Effect of Financial Indicators on Corporate Social Responsibility: Evidence from Emerging Economies

Abstract

The relationship between corporate social responsibility (CSR) and financial performance remains a subject of ongoing debate, particularly regarding the determinants of CSR in emerging economies. This study examines the effect of financial indicators on the level of corporate social responsibility. This study presents an integrated perspective to determine factors that can impact socially responsible behavior in developing and emerging countries. We drew our sample from 110 firms from 20 emerging economies from 2016 to 2020. We applied an instrumental variable estimation technique to address potential endogeneity and heterogeneity issues. The results revealed that financial performance is a weak determinant of socially responsible behavior in developing and emerging regions. Additionally, weak enforcement mechanisms and regulatory frameworks play a significant role in shaping CSR behaviors. Contrary to conventional assumptions, firms with higher organizational slack do not necessarily allocate additional resources toward CSR initiatives. This study contributes to the literature by providing empirical insights into the financial and institutional drivers of CSR in emerging markets and offers implications for policymakers, regulators, and corporate decision-makers aiming to enhance socially responsible business practices.

1. Introduction

This era is marked by the growing role attributed to business in social and environmental issues, bringing to the spotlight the concept of Corporate Social Responsibility (CSR). Though no single definition exists (Godfrey & Hatch, 2007), in general, CSR refers to the responsibility of a business to incorporate social and environmental matters in its operations, as well as to assess the impact of its business activity on various stakeholders. The importance of CSR in the global business agenda can be evidenced by spectacular growth in sustainability disclosure, enhancement of the reporting standards, and increased sustainable investment. According to the KPMG Survey of Sustainability Reporting, 96% of G250 firms report on sustainability or ESG issues (KPMG, 2022). The practice of sustainability reporting is promoted by the development of such standards as the Global Reporting Initiative (GRI), designed to encourage businesses to take responsibility for their impacts on society and the environment, as well as standardizing the way firms communicate these impacts. The investment based on sustainability criteria is also gaining momentum, increasing from USD13.6 trillion in 2012 to almost USD30.3 trillion in 2022, representing a growth of 123% (GSIA, 2022). Many firms worldwide incorporate 17 Sustainable Development Goals (SDGs) set by the United Nations (UN) in 2015 as part of their strategic agenda. This reflects a modern reality that mutual dependency between society and business has been strengthening, making business an integral part of the ecosystem whose responsibilities extend far beyond the generation of profits.

In the academic agenda, the CSR topic has also been gaining wide coverage, addressing CSR in the spectrum of interdisciplinary journals, thereby reflecting its multidimensional nature. Emerging as a topic in management research, it has extended to the accounting and finance literature, driven by the recognition of the impact of CSR not only on firms’ financials, but also acknowledging its footprint on the financial industry, such as the application of asset screening on social responsibility criteria, incorporation of Environmental, Social, and Governance (ESG) criteria in the investment decision-making, establishment of CSR rating agencies, sustainability indices and funds. However, in finance and accounting-related research, finding the link between CSR and a firm’s financial performance presents a topic of ongoing debate. Shedding light on the relationship between a firm’s level of CSR and its financials presents particular interest, given that CSR involves a contribution of the firm’s resources. Institutional theory suggests that firms adopt CSR practices due to institutional pressures, including regulatory frameworks, social norms, and cultural expectations (DiMaggio & Powell, 1983). J. L. Campbell (2007) argued that weak enforcement mechanisms and regulatory inconsistencies influence firms’ CSR behaviors in emerging economies. In a similar vein, Matten and Moon (2008) argued that firms operating in stronger institutional frameworks tend to engage in more structured and substantive CSR activities, whereas those in weaker institutional settings are more likely to adopt symbolic CSR initiatives. Stakeholder theory posits that businesses should consider the interests of various stakeholders, including investors, employees, customers, and communities (Freeman, 2010). Scholars argued that stakeholder activism and consumer awareness are rising in emerging markets and influencing firms to engage in CSR to maintain legitimacy and competitive advantage (Arslan et al., 2021; Garcia-Sanchez et al., 2013; Orazayeva & Arslan, 2022, 2024).

The relationship between CSR and financial performance has received extensive attention in academic literature. However, there is no consensus on either the magnitude or direction of this relationship. The inconsistencies in prior findings may stem from various factors, including the ambiguous definition of CSR, diverse assessment and research methods, differing study settings, and varying research contexts. This study recognizes the growing role that business plays in the environment and society, as well as the increasing place that CSR is getting in the business agenda. This research explores the causalities of socially responsible behavior by firms, addressing the “why CSR” question, with special attention given to the financial indicators. Prior research mainly concentrated on the effects of CSR on various business aspects, however, the motivators of socially responsible behavior were strikingly neglected (Gulema & Roba, 2021), creating the research problem of unclear causalities of social behavior by firms. According to the results of a meta-analysis by Margolis and Walsh (2003), 85% of prior studies utilized CSR as an explanatory variable, while the examples where CSR acts as a variable of interest are quite limited. This study addresses the issue by exploring whether financial indicators contribute to the eagerness of firms to engage in socially responsible practices. This study is particularly interested in examining potential determinants of CSR in the context of emerging and developing markets region. Inherent differences in national-level institutions (D. J. Campbell, 2000; Matten & Moon, 2008) influence the degree of CSR implementation, as well as the way businesses and society perceive socially responsible practices in different countries. Prior literature evidenced that CSR in developing regions is generally less politically oriented (Visser, 2008), has a more spontaneous and altruistic nature, relies on a combination of personal and religious beliefs (Jamali et al., 2009), and is less formal and more philanthropic (Amaeshi et al., 2006). In the context of developing economies, CSR discussions are specifically relevant, considering the presence of social and environmental issues that these regions are facing (Lund-Thomsen, 2004).

The main goal of this study is to examine the role of financial indicators on the degree of CSR implementation in the context of developing and emerging regions. As proxies of the financial indicators, three categories of financial performance are applied, namely profitability, organizational slack, and leverage. The following research objectives are set:

(1)

Identify the direction and significance of the impact of profitability of firms from the developing and emerging economies on their overall level of CSR.

(2)

Identify the direction and significance of the impact of organizational slack of firms from the developing and emerging economies on their overall level of CSR.

(3)

Identify the direction and significance of the impact of leverage of firms from developing and emerging economies on their overall level of CSR.

When addressing research objectives, the instrumental variable estimation technique is employed to minimize potential endogeneity and heterogeneity issues, which present potential shortcomings in some of the prior related research on the topic. In addition to financial indicators, this study adds macro-level variables to the estimation model, thereby showing the complexity of the CSR concept and the underlying forces that shape it. Statistical data processing was performed by applying Microsoft Excel and E-views 12 statistical package.

The theoretical contribution of this study comes from presenting novel evidence on the debatable topic while applying improved research specifications. This leads to a better understanding of the forces that impact CSR in the developing and emerging parts of the world. From a practical perspective, the results of this study can be useful for implementing, developing, and improving CSR practices in emerging and developing regions. A better understanding of the role of financial indicators in molding CSR suggests an avenue for the encasement of corporate strategies with consideration of CSR initiatives. The stimulus of firms to engage in CSR can serve as a ground for more effective reporting and monitoring mechanisms, leading to higher commitment to CSR, and thus better overall environmental and social well-being.

The scientific novelty of this research comes from suggesting the research framework which contributes new evidence to the ongoing discussion regarding the relationship between CSR and firms’ financial performance. In this study, CSR is a variable of interest, while prior works were mainly focused on finding whether financial performance is driven by CSR. In addition, non-common financial indicators are applied, such as measures of slack resources and leverage, in addition to traditional profitability measures observed in prior research. This study also applies a multi-layered approach by including in the estimation model not only firm-specific factors but also external forces, thereby addressing the complexity of the CSR concept. Namely, macro-level measures, such as government effectiveness, and the power of public voice are tested. Importantly, by utilizing a dynamic linear model, the Generalized Method of Moments (GMM) estimator, the study addresses the critique that prior related research faced concerning biased estimation methods. Finally, applying multiple-country research settings helps to draw a generalized picture of the drivers of CSR in developing and emerging regions.

The remainder of this paper is structured as follows. Section 2 summarizes various factors influencing the level of firms’ CSR and CSR disclosure observed in prior literature. Special attention is devoted to the discussion of previous studies that examined the relationship between CSR and firms’ financial indicators. The sources of inconsistencies in prior results are also discussed. The section concludes by presenting the theoretical base of this study and formulating study hypotheses. The sources of data and justification of the research methodology are presented in Section 3. The results and analysis highlighting key findings are demonstrated in Section 4. The final section concludes the study by presenting the theoretical contribution and practical implications of the results, study limitations, and opportunities for further research.

2. Literature Review and Hypothesis Formulation

2.1. The Determinants of CSR

The predecessors of CSR, especially in the context of developing and emerging economies, received limited academic coverage. The focus of prior research was mainly a study of the impact of CSR on various aspects of firm performance, rather than the examination of the driving forces of CSR itself. Among the limited number of studies on the topic of CSR drivers, internal and external factors can be identified. The former factors refer to the firm’s characteristics, such as size, industry, and financial performance. Larger firms tend to exhibit more socially responsible behavior pushed by higher public scrutiny and visibility (D. Campbell & Slack, 2006). According to some prior works, the type of industry also influences the degree of social responsibility (Useem, 1988). Financial performance appears in the early, limited literature on the determinants of CSR (Ullmann, 1985; Roberts, 1992). More recently, based on a sample of Chinese firms, Xiong et al. (2023) concluded that the relationship between CSR and financial performance is very complex, with a positive impact observed for firms with more funds; however, some exceptions were also noted. Regarding the external factors, prior research utilized various factors, such as ownership, public pressure, and legal enforcement. The summary of the prior studies on the motivators of CSR is presented in

Table 1. Determinants of CSR and CSR disclosure in prior research.

Authors	Country	Determinants
		Internal	External
1	2	3	4
Cormier et al. (2005)	Germany	Size (+), Industry (+), age of assets (+), risk (+), financial performance (0)	Public pressure (+), ownership (+)
Tagesson et al. (2009)	Swedish	Size (+), industry (+), financial performance (+)	Ownership (+)
Chih et al. (2010)	34 countries	Size (+), financial performance (0)	Legal enforcement (+)
Hou and Reber (2011)	USA	Size (+), industry (+)
Haniffa and Cooke (2005)	Malaysia	Size (+), industry (+), multiple listing, financial performance (+)
Alsaeed (2006)	Saudi Arabia	Size (+), industry (0), financial performance (0), firm age (0)
Rizk et al. (2008)	Egypt	Industry (+)	Ownership (+)
Sobhani et al. (2009)	Bangladesh	Industry (+)
Buniamin (2010	Malaysia	Size (+), Industry (+)
Huang and Kung (2010)	Taiwan	Industry (+), leverage (−)	Govt (+), consumers (+), suppliers (−), employees (+), competitors (+), shareholding concentration (−)
H. Khan (2010)	Bangladesh	Size (+), financial performance (+)
Saleh et al. (2010)	Malaysia	Size (+), financial performance (0)	Institutional ownership (+)
A. Khan et al. (2013)	Bangladesh		Managerial ownership (−), public ownership (+), foreign ownership (+)
Kansal et al. (2014)	India	Size (+), industry (+)
Bhatia and Makar (2019)	Russia	Industry (+)	International listing (+), board size (+), board independence (+)
Menassa and Dagher (2019)	UAE	Size (+), financial performance (+)
Malik et al. (2020	Pakistan	Size (+)	Ownership (0)
Fahad and Nidheesh (2021)	India	Firm age, financial leverage with different effects on CSR pillars, firm size (+)	Ownership with different effects on CSR pillars
Abdul Rahman and Alsayegh (2021)	20 Asian countries	Firm size (+), financial performance (+), leverage (+)
Soobaroyen et al. (2023)	Mauritius		cross-directorships (−)

Note—Complied by the authors.

.

2.2. Prior Research on CSR and Financial Indicators

No consensus on the topic regarding the degree and direction of the relationship between CSR and financial performance is observed in prior literature, though a positive link between the two is commonly reported (Orlitzky et al., 2003). According to Waddock and Graves (1997), the relationship can be characterized as a “virtuous circle”, with causation that can occur in both ways. Similarly, Coelho et al. (2023), based on the systematic literature review of 53 articles published during the period from 1984 to 2021, suggested that CSR has a direct impact on a firm’s financial performance, with a growing effect as a firm’s ESG score improves.

Three streams of studies can be identified: (1) CSR and accounting-based performance, (2) CSR and market-based performance, and (3) CSR and cost of financing. However, it should be noted that previous studies mainly focused on studying the impact of CSR on a firm’s financial performance, while the effect of financials on the socially responsible behavior of the firm received low interest from the academic side.

2.2.1. CSR and Firm’s Profitability

Studies that observed a positive impact of CSR on accounting-based indicators (ROA and ROE) attributed the results to higher operational efficiency and cost reduction via active stakeholder engagement (Stojanovic et al., 2020). The positive effect of CSR on sales was argued to be a consequence of boosted employee motivation and loyalty, as well as endorsement by customers (Ruf et al., 2001). A significant positive impact of CSR on a firm’s financial performance was observed by Aftab et al. (2024) based on a large sample of manufacturing firms from Pakistan. Furthermore, these authors observed that the relationship between CSR and financial performance can be mediated by Green Innovation, through decreasing environmental costs.

On the other hand, irresponsible behavior can be punished, destroying a firm’s revenues (Russell et al., 2016). A negative and non-significant link between CSR and accounting-based performance was also evidenced in prior research (Nollet et al., 2016). According to Franco et al. (2020), CSR involves not only benefits but also costs. Sharma and Chakraborty (2024) examined a sample of 134 Indian firms and found a negative relationship between corporate social responsibility (CSR) and financial performance. Their findings suggest that CSR does not significantly enhance firm value.

The research studying the link between CSR and market value also delivered mixed results. A positive relationship was documented by some works, suggesting that social initiatives can be a positive signal to the market regarding the prospects of positive cash flows (Cahan et al., 2016). However, some studies found a negative relationship between social performance and stock returns, attributing the result to investor altruism or penalty for excessive engagement in unrelated activities (Brammer et al., 2006).

2.2.2. CSR and Financial Slack

Financial slack refers to unabsorbed financial resources with no immediate use. As CSR is generally considered to be a voluntary activity, the availability of slack resources presents a logical argument in the context of the ongoing CSR-financial performance discussion. Social and environmental ideas and projects can be driven by a higher degree of freedom in terms of business resources. However, the slack resource hypothesis is not commonly observed in prior literature. According to Margolis and Walsh (2003), out of 127 prior studies on the topic, only 22 utilized the slack-resource hypothesis. Heubeck and Ahrens (2024) proposed that the relationship between organizational slack and CSR follows a U-shaped curve. Their findings indicate that at low levels of organizational slack, the resource-based view provides stronger support, whereas at high levels, the results align more closely with agency theory.

2.2.3. CSR and Firm’s Financing

According to prior literature, sustainability practices can help firms to reduce their financing costs, due to the lower risk associated with CSR (Starks, 2009). Building relationships with different stakeholders can lead to a lower risk of litigation and supervision (Dhaliwal et al., 2012). Moreover, CSR can contribute to a better reputation and moral image (Godfrey, 2005), leading to less vulnerability of such firms in crisis periods (Dhaliwal et al., 2011). Strong CSR can also reduce information asymmetry as a higher amount of information is disclosed to the public (Razali et al., 2018). A negative link between CSR and a firm’s cost of capital was observed in several empirical studies (El Ghoul et al., 2011). Based on a sample of Chinese firms, Liu et al. (2025) found that higher levels of CSR increase loan amounts but have no significant effect on loan approval likelihood.

2.2.4. Explaining Inconsistent Results of Previous Literature

Prior studies delivered inconsistent results about the direction and magnitude of the CSR-financial performance relationship, which can be explained by the following causes. Firstly, inconsistent findings can be attributed to differences in the research design and other technical issues such as the choice of sample and variables (McWilliams et al., 2006). The ambiguity of the relationship between CSR and financial performance can also be a result of the absence of a unique definition of CSR (Wang et al., 2015) due to its diverse and multidimensional nature (Endrikat, 2016). Some authors argue that moderating factors, or contingency factors impact the strength of the relationship between CSR and financial performance and thus should not be omitted from the model specifications (Aguinis et al., 2017).

Based on the review of prior literature, this study intends to fill in the following literature gaps. Firstly, CSR is utilized as a dependent variable in this study, whereas prior literature mainly focused on the impacts of CSR itself. In addition, the range of financial indicators is extended as prior studies mainly examined the relationship between CSR and accounting-based profitability measures such as ROA and ROE. This study examines the effects of organizational slack and leverage in addition to the traditionally employed profitability measures. Moreover, a multilayered approach is applied, by adding potential catalysts of CSR at the macro-level, such as government effectiveness and public voice. Finally, the study is based on the countries from developing and emerging regions, thereby allowing the suggestion of more specific CSR solutions applicable to non-Western parts of the world.

