style

Vatis SE, Drogalas G, Persakis A, Chytis E. The Impact of ESG on Earnings Quality and Real Earnings Management: The Role of Firm Size. Sustainability. 2025; 17(11):5027. https://doi.org/10.3390/su17115027

Chicago/Turabian Style

Vatis, Stylianos Efstratios, George Drogalas, Antonios Persakis, and Evangelos Chytis. 2025. "The Impact of ESG on Earnings Quality and Real Earnings Management: The Role of Firm Size" Sustainability 17, no. 11: 5027. https://doi.org/10.3390/su17115027

APA Style

Vatis, S. E., Drogalas, G., Persakis, A., & Chytis, E. (2025). The Impact of ESG on Earnings Quality and Real Earnings Management: The Role of Firm Size. Sustainability, 17(11), 5027. https://doi.org/10.3390/su17115027

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop