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Purpose

This study aims to investigate the institutional determinants of firms’ environmental, social and governance (ESG) performance. Drawing on stakeholder and institutional theories, the authors examine how three distinct types of national institutions – formal, informal and financial market institutions – shape firms’ ESG performance. By analyzing the differential effects of these institutional types on each component of ESG performance, the study offers a more nuanced understanding of the relationship between institutional environments and corporate social performance.

Design/methodology/approach

The authors developed and tested a multi-source data set analyzing the ESG performance of 3,872 firms across 49 countries over 15 years (2002–2016) using data from the ASSET4 database. To assess the relationships between the three types of institutions and firms’ ESG performance, the authors used multilevel random-effects models with lagged independent variables.

Findings

This research provides a comprehensive analysis that links specific types of institutions to each component of ESG performance, rather than to aggregate corporate social responsibility performance. It advances stakeholder and institutional theories by demonstrating how distinct stakeholder groups – represented through different institutional arrangements – shape specific ESG outcomes, thereby offering valuable insights for policymakers and managers.

Originality/value

This study provides a comprehensive examination of how distinct types of institutions are linked to individual ESG dimensions.

Firms operate within institutional environments that shape the nature and extent of their responsible behavior. Scholars have long recognized that institutions matter for corporate social responsibility (CSR), which in turn leads to corporate social performance (CSP) (Campbell, 2007; Ioannou and Serafeim, 2012; Matten and Moon, 2008). A vast body of literature has examined how national institutions including legal systems and cultural norms influence CSR activities (Chapple and Moon, 2005; Jackson and Apostolakou, 2010; Young and Makhija, 2014). For example, firms from different institutional contexts display distinct CSR profiles (Matten and Moon, 2008, 2020), and regulatory, normative and cognitive pressures have been shown to empower stakeholders, such as governments, nongovernmental organizations (NGOs) and customers, to press firms toward enhanced social performance (Brammer et al., 2012; Kim et al., 2013; Oetzel and Doh, 2009).

Despite their recognized importance, the effects of national institutions on CSR activities remain underexplored. While recent studies have begun to examine differential institutional effects on CSR (e.g. Luo et al., 2017; Marquis and Qian, 2014), these studies have not yet systematically linked distinct institutional pillars (formal, informal and financial market institutions) to specific environmental, social and governance (ESG) dimensions. Much of the literature treats both institutions and CSP as aggregate constructs, overlooking the heterogeneity of institutional mechanisms and the multidimensionality of corporate responsibility (El Ghoul et al., 2019; Hartmann and Uhlenbruck, 2015). Although prior research has examined formal institutions (e.g. regulatory and legal systems; Jackson and Apostolakou, 2010; Matten and Moon, 2008), informal institutions (e.g. cultural norms and societal values; Miska et al., 2018; Ringov and Zollo, 2007) and, more recently, financial market institutions (e.g. investor systems and market regulations; Dyck et al., 2019; Gillan et al., 2021), these streams have rarely been integrated within a unified framework. Furthermore, ESG performance, now widely recognized as the operational dimensions of CSP, has only recently begun to be conceptualized as comprising distinct yet interrelated domains. Together, these two forms of aggregation limit our ability to explain why firms operating in similar institutional environments may exhibit uneven ESG profiles, excelling in some dimensions while lagging in others.

We address these gaps by disaggregating both sides of the relationship, institutional environments and CSP outcomes. Disaggregating both institutional pillars and ESG outcomes is therefore theoretically valuable because it helps identify the specific stakeholder pressures – regulatory, normative and market-based – through which institutions influence corporate sustainability. It is also practically important, as policymakers and multinational enterprises (MNEs) increasingly rely on ESG metrics while operating under heterogeneous institutional conditions that prioritize different sustainability concerns. Without such disaggregation, ESG policies risk becoming overly generic and misaligned with the institutional realities firms face across countries. Thus, conceptually, we distinguish formal, informal and financial market institutions, each associated with coercive, normative and market-based pressures, respectively. Analytically, we link these three types of institutional environments to the three ESG dimensions that operationalize CSP (Eccles et al., 2014).

Building on institutional and stakeholder perspectives, we theorize that different institutional pillars are associated with distinct mechanisms through which firms are encouraged to attend to ESG concerns. Importantly, these associations reflect theoretical correspondence rather than empirical comparisons of relative strength, and do not imply that any institutional pillar affects only a single ESG dimension. Accordingly, this study seeks to answer the following research question:

RQ1.

How do formal, informal and financial market institutions shape firms’ environmental, social and governance dimensions of corporate sustainability, and through what mechanisms?

By addressing this question, we contribute to both theory and practice. Theoretically, we move beyond the general proposition that “institutions matter” (Campbell, 2007) toward a more nuanced, theoretically grounded account of how and why different institutional conditions shape distinct aspects of ESG performance. Drawing on stakeholder salience theory (Mitchell et al., 1997), we clarify how institutional environments condition the power and legitimacy of stakeholder groups, thereby channeling firm attention toward specific ESG dimensions. Practically, recognizing institutional heterogeneity offers important insights for policymakers and MNEs, suggesting that global ESG standardization efforts and policy initiatives must account for differences in various institutional forms across countries to be effective (Linnenluecke, 2022; Xu et al., 2021).

The following section builds the theoretical foundation for this argument by clarifying key concepts and situating our study within the broader CSR and institutional literature.

Research on corporate responsibility has evolved from normative discussions of CSR to measurable assessments of CSP and, more recently, to investment-relevant ESG performance (Carroll, 1979; Eccles et al., 2014; Ioannou and Serafeim, 2012; Wartick and Cochran, 1985; Wood, 1991). To ensure conceptual clarity, we distinguish among these three related but conceptually distinct constructs. CSR refers to the overarching normative principles and values that define firms’ moral and strategic obligations to stakeholders and society (Carroll, 1979; Donaldson and Preston, 1995). CSP captures the measurable outcomes of CSR initiatives – how effectively firms translate these responsibilities into concrete social and environmental achievements (Wartick and Cochran, 1985; Wood, 1991). ESG performance represents the empirical operationalization of CSP, disaggregating it into three investment-relevant dimensions of ESG that provide standardized indicators used by investors and policymakers to evaluate firms’ nonfinancial sustainability (Eccles et al., 2014; El Ghoul et al., 2017; Ioannou and Serafeim, 2012).

Following this conceptual–analytical–empirical hierarchy, ESG performance serves as the operational manifestation of CSP, which in turn embodies the normative foundations of CSR. Accordingly, we use ESG in our empirical analyses while referring to CSP and CSR when discussing performance and ethical underpinnings, respectively.

Stakeholder theory posits that firms’ long-term success depends on maintaining productive relationships with diverse stakeholder groups such as investors, customers, employees, communities and governments, each of which holds varying degrees of power and interest (Donaldson and Preston, 1995; Freeman et al., 2010; Mitchell et al., 1997). Firms prioritize stakeholders whose claims are most salient, responding through targeted CSR activities that enhance legitimacy and reputation (Ng and Rezaee, 2015; Orlitzky et al., 2017). Practically and empirically, these activities serve as the foundation for measuring ESG performance (Cheng et al., 2014; Eccles et al., 2014).

The environmental dimension captures firms’ management of ecological impacts throughout their operations, such as energy efficiency, waste reduction and CO2 emissions control (ISO, 1999; Trumpp et al., 2015). Firms demonstrating superior environmental performance reduce pollution, improve resource efficiency and generate new opportunities through eco-innovation and cleaner technologies (Rao and Holt, 2005; Russo and Fouts, 1997). Such pro-environmental practices – exemplified by ISO 14001 certification – are increasingly valued by regulators, consumers and investors as indicators of responsible management (Flammer, 2015; Klassen and McLaughlin, 1996). The social dimension reflects how firms manage relationships with employees, customers and communities. High social performance entails fostering safe and equitable workplaces, respecting human rights, investing in employee development and supporting community well-being (El Ghoul et al., 2017). Firms that neglect social concerns risk reputational damage and stakeholder conflict (Berman et al., 2017; Lutz, 2015), whereas those engaging in community and employee initiatives build trust and lower operational risks (Porter and Kramer, 2006; Waddock and Graves, 1997). Finally, the governance dimension concerns the structures and processes by which firms are directed and held accountable (Dahya et al., 1996; Jo and Kim, 2008). Strong governance systems are characterized by transparent disclosure, independent oversight and effective monitoring. Such systems reduce agency problems, align management with stakeholder interests and support long-term strategic consistency (Amel-Zadeh and Serafeim, 2018; Husted and de Sousa-Filho, 2017). Together, these dimensions capture how firms use stakeholder management through measurable aspects of CSP.

Institutional theory complements the stakeholder perspective by explaining why stakeholder salience differs across countries. Institutions shape both the expectations of stakeholders and the mechanisms through which they exert influence on firms (Campbell, 2007; Matten and Moon, 2008; North, 1990). When institutional pressures are strong, firms face higher expectations for ESG performance and greater penalties for non-compliance (Aguilera et al., 2007; Shin et al., 2023). Recent work underscores the need to integrate corporate governance, stakeholder and institutional perspectives to better explain how firms manage environmental and social sustainability within diverse governance systems (Aguilera et al., 2021).

Recent literature further cautions against treating national institutional context as monolithic. Andrews et al. (2024) depict formal institutions as a “layer cake” of governance levels whose interactions can generate misaligned pressures for firms. Keig et al. (2015) showed that formal and informal institutions are distinct and independently consequential, such that focusing on only one can mischaracterize firms’ environments. Lewellyn and Muller-Kahle (2024) likewise demonstrated that institutional conditions combine with internal governance in equifinal configurations to shape ESG performance. Together, these studies shift the conversation from whether institutions matter to how institutional pillars combine, interact and sometimes conflict in shaping firms’ responsibility outcomes. However, this emerging work has not yet systematically linked distinct institutional pillars to the separate E, S and G domains, nor has it clarified which pillar is most salient for which ESG dimension. Addressing this gap, we theorize and test pillar-dimension linkages across formal, informal and financial market institutions.

First, formal institutions reflect the coercive power and legitimacy of government and regulatory stakeholders, who impose coercive pressures through laws, disclosure mandates and enforcement systems. Such institutions increase transparency, codify stakeholder rights and raise expectations for responsible conduct (Antal and Sobczak, 2007; Campbell, 2007; Liang and Renneboog, 2017). Second, informal institutions are defined by the normative legitimacy of society-at-large and include cultural values, norms and moral expectations that create normative pressures for fairness, equity and collective well-being (Husted and de Sousa-Filho, 2017; Ringov and Zollo, 2007). In societies emphasizing trust and social solidarity, firms often go beyond compliance to address community and employee welfare (Eden and Miller, 2004; Ho et al., 2024; Miska et al., 2018; Shin et al., 2023). Third, financial market institutions combine formal rules with cognitive logics embedded in capital markets, shaping firms’ access to finance and the monitoring role of investors and intermediaries (Dyck et al., 2019; Gillan and Starks, 2000; Ioannou and Serafeim, 2012). Market-based systems emphasize shareholder value and transparency, whereas bank-based systems foster longer-term stakeholder relationships (Fiss and Zajac, 2004; Jackson and Apostolakou, 2010). The expansion of socially responsible investment (SRI) and sustainability indices further amplifies investor attention to ESG performance (Eccles et al., 2014; Revelli, 2017).

From a stakeholder salience perspective, institutional pillars determine which stakeholder groups possess the greatest influence and how their interests, in turn, shape the ESG issues that firms prioritize (Marano and Kostova, 2016; Mitchell et al., 1997). Specifically, institutional arrangements can amplify stakeholders’ ability to influence firms (power), elevate the perceived appropriateness of their claims (legitimacy) and, in some contexts, heighten the immediacy of those claims (urgency). As a result, institutional environments do not merely constrain firm behavior, they systematically channel firms’ attention and resource allocation toward ESG performance.

Recent literature further underscores the dynamic interaction between shareholders and other stakeholders. DesJardine et al. (2023) showed that shareholder engagement and activism can either complement or conflict with broader stakeholder interests depending on institutional conditions. Integrating this insight, we argue that national institutions shape how investors, regulators and societal actors interact, ultimately influencing the salience of different ESG domains. This framework highlights the interdependence between institutional environments and stakeholder relationships as the foundation for cross-national variation in ESG performance.

While all institutional pillars can shape multiple dimensions of ESG performance, our framework focuses on the dominant theoretical linkages, connecting each pillar to its most salient stakeholder group and the primary mechanisms proposed by institutional and stakeholder salience perspectives. Stakeholder salience depends on power, legitimacy and urgency (Mitchell et al., 1997). Formal institutions shape salience by granting legal standing, strengthening enforcement (power/legitimacy) and increasing the credibility of sanctions and compliance expectations (urgency). Informal institutions, such as social norms and the media, influence legitimacy and urgency through reputational pressures and strengthen stakeholder power by facilitating information diffusion and collective scrutiny (Hong et al., 2024; Mitchell et al., 1997). Financial market institutions enhance investors’ monitoring capacity and capital-allocation leverage (power) while standardizing disclosure expectations (legitimacy). This emphasis is intended to clarify the main pathways in the literature, not to imply exclusivity. Other mechanisms may also operate and institutional domains can jointly influence multiple ESG dimensions, including through complementarities across domains.

Formal institutions which are laws, regulations and enforcement mechanisms shape firms’ behaviors by defining the formal “rules of the game” and strengthening the coercive authority of governments and regulators (Campbell, 2007; Powell and DiMaggio, 1991; North, 1990). When corruption is low, government functions efficiently and the rule of law is strong, regulatory agencies have the legitimacy and capacity to establish and enforce clear standards for corporate conduct. Such credible and consistent enforcement heightens firms’ compliance pressures and motivates proactive corporate responsibility. These external pressures from government stakeholders often lead firms to prioritize their CSR initiatives (Brammer et al., 2012; Matten and Moon, 2008).

Beyond broad CSR initiatives, these formal institutional pressures are most visibly expressed in the environmental domain, where regulation directly targets measurable corporate externalities. Governments and regulatory agencies possess both the legitimacy and capacity to set, monitor and enforce environmental standards, compelling firms to reduce pollution, manage emissions and improve operational efficiency (Lanoie et al., 2011; Rivera and Oh, 2013). These coercive pressures are particularly salient for the environmental dimension of ESG because environmental externalities, such as pollution or resource depletion, are directly subject to public regulation and oversight (Murillo‐Luna et al., 2008). Strong formal institutions thus provide a stable and credible policy environment that encourages firms to internalize environmental responsibilities as part of their compliance and legitimacy strategies.

While formal institutions may also influence social and governance outcomes, their most direct and consistent effects are expected in the environmental domain, where policy design, enforcement capacity and legal accountability mechanisms converge:

H1.

Strong formal institutions (as reflected in corruption, government efficiency and rule of law) positively affect firms’ environmental performance.

Informal institutions reflect the shared values, norms and moral expectations of a society and thereby shape firms’ legitimacy processes. Unlike formal regulatory institutions, informal institutions derive their influence through normative pressures that emerge from society-at-large, including communities, the media and NGOs (Brammer et al., 2021; Doh et al., 2010). These societal actors collectively constitute the informal institutional environment that defines what constitutes socially appropriate or desirable behavior for firms (Suchman, 1995). The literature on the social license to operate underscores that receiving and maintaining social approval from these groups is critical for firms’ sustained operations and legitimacy (Demuijnck and Fasterling, 2016; Ho et al., 2024; Park, 2023). Failure to meet societal expectations can provoke public backlash, NGO campaigns or community opposition, leading to reputational damage and operational disruptions (Mele and Armengou, 2016; Oh et al., 2023).

In institutional environments characterized by freedom of the press, high levels of social trust and active civic engagement, firms are subject to more intense societal scrutiny. The media and NGOs play important roles in monitoring corporate behavior and shaping public discourse around fairness, welfare and social equity. These normative pressures increase the salience of societal stakeholders, compelling firms to align with prevailing social norms and values. Because legitimacy concerns are directly tied to how firms treat employees, customers and communities, the effects of informal institutions are most pronounced in the social (S) dimension of ESG, where legitimacy depends on fulfilling relational and moral obligations toward these stakeholder groups.

While informal institutions can also influence firms’ environmental behavior by shaping general societal attitudes toward sustainability (e.g. Ringov and Zollo, 2007), their primary mechanisms, including social approval, moral alignment and reputational standing, are inherently social. These mechanisms operate through immediate and visible relationships with societal stakeholders, making social performance the most responsive domain of firm behavior (Aguilera et al., 2007). Hence, in countries characterized by strong informal institutions that promote trust, transparency and civic participation, firms are more likely to engage in socially responsible practices to maintain legitimacy and social approval:

H2.

Strong informal institutions (as reflected in press freedom, interpersonal trust and NGO activity) positively affect firms’ social performance.

Financial market institutions represent a distinct institutional pillar, separate from formal and informal institutions, because they influence firms through capital allocation, investor oversight and market discipline rather than through regulatory coercion or societal norms. In this context, individual and institutional investors act as principal stakeholders who can exert significant pressure on firms to change their behavior (Orlitzky et al., 2017). When financial markets are well developed, investors and financial intermediaries become salient actors capable of shaping firms’ strategic decisions and holding them accountable for governance quality (Aguilera and Jackson, 2003; Dyck et al., 2019).

Investors influence firms by linking access to external financing with expectations of responsible governance. Through mechanisms such as shareholder activism and SRI, they signal market preferences for transparency, accountability and ethical behavior (Chhaochharia and Grinstein, 2007; Oh et al., 2013; Scholtens, 2006). These practices collectively represent market-based channels through which financial market institutions exert normative and economic pressures on firms to strengthen their governance systems and align managerial incentives with broader stakeholder interests.

The influence of financial market institutions is therefore most visible in the governance (G) dimension of ESG performance, where capital market scrutiny and investor engagement serve as de facto governance mechanisms. Prior research suggests that systems characterized by active investors and diverse financial sources tend to produce stronger corporate governance and ethical oversight (Aguilera and Jackson, 2003; Arora and Dharwadkar, 2011; Harjoto and Jo, 2011). Moreover, empirical evidence shows that investor-oriented institutional environments, such as those with strong investor protection, enhance board composition for more effective monitoring and strategic advising (Kim and Ozdemir, 2014), reinforcing firms’ governance quality. In such contexts, firms face ongoing monitoring from investors and regulatory authorities, which fosters improved board effectiveness, accountability and transparency. Although financial markets may also shape firms’ environmental and social activities, for instance, when investors integrate ESG criteria into investment decisions (Oh et al., 2013), their most direct and consistent effects arise in the governance (G) domain, where investor oversight and regulatory enforcement jointly reinforce managerial accountability and governance integrity:

H3.

Strong financial market institutions (as reflected in investor protection, soundness of bank, regulation of securities exchange) positively affect firms’ governance performance.

Our empirical analysis draws on a multi-country panel of 3,872 publicly listed firms, comprising 25,233 firm-year observations across 49 countries. The data set spans 2002–2016, offering rich longitudinal coverage across a formative phase in the global ESG landscape, including the post-Kyoto Protocol years, the diffusion of the UN Global Compact and the lead-up to the Paris Agreement. While more recent ESG data have become available, this earlier window offers a valuable historical baseline for understanding how institutional environments initially shaped corporate ESG practices, before the widespread adoption of mandatory disclosure regimes and global reporting standards. Table 1 shows the sample composition: the number of observations by country and year.