2.3. Hypothesis Formulation

This study examines a resource-based perspective, according to which the availability of resources influences the degree of the firm’s CSR commitment. According to prior studies, organizational slack can enhance innovations and strategic behaviors (Su et al., 2009), increasing the likelihood of undertaking sustainability practices. In addition, viewing CSR as an area of managerial discretion due to its voluntary nature, resource availability should play an important role in decision-making (Cui et al., 2015). Moreover, in the context of developing countries, the inclusion of CSR initiatives in a firm’s strategic agenda can be dictated by its profitability. Thus, taking a resource-based view, this study applies two financial measures as proxies for organizational slack: financial profitability and current ratio. The following hypotheses are tested:

H1a1: 

There is a positive and significant relationship between accounting-based profitability and CSR.

H1a2: 

There is a positive and significant relationship between market-based profitability and CSR.

H2: 

There is a positive and significant relationship between organizational slack proxied by the current ratio and CSR.

In addition to the resource-based view, this study applies stakeholder theory to assess the role of the level of debt in the degree of a firm’s CSR. The negative impact of the amount of leverage on CSR is assumed, based on the prior findings which observed that socially responsible firms tend to be less leveraged (Bae et al., 2011) and have lower bankruptcy risk (Brammer & Pavelin, 2006). The hypothesis is formulated as follows:

H3: 

A firm’s level of leverage has a negative impact on a firm’s CSR.

In addition to firm-level factors, this study examines the effects of external forces on the extent of a firm’s CSR, thereby recognizing the complexity of the CSR concept and its multidimensional nature. In particular, government effectiveness and the power of stakeholders are suggested to influence CSR based on the premises of institutional theory and stakeholder theory, respectively. Institutional ecosystems were found to influence the commitment of firms to social matters (Orlitzky et al., 2003), and higher stakeholder activism can put pressure on businesses (Garcia-Sanchez et al., 2013) as observed in previous studies.

3. Data and Methodology

3.1. Data

This study encompasses developing and emerging countries from five regions: Asia, Africa, Latin America, East Europe, and the Middle East (Appendix A). Academic research on these regions remains limited, primarily due to incomplete and often unreliable archival data. The decision to adopt a multi-country approach is driven by the need to provide a comprehensive overview of CSR practices across these regions, offering broader insights into their state of development and implementation. Additionally, the selection of countries for this study was driven by the availability of reliable financial and CSR data from non-Western regions. The classification of these countries as developing or emerging aligns with global indices such as the MSCI Emerging Markets Index and categorizations by international organizations, including the World Bank and the IMF.

The study covers a five-year period from 2016 to 2020 for several reasons. First, the period starting in 2016 witnessed significant advancements in CSR reporting standards, notably the introduction of the Global Reporting Initiative (GRI) Standards (GRI, 2016). In 2018, the Sustainability Accounting Standards Board (SASB, 2018) introduced industry-specific sustainability standards. Second, this timeframe represents the pre-COVID-19 era, during which CSR initiatives were shaped by evolving regulations, stakeholder expectations, and economic growth. The pandemic prompted a reassessment of corporate values, shifting business priorities toward employee well-being and societal contributions (Zhao, 2021). Thus, the selected period provides a stable foundation for analyzing CSR motivations before the impact of major global disruptions, facilitating comparisons with post-pandemic developments.

Publicly traded firms from the list were checked for the availability of financial and CSR data, resulting in a total of 20 countries, 110 firms, and 519 observations of unbalanced data. The industry set excludes the financial sector (banks, valuation, insurance, and real estate agencies), due to industry specifics in terms of CSR and financial indicators unrelated to the purposes of this study. The breakdown by industry is summarized in Appendix B.

Data is gathered from secondary sources. In particular, CSR scores and financial indicators are obtained from the LSEG Data and Analytics database, which presents one of the most comprehensive databases in the finance field. Relying on publicly available CSR scores increases the comparability and replicability of the results. The source of the macro-level variables is World bank ratings based on opinion surveys, and the World Bank macro-indicators.

3.2. Variable Specification

As the main interest of this study is to find forces that impact the level of a firm’s CSR in developing and emerging regions, the overall CSR score is selected as a dependent variable. While there is no unified method to measure CSR, with both advantages and disadvantages attributable to different approaches, this study applies ESG scores developed by LSEG as a proxy of CSR. The advantage of such an approach lies in its transparency, replicability, and accounting for industry differences. Utilizing the overall CSR score provides a holistic view of a firm’s CSR performance, ensuring a balanced assessment without bias toward any specific CSR category.

For the independent variables, financial indicators, macro-level factors, and control variables are utilized. With regard to financial indicators, three categories are applied: profitability (accounting- and market-based performance), organizational slack (proxied by current ratio), and leverage (proxied by debt-to-assets). For market-based factors, government effectiveness and public voice were proxied by the World Bank Government Indicators (World Bank, 2021). These indicators are argued to present the most relevant index to assess the effectiveness of a country’s government (Kaufman et al., 2004) based on the responses of a large number of surveys. For control variables, which present extraneous variables included to remove their impact on other variables, the firm-level (firm’s size) and macro-level (country’s GDP) are chosen. There is extensive evidence of a strong relationship between CSR and a firm’s size, due to higher public scrutiny and litigation risks for ignorance of social and environmental issues (D. Campbell & Slack, 2006). As a second control variable, GDP per capita is selected to control for specific country-related effects. The summary of variables applied in the study is presented in

Table 2. Variable specification.

Variable Name	Measurement	Code
1	2	3
Dependent variables
(1) CSR	Overall CSR score	CSR
Independent variables
(1) Financial indicators
(a) Profitability
Accounting-based performance	Return on Assets	ROA
Market-based performance	Tobin’s Q is measured as the sum of equity’s market value and debt’s book value by the total firm’s assets	TQ
(b) Organizational slack	Current Assets to Current Liabilities	CR
(c) Leverage	Debt as a percentage of Total assets	LEV
(2) Macro-level variables
(a) Government effectiveness	World Bank Government Indicators	GOVEFF
(b) Voice of stakeholders	World Bank Government Indicators	VOI
(3) Control variables:
Size	Natural logarithm of Total Assets	LnTA
GDP per capita	Natural logarithm of GDP per capita	LnGDP

Note—Complied by the Authors.

