To investigate the relationship between corporate environmental, social and governance (ESG) performance and trade credit, with a particular focus on the moderating role of institutional variables.
An empirical analysis was conducted using a comprehensive panel dataset of European listed firms included in the STOXX 600 Index from 2015 to 2022. The research employs panel regression techniques, with an ordinary least squares estimation with robust standard error and the application of the Heckman two-stage test to address the possible issue of sample selection bias.
Higher ESG performance improves trade credit use by fostering supplier trust and reducing information asymmetry. Crucially, this relationship is moderated by the institutional environment. Firms operating in countries with a less efficient legal system and/or higher corruption rely more heavily on their ESG performance to secure trade credit, indicating a compensatory effect.
Financial managers in countries with weak institutional frameworks should prioritise their ESG performance, as it serves as a high-impact signal for accessing trade credit. For policymakers, the results stress the importance of integrating sustainability indicators and institutional reforms into policies that promote access to trade credit.
This article offers novel evidence by highlighting the dual role of ESG performance as both a general signal of creditworthiness and a compensatory mechanism in institutional contexts characterised by weak rule of law and high corruption. It extends the trade credit literature by integrating firm-level ESG factors with country-level institutional quality.
1. Introduction
Environmental, social, and governance (ESG) criteria form an optimal system designed to be embedded into an organisation's strategy to implement sustainable development. These criteria oblige companies to focus on aspects such as environmental impact, social development, and stakeholders' interests in their business management to achieve both a sustainable growth target and economic benefits (Hao and He, 2022). Researchers worldwide are increasingly examining ESG criteria and their implications in various areas, which demonstrates the growing relevance of these considerations in modern corporate practices.
The inclusion of ESG criteria in investment decision-making implies that traditional parameters of profitability and risk should be complemented by environmental, social responsibility, and sound governance indicators, which collectively foster what is called “sustainable development” (Henao, 2014). However, a commitment to sustainability requires more than solely implementing responsible projects: it also involves securing suitable financing instruments. According to Mishra et al. (2023), companies can obtain sustainable finance through financial markets or various other types of investors or lenders. Green, social, sustainable, and sustainability-linked bonds have emerged in recent years; likewise, green and sustainable loans are already available in the financial sector. Yet there are other financial resources, such as trade credit, which, although not explicitly called “green“ or “sustainable“, can also be a key factor in supporting sustainability goals.
Trade credit, also called trade payable, is vital for non-financial companies due to its crucial role in financing working capital and the supply chain (Demirgüç-Kunt and Maksimovic, 2001). This resource is especially relevant for companies that encounter challenges in securing financial resources (Berger and Udell, 1998; Palacín-Sánchez et al., 2019), thereby acting as a pivotal resource that often safeguards firms' survival by making suppliers the lenders of last resort (Cuñat, 2007). Over the past 25 years, trade payables have represented roughly 20% of world gross domestic product (Boissay et al., 2020), which underscores their importance not only in developing countries (Li et al., 2018) but also in developed economies (Afrifa and Gyapong, 2017).
This universal presence of trade credit in corporate financing makes it an opportune mechanism for the incorporation of ESG considerations into financial decision-making, thereby supporting sustainable economic growth (Bancilhon et al., 2018). From a theoretical perspective, the integration of ESG performance into trade credit evaluation adds an innovative dimension to our understanding of financial practices: firms with stronger ESG indicators may be viewed by suppliers as less risky and more aligned with long-term sustainability objectives. Consequently, better corporate ESG performance can enhance trust of their suppliers, leading to greater trade credit use (Luo et al., 2023). This approach advances the existing literature by highlighting how ESG-driven policies and practices can influence suppliers' disposition to grant trade credit.
Our study aims to analyse the impact of ESG outcomes on funding received from suppliers, by carrying out an empirical study on a sample of European listed companies included in the STOXX 600 Index for the years 2015–2022. It should be borne in mind that, in our study, ESG performance is measured using a comprehensive ESG rating that reflects the company's sustainability efforts across ESG criteria. Moreover, our work adopts an unequivocal definition of trade credit as financing from suppliers (Yang, 2011; Palacín-Sánchez et al., 2019), which differs from prior relevant studies in ESG and trade credit (Luo et al., 2023). In doing so, we expand the theoretical understanding of trade credit determinants (Petersen and Rajan, 1997; Canto-Cuevas et al., 2016) to include ESG as a relatively innovative factor.
The financial empirical literature regarding the effect of ESG performance on trade credit is scarce, and this relationship has not been completely explored (Wang, 2023; Wu, 2023). Furthermore, the majority of these studies are predominantly focused on China (Tian and Tian, 2022; Luo et al., 2023; Wang, 2023; Wu, 2023; Zheng and Aishan, 2023; Huang et al., 2023), a country with an imperfect financial market where bank credit discrimination is widespread, which has led firms to depend on financial resources of a more informal nature, such as trade credit, to cover their bank financing constraints (Allen et al., 2005; Wu et al., 2014; Wang, 2023). Consequently, by investigating this relationship in Europe (a world leader in the promotion of sustainability), a major gap in the literature is filled.
Furthermore, our cross-country study considers the impact of institutional environments on trade credit usage (Palacín-Sánchez et al., 2019), by incorporating factors such as the efficiency of the legal system and the level of corruption. These additions enable, firstly, the examination of the effect of such institutional factors on supplier financing and, secondly, the analysis of the moderating role of country-specific factors and their interrelation with ESG performance and trade credit use. The scarce previous empirical literature has predominantly focused on analysing the effect of ESG results on trade credit within single countries, with institutional factors typically referring to differences across regions within the same country, such as in Wu (2023). Furthermore, there are only two notable cross-country studies on this topic: Zhang and Lucey (2022), which examines listed companies from 47 countries, and Heo (2024), which analyses a sample of Organisation for Economic Co-operation and Development (OECD) countries. However, neither of these articles considers institutional factors. Therefore, our study constitutes a relevant advancement of the previous literature by incorporating cross-country factors into the study of this relationship.
Our findings suggest that the relationship between ESG performance and trade credit use is positive among European listed firms. However, this relationship is moderated by institutional factors, with ESG outcomes playing a crucial role in securing trade credit in those countries with weaker legal systems and higher levels of corruption.
Regarding the following sections of the article: in Section II, the literature review of this topic is presented, and the hypotheses are also stated. Section III describes the variables, the descriptive statistics, the model, and the methodology used in our research. Section IV shows the empirical results, and Section V provides the conclusions.
2. Literature review and hypotheses development
Trade credit is a form of short-term financing that emerges when suppliers of goods and services extend payment terms to their customers, thereby enabling deferred settlement of invoices. This arrangement is generally based on a relationship of trust, in which the buyer assumes the role of a debtor, while the seller acts as a lender. This mechanism is used worldwide to finance the supply chain and has been explained from several theoretical perspectives.
From a commercial approach, trade credit may be granted to increase sales and promote growth (Nadiri, 1969), whereas the quality guarantee theory posits that suppliers grant more time for buyers to verify the quality of goods (Long et al., 1993). Moreover, the transaction cost hypothesis suggests that the use of trade credit reduces the costs of frequent transactions (Petersen and Rajan, 1997). From a financial perspective, the financing advantage theory explains that suppliers possess privileged information about clients' operations and prospects, thereby reducing asymmetric information (Smith, 1987). In this context, the theory of signals, a strand of asymmetric information theory, initiated by Ross (1977) and Brealey et al. (1977), maintains that investors' decisions to finance a company are shaped by signals, such as transparency, social image, and reputation, which reduce risk and facilitate lending. Hence, a firm's good social image and reputation are optimal signals that can alleviate problems created by information asymmetries and can lead suppliers to increase the granting of trade credit.
2.1 The effect of ESG performance on trade credit
The ESG principles, originally promoted by the Principles for Responsible Investment under the auspices of the United Nations (UNPRI), have established ESG aspects as relevant indicators of corporate sustainability. Standing as an important proxy for the assessment of sustainable development, ESG ratings are often relied upon by suppliers to gauge a company's risk profile and responsible practices, thereby serving as a basis for granting business credit (Wang, 2023).
Early empirical studies examining the influence of ESG on trade credit often considered only individual components of ESG, by focusing solely on “green supply chain“ strategies that help reduce carbon emissions problems (Wu et al., 2019; An et al., 2021; Zhu et al., 2023), and/or on corporate social responsibility (CSR) in areas that include philanthropy or employee welfare (Zhu et al., 2016; Jiraporn et al., 2014; El Ghoul et al., 2011), and/or on corporate governance practices (Mugova and Sachs, 2017).
A more recent set of studies on the effect of ESG performance on trade credit considers a holistic view of ESG. Our study follows this approach. This evidence remains scarce (Wang, 2023; Wu, 2023), with the majority of empirical studies focusing on China, where imperfect financial markets and bank credit discrimination push firms to rely on supplier financing (Allen et al., 2005; Wu et al., 2014). The majority of existing studies show a positive relationship between ESG performance and trade credit, due to the role of ESG in reducing information asymmetry, operational risk, and corporate inefficiencies, which in turn enhances suppliers' trust (Tian and Tian, 2022; Luo et al., 2023; Wang, 2023; Huang et al., 2023). Moreover, ESG performance increases its effect over trade credit when companies are audited by one of the Big 4 (Wu, 2023). In contrast, a few studies suggest that this relationship is not always linear, or even negative. Zheng and Aishan (2023) find that moderate ESG ratings improve supplier financing, while excessively high ESG ratings may increase costs and agency problems, ultimately reducing trade credit availability. Similarly, Wang and Yang (2024) show that although firms with a better ESG rating generally receive more supplier financing, this effect becomes weaker in the more mature firms. Lastly, Xin et al. (2024) observe a negative influence of ESG on trade credit.
Among the few studies existing outside the Chinese context, Zhang and Lucey (2022), who consider listed companies from 47 countries, and Heo (2024), who analyses firms from OECD countries, highlight the role of ESG outcomes in facilitating trade credit use and easing financial constraints. Their findings reinforce the relevance of ESG as a factor considered by suppliers and suggest that this effect may be even more pronounced in Europe: a region strongly committed to sustainable development. Therefore, the first of our hypotheses can be formulated:
Firms with better ESG performance use more trade credit.
2.2 Institutional factors and trade credit
The institutional environment in which firms operate significantly influences trade credit, since it affects the availability and conditions of credit (Demirgüç-Kunt and Maksimovic, 2001). Among the institutional factors that explain cross-country heterogeneity, the efficiency of the legal system and the corruption level are considered.
The previous literature supports the link between trade credit and the legal system efficiency. A strong framework system that enforces contracts and protects creditors' rights promotes confidence in all forms of borrowing, including trade credit financing (Demirgüç-Kunt and Maksimovic, 2001). In principle, this indicates a positive influence of the efficiency of the legal system on the availability of trade credit. However, in weak legal environments where investor protection is low, traditional lenders impose stricter credit conditions, thereby limiting firms' access to institutional finance. This leads to a substitution effect, where firms increase the use of trade credit in order to replace the lack of other financial resources (Palacín-Sánchez et al., 2019). Suppliers, due to their close business relationships with customers, enjoy monitoring advantages (Petersen and Rajan, 1997) and enforcement mechanisms (such as withholding future deliveries and reclaiming goods) that reduce credit risk. These advantages make suppliers more willing to extend credit even when legal protection is weak (Fisman and Love, 2003). Based on these arguments, the second of our hypotheses is proposed as follows:
Lower legal system efficiency is positively related to increased use of trade credit.
A more corrupt environment is a major problem in developing countries, which tend to be more financially constrained. Lax laws and regulations can encourage corruption in lending, affecting the proper allocation of credit (Beck et al., 2008). The inadequate monitoring of lending decisions, weak supervision of bank managers, and poor oversight of the banking system can lead to excessive and misallocated financing, including lending to less creditworthy firms (Jiang and Wang, 2024). In such cases, firms may rely less on supplier financing since they have easy access to other financial resources (Cai et al., 2023).
Conversely, a higher level of corruption can also create obstacles and delays in bank lending, leading to inconsistencies in credit allocation (Weill, 2011). Lack of transparency and compliance prevents banks from performing their lending functions properly. As a result, firms may face unfavourable lending conditions and limited access to financial institutions, thereby forcing them to turn to alternative sources such as trade credit. Empirical evidence supports this view. Wang (2012) finds that corruption distorts the banking system, leading firms to rely more on supplier financing as a substitute for bank credit. Similarly, Ahmed and Farooq (2020) show that in financially developed countries, corruption shapes firms' financing choices: under high corruption, companies rely more on bank financing, but when corruption interacts with financial development, trade credit becomes a substitute for bank credit. Corruption fosters mistrust of financial institutions, which makes firms feel insecure when dealing with banks, while strong supplier relationships provide a more reliable financing alternative (Yano and Shiraishi, 2014).
Given that our study focuses on European countries that are financially developed but likely to have varying levels of corruption, our third hypothesis can be proposed:
A higher level of corruption is positively related to an increased use of trade credit.
2.3 Moderating effects of institutional factors
Institutional conditions not only influence firms' access to trade credit, but also play a decisive role in shaping corporate ESG policies and performance (Jiménez-Parra et al., 2025). It is therefore reasonable to expect that the effects of ESG performance on trade credit may be influenced by institutional factors. Xin et al. (2024) point out that discrepancies in the results of empirical studies may be due to differences in the institutional environments. In regions belonging to a single country such as China, Wu (2023) finds that the positive effect of ESG ratings on trade credit is more pronounced in regions with stronger legal systems and well-developed financial institutions. A well-regulated legal environment enhances the reliability of ESG disclosures, thereby both making it harder for firms to manipulate ESG ratings and increasing supplier trust. This transparency strengthens the link between ESG performance and trade credit use. In contrast, Luo et al. (2023) show that in Chinese regions with weaker legal systems and higher business risks, ESG performance may be utilised as a compensatory mechanism to reduce firm risk and enhance trade credit usage. Wei et al. (2023) find similar results for CSR and suggest that, in regions with weaker institutions, CSR serves as a substitute for trust in helping firms secure trade credit financing.
In contrast, in a cross-country study conducted during the COVID-19 crisis and focused on CSR, Dewally et al. (2025) show that firms with better CSR results were granted an increase in their trade credit, especially in countries with weaker institutions. In this case, actions that are morally and voluntarily socially responsible played a crucial role in maintaining business relationships. This suggests that, in weaker institutional contexts, companies rely more on ESG practices to build credibility and secure trade credit.
In conclusion, the scarcity of cross-country studies that explicitly analyse the influence of the institutional environment on the relationship between ESG and trade credit, and the ambiguous results of the aforementioned evidence, highlight the need to explore this relationship. On this basis, the fourth of our hypotheses is formulated.
The relationship between ESG results and trade credit is moderated by institutional factors such as the efficiency of the legal system and the level of corruption.
3. Research methodology
3.1 Data
The data utilised in this study was sourced from the Refinitiv Eikon Datastream database, a comprehensive global repository of financial and macroeconomic time-series data. This database provides extensive information on equities, stock market indices, currencies, corporate fundamentals, and other relevant financial and non-financial indicators, and covers 175 countries and 60 markets worldwide.
The sample contains European listed companies included in the STOXX 600 Index during the period 2015 to 2022. This index contains a permanent number of 600 large, medium, and small capitalisation firms across 17 European countries (not only from the Eurozone). The index covers approximately 90% of the free-float market capitalisation of the European stock market. The firms belong to a wide variety of countries: Austria, Belgium, Denmark, France, Finland, Germany, Ireland, Italy, Luxembourg, Norway, Poland, Portugal, Spain, Sweden, Switzerland, the Netherlands, and the United Kingdom. Table AI of Appendix shows the number of observations in our sample by country. This index provides a representative sample of companies across Europe and enables a comprehensive analysis to be performed in a region with a strong commitment to ESG objectives. Firms in the financial sector are excluded in order to obtain a homogeneous sample of companies with comparable characteristics in terms of supplier financing. We also highlight that the sample period begins in 2015 because Refinitiv substantially expanded its ESG coverage from that year onward, incorporating additional indices and improving the consistency of its scoring methodology across European listed firms. As stated in Refinitiv (2022), “it was from 2015 onwards when Refinitiv added more new indices to calculate the ESG score ratings,“ which significantly increased the reliability and comparability of ESG data across countries. Restricting the period to 2015–2022 ensures homogeneous data availability for firms included in our sample. Lastly, an unbalanced panel of 2,914 firm-year observations is obtained.
3.2 Variables
3.2.1 Firm variables
Firstly, the dependent variable is defined in an unequivocal manner to clearly capture the concept of trade credit. To this end, accounts payable is considered as a financial resource that arises when companies act as buyers and demand trade credit from sellers. Trade credit (TCPAY) is the ratio of accounts payable to total assets, following the approach of Yang (2011) and Palacín-Sánchez et al. (2019).
Secondly, an indicator of corporate ESG performance is also required. This rating, ESGSCORE, is our main independent variable. The ESGSCORE represents an aggregate company-level score derived from firm-reported data on the three pillars of environmental, social, and corporate governance (ESG), and is sourced from the Refinitiv Eikon database. Refinitiv's ESG scores offer the advantage of being designed to transparently and objectively assess a company's relative ESG performance, commitment, and effectiveness, based exclusively on publicly disclosed information. Furthermore, ESG scores from Refinitiv are updated once a year in line with companies' own ESG disclosures. The ESGSCORE rating takes values from 0 to 1, and are calculated from the percentile rank scoring of Refinitiv.
Lastly, classic firm-level determinants of trade credit are also included in the analysis as control variables. Following García-Teruel and Martínez-Solano (2010) and Huang et al. (2011), short-term debt, STDEBT, represents the proportion of bank debt payable within one year to total assets. Long-term debt (LTDEBT) is the ratio of all long-term interest-bearing financial obligations to total assets (Yazdanfar and Öhman, 2017). Following Love et al. (2007) and Kestens et al. (2012), liquidity (LIQUIDITY) is measured as the ratio of cash holdings to total assets. Current assets (CURRAS) are expressed as a proportion of total assets (Canto-Cuevas et al., 2016). The size of companies (SIZE) is proxied by the natural logarithm of total assets (Yang, 2011; McGuinness and Hogan, 2016). Market valuation is captured by Tobin's Q (TOBINQ), calculated as the sum of the market value of equity and the book value of debt divided by the book value of total assets (Luo et al., 2023). Growth (GROWTH) is defined as the annual percentage change in sales (Petersen and Rajan, 1997; García-Teruel and Martínez-Solano, 2010), while firm age (AGE) is measured as the number of years the firm has been publicly listed (Cai et al., 2023).
3.2.2 Institutional variables
Two institutional factors are employed herein as independent variables. Firstly, legal system efficiency (LEGALSYSTEM) is measured using an indicator provided by the World Bank, available for each year in all the countries analysed. This metric reflects perceptions of how much trust agents place in societal norms and, in particular, the quality of contract enforcement, property rights, and the judicial system. Each country's score follows a standardised normal distribution, ranging approximately from −2.5, which indicates a weak legal framework, to 2.5, which reflects a highly efficient legal system. This indicator has been employed in previous empirical research, including studies by Demirgüç-Kunt and Maksimovic (2001) and Palacín-Sánchez et al. (2019), to assess the institutional quality across countries.
Secondly, the proxy of the level of corruption (CORRUPTION) is also obtained from the World Bank, which provides a comparative measure of institutional integrity across countries. This indicator captures perceptions regarding the extent to which public authority is exploited for private benefit, by encompassing both minor and major forms of corruption together with the systemic “capture“ of the state by elites and private interests. The score is presented as an estimate on a standardised normal distribution, ranging from approximately −2.5, which indicates high corruption, to 2.5, which signifies the absence of this problem in the country.
3.3 Descriptive statistics
Table 1 presents the descriptive statistics for all the variables of the study in the whole sample. It shows that European listed firms have a mean of 0.6725 in the ESG score and that they make a non-negligible use of trade credit. In our sample, accounts payable represents, on average, 9.1% of their financial resources. Table 2 presents the correlation matrix. The correlations among the independent variables are sufficiently low to indicate that multicollinearity is unlikely to pose a significant concern [1].
Descriptive statistics
| Variable | Mean | Std. Dev | Min | Max | Obs |
|---|---|---|---|---|---|
| TCPAY | 0.0910 | 0.0754 | 0 | 0.6559 | 2,914 |
| ESGSCORE | 0.6725 | 0.1726 | 0.0391 | 0.9598 | 2,914 |
| STDEBT | 0.0498 | 0.0532 | 0 | 0.5950 | 2,914 |
| LTDEBT | 0.2131 | 0.1386 | 0 | 0.9988 | 2,914 |
| LIQUIDITY | 0.1275 | 0.1119 | 0.0002 | 0.9789 | 2,914 |
| CURRAS | 0.3981 | 0.1967 | 0.0176 | 0.9923 | 2,914 |
| SIZE | 15.9503 | 1.4663 | 10.5578 | 20.1287 | 2,914 |
| TOBINQ | 2.1553 | 3.3852 | 0.1709 | 58.4271 | 2,914 |
| GROWTH | 0.0982 | 0.4732 | −0.8617 | 16.7713 | 2,914 |
| AGE | 45.7248 | 37.4011 | 0 | 195 | 2,914 |
| LEGALSYSTEM | 1.4945 | 0.40472 | 0.20770 | 2.05346 | 2,914 |
| CORRUPTION | 1.6575 | 0.40586 | 0.027109 | 2.402744 | 2,914 |
| Variable | Mean | Std. Dev | Min | Max | Obs |
|---|---|---|---|---|---|
| TCPAY | 0.0910 | 0.0754 | 0 | 0.6559 | 2,914 |
| ESGSCORE | 0.6725 | 0.1726 | 0.0391 | 0.9598 | 2,914 |
| STDEBT | 0.0498 | 0.0532 | 0 | 0.5950 | 2,914 |
| LTDEBT | 0.2131 | 0.1386 | 0 | 0.9988 | 2,914 |
| LIQUIDITY | 0.1275 | 0.1119 | 0.0002 | 0.9789 | 2,914 |
| CURRAS | 0.3981 | 0.1967 | 0.0176 | 0.9923 | 2,914 |
| SIZE | 15.9503 | 1.4663 | 10.5578 | 20.1287 | 2,914 |
| TOBINQ | 2.1553 | 3.3852 | 0.1709 | 58.4271 | 2,914 |
| GROWTH | 0.0982 | 0.4732 | −0.8617 | 16.7713 | 2,914 |
| AGE | 45.7248 | 37.4011 | 0 | 195 | 2,914 |
| LEGALSYSTEM | 1.4945 | 0.40472 | 0.20770 | 2.05346 | 2,914 |
| CORRUPTION | 1.6575 | 0.40586 | 0.027109 | 2.402744 | 2,914 |
Correlation matrix
| TCPAY | ESGSCORE | STDEBT | LTDEBT | LIQUIDITY | CURRAS | SIZE | TOBINQ | GROWTH | AGE | LEGALSYSTEM | CORRUPTION | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TCPAY | 1 | ||||||||||||||||||||||
| ESGSCORE | 0.0738 | *** | 1 | ||||||||||||||||||||
| STDEBT | −0.0575 | *** | 0.0878 | *** | 1 | ||||||||||||||||||
| LTDEBT | −0.0989 | *** | 0.093 | *** | 0.0986 | *** | 1 | ||||||||||||||||
| LIQUIDITY | 0.1413 | *** | −0.0535 | *** | −0.0966 | *** | −0.1851 | *** | 1 | ||||||||||||||
| CURRAS | 0.361 | *** | −0.1 | *** | −0.117 | *** | −0.5296 | *** | 0.609 | *** | 1 | ||||||||||||
| SIZE | −0.1946 | *** | 0.4132 | *** | 0.2066 | *** | −0.0163 | −0.2684 | *** | −0.324 | *** | 1 | |||||||||||
| TOBINQ | 0.0078 | −0.1358 | *** | −0.0903 | *** | −0.0704 | *** | 0.2999 | *** | 0.2183 | *** | −0.4155 | *** | 1 | |||||||||
| GROWTH | 0.0017 | −0.0576 | *** | −0.0195 | 0.003 | 0.1007 | *** | 0.0135 | −0.0769 | *** | 0.0466 | *** | 1 | ||||||||||
| AGE | 0.0644 | *** | 0.1656 | *** | 0.0951 | *** | −0.0458 | *** | −0.0557 | *** | 0.0671 | *** | 0.1497 | *** | −0.0628 | *** | −0.0337 | *** | 1 | ||||
| LEGALSYSTEM | −0.0184 | −0.1158 | *** | −0.1703 | *** | −0.11 | *** | 0.0245 | * | 0.1228 | *** | −0.1722 | *** | 0.0756 | *** | 0.0017 | −0.0443 | *** | 1 | ||||
| CORRUPTION | −0.0354 | ** | −0.1173 | *** | −0.1724 | *** | −0.1175 | *** | 0.0188 | 0.1271 | *** | −0.1934 | *** | 0.0983 | *** | 0.0168 | −0.0418 | *** | 0.9449 | *** | 1 | ||
| TCPAY | ESGSCORE | STDEBT | LTDEBT | LIQUIDITY | CURRAS | SIZE | TOBINQ | GROWTH | AGE | LEGALSYSTEM | CORRUPTION | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TCPAY | 1 | ||||||||||||||||||||||
| ESGSCORE | 0.0738 | *** | 1 | ||||||||||||||||||||
| STDEBT | −0.0575 | *** | 0.0878 | *** | 1 | ||||||||||||||||||
| LTDEBT | −0.0989 | *** | 0.093 | *** | 0.0986 | *** | 1 | ||||||||||||||||
| LIQUIDITY | 0.1413 | *** | −0.0535 | *** | −0.0966 | *** | −0.1851 | *** | 1 | ||||||||||||||
| CURRAS | 0.361 | *** | −0.1 | *** | −0.117 | *** | −0.5296 | *** | 0.609 | *** | 1 | ||||||||||||
| SIZE | −0.1946 | *** | 0.4132 | *** | 0.2066 | *** | −0.0163 | −0.2684 | *** | −0.324 | *** | 1 | |||||||||||
| TOBINQ | 0.0078 | −0.1358 | *** | −0.0903 | *** | −0.0704 | *** | 0.2999 | *** | 0.2183 | *** | −0.4155 | *** | 1 | |||||||||
| GROWTH | 0.0017 | −0.0576 | *** | −0.0195 | 0.003 | 0.1007 | *** | 0.0135 | −0.0769 | *** | 0.0466 | *** | 1 | ||||||||||
| AGE | 0.0644 | *** | 0.1656 | *** | 0.0951 | *** | −0.0458 | *** | −0.0557 | *** | 0.0671 | *** | 0.1497 | *** | −0.0628 | *** | −0.0337 | *** | 1 | ||||
| LEGALSYSTEM | −0.0184 | −0.1158 | *** | −0.1703 | *** | −0.11 | *** | 0.0245 | * | 0.1228 | *** | −0.1722 | *** | 0.0756 | *** | 0.0017 | −0.0443 | *** | 1 | ||||
| CORRUPTION | −0.0354 | ** | −0.1173 | *** | −0.1724 | *** | −0.1175 | *** | 0.0188 | 0.1271 | *** | −0.1934 | *** | 0.0983 | *** | 0.0168 | −0.0418 | *** | 0.9449 | *** | 1 | ||
Note(s): Statistical significance is indicated by asterisks at the 10% (*), 5% (**) and 1% (***) levels
3.4 Methodology
In order to verify Hypotheses 1, 2, and 3 regarding the influence of ESG performance on trade credit and the influence of institutional factors on trade credit, a model for the baseline study is constructed. This model incorporates firm-specific variables. Country, sector, and time dummies are also included as control variables. The equation is as follows:
TCPAYit = α0 + β1ESGSCOREit + β2STDEBTit + β3LTDEBTit + β4LIQUIDITYit + β5CURRASit + β6SIZEit + β7TOBINQit + β8GROWTHit + β9AGEit + β10INSTITUTIONAL VARIABLEjt + sector dummies + country dummies + time dummies + εit (1)
where α0 stands for the constant term, i denotes the firm, t indicates the year, j is the country, and εi,t is the disturbance term. The 94.5% correlation shown in Table 2 between our two institutional variables, LEGALSYSTEM and CORRUPTION, suggests strong collinearity. To avoid multicollinearity, these two variables are not used at the same time in our model.
The ordinary least squares (OLS) estimation with robust standard error is utilised to estimate the relationship between ESG score and trade credit while accounting for potential endogeneity issues. This estimation method enables unbiased and efficient estimates of the coefficients to be obtained while considering potential heteroscedasticity and correlation of errors.
4. Empirical results
4.1 Baseline analysis
Regressions that estimate the effect of ESG rating and institutional variables on trade credit are presented in this section. Table 3 shows the results of the model, which controls for firm-specific characteristics as well as year, sector, and country dummy variables, where Column I includes LEGALSYSTEM as a proxy for the institutional environment and Column II includes CORRUPTION. Our results show a positive relationship between ESGSCORE and TRADECREDIT, which is highly significant at the 1% level. This suggests that firms with higher ESG ratings tend to receive more trade credit. Therefore, H1 is verified, and it is shown that firms with better ESG performance are more trustworthy to suppliers due to the signals from their ESG score, which reduces the problems of asymmetric information, and therefore implies a lower risk of non-payment. These results are in line with previous studies using listed firms around the world, such as Zhang and Lucey (2022) and Heo (2024), or in the context of China (Tian and Tian, 2022; Luo et al., 2023; Wang, 2023; Huang et al., 2023).
Effects of ESG performance on trade credit
| Variables | (I) | (II) | ||
|---|---|---|---|---|
| ESGSCORE | 0.0340 | *** | 0.0322 | *** |
| (0.0101) | (0.0102) | |||
| STDEBT | −0.0370 | −0.0384 | ||
| (0.0256) | (0.0256) | |||
| LTDEBT | −0.0238 | ** | −0.0224 | * |
| (0.0121) | (0.0121) | |||
| LIQUIDITY | −0.1985 | *** | −0.2017 | *** |
| (0.0169) | (0.0170) | |||
| CURRAS | 0.2281 | *** | 0.2282 | *** |
| (0.0102) | (0.0103) | |||
| SIZE | 0.0027 | ** | 0.0026 | ** |
| (0.0013) | (0.0013) | |||
| TOBINQ | −0.0023 | *** | −0.0023 | *** |
| (0.0004) | (0.0004) | |||
| GROWTH | 0.0000 | 0.0000 | ||
| (0.0000) | (0.0000) | |||
| AGE | −0.0001 | *** | −0.0001 | *** |
| (0.0000) | (0.0000) | |||
| CORRUPTION | −0.0255 | *** | ||
| (0.0035) | ||||
| LEGALSYSTEM | −0.0381 | *** | ||
| (0.0046) | ||||
| CONSTANT | 0.0412 | * | 0.0260 | |
| (0.0224) | (0.0221) | |||
| YEAR | Yes | Yes | ||
| SECTOR | Yes | Yes | ||
| COUNTRY | Yes | Yes | ||
| R-Squared | 0.2764 | 0.2719 | ||
| Obs | 2393 | 2393 |
| Variables | (I) | (II) | ||
|---|---|---|---|---|
| ESGSCORE | 0.0340 | *** | 0.0322 | *** |
| (0.0101) | (0.0102) | |||
| STDEBT | −0.0370 | −0.0384 | ||
| (0.0256) | (0.0256) | |||
| LTDEBT | −0.0238 | ** | −0.0224 | * |
| (0.0121) | (0.0121) | |||
| LIQUIDITY | −0.1985 | *** | −0.2017 | *** |
| (0.0169) | (0.0170) | |||
| CURRAS | 0.2281 | *** | 0.2282 | *** |
| (0.0102) | (0.0103) | |||
| SIZE | 0.0027 | ** | 0.0026 | ** |
| (0.0013) | (0.0013) | |||
| TOBINQ | −0.0023 | *** | −0.0023 | *** |
| (0.0004) | (0.0004) | |||
| GROWTH | 0.0000 | 0.0000 | ||
| (0.0000) | (0.0000) | |||
| AGE | −0.0001 | *** | −0.0001 | *** |
| (0.0000) | (0.0000) | |||
| CORRUPTION | −0.0255 | *** | ||
| (0.0035) | ||||
| LEGALSYSTEM | −0.0381 | *** | ||
| (0.0046) | ||||
| CONSTANT | 0.0412 | * | 0.0260 | |
| (0.0224) | (0.0221) | |||
| YEAR | Yes | Yes | ||
| SECTOR | Yes | Yes | ||
| COUNTRY | Yes | Yes | ||
| R-Squared | 0.2764 | 0.2719 | ||
| Obs | 2393 | 2393 |
Note(s): The institutional variable is LEGALSYSTEM in Column (I) and CORRUPTION in Column (II). Statistical significance is indicated by asterisks at the 10% (*), 5% (**) and 1% (***) levels. Standard errors are reported in parentheses beneath the estimated coefficients
The two institutional factors, legal system efficiency (LEGALSYSTEM) in Column I and level of corruption (CORRUPTION) in Column II, are statistically significant in explaining supplier financing. The signs of their relationships with our dependent variable are negative. These findings verify H2 and H3. These results suggest that countries with less efficient legal systems and more corruption favour a greater use of supplier financing. This could mean that the relationships between suppliers and customers are built on their knowledge and trust, which means less information asymmetry. Thus, suppliers can grant trade credit when the legal system is less effective and when there is a greater level of corruption. Furthermore, suppliers may be able to resolve disputes with their customers (such as by threatening to suspend deliveries) without resorting to formal legal procedures. The influence of the legal system on trade credit has also been documented in previous studies on publicly listed firms (Demirgüç-Kunt and Maksimovic, 2001). Concerning the level of corruption, this relationship was previously observed for listed companies in China (Wang, 2012).
Regarding control variables, the negative coefficients of LTDEBT show that supplier financing increases when firms experience difficulties using long-term debt (Yazdanfar and Öhman, 2017), which suggests a substitution effect with trade credit. The negative coefficient of LIQUIDITY indicates that highly liquid firms need less credit from their suppliers to finance their assets (Yang, 2011), which shows that these firms depend less on trade credit. The positive coefficient of CURRAS shows that companies with a higher proportion of current assets tend to rely more heavily on trade credit to finance their short-term assets (Petersen and Rajan, 1997). SIZE presents a positive relationship with supplier financing, which shows a greater use of trade credit by larger firms. Large firms may impose their own conditions regarding trade credit received from smaller suppliers, by exploiting their market power (Marotta, 2005), and obtaining more payables. TOBINQ is inversely related with supplier financing (Luo et al., 2023), and AGE presents a negative coefficient, which suggests that those companies that have been listed for fewer years use more supplier financing (Cai et al., 2023). The remaining firm-level determinants of trade credit do not show a statistically significant relationship.
4.2 Moderating effects of institutional factors
To examine whether institutional quality moderates the relationship between ESG performance and trade credit, we employ a categorical moderator approach by stratifying countries into three groups depending on the score of each country regarding the LEGALSYSTEM and CORRUPTION [2]. The first group (LS1) comprises countries with the lowest tertial of legal systems and control of corruption: Italy, Poland, Spain, and Portugal. The second group (LS2) includes countries in the middle tertial and with moderate institutional factors, namely France, Belgium, Ireland, the United Kingdom, and Germany. Finally, the third group (LS3) consists of countries with the strongest legal systems and lowest corruption levels, including Luxembourg, the Netherlands, Austria, Sweden, Switzerland, Denmark, Norway, and Finland. We then created interaction terms multiplying ESG scores by each group dummy (ESGSCORE*LS1, ESGSCORE*LS2, ESGSCORE*LS3) to test whether ESG's effect on trade credit varies across different institutional contexts.
We chose this categorical approach rather than a single continuous interaction for several theoretical and empirical reasons. First, Institutional Theory suggests that institutional quality operates through distinct regimes rather than smoothly along a continuum (North, 1990; Khanna and Palepu, 2000). Countries with weak legal system and poor corruption control create fundamentally different environments where ESG compensates for missing formal enforcement, while strong institutional frameworks already provide formal protections that reduce ESG's role as a signalling mechanism. A single continuous interaction would impose linearity that obscures these threshold-based, non-monotonic relationships. Second, the categorical approach is more robust to measurement error and permits flexible non-linear moderating effects. Third, it offers clearer interpretation for managers and policymakers by directly showing how the ESG-trade credit relationship differs across weak, medium, and strong institutional settings.