Dependent variables. We collected information on firm-level ESG performance from the ASSET4 database provided by Thomson Reuters (now renamed LSEG). The ASSET4 database offers comprehensive ESG information for individual firms based on 280 key performance indicators and 750 individual items. Thomson Reuters gathered various CSR-related information from over 900 public sources, such as firms’ CSR annual reports and news articles. Using this information, we measure firms’ ESG outcomes using the environmental (E), social (S) and governance (G) scores, which are constructed by aggregating category-level assessments and scaled to a 0–100 range. We also compute an overall ESG score as the simple average of E, S and G (de Villiers et al., 2022).

Independent variables. To measure each institution as a composite, we combined three sources. Building on institutional theory in international business, we conceptualize formal institutions as codified rules and enforcement systems administered through state authority (Dau et al., 2022; Holmes et al., 2013; Solarino and Boyd, 2023). First, formal institutions consist of the Corruption Perception Index (CPI) from Transparency International, Government Efficiency from the World Economic Forum’s Global Competitiveness Index (GCI) and the rule of law from the World Bank’s Worldwide Governance Indicators (WGI). We operationalize formal institutional quality using these indicators because they capture the integrity and capacity of the state’s legal-administrative apparatus – specifically, whether formal rules are credibly implemented and enforced. This approach is consistent with established IB research that measures formal institutional environments using such governance indicators (e.g. Correa da Cunha et al., 2024; Donnelly et al., 2024). The CPI ranges from 0 to 100, with a higher score indicating lower perceived corruption. The GCI measure of government efficiency is based on a 1–7 scale, where higher values indicate more efficient and credible public administration. The WGI’s Rule of Law index ranges from −2.5 to + 2.5, where higher scores represent stronger adherence to rules and regulations. Because the three measures use different scales, we standardized each indicator to a z-score and then averaged the standardized values to create the composite index of formal institutional quality. Higher values of this index represent stronger formal institutional environments.

Second, to capture informal institutional quality, we draw on the conceptualization of informal institutions as non-codified norms and information or monitoring arrangements that shape behavior through social approval, reputational sanctioning and civil-society channels (Dau et al., 2022; Holmes et al., 2013; Solarino and Boyd, 2023). Accordingly, we used three indicators from three different sources: the Press Freedom Index from Reporters Without Borders (RSF), generalized trust from the World Values Survey (WVS) and the density of NGOs per capita. Interpersonal trust is a canonical informal institution widely used in IB research (Dau et al., 2022; Gaur et al., 2022; Lu et al., 2018). Press freedom reflects de facto openness of information flows and public scrutiny – key mechanisms of social monitoring and reputational sanctioning – even when formal laws exist but are weakly enforced (Holmes et al., 2013; Lu et al., 2018; Solarino and Boyd, 2023). NGO density serves as a proxy for the strength of organized civil society, which increases capacity for decentralized oversight and informal norm enforcement (Dau et al., 2022). The RSF Press Freedom Index ranges from 0 to 100, where lower values indicate greater freedom. To ensure consistency across indicators, we reverse-coded this index so that higher scores correspond to higher levels of press freedom. The generalized trust variable measures the proportion of respondents based on the statement that “most people can be trusted” in each country. Because WVS data are collected every four years, we interpolated and forward-filled values to minimize missing values within the panel. NGO density was manually compiled as the number of registered NGOs divided by the national population, with population data obtained from the World Bank’s World Development Indicators (WDI). Each indicator was standardized to a z-score and then averaged to form the composite index of informal institutions. This index allows us to capture the normative and cognitive dimensions of the informal institutional environment, reflecting media integrity, social trust and civic engagement.

Third, the composite measure of financial market institutions consists of the protection of minority shareholders’ interests by the legal system, strength of investor protection and soundness of banks. We obtained all of these from the GCI. Since these three variables range from 1 to 7, with a higher score indicating better performance of each indicator, we averaged them to construct a composite index of financial market development. In this index, a value of 7 represents the most developed financial market, whereas a value of 1 represents the least developed one.

Control variables. For the control variables, we included the firms’ characteristics, which can affect the firms’ CSP. The firm-level information was collected from Datastream provided by Thomson Reuters. The firm characteristics included return on assets (ROA) to control for financial performance, the log of total assets to control for firm size, the debt ratio to control for financial risk and foreign sales to total sales to control for the degree of internationalization. We also controlled for firms’ industries using the standard industrial classification (SIC) 1-digit code, which is similar to the 2-digit North American Industry Classification System (NAICS).

For the country-level control variables, we included several variables that might affect firms’ economic and social behaviors. We included GDP annual growth, population (log), unemployment rate, land size (log) and foreign direct investment inflow (log) to control for macro socio-economic factors. We also included labor market efficiency indices to control for the characteristics of product and labor market development. The sources of these data are the WDI and the GCI.

After matching these data sets, the number of firm-year observations is 25,233 for 3,872 firms in 49 countries between 2002 and 2016. We used a multi-level mixed-effects regression model to test the three hypotheses. In our data, the firm-level data is nested within the industry- and country-level, so we apply multi-level modeling. As firm characteristics within industries and countries may not be independently distributed, ignoring the interdependency between firm-, industry- and country-level characteristics can produce biased empirical results (Hofmann, 1997).

Formal, informal and financial market institutions are interdependent components of national institutional systems and may be correlated to some degree. We view this covariance as reflecting institutional embeddedness rather than conceptual redundancy. Empirically, the key consideration is whether including these pillars jointly still allows us to identify theoretically distinct associations with ESG outcomes. Table 2 presents the summary statistics and correlation matrix for all the variables. The correlation matrix and variance inflation factor (VIF) diagnostics indicate that multicollinearity is not a concern. The maximum VIF is 3.60 (below the conventional cutoff of 5) and tolerance values range from 0.28 to 0.99 (above the commonly used threshold of 0.20), indicating no evidence of problematic collinearity among the institutional variables. All the independent and control variables are lagged by one year to reduce the issue of reverse causality. We also include year and industry (1-digit SIC) fixed effects.

Table 3 presents the results from our multi-level mixed-effects regression models. In Model (1), we only included the control variables and used a composite measure for ESG (i.e. an average of the ESG performance) as the dependent variable. All of the firm-level control variables have positive impacts on the different dimensions of the firms’ CSP. These results show that large firms with high levels of profitability, leverage and international sales have high levels of CSP. Regarding the country-level control variables, FDI inflows had consistently negative impacts on the different dimensions of the firms’ CSP. With firm-level control variables, ROA, total assets and foreign sales to total sales had consistently positive impacts on the different CSP dimensions.

In Model (2), we added three institutional variables (i.e. formal, informal and financial market institutions) to Model (1). These composite formal and informal institutional indices are significantly associated with the composite measure of CSP. Specifically, strong formal institutions (β = 1.6226, p < 0.1) and high-quality informal institutions (β = 4.4092, p < 0.001) improve CSP. Like previous studies that argued the positive impact of well-developed institutions on CSP (Campbell, 2007; Rathert, 2016; Young and Makhija, 2014), formal and informal institutions positively affected the firms’ overall CSP. Furthermore, the composite index of financial market institutions, which is conceptually distinct from both formal and informal institutions, is significantly associated with firms’ overall CSP (β = 1.1884, p < 0.05).