. Since the CSR scores and their pillar weights used as dependent variables in this study are already adjusted for industry effects, additional industry controls are not included. Specifically, in calculating the CSR score, LSEG applies pillar weights based on a materiality matrix that accounts for the relevance of each ESG topic across different industries, thereby incorporating industry-specific variations.

3.3. Methodology

The empirical analysis was performed with the help of the EViews 12 statistical package. As the first part of the analysis, data was checked for the presence of heteroskedasticity and multicollinearity. To identify the presence of heteroscedasticity, the likelihood ratio (LR) test with a null hypothesis of homoscedastic residuals was tested. With regard to multicollinearity, the Variance Inflation Factor (VIF) test was applied.

The choice of the estimation model was dictated by recognition of the potential endogeneity issue, which can affect the results regarding the direction and magnitude of the relationship between variables. In the case of the study of the CSR and financial performance relationship, endogeneity issues can occur as a result of the variable omission or unobserved heterogeneity as potential forces that shape CSR can be extensive (Ben Lahouel et al., 2019). In addition, the endogeneity can be driven by the potential reverse causality of CSR and financial performance variables. Moreover, building an experiment based on a sample from countries with limited disclosure can bring additional errors (Boulouta & Pitelis, 2014). Failing to control for endogeneity can lead to biased estimates and incorrect inferences regarding the relationship between CSR and financial performance (Jo & Harjoto, 2011). The presence of endogeneity of the explanatory variable in this study was tested by the means of the Hausman test, which represents the most widely methods to detect the endogeneity of regressors (Box, 1979).

Thus, to minimize the issue of potential endogeneity, the Generalized Method of Moments (GMM) is utilized, as it is argued to deliver consistent estimates in the context of the presence of endogeneity and measurement errors (Arellano & Bond, 2020). In particular, the first-difference GMM estimation is implemented, with the lagged value of the explanatory variable used as the instrumental variable. The lag of the dependent variable is argued to control for the potential problem of reverse causality (Gretz & Malshe, 2019) and serial autocorrelation in the model. The consistency of utilized GMM estimators was tested by applying a test of second-order serial correlation.

In consistency with the tested hypotheses, the following empirical models are specified:

H1a1: 

CSR and accounting-based profitability

$${CSR}_{i,j,t}=a_0+{CSR}_{i,j,t-1}+{AP}_{i,j,t}{+{{MF}_{i,j,t}+X}_{i,j,t}+\epsilon }_{it}$$

H1a2: 

CSR and market-based profitability

$${CSR}_{i,j,t}=a_0+{CSR}_{i,j,t-1}+{MP}_{i,j,t}{+{{MF}_{i,j,t}+X}_{i,j,t}+\epsilon }_{it}$$

H2: 

CSR and organizational slack

$${CSR}_{i,j,t}=a_0+{CSR}_{i,j,t-1}+{CR}_{i,j,t}{+{{MF}_{i,j,t}+X}_{i,j,t}+\epsilon }_{it}$$

H3: 

CSR and leverage

$${CSR}_{i,j,t}=a_0+{CSR}_{i,j,t-1}+{LEV}_{i,j,t}{+{{MF}_{i,j,t}+X}_{i,j,t}+\epsilon }_{it}$$

where CSR is the overall social responsibility score for sample firm i of country j at year t, AP and MP proxies for accounting- and market-based profitability ratios, CR proxies for current ratio, LEV proxies for the ratio of debt to assets, MF states for macro-effects of government effectiveness and public voice, X refers to control variables, namely firm’s size and country’s GDP.

In addition to the main model, the results of the classical techniques, namely OLS and 2SLS are demonstrated for comparison purposes.

4. Results

4.1. Descriptive Statistics

Table 3. Descriptive statistics.

Description	Panel A				Panel B				Panel C
	(1) CSR	(2) ENV	(3) SOC	(4) GOV	(5) ROA	(6) TQ	(7) CR	(8) LEV	(9) GOVEFF	(10) VOI
Mean	49.02	46.92	51.67	49.59	6.14	1.26	1.63	81.72	64.15	51.73
Median	54.15	54.15	62.50	54.15	5.36	0.99	1.23	64.05	65.38	51.72
Maximum	87.50	95.37	99.41	99.23	72.50	12.76	11.87	599.37	100.00	81.64
Minimum	4.17	1.00	3.81	0.08	−81.51	0.15	0.15	0	27.88	4.83
Std. Dev.	18.74	25.03	23.83	20.87	8.63	1.32	1.47	92.48	15.75	22.13
Skewness	−0.48	−0.12	−0.37	−0.10	−1.12	3.96	4.07	2.10	0.21	−0.41
Kurtosis	2.71	2.04	2.33	2.44	33.32	24.59	23.92	8.66	2.83	2.01
Jarque-Bera	21.81 **	21.38 **	21.47 **	7.66 **	19,991.69 **	11,440.53 **	10,893.38 **	1073.7 **	4.51	35.67 **
Numb. of observations	519	519	519	519	519	519	519	519	519	519

Note: Sign ** indicates significance at 5% level.

presents the results of descriptive statistics. The mean CSR score is 49.02% and indicates a relatively satisfactory level of CSR and moderate transparency in CSR reporting across the sample. Maximum and minimum CSR scores are 87.50% and 4.17%, respectively, indicating that both excellent and poor CSR performance are presented in the sample countries set. A slight divergence of CSR scores from the properties of normal distribution is detected. Specifically, CSR scores reveal a slight negative skew, with a longer tail on the left side of the distribution compared to the right. Moreover, the distribution is slightly flatter than normal. The significant Jarque-Bera (J-B) test statistic further suggests deviations from normality.

With regard to financial indicators, the mean ROA value is 6.14%, while maximum and minimum ROA values are 72.50% and −81.51%, respectively. Significant J-B statistics, negative skewness, and high kurtosis indicate a non-normal distribution of ROA. The distribution of Tobin’s Q also diverges from normality. The current ratio (CR) has a mean value of 1.63, a maximum of 11.87, and a minimum of 0.15, with non-normal distribution. Average debt as a percentage of total assets is 81.72% for the sample under examination. The average value of government effectiveness is 64.15%, while the average level of public voice is 51.73%.