We report results using OLS with robust standard errors (Table 4) and Heckman two-stage estimation to address sample selection bias (Table 5). The interaction terms in the Heckman second stage directly test the moderation hypothesis. Significant differences in interaction coefficients across groups indicate that institutional context moderates the ESG-trade credit relationship. This categorical framework follows established practices in leading finance journals examining institutional moderation (La Porta et al., 1998; Djankov et al., 2007).
Influence of ESG score on trade credit across the three institutional environments
| Variables | TCPAY | |
|---|---|---|
| ESGSCORE*LS1 | 0.0713 | *** |
| (0.0129) | ||
| ESGSCORE*LS2 | 0.0349 | *** |
| (0.0104) | ||
| ESGSCORE*LS3 | 0.0195 | * |
| (0.0104) | ||
| STDEBT | −0.0312 | |
| (0.0257) | ||
| LTDEBT | −0.0206 | * |
| (0.0122) | ||
| LIQUIDITY | −0.1947 | *** |
| (0.0170) | ||
| CURRAS | 0.2292 | *** |
| (0.0103) | ||
| SIZE | 0.0036 | *** |
| (0.0013) | ||
| TOBINQ | −0.0023 | *** |
| (0.0004) | ||
| GROWTH | 0.0000 | |
| (0.0000) | ||
| AGE | −0.0001 | *** |
| (0.0000) | ||
| CONSTANT | −0.0351 | * |
| (0.0199) | ||
| YEAR | Yes | |
| SECTOR | Yes | |
| COUNTRY | Yes | |
| R-squared | 0.2674 | |
| Obs | 2393 |
| Variables | TCPAY | |
|---|---|---|
| ESGSCORE*LS1 | 0.0713 | *** |
| (0.0129) | ||
| ESGSCORE*LS2 | 0.0349 | *** |
| (0.0104) | ||
| ESGSCORE*LS3 | 0.0195 | * |
| (0.0104) | ||
| STDEBT | −0.0312 | |
| (0.0257) | ||
| LTDEBT | −0.0206 | * |
| (0.0122) | ||
| LIQUIDITY | −0.1947 | *** |
| (0.0170) | ||
| CURRAS | 0.2292 | *** |
| (0.0103) | ||
| SIZE | 0.0036 | *** |
| (0.0013) | ||
| TOBINQ | −0.0023 | *** |
| (0.0004) | ||
| GROWTH | 0.0000 | |
| (0.0000) | ||
| AGE | −0.0001 | *** |
| (0.0000) | ||
| CONSTANT | −0.0351 | * |
| (0.0199) | ||
| YEAR | Yes | |
| SECTOR | Yes | |
| COUNTRY | Yes | |
| R-squared | 0.2674 | |
| Obs | 2393 |
Note(s): Statistical significance is indicated by asterisks at the 10% (*), 5% (**) and 1% (***) levels. Robust standard errors are reported in parentheses
Moderating effect of legal system on ESG-Trade credit relationship: Heckman 2-step with interaction terms
| Variables | Desgscore | TCPAY | ||
|---|---|---|---|---|
| ESGSCORE*LS1 | 0.0775 | *** | ||
| (0.0288) | ||||
| ESGSCORE*LS2 | 0.0531 | * | ||
| (0.0277) | ||||
| ESGSCORE*LS3 | 0.0471 | * | ||
| (0.0276) | ||||
| STDEBT | −0.4737 | −0.0575 | *** | |
| (0.5314) | (0.0347) | |||
| LTDEBT | 0.9749 | *** | −0.0728 | *** |
| (0.2152) | (0.0165) | |||
| LIQUIDITY | −0.5654 | * | −0.1716 | *** |
| (0.3101) | (0.0242) | |||
| CURRAS | 0.6712 | *** | 0.2023 | *** |
| (0.1923) | (0.0146) | |||
| SIZE | 0.5498 | *** | −0.0024 | |
| (0.0234) | (0.0024) | |||
| TOBINQ | 0.0087 | −0.0116 | *** | |
| (0.0175) | (0.0013) | |||
| GROWTH | 0.0001 | |||
| (0.0001) | ||||
| AGE | 0.0000 | |||
| (0.0000) | ||||
| CONSTANT | −9.2986 | *** | 0.0860 | * |
| (0.4065) | (0.0450) | |||
| YEAR | Yes | |||
| SECTOR | Yes | |||
| COUNTRY | Yes | |||
| Wald chi | 547.03 | |||
| Obs | 2893 | 1322 | ||
| Variables | Desgscore | TCPAY | ||
|---|---|---|---|---|
| ESGSCORE*LS1 | 0.0775 | *** | ||
| (0.0288) | ||||
| ESGSCORE*LS2 | 0.0531 | * | ||
| (0.0277) | ||||
| ESGSCORE*LS3 | 0.0471 | * | ||
| (0.0276) | ||||
| STDEBT | −0.4737 | −0.0575 | *** | |
| (0.5314) | (0.0347) | |||
| LTDEBT | 0.9749 | *** | −0.0728 | *** |
| (0.2152) | (0.0165) | |||
| LIQUIDITY | −0.5654 | * | −0.1716 | *** |
| (0.3101) | (0.0242) | |||
| CURRAS | 0.6712 | *** | 0.2023 | *** |
| (0.1923) | (0.0146) | |||
| SIZE | 0.5498 | *** | −0.0024 | |
| (0.0234) | (0.0024) | |||
| TOBINQ | 0.0087 | −0.0116 | *** | |
| (0.0175) | (0.0013) | |||
| GROWTH | 0.0001 | |||
| (0.0001) | ||||
| AGE | 0.0000 | |||
| (0.0000) | ||||
| CONSTANT | −9.2986 | *** | 0.0860 | * |
| (0.4065) | (0.0450) | |||
| YEAR | Yes | |||
| SECTOR | Yes | |||
| COUNTRY | Yes | |||
| Wald chi | 547.03 | |||
| Obs | 2893 | 1322 | ||
Note(s): Statistical significance is indicated by asterisks at the 10% (*), 5% (**) and 1% (***) levels. Standard errors are reported in parentheses
The results provide evidence of a positive relationship, which is statistically significant, between ESG performance and trade credit across all three groups. Nevertheless, variations in the magnitude of these coefficients can be observed, which suggest potential heterogeneity in their effects. The coefficient for the interactive variable is largest in LS1, which represents countries with the weakest legal systems and highest corruption levels. The coefficient decreases for LS2, comprising countries with moderate institutional factors, and is the smallest for LS3, which represents countries with the strongest legal systems and lowest corruption levels. These results suggest that the relationship between ESG performance and trade credit varies across different institutional environments. The fourth hypothesis (H4) is therefore confirmed, which shows the moderating role of institutional factors, such as legal system efficiency and the level of corruption, on the relationship between ESG performance and trade credit.
Regarding the magnitude of the coefficients across all three groups, the results reveal that the influence of the ESG practices on supplier financing is strongest in those countries that have the weakest institutional environments, and that this influence diminishes as the strength of the legal systems improves and corruption levels decrease. The results show that ESG practices perform a significant role in granting trade credit in environments where institutional safeguards are less robust. This is possible due to suppliers maintaining a close relationship with their customers and to their better knowledge of said customers, which could help them incorporate the information obtained from ESG ratings.