In Models (3)–(5), we used an individual dimension of CSP (i.e. E, S and G, respectively) as the dependent variable. These results show that formal, informal and financial market institutions enhance different dimensions of CSP. In Model (3), we used environmental performance as the dependent variable and the results show that formal institutions are positively and significantly associated with environmental performance (β = 3.4679, p < 0.01). In addition, informal institutions are positively and significantly associated with environmental performance (β = 4.1902, p < 0.001), while financial market institutions are not significant (β = 0.1927, p = n.s.). Firms in transparent, efficient and credible policy environments are more likely to be environmentally responsible.

In Model (4), informal institutions are significantly associated with social performance (β = 2.4227, p < 0.01), while formal institutions (β = 1.7594, p = n.s.) and financial market institutions (β = 1.1616, p = n.s.) are not. In other words, firms in societies with high trust, ethical norms and civic engagement are more likely to be socially responsible in that they care about their employees, customers and communities.

Finally, financial market institutions enhance corporate governance performance in Model (5) (β = 1.8820, p < 0.001). In addition, informal institutions are also significant in regard to improving corporate governance performance (β = 6.2010, p < 0.001). By contrast, formal institutions have a statistically insignificant effect (β = −1.4321, p = n.s.). Firms embedded in sound financial systems and socially cohesive environments tend to adopt stronger corporate governance structures and more transparent management practices. These results support all three hypotheses.

To ensure robustness, we re-estimated the models using alternative indicators of the three institutional pillars. Table 4 presents results by replacing each pillar with an alternative measurement one at a time, whereas Table 5 provides results when three alternative measures are included together. For formal institutions, we used a principal component analysis-based measurement that integrates the La Porta legal origin classification, the Corruption Perceptions Index from transparency international and government efficiency from GCI. Consistent with the main results, the alternative measure of formal institutions remained positively and significantly associated with firms’ environmental performance (β = 7.0760, p < 0.05 in Model (1) of Table 4; β = 6.2952, p < 0.05 in Model (2) of Table 5).

For informal institutions, we used an alternative composite measure that averages media integrity from V-Dem, trust from WVS and ethical behavior of firms from GCI. The results were robust, showing that this alternative measure remained significantly associated with social performance (β = 3.1690, p < 0.01 in Model (2) of Table 4; β = 2.9548, p < 0.01 in Model (3) of Table 5).

For financial market institutions, we used an alternative composite measure based on the business extent of disclosure index from the doing business project, market capitalization of listed domestic companies from WDI and voice and accountability from WGI and all these three variables are from the World Bank. The results showed that these alternative financial institutions also positively affect firms’ corporate governance performance (β = 6.6769, p < 0.001 in Model (3) of Table 4; β = 6.1062, p < 0.001 in Model (3) of Table 5).

Over the last two decades, scholars have increasingly examined CSP and related topics (Kolk and van Tulder, 2010). Yet systematic attention to how country institutions shape CSP has emerged more recently and the institutional sources of cross-country differences in sustainability outcomes remain incompletely understood. Kolk (2016) noted that much of the sustainability literature does not yet fully incorporate cross-national variation and related work calls for closer attention to both formal and informal institutions to address this gap (Brammer et al., 2012; Tolmie et al., 2020).

Drawing on institutional theory and stakeholder salience (Mitchell et al., 1997), this study provides theoretical and empirical evidence on how and why different types of country institutions are associated with firms’ CSP differently across ESG dimensions. From a stakeholder salience perspective, institutional pillars shape which stakeholder groups become most influential in a given national context and, in turn, which ESG issues firms prioritize (Marano and Kostova, 2016; Mitchell et al., 1997). We do not directly measure stakeholder power, legitimacy or urgency; rather, we use stakeholder salience as an organizing theoretical lens to explain why distinct institutional pillars are likely to elevate different stakeholder demands and channel sustainability pressures toward particular ESG domains. Using a multi-level analysis of 25,233 observations of 3,872 firms across 49 countries from 2002 to 2016, we find that formal institutions are positively associated with environmental performance, informal institutions with social performance and both formal and financial market institutions with corporate governance performance.

Taken together, these findings underscore that institutional environments generate patterned variation across ESG dimensions, helping to explain why firms operating in similar national contexts may excel in certain aspects of CSP while lagging in others. By disaggregating both institutional pillars and ESG outcomes, our study helps reconcile previously mixed findings in cross-national CSP research. Rather than asking whether institutions matter for sustainability, we clarify which institutional domains matter for which ESG outcomes and provide a theoretically grounded explanation for this pattern. This interpretation complements recent work on layered institutional systems in which multiple institutional domains coexist and exert differentiated influences on firm behavior (Andrews et al., 2024), by specifying how institutional heterogeneity translates into systematic differences in ESG performance.

This study contributes to the literature on country institutions and CSP by providing a mechanism-based explanation of how institutional heterogeneity translates into differentiated ESG outcomes. Building on earlier research linking national institutions to CSR and CSP (Chapple and Moon, 2005; Ioannou and Serafeim, 2012; Young and Makhija, 2014), we move beyond treating institutions and CSP as aggregate constructs and instead theorize how distinct institutional pillars map onto specific ESG dimensions.

First, drawing on stakeholder salience theory (Mitchell et al., 1997), we explain how institutional environments shape not only the intensity of sustainability pressures but also their direction by elevating the influence of different stakeholder groups. Although we do not directly measure power, legitimacy or urgency in our empirical models, the stakeholder salience framework helps specify why different institutional pillars are likely to make particular stakeholder claims more consequential for firms and thus more likely to be reflected in ESG outcomes. Stronger formal institutions increase the credibility and enforceability of regulatory demands, making environmental expectations more difficult to ignore and more strongly associated with environmental performance. Stronger informal institutions amplify societal expectations communicated by communities, NGOs and civil society, making social concerns more salient and more strongly associated with social performance. Stronger financial market institutions intensify investor monitoring and voice through disclosure demands, capital allocation and shareholder influence, making governance-related expectations more strongly associated with corporate governance performance. In this way, our framework clarifies why firms allocate attention unevenly across ESG domains depending on which stakeholder groups are institutionally empowered.