4.2. Results of Preliminary Tests

The likelihood ratio analysis indicated that heteroskedasticity is present as the null hypothesis of homoscedastic residuals was rejected for all three main hypotheses regarding profitability, slack resources, and leverage, favoring the application of the dynamic linear estimation model as utilized in this study. The test on the presence of multicollinearity showed VIFs for the variables of all the hypotheses around 1, indicating that only a small portion of correlation among predictor variables exists. Finally, the Hausman test demonstrated the presence of random effects in all the hypotheses.

4.3. Regression Results

Profitability as a Motivator of CSR

H1a1: 

Accounting-based profitability and CSR

The results from the GMM model presented in

Table 4. Regression results for the impact of ROA on CSR (H1a1).

Panel A				Panel B				Panel C
GMM				2SLS				OLS
Variable	Coeff.	Std. Error	Prob.	Variable	Coeff.	Std. Error	Prob.	Variable	Coeff.	Std. Error	Prob.
CSR(−1)	0.426 **	0.204	0.040	C	0.292	0.354	0.411	C	0.066	0.119	0.579
ROA	0.232	0.192	0.230	ROA	0.099	0.075	0.192	ROA	0.049	0.110	0.656
GOVEFF	0.004	0.009	0.634	GOVEFF	0.003	0.003	0.259	GOVEFF	0.000	0.001	0.889
VOI	0.014	0.012	0.248	VOI	0.005	0.004	0.238	VOI	0.002 **	0.001	0.014
LnTA	0.330 *	0.172	0.057	LnTA	0.019	0.027	0.484	LnTA	0.041 ***	0.010	0.000
LnGDP	0.003	0.007	0.651	LnGDP	0.004	0.004	0.337	LnGDP	0.015 *	0.009	0.095
Effects Specification				Effects Specification				Effects Specification
S.E. of regression			0.138	S.E. of regression			0.083	S.E. of regression			0.173
Sum squared resid.			5.750	Sum squared resid.			2.099	Sum squared resid.			15.653
J-statistic			4.672	Durbin-Watson stat.			1.859	Durbin-Watson stat.			0.386
Prob(J-statistic)			0.457	F-statistic			14.847 ***	F-statistic			23.432 ***
				R-squared			0.851	R-squared			0.183

Notes: Signs *, **, *** indicate significance at 10%, 5%, and 1% levels, respectively. Abbreviations used in the table indicate the following: Hij—hypothesis number i type j, CSR—Corporate Social Responsibility, ENV—environmental responsibility, SOC—social responsibility, GOV—corporate governance, ROA—return on assets, TQ—Tobin’s Q, CR—current ratio, LEV—leverage, GOVEFF—government effectiveness, VOI—public voice, LnTA—natural logarithm of total assets, lnGDP—natural logarithm of GDP per capita. Compiled by the author.

demonstrate an insignificant relationship between ROA and CSR (β = 0.232, p-value = 0.230). Thus, H1a1 is not supported. The relation between CSR and macro-variables, namely public voice (VOI) and government effectiveness (GOVEFF) is also observed to be insignificant (β = 0.014, p-value = 0.248 for VOI and β = 0.004, p-value = 0.634). CSR demonstrates a positive significant relationship at a 10% level with the firm’s size measured by total assets (β = 0.330, p-value = 0.057). A positive insignificant relationship with GDP (β = 0.003, p-value = 0.651) is observed. The J-test shows a significant p-value for the J-statistic, which indicates that the instruments are exogenous.

Among the three models, the OLS model demonstrates the highest sum of squares of residuals, indicating a discrepancy between the data and estimation model (sum of sq. resid. = 15.653), and the highest size of the equation errors (S.E. regression = 0.173), low Durbin-Watson statistic (DW = 0.386), and a small R-squared (R² = 0.183). These results suggest that OLS presents a relatively weak estimation approach in the context of examining the CSR-financial performance link.

The finding of an insignificant relationship between ROA and CSR suggests that financial performance is a weak predictor of socially responsible behavior. This aligns with a body of literature that has also reported an insignificant link between these variables (e.g., Dyduch & Krasodomska, 2017; Kuzey & Uyar, 2017; Aras & Crowther, 2009). This finding also suggests that the incorporation of CSR agenda in strategic decisions for the sample under examination is weak, and more profitable firms are not necessarily those that invest more in CSR.

H1a2: 

Market-based performance and CSR

The regression analysis results on the effects of the market-based indicator on CSR, presented in

Table 5. Regression results for the impact of Tobin’s Q on CSR (H1a2).

Panel A				Panel B				Panel C
GMM				2SLS				OLS
Variable	Coeff.	Std. Error	Prob.	Variable	Coeff.	Std. Error	Prob.	Variable	Coeff.	Std. Error	Prob.
CSR(−1)	0.395 *	0.201	0.052	C	0.257	0.284	0.366	C	0.051	0.121	0.673
TQ	−0.022	0.103	0.831	TQ	−0.001	0.008	0.881	TQ	−0.002	0.008	0.854
GOVEFF	0.011	0.009	0.188	GOVEFF	0.002	0.002	0.126	GOVEFF	0.000	0.001	0.878
VOI	0.018	0.012	0.130	VOI	0.004 *	0.003	0.092	VOI	0.002 **	0.001	0.013
LnTA	0.280	0.174	0.111	LnTA	0.023	0.022	0.300	LnTA	0.042 ***	0.010	0.000
LnGDP	0.049	0.070	0.480	LnGDP	0.041	0.035	0.252	LnGDP	0.145	0.088	0.103
Effects Specification				Effects Specification				Effects Specification
S.E. of regression			0.138	S.E. of regression			0.084	S.E. of regression			0.173
Sum squared resid.			5.692	Sum squared resid.			2.118	Sum squared resid.			15.664
J-statistic			5.806	Durbin-Watson stat.			1.848	Durbin-Watson stat.			0.378
Prob(J-statistic)			0.326	F-statistic			14.689 ***	F-statistic			23.341 ***
				R-squared			0.849	R-squared			0.182