The moderating effects tested in the analysis can also be illustrated graphically. The marginal effects of ESG scores are analysed across the three institutional groups (LS1, LS2, and LS3). Figure 1 shows that, in LS1, higher ESG scores are strongly related to an increase in the use of supplier financing, as indicated by the steep slope of the line. For LS2, this positive relationship persists, but the slope is notably less pronounced, indicating a weaker effect. Lastly, for LS3, the line is nearly horizontal, which suggests that the effect of ESG scores on supplier financing is minimal in contexts characterised by highly efficient legal systems.
The line graph illustrates the relationship between “E S G score” and a predicted outcome. The horizontal axis is labeled “E S G score” and ranges from “0” to “95” in increments of “10” units. The vertical axis is labeled “Linear Prediction” and ranges from “0.06” to “0.14” in increments of “0.02” units. The graph displays three separate lines, each with vertical error bars representing confidence intervals. A legend at the bottom identifies the lines as “L S 1”, “L S 2”, and “L S 3”. All three lines originate from the same starting point of “0.076” when the “E S G score” is “0”. The first line, “L S 1”, rises steeply, passing through “0.100” at an “E S G score” of “40” and reaching “0.133” at an “E S G score” of “95”. The second line, “L S 2”, increases steadily, passing through “0.087” at an “E S G score” of “40” and reaching “0.102” at an “E S G score” of “95”. The third line, “L S 3”, shows a slight upward trend, passing through “0.080” at an “E S G score” of “40” and reaching “0.085” at an “E S G score” of “95”. Note: All numerical values are approximate.Impact of ESG performance on trade credit across the three institutional environments. Note: This figure reports the moderating effects of institutional environment (grouped as LS1, LS2 and LS3) on the relationship between ESGSCORE and trade credit. Specifically, LS1 comprises countries with the weakest legal systems and the highest levels of corruption; LS2 includes countries with moderate institutional factors; and LS3 consists of countries with the strongest legal systems and the lowest corruption levels
The line graph illustrates the relationship between “E S G score” and a predicted outcome. The horizontal axis is labeled “E S G score” and ranges from “0” to “95” in increments of “10” units. The vertical axis is labeled “Linear Prediction” and ranges from “0.06” to “0.14” in increments of “0.02” units. The graph displays three separate lines, each with vertical error bars representing confidence intervals. A legend at the bottom identifies the lines as “L S 1”, “L S 2”, and “L S 3”. All three lines originate from the same starting point of “0.076” when the “E S G score” is “0”. The first line, “L S 1”, rises steeply, passing through “0.100” at an “E S G score” of “40” and reaching “0.133” at an “E S G score” of “95”. The second line, “L S 2”, increases steadily, passing through “0.087” at an “E S G score” of “40” and reaching “0.102” at an “E S G score” of “95”. The third line, “L S 3”, shows a slight upward trend, passing through “0.080” at an “E S G score” of “40” and reaching “0.085” at an “E S G score” of “95”. Note: All numerical values are approximate.Impact of ESG performance on trade credit across the three institutional environments. Note: This figure reports the moderating effects of institutional environment (grouped as LS1, LS2 and LS3) on the relationship between ESGSCORE and trade credit. Specifically, LS1 comprises countries with the weakest legal systems and the highest levels of corruption; LS2 includes countries with moderate institutional factors; and LS3 consists of countries with the strongest legal systems and the lowest corruption levels
Our findings align with studies carried out in China (Luo et al., 2023; Wei et al., 2023), and with the cross-country study on CSR by Dewally et al. (2025), confirming that ESG outcomes play a more relevant role in granting trade credit in weaker institutional environments. Our study contributes towards addressing the gap in cross-country research in this field and provides empirical evidence on how legal system efficiency and corruption level moderate the ESG-trade credit relationship across different European institutional contexts.
In Table 5, the interaction terms provide direct evidence of the moderating role of institutional quality. The coefficient on ESGSCORE*LS1 (0.0775) is significantly larger than ESGSCORE*LS3 (0.0471), indicating that the positive effect of ESG performance on trade credit is substantially stronger in countries with weak legal systems. This pattern supports our hypothesis H4 that ESG serves as a compensatory signalling mechanism in institutional environments characterised by weak rule of law.
Specifically, a one-standard-deviation increase in ESG score is associated with a 7.75% point increase in trade credit ratio for firms in weak institutional environments (LS1), compared to only 4.71% points in strong institutional contexts (LS3). This 64% difference in effect magnitude (7.75 vs. 4.71) demonstrates that institutional context fundamentally shapes how ESG performance translates into supplier trust and trade credit access.
The moderating effect is economically meaningful: in weak institutional environments, where formal legal enforcement and contract protection are limited, suppliers place greater weight on ESG signals as indicators of payment reliability and long-term partnership viability. Conversely, in strong institutional settings, formal mechanisms (e.g. efficient courts, low corruption) already reduce information asymmetry, making ESG a less critical, though still positive signal.
4.3 Robustness test
The issue of endogeneity is a common challenge in empirical research, particularly in the context of ESG studies. To mitigate concerns that the documented ESG–trade credit relationship may be driven by differences unobservable firm characteristics (i.e. non-random selection into high ESG performance), we conduct a robustness test based on propensity score matching. Specifically, we define a treatment indicator equal to 1 for firm-year observations with an ESG score above the sample median and 0 otherwise. We then estimate propensity scores using a set of covariates commonly associated with trade credit demand and financing capacity -TCPAY, STDEBT, LTDEBT, LIQUIDITY, CURRAS, SIZE TOBINQ, GROWTH, and AGE- and we also account for sector and year effects in the matching procedure. After matching treated (high-ESG) and control (low-ESG) observations with similar propensity scores, we build a matched sample and re-estimate our baseline trade credit specification on this balanced dataset. The results (reported in Table 6) remain consistent with our main findings, supporting the conclusion that higher ESG performance is associated with greater use of trade credit, and suggesting that our baseline evidence is not solely attributable to observable differences between high- and low-ESG firms.
Moderating effect of legal system on ESG-Trade Credit relationship: propensity score match with interaction terms
| Variables | TCPAY | |
|---|---|---|
| ESGSCORE*LS1 | 0.0605 | *** |
| 0.0163 | ||
| ESGSCORE*LS2 | 0.0340 | *** |
| 0.0128 | ||
| ESGSCORE*LS3 | 0.0256 | ** |
| 0.0126 | ||
| STDEBT | −0.0945 | *** |
| 0.0332 | ||
| LTDEBT | −0.0092 | |
| 0.0161 | ||
| LIQUIDITY | −0.2304 | *** |
| 0.0286 | ||
| CURRAS | 0.2564 | *** |
| 0.0224 | ||
| SIZE | 0.0058 | *** |
| 0.0019 | ||
| TOBINQ | −0.0039 | *** |
| 0.0010 | ||
| GROWTH | 0.0000 | |
| 0.0000 | ||
| AGE | −0.0001 | ** |
| 0.0000 | ||
| CONSTANT | −0.0649 | ** |
| 0.0305 | ||
| YEAR | Yes | |
| SECTOR | Yes | |
| COUNTRY | Yes | |
| R-squared | 0.299 | |
| Obs | 1513 |
| Variables | TCPAY | |
|---|---|---|
| ESGSCORE*LS1 | 0.0605 | *** |
| 0.0163 | ||
| ESGSCORE*LS2 | 0.0340 | *** |
| 0.0128 | ||
| ESGSCORE*LS3 | 0.0256 | ** |
| 0.0126 | ||
| STDEBT | −0.0945 | *** |
| 0.0332 | ||
| LTDEBT | −0.0092 | |
| 0.0161 | ||
| LIQUIDITY | −0.2304 | *** |
| 0.0286 | ||
| CURRAS | 0.2564 | *** |
| 0.0224 | ||
| SIZE | 0.0058 | *** |
| 0.0019 | ||
| TOBINQ | −0.0039 | *** |
| 0.0010 | ||
| GROWTH | 0.0000 | |
| 0.0000 | ||
| AGE | −0.0001 | ** |
| 0.0000 | ||
| CONSTANT | −0.0649 | ** |
| 0.0305 | ||
| YEAR | Yes | |
| SECTOR | Yes | |
| COUNTRY | Yes | |
| R-squared | 0.299 | |
| Obs | 1513 |
Note(s): Statistical significance is indicated by asterisks at the 10% (*), 5% (**) and 1% (***) levels. Standard errors are reported in parentheses
5. Conclusions
This study contributes to the growing field of sustainable finance by analysing the influence of a firm's ESG performance on its use of trade credit financing, with a particular attention to the moderating role of institutional factors. The results show a significant and positive relationship between ESG performance and access to trade credit, suggesting that firms with stronger sustainability practices are perceived by their suppliers as more reliable and creditworthy. In this regard, ESG performance, acts as a mechanism that enhances transparency, reduces information asymmetry, and supports trust-based relationships within supply chains.