Second, our study advances institutional theory by specifying how layered institutional environments generate differentiated firm responses. While recent work highlights institutional heterogeneity and layered systems (Andrews et al., 2024; Brammer et al., 2021; Sun et al., 2021), direct evidence on how such heterogeneity translates into concrete outcome differences remains limited. Importantly, our findings do not imply that institutional pillars operate in isolation or that each pillar influences only one ESG dimension. Rather, they highlight dominant – but non-exclusive – pathways through which layered institutional environments shape corporate sustainability outcomes. Consistent with this view, we also find a positive association between formal institutions and corporate governance performance, even though we do not hypothesize this relationship. Because corporate governance is partly an enforcement and accountability system grounded in legal and regulatory frameworks (Jamali et al., 2008), stronger formal institutions can enhance governance outcomes, aligning with research showing that governance evolves with stronger regulation (Larcker et al., 2011; Martynova and Renneboog, 2011). At the same time, our results indicate that financial market institutions can improve governance outcomes even in contexts where formal legal systems are less developed, highlighting a market-based channel that can substitute for weaker regulatory infrastructure.

Third, we extend prior institutional research by explicitly incorporating financial market institutions as a distinct institutional domain alongside formal and informal institutions. Whereas prior work often emphasizes political, legal or cultural institutions (Goerzen et al., 2023; Ioannou and Serafeim, 2012; Peng et al., 2023), our results show that financial market institutions play an independent role in shaping governance-related ESG outcomes. Financial market institutions differ from formal and informal institutions in that they exert pressure through capital allocation, monitoring and investor voice. Consistent with institutional arguments about legitimacy-seeking and isomorphic pressures (Powell and DiMaggio, 1991), investors can leverage financing conditions and market expectations to influence governance practices (David et al., 2007). Mechanisms such as SRI and shareholder activism therefore constitute important sources of institutional pressure shaping corporate governance (Oh et al., 2013). Our evidence suggests that these market-based pressures can operate even when other institutional supports are weaker, aligning with recent work on configurational patterns of ESG performance (Lewellyn and Muller-Kahle, 2024). More broadly, this distinction is theoretically important because financial markets combine regulatory requirements with market expectations regarding transparency, disclosure and fiduciary responsibility – pressures that are not fully captured by formal or informal institutions alone.

Our findings offer several implications for MNEs managing ESG initiatives across diverse institutional contexts. Prior research suggests that firms benefit from achieving an optimal fit between institutional environments and CSR practices (Brammer and Pavelin, 2006). Extending this logic, our results highlight that effective ESG strategies require alignment with three key institutional pillars – formal, informal and financial market institutions – which shape different stakeholder pressures and ESG priorities across countries.

Building on institutional theory (North, 1990; Scott, 1995), national governance systems can be understood as comprising these three distinct institutional pillars, each exerting different types of pressures on firms through different stakeholder groups. Formal institutions primarily empower regulators and shape environmental externalities through legal rules and enforcement; informal institutions elevate societal actors such as employees, communities and NGOs, shaping social expectations and legitimacy; and financial market institutions strengthen investor oversight, reinforcing governance standards through capital-market discipline. This tripartite structure provides a theoretically grounded basis for understanding why firms prioritize certain ESG dimensions in specific institutional contexts.

For MNE managers, this implies that ESG strategies should be tailored rather than standardized across countries. When operating in environments with strong formal institutions, MNE subsidiaries should prioritize compliance-driven environmental initiatives and invest in systems that meet stringent regulatory expectations. In contexts characterized by strong informal institutions, firms benefit from strengthening community engagement, employee welfare and social programs to secure local legitimacy. Where financial market institutions are dominant, MNEs should focus on governance-related practices such as transparency, board oversight and investor communication to meet capital-market expectations.

Taken together, these differentiated strategies enable MNEs to navigate institutional distance across their global operations and allocate ESG resources more effectively. More broadly, our findings underscore that ESG performance is not solely a matter of firm-level commitment; it is also shaped by country institutional environments that determine which stakeholder claims carry the greatest weight in a given country.

As an extension of this study, we would like to provide some research avenues related to the relationship between institutions and CSP. First, it would be worthwhile to have in-depth analyses of some of the ESG dimensions. Considering that more firms operate internationally, we need to investigate the differences in firms’ CSP between home and host countries. It is important to look at how foreign firms adopt strong (or weak) institutional pressures for advanced CSR practices and coordinate their operations between their home and host countries. We do not expect each type of institution to have the same impact on the adoption of CSR and the coordination of firms’ international operations.

Second, scholarly research has recently emphasized the importance of the social dimension of CSR and the relationship between multinational corporations (MNCs) and stakeholders in host countries, such as communities and consumers (Dorobantu et al., 2017; Henisz et al., 2014; Moon et al., 2015; Oh and Shin, 2025). Conflicts with stakeholders in host countries cause a great deal of risks to MNCs. MNCs’ operations can be delayed or suspended due to such conflicts (Grzybowski, 2012). Future research should discuss how firms can find the best fit between informal institutions and CSR by accommodating the demands of employees, customers and communities and lowering the number of conflicts with them in their host countries. While more scholarly attention should be paid to this stream of research, we hope that our paper establishes an understanding of the importance of informal institutions and CSP.

Third, while our focus was on disentangling the dominant institutional mechanisms driving distinct ESG dimensions, future research could further examine how formal, informal and financial market institutions interact in shaping firms’ sustainability strategies. These institutional domains may complement one another when regulatory systems, societal norms and capital market expectations align or conflict when their underlying logics diverge. Exploring such interactions across national and regional contexts would deepen our understanding of how institutional complementarities and tensions jointly influence the direction and effectiveness of firms’ ESG initiatives. In particular, the comparatively strong influence of informal institutions across the ESG dimensions suggests that they function as a form of broad societal monitoring. By amplifying the urgency and salience of expectations related to these three dimensions, informal institutions may help explain why information institutions have consistent effects across all pillars (Hong et al., 2024; Yoshikawa et al., 2021). Future research could further disentangle the mechanisms of informal institutions to better understand how this broad societal monitoring operates.

Fourth, while our use of lagged, country-level institutional variables mitigates concerns about reverse causality, we acknowledge that the relationship between institutions and firm behavior may not be entirely unidirectional. For example, large or highly visible firms may, in rare cases, influence national policies or shape societal norms through lobbying or exemplary ESG practices. However, as shown by the sample composition in Table 1, our sample is largely composed of firms from advanced economies, where market-driven competition makes it unlikely for individual firms to influence national policies, societal norms or financial market conditions and likely impossible in the short term.

Finally, our data set concludes in 2016 and thus does not reflect more recent institutional changes, such as the EU Corporate Sustainability Reporting Directive, the implementation phase of the Paris Agreement or widespread carbon neutrality commitments. While our results provide a robust historical benchmark for understanding institutional effects on ESG performance, we encourage future research to replicate and extend our analysis with post-2016 data to assess the impact of these more recent developments.