Notes: Signs *, **, *** indicate significance at 10%, 5%, and 1% levels, respectively. Abbreviations used in the table indicate the following: Hij—hypothesis number i type j, CSR—Corporate Social Responsibility, ENV—environmental responsibility, SOC—social responsibility, GOV—corporate governance, ROA—return on assets, TQ—Tobin’s Q, CR—current ratio, LEV—leverage, GOVEFF—government effectiveness, VOI—public voice, LnTA—natural logarithm of total assets, lnGDP—natural logarithm of GDP per capita. Compiled by the authors.

, show that H1a2 is not supported (β = −0.022, p-value = 0.831). The model reveals that the relationship between CSR and the other variables is insignificant. The J-test for overidentifying restrictions reveals a p-value for the J-statistic greater than 10%, suggesting that the instruments are exogenous. Both the 2SLS and OLS regressions generate similar conclusions regarding the relationship between CSR and the market-based performance measure, as indicated by the insignificant coefficients. The OLS regression results are more biased compared to the other models, as validated by a low R-squared (R² = 0.182), a Durbin-Watson statistic below 2 (DW = 0.378), and the highest sum of squared residuals (sum of sq. resid. = 15.664). However, the F-statistics for both models suggest that the variables are jointly significant. The finding of an insignificant impact of the market-based indicator may indicate that enforcement mechanisms and regulations promoting the adoption of CSR initiatives are weak in the sample under examination.

H2: 

Organizational slack and CSR

The results of GMM regression, with an organizational slack utilized as a predictor variable of CSR, are presented in

Table 6. Regression results for the impact of organizational slack on CSR (H2).

Panel A				Panel B				Panel C
GMM				2SLS				OLS
Variable	Coeff.	Std. Error	Prob.	Variable	Coeff.	Std. Error	Prob.	Variable	Coeff.	Std. Error	Prob.
CSR(−1)	0.414	0.228	0.072	C	0.247	0.279	0.377	C	0.104	0.056	0.063
CR	−0.043	0.034	0.214	CR	−0.000	0.009	1.000	CR	−0.012 ***	0.005	0.010
GOVEFF	0.009	0.009	0.284	GOVEFF	−0.002	0.002	0.129	GOVEFF	−0.000	0.001	0.828
VOI	0.016	0.012	0.183	VOI	0.004 *	0.003	0.090	VOI	0.002 ***	0.000	0.000
LNTA	0.303 *	0.170	0.078	LNTA	0.023	0.022	0.282	LNTA	0.039 ***	0.005	0.000
LNGDP	0.005	0.007	0.443	LNGDP	0.004	0.004	0.249	LNGDP	0.014 ***	0.005	0.004
Effects Specification				Effects Specification				Effects Specification
S.E. of regression			0.139	S.E. of regression			0.084	S.E. of regression			0.172
Sum squared resid.			5.815	Sum squared resid			2.118	Sum squared resid.			15.466
J-statistic			6.300	Durbin-Watson stat.			1.847	Durbin-Watson stat.			0.380
Prob(J-statistic)			0.278	F-statistic			14.687 ***	F-statistic			24.979 ***
				R-squared			0.849	R-squared			0.193

Notes: Signs * and *** indicate significance at 10% and 1% levels, respectively. Abbreviations used in the table indicate the following: Hij—hypothesis number i type j, CSR—Corporate Social Responsibility, ENV—environmental responsibility, SOC—social responsibility, GOV—corporate governance, ROA—return on assets, TQ—Tobin’s Q, CR—current ratio, LEV—leverage, GOVEFF—government effectiveness, VOI—public voice, LnTA—natural logarithm of total assets, lnGDP—natural logarithm of GDP per capita. Compiled by the authors.

. The relationship was found to be negative and statistically insignificant (β = −0.043, p-value = 0.214), therefore, H2 is not supported. The results of OLS and 2SLS also support the conclusion of GMM estimator. This finding contrasts with the initial prediction of this study, demonstrating no support for the resource-based theory. This result indicates that firms with higher organizational slack are not necessarily willing to devote extra resources to unrelated business activities such as CSR. Conclusion of low prioritization of CSR initiatives in corporate business decisions is inferred from this. Some of the prior studies also found a poor link between CSR and prior organizational slack (Sayekti, 2017; Julian & Ofori-Dankwa, 2013).

H3: 

Leverage and CSR

With regard to leverage, no significant relationship between leverage and CSR is found as demonstrated in

Table 7. Regression results for the impact of leverage on CSR (H3).

Panel A				Panel B				Panel C
GMM				2SLS				OLS
Variable	Coeff.	Std. Error	Prob.	Variable	Coeff.	Std. Error	Prob.	Variable	Coeff.	Std. Error	Prob.
CSR(−1)	0.468	0.255	0.070	C	0.060	0.118	0.614	C	0.077	0.118	0.516
LEV	−0.001	0.001	0.244	LEV	−0.000	0.000	0.215	LEV	−0.000	0.000	0.265
GOVEFF	0.001	0.009	0.940	GOVEFF	−0.001	0.001	0.285	GOVEFF	−0.000	0.001	0.811
VOI	0.012	0.011	0.277	VOI	0.002 ***	0.001	0.010	VOI	0.002 **	0.001	0.021
LNTA	0.243 **	0.122	0.049	LNTA	0.047 ***	0.012	0.000	LNTA	0.042 ***	0.010	0.000
LNGDP	0.004	0.006	0.497	LNGDP	0.003	0.004	0.502	LNGDP	−0.013	0.009	0.132
Effects Specification				Effects Specification				Effects Specification
S.E. of regression			0.133	S.E. of regression			0.086	S.E. of regression			0.171
Sum squared resid			5.096	Sum squared resid			3.005	Sum squared resid			14.914
J-statistic			5.173	Durbin-Watson stat			1.300	Durbin-Watson stat			0.372
Prob(J-statistic)			0.395	F-statistic			7.489 ***	F-statistic			22.494 ***
				R-squared			0.084	R-squared			0.180