The findings also highlight the relevance of institutional quality—specifically legal system efficiency and the level of corruption—in the relationship between ESG and trade credit. In countries with weaker institutional environments, characterised by less effective legal systems and higher levels of corruption, the positive effect of ESG performance on trade credit becomes even stronger. Conversely, in countries with stronger institutional frameworks, this effect remains positive but weaker. This pattern suggests that, in a weaker institutional environment where formal legal protection is less developed, ESG practices may partially compensate for institutional shortcomings, leading suppliers to rely more heavily on ESG signals when assessing customer reliability.
These findings generate several important implications for theoretical development, managerial practice, and policy-making. From a theoretical perspective, the study extends the traditional trade credit literature by incorporating ESG performance as a relevant determinant of supplier financing decisions and by demonstrating that its financial relevance depends on the surrounding institutional environment. From a managerial perspective, the findings underline that ESG performance is not only a matter of regulatory compliance or reputation management, but also a strategic financial asset that can improve access to short-term financing. This is particularly relevant for firms operating in institutional settings characterised by legal inefficiencies or higher corruption, where ESG engagement can help build credibility and reinforce supplier relationships. From a policy perspective, the evidence suggests that policies promoting ESG transparency and standardisation may indirectly facilitate access to trade credit and support more inclusive financing conditions, especially in countries with institutional weaknesses. Policymakers should therefore consider the complementarities between institutional reforms and sustainability policies, as both dimensions jointly influence firms' financing opportunities and the functioning of supply chains.
Future research could extend this analysis to other geographical regions, incorporate additional institutional factors, or other effects, such as sector-specific effects. Furthermore, ongoing improvements in ESG measurement and the growing availability of comparable indicators for small and medium-sized firms will allow deeper exploration of the financial and institutional mechanisms underlying sustainable business practices.
In summary, this research confirms that ESG performance is a crucial determinant factor in trade credit financing decisions, although its impact is strongly conditioned by the institutional environment. These findings enrich the understanding of how sustainability, institutional quality, and financial dynamics interact to advance long-term and economic development.
APPENDIX
Number of observations per country
| Country | Frequency | % |
|---|---|---|
| Austria | 41 | 1.41 |
| Belgium | 47 | 1.62 |
| Denmark | 122 | 4.2 |
| France | 414 | 13.76 |
| Finland | 95 | 3.27 |
| Germany | 364 | 12.52 |
| Ireland | 61 | 2.1 |
| Italy | 112 | 3.85 |
| Luxembourg | 36 | 1.24 |
| Norway | 71 | 2.44 |
| Poland | 30 | 1.03 |
| Portugal | 18 | 0.62 |
| Spain | 118 | 4.06 |
| Sweden | 288 | 9.77 |
| Switzerland | 272 | 9.36 |
| Netherlands | 183 | 6.3 |
| United Kingdom | 642 | 22.08 |
| Total obs | 2914 | 100 |
| Country | Frequency | % |
|---|---|---|
| Austria | 41 | 1.41 |
| Belgium | 47 | 1.62 |
| Denmark | 122 | 4.2 |
| France | 414 | 13.76 |
| Finland | 95 | 3.27 |
| Germany | 364 | 12.52 |
| Ireland | 61 | 2.1 |
| Italy | 112 | 3.85 |
| Luxembourg | 36 | 1.24 |
| Norway | 71 | 2.44 |
| Poland | 30 | 1.03 |
| Portugal | 18 | 0.62 |
| Spain | 118 | 4.06 |
| Sweden | 288 | 9.77 |
| Switzerland | 272 | 9.36 |
| Netherlands | 183 | 6.3 |
| United Kingdom | 642 | 22.08 |
| Total obs | 2914 | 100 |
Country institutional clusters based on LEGALSYSTEM and CORRUPTION
| Country | LEGALSYSTEM | CORRUPTION | Group |
|---|---|---|---|
| Italy | 0.2794 | 0.2863 | 1 |
| Poland | 0.4928 | 0.6324 | 1 |
| Spain | 0.9187 | 0.6380 | 1 |
| Portugal | 1.1044 | 0.7897 | 1 |
| France | 1.3279 | 1.2494 | 2 |
| Germany | 1.5894 | 1.8181 | 2 |
| Belgium | 1.3518 | 1.4606 | 2 |
| Ireland | 1.4766 | 1.5844 | 2 |
| United Kingdom | 1.5629 | 1.7465 | 2 |
| Luxembourg | 1.7542 | 1.9891 | 3 |
| Netherlands | 1.7570 | 1.8895 | 3 |
| Austria | 1.7900 | 1.4424 | 3 |
| Sweden | 1.8117 | 2.1009 | 3 |
| Switzerland | 1.8442 | 1.9888 | 3 |
| Finland | 1.8643 | 2.1881 | 3 |
| Norway | 1.9269 | 2.1083 | 3 |
| Denmark | 2.0156 | 2.2288 | 3 |
| Country | LEGALSYSTEM | CORRUPTION | Group |
|---|---|---|---|
| Italy | 0.2794 | 0.2863 | 1 |
| Poland | 0.4928 | 0.6324 | 1 |
| Spain | 0.9187 | 0.6380 | 1 |
| Portugal | 1.1044 | 0.7897 | 1 |
| France | 1.3279 | 1.2494 | 2 |
| Germany | 1.5894 | 1.8181 | 2 |
| Belgium | 1.3518 | 1.4606 | 2 |
| Ireland | 1.4766 | 1.5844 | 2 |
| United Kingdom | 1.5629 | 1.7465 | 2 |
| Luxembourg | 1.7542 | 1.9891 | 3 |
| Netherlands | 1.7570 | 1.8895 | 3 |
| Austria | 1.7900 | 1.4424 | 3 |
| Sweden | 1.8117 | 2.1009 | 3 |
| Switzerland | 1.8442 | 1.9888 | 3 |
| Finland | 1.8643 | 2.1881 | 3 |
| Norway | 1.9269 | 2.1083 | 3 |
| Denmark | 2.0156 | 2.2288 | 3 |
Notes
The correlation between LEGALSYSTEM and CORRUPTION are discussed in the Methodology Section below.