In this study, we examined a cross-country comparison of country institutions and their impacts on environmental, social and corporate governance (ESG) performance. We found that different types of country institutions have different impacts on ESG performance. Specifically, a country’s formal institutions have a significant impact on firms’ environmental and corporate governance performances, and a country’s informal institutions have a significant impact on firms’ social performance. We used financial market institutions as a distinctive type of country institution that could be separated from formal and informal institutions in the case of CSP and found a positive relationship with firms’ corporate governance performance.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The authors sincerely acknowledge the invaluable guidance and insightful comments of the journal editor, Tazeeb Rajwani and the two anonymous reviewers.

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Data & Figures

Table 1.

Sample composition by country and year

CountryFreq.%Cum.YearFreq.%Cum.
Australia1,4535.765.7620025562.22.2
Austria1570.626.3820035672.254.45
Belgium1650.657.0320049703.848.29
Brazil3471.388.4120051,262513.3
Canada1,4915.9114.3220061,0874.3117.6
Chile840.3314.6520071,4845.8823.49
China5872.3316.9820081,766730.48
Czech Republic150.0617.0420092,0228.0138.5
Denmark2130.8417.8820102,4769.8148.31
Finland2060.8218.720112,3019.1257.43
France7883.1221.8220122,86611.3668.79
Germany7222.8624.6820132,91111.5480.32
Greece1550.6125.320143,10012.2992.61
Hong Kong6982.7728.0620151,8657.39100
Hungary140.0628.12Total25,233100
India3001.1929.31
Indonesia1570.6229.93
Ireland1190.4730.4
Israel900.3630.76
Italy3211.2732.03
Japan2,80611.1243.15
South Korea4241.6844.83
Kuwait120.0544.88
Luxembourg150.0644.94
Malaysia2200.8745.81
Mexico1320.5246.33
Morocco160.0646.4
Netherlands3361.3347.73
New Zealand630.2547.98
Norway1780.7148.68
Panama60.0248.71
Peru60.0248.73
Philippines740.2949.02
Poland1210.4849.5
Portugal910.3649.86
Qatar180.0749.93
Russian Federation1950.7750.71
Singapore3361.3352.04
South Africa4641.8453.88
Spain3851.5355.4
Sweden4391.7457.14
Switzerland5282.0959.24
Thailand890.3559.59
United Arab Emirates1059.59
Turkey1150.4660.05
Ukraine50.0260.07
Egypt, Arab Rep.320.1360.19
UK2,3839.4469.64
USA7,66130.36100
Total25,233100
Source(s): Authors’ own work
Table 2.

Descriptive statistics and correlations

VariablesMeanSDMin.Max.(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)
(1) Environmental performance52.30832.0808.32097.4901.000
(2) Social performance40.86323.0310.05098.6300.632*1.000
(3) Governance performance49.08222.6020.25099.3500.382*0.414*1.000
(4) Formal institution0.0650.855−2.9971.7880.036*0.0030.0031.000
(5) Informal institution0.0840.724−2.2046.0330.056*0.050*0.0070.651*1.000
(6) Financial market institution0.0230.729−3.2461.513−0.111*−0.062*−0.0040.515*0.165*1.000
(7) GDP growth2.0592.590−9.77326.276−0.132*−0.114*−0.017*−0.224*−0.220*0.049*1.000
(8) Population (log)18.2231.39113.03521.034−0.083*−0.015*−0.010−0.450*−0.422*−0.259*0.066*1.000
(9) Unemployment rate6.693.6790.16027.4700.061*0.162*0.031*−0.259*−0.120*−0.077*−0.211*−0.015*1.000
(10) FDI net inflow (log)24.6771.51917.34827.322−0.177*0.032*−0.018*0.121*0.057*0.102*0.102*0.492*−0.106*1.000
(11) Size of land (log)13.9182.3566.53516.611−0.157*0.014*0.013*−0.247*−0.137*−0.035*−0.0120.688*0.134*0.445*1.000
(12) Labor market efficiency5.7973.3491.10725.243−0.025*−0.104*−0.033*0.397*0.237*0.271*0.062*−0.097*−0.197*0.096*−0.073*1.000
(13) Return on assets4.82412.360−521.595647.6840.0040.022*0.0070.008−0.0110.018*0.070*0.0060.014*0.028*−0.032*0.037*1.000
(14) Total asset46.303198.3560.0043765.0350.173*0.190*0.161*−0.0060.006−0.054*−0.0040.019*0.0050.018*−0.015*0.006−0.058*1.000
(15) Debt ratio24.94918.8340.000345.9570.021*0.042*−0.012−0.060*−0.050*−0.052*−0.024*0.048*0.0100.033*0.036*−0.020*−0.167*−0.018*1.000
(16) Foreign sales to total sales35.88297.172−161.1514466.0100.082*0.054*0.025*0.080*0.073*0.011−0.024*−0.095*−0.004−0.025*−0.071*0.022*−0.004−0.013*−0.039*
Note(s):

N = 25,233; *shows significant level at 0.05

Source(s): Authors’ own work
Table 3.

Main results

Model (1)Model (2)Model (3)Model (4)Model (5)
Dependent variableComposite measure of ESGComposite measure of ESGEnvironmental performanceSocial performanceGovernance performance
Independent variables
Formal institution1.6226† (0.8849)3.4679** (1.0994)1.7594 (1.1229)−1.4321 (0.8725)
Informal institution4.4092*** (0.6732)4.1902*** (0.8469)2.4772** (0.8579)6.2010*** (0.6485)
Financial market institution1.1884* (0.5514)0.1927 (0.7092)1.1616 (0.7079)1.8820*** (0.5105)
Country-level control variables
GDP growth−0.1038 (0.1062)−0.0530 (0.1078)−0.1204 (0.1403)0.1100 (0.1389)−0.1665† (0.0983)
Population (log)−2.6793 (1.8338)0.1266 (1.5334)3.1134† (1.6509)0.7898 (1.8471)−6.1543** (2.3391)
Unemployment rate0.1474 (0.0955)0.3704*** (0.1054)0.6788*** (0.1348)0.3192* (0.1351)0.0310 (0.0987)
FDI net inflow (log)−0.6301** (0.2079)−0.8508*** (0.2112)−1.0436*** (0.2750)−1.0691*** (0.2722)−0.4768* (0.1924)
Size of land (log)1.3214 (1.3120)1.0901 (1.0370)−0.4667 (1.0878)0.7370 (1.2384)4.2258* (1.6685)
Labor market efficiency−0.4188** (0.1385)−0.3872** (0.1384)−0.5532** (0.1805)−0.5200** (0.1784)−0.1030 (0.1259)
Firm-level control variables
Return on assets0.0386*** (0.0109)0.0389*** (0.0109)0.0348* (0.0143)0.0636*** (0.0141)0.0184† (0.0099)
Total asset0.0275*** (0.0007)0.0275*** (0.0007)0.0341*** (0.0009)0.0317*** (0.0009)0.0167*** (0.0006)
Debt ratio0.0130† (0.0073)0.0121† (0.0073)0.0364*** (0.0096)−0.0146 (0.0094)0.0147* (0.0066)
Foreign sales to total sales0.0097*** (0.0014)0.0097*** (0.0014)0.0110*** (0.0018)0.0124*** (0.0018)0.0055*** (0.0012)
Constant82.5017*** (22.4954)42.7191* (19.3489)17.1442 (21.2458)47.6597* (23.4577)93.6230** (28.6712)
Log of random-effects parameter  2.6012*** (0.1127)2.3556*** (0.1167)2.3939*** (0.1168)2.5298*** (0.1153)2.8579*** (0.1123)
Number of observations25,23325,23325,23325,23325,233
Log-likelihood−112,346.08−112,321.53−119,083.55−118,752.19−109,847.80
AIC224,760.16224,717.07238,241.10237,578.39219,769.61
Note(s):