Notes: Signs ** and *** indicate significance at 5%, and 1% levels, respectively. Abbreviations used in the table indicate the following: Hij—hypothesis number i type j, CSR—Corporate Social Responsibility, ENV—environmental responsibility, SOC—social responsibility, GOV—corporate governance, ROA—return on assets, TQ—Tobin’s Q, CR—current ratio, LEV—leverage, GOVEFF—government effectiveness, VOI—public voice, LnTA—natural logarithm of total assets, lnGDP—natural logarithm of GDP per capita. Compiled by the authors.

(β = −0.001, p-value = 0.244). Thus, H3 is not supported. While negative coefficient between CSR and leverage is observed, due to lack of significance, it can be inferred that the power of leverage to discourage CSR initiatives is small. Prior literature presented mixed results on the relationship of CSR and leverage, with evidence of an inverse, positive, and insignificant relationship (Alsaeed, 2006; Hossain & Hammami, 2009).

The significant effect of firm size remains robust in playing a significant role in explaining the level of CSR in the sample under examination, which is consistent with findings from most of the previous literature covering a developing region. This could be attributed to the higher visibility of larger firms and stricter legitimacy requirements.

The results of the GMM estimator were tested by employing a serial correlation test, which showed significant first-order correlation (AR1), while the second-order correlation (AR2) was not statistically significant, indicating a robust estimator.

5. Conclusions

The results of this study suggest that financial performance is a weak determinant of socially responsible behavior in developing and emerging regions. This conclusion has several implications. Firstly, these results suggest that incorporation of the CSR agenda in strategic decisions of firms in developing and emerging economies is weak. Secondly, higher profitability does not necessarily motivate firms to exhibit more socially responsible behavior. Finding positive insignificant impact of accounting-based profitability suggests the presence of the symbolic CSR, which satisfies a basic level of public expectations.

The insignificant effect of organizational slack on CSR suggests that extra resources of firms from the covered economies are not immediately directed to social matters, thereby indicating that the prioritization of CSR initiatives strategic agenda is quite low. Finally, finding negative insignificant impact of leverage of firms from developing and emerging countries suggests that higher debt discourages CSR activities, although this negative effect is small.

Regarding macro-level determinants of CSR, the results of this study suggest that the effect of government and public voice is insignificant on firms’ social responsibility levels.

Though the main study hypotheses were not supported, observing insignificant results can help to find the gaps and suggest the following recommendations to enhance CSR development in developing and emerging regions. In particular, it is suggested to motivate CSR both from the side of the government, as well as developing internal motivation, as exhibited in Figure 1.

From the government side, it is suggested to create incentives for firms to engage in CSR initiatives. This can include tax incentives, more stringent disclosure and reporting mechanisms, and government subsidies. There is evidence in prior literature that government subsidies can stimulate CSR. For example, Duan et al. (2022) observed a positive significant effect of government subsidies on the promotion of social responsibility in a sample of Pakistani firms. Tang and Wang (2022) found a positive impact of tax incentives on the CSR of Chinese firms. Firms in stricter regulatory environments are more likely to integrate CSR as a core strategy. Nonetheless, countries with strong regulatory frameworks (e.g., China, Singapore) may enforce CSR compliance more effectively than those with weaker enforcement (e.g., Egypt, Argentina).

From a firm-level perspective, it is suggested to consider CSR expenses as part of the corporate strategy. By integrating a CSR mindset as part of the operational activity, stronger connections with firms’ finances can be made, leading to more informed decisions in terms of CSR. The availability of financial resources influences CSR commitment. It implies that firms in Singapore and Malaysia, with higher profitability and financial slack, can invest in CSR, whereas companies in Colombia and Thailand may prioritize financial stability over CSR. In Saudi Arabia and Qatar, CSR aligns with Islamic philanthropic principles, whereas in Brazil and South Africa, social activism and labor rights movements drive CSR initiatives. Thus, the competitive pressures shape CSR strategies. Further, the firms in South Africa and Poland face high ESG scrutiny from international investors, incentivizing strong CSR policies, while firms in Turkey and the Philippines experience less external pressure.

Therefore, budgeting, monitoring, and controlling of the results of CSR initiatives are suggested. Cost-benefit analysis is recommended for investment projects (Jenkins et al., 2018), which will help to detect weak points of environmental and social responsibility, which in turn will result in more disciplined decisions with regard to CSR. Furthermore, by integrating CSR into corporate strategy, the view of CSR expenses as an unnecessary burden can be gradually changed.

This study is novel in several ways. Most notably, it explores the relationship between CSR and financial performance from the perspective of how financial performance impacts CSR, rather than the reverse. Previous studies primarily focused on the effect of enhanced social and environmental responsibility on financial profitability. As such, this study offers a fresh perspective, contributing new insights to the ongoing discussion about the role of CSR in the business context. Additionally, the research framework is unique in terms of model selection and variable specification.

The study has following limitations. Firstly, due to limited information on CSR, the sample period and number of firms are limited. Secondly, while relying on readily available rankings on CSR has its advantages, such an approach omits firms without CSR rankings and private firms. Thirdly, producing a multi-level study with multi-country settings can produce generalized conclusions. In addition, it should be recognized that CSR presents a very comprehensive concept, with various potential determinants, including the ones that are difficult to measure, such as religion or historical background. For future research, it is suggested to examine other potential factors which can impact CSR, such as informal institutions (e.g., culture and religiosity) or introducing the interaction term, which involves examining the combined effect of multiple variables on CSR.