Year and industry fixed effects are included but are not reported here. Multilevel regression with random intercept modelling is used to estimate the results. Standard errors are in parentheses. ***p < 0.001, ** p < 0.01, *p < 0.05, †p < 0.10. All explanatory and control variables are lagged by One year to minimize the endogeneity concern. AIC = Akaike information criterion

Source(s): Authors’ own work
Table 4.

Robustness checks (1)

Model (1)Model (2)Model (3)
Dependent variableEnvironmental performanceSocial performanceGovernance performance
Independent variables
Formal institution7.0760* (2.7592)1.1210 (1.1602)−1.4896† (0.8324)
Informal institution4.1087*** (0.8999)3.1690** (1.0083)5.9758*** (0.6403)
Financial market institution0.6979 (0.6827)0.6960 (0.7106)6.6769*** (0.6905)
Country-level control variables
GDP growth−0.0670 (0.1431)0.0920 (0.1380)−0.1185 (0.0983)
Population (log)2.5535 (1.6888)0.6051 (1.8514)−5.9946** (2.1159)
Unemployment rate0.6570*** (0.1366)0.3020* (0.1347)−0.0719 (0.0913)
FDI net inflow (log)−0.9992*** (0.2749)−0.9805*** (0.2700)−0.2272 (0.1937)
Size of land (log)−0.9391 (1.2216)0.6071 (1.2507)4.2526** (1.4948)
Labor market efficiency−0.5651** (0.1872)−0.4733** (0.1795)−0.1243 (0.1256)
Firm-level control variables
Return on assets0.0361* (0.0144)0.0638*** (0.0141)0.0177† (0.0099)
Total asset0.0345*** (0.0009)0.0317*** (0.0009)0.0167*** (0.0006)
Debt ratio0.0421*** (0.0096)−0.0142 (0.0094)0.0151* (0.0066)
Foreign sales to total sales0.0111*** (0.0018)0.0124*** (0.0018)0.0055*** (0.0012)
Constant31.6573 (21.1451)50.7346* (23.4481)88.1420*** (26.0021)
Log of random-effects parameter  2.4155*** (0.1244)2.5393*** (0.1146)2.7421*** (0.1107)
Number of observations24,53525,23325,233
Log-likelihood−115,740.18−118,751.40−109,808.79
AIC231,554.37237,576.80219,691.59
Note(s):

Year and industry fixed effects are included but are not reported here. Multilevel regression with random intercept modelling is used to estimate the results. Standard errors are in parentheses. ***p < 0.001, **p < 0.01, *p < 0.05, †p < 0.10. All explanatory and control variables are lagged by One year to minimize the endogeneity concern. AIC = Akaike information criterion

Source(s): Authors’ own work
Table 5.

Robustness checks (2)

Model (1)Model (2)Model (3)Model (4)
Dependent variableComposite measure of ESGEnvironmental performanceSocial performanceGovernance performance
Independent variables
Formal institution0.8592 (2.2983)6.2952* (2.7320)5.2215† (2.8522)−8.2215*** (2.3887)
Informal institution4.6604*** (0.8209)2.9367** (1.0255)2.9548** (1.0399)8.5205*** (0.7905)
Financial market institution2.8320*** (0.8165)1.6347 (1.0483)0.8336 (1.0458)6.1062*** (0.7575)
Country-level control variables
GDP growth−0.0037 (0.1091)−0.1185 (0.1423)0.1028 (0.1406)−0.0273 (0.0989)
Population (log)−0.2044 (1.5344)2.1053 (1.6771)0.8134 (1.8325)−4.2170* (1.9833)
Unemployment rate0.3121** (0.0993)0.6157*** (0.1277)0.2079 (0.1273)0.1147 (0.0916)
FDI net inflow (log)−0.6458** (0.2109)−0.7985** (0.2747)−0.9217*** (0.2718)−0.2438 (0.1915)
Size of land (log)0.4266 (1.1280)−1.2437 (1.2143)0.0601 (1.3386)2.9832* (1.4961)
Labor market efficiency−0.3499* (0.1446)−0.5597** (0.1890)−0.5389** (0.1866)0.0630 (0.1307)
Firm-level control variables
Return on assets0.0402*** (0.0110)0.0356* (0.0144)0.0643*** (0.0142)0.0205* (0.0099)
Total asset0.0278*** (0.0007)0.0345*** (0.0009)0.0320*** (0.0009)0.0169*** (0.0006)
Debt ratio0.0168* (0.0073)0.0429*** (0.0096)−0.0108 (0.0095)0.0183** (0.0066)
Foreign sales to total sales0.0098*** (0.0014)0.0111*** (0.0018)0.0126*** (0.0018)0.0058*** (0.0012)
Constant54.4014** (18.7527)39.7836† (20.8997)55.4820* (22.5694)73.9982** (23.6758)
Log of random-effects parameter  2.3432*** (0.1175)2.4068*** (0.1290)2.5102*** (0.1197)2.6457*** (0.1081)
Number of observations24,53524,53524,53524,535
Log-likelihood−109,109.78−115,744.86−115,388.71−106,530.10
AIC218,293.56231,563.73230,851.43213,134.20
Note(s):

Year and industry fixed effects are included but are not reported here. Multilevel regression with random intercept modelling is used to estimate the results. Standard errors are in parentheses. ***p < 0.001, **p < 0.01, *p < 0.05, †p < 0.10. All explanatory and control variables are lagged by One year to minimize the endogeneity concern. AIC = Akaike information criterion

Source(s): Authors’ own work

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