The Relationship Between ESG and Corruption Events in the Banking Industry: An Empirical Analysis

Abstract

We analyse the relationship between Environmental, Social, and Governance (ESG) practices and the probability of being involved in corruption in banking. We draw on two opposing theoretical frameworks (i.e., the stakeholder view and the impression management view), to assess whether ESG-oriented banks are genuinely concerned about anti-corruption practices or potentially involved in deceptive strategies. Using a sample of 46 Eurozone banks over the 2013–2022, our results suggest the existence of a beneficial negative association between ESG and corruption supporting the stakeholder theory. We show that such result is mainly driven by the G pillar, highlighting the key role in corporate governance in preventing corruption events. Moreover, we find that bank-level characteristics, corporate governance mechanisms, and country-level institutional factors play a key role in shaping such relationship. Our findings have important policy and managerial implications in terms of prevention of corruption scandals that can jeopardise the effectiveness of bank financial intermediation.

Jel classification: G21, G28, K20

Keywords

corruption banking ESG stakeholder theory impression management view

Introduction

During the last decades, banks have been involved in several corruption scandals and have been subject to ethical concerns all over the world. Examples of this reality include the episode of the Global Financial Crisis (GFC) in 2007/2008 as well as problems derived of practices related to money laundering and terrorism financing, among others (Chong & Lopez-De-Silanes, 2015; Limodio, 2022).

Corruption scandals can in fact jeopardise the effectiveness of the financial intermediation function traditionally carried out by the banking industry. These types of events may create important reputational costs for banks and imply difficulties in raising funds, provoking inefficiencies in the allocation of credit sometimes related to the ineffectiveness in the assessment of borrowers’ creditworthiness (Wu & Shen, 2013).

At the same time, there is an increasing trend in fostering the ESG efforts made by financial institutions, given that Environmental, Social and Governance (ESG) aspects are now under the spotlight in the banking industry as a response to the ethical concerns affecting banks during the recent periods, from the one side, but also because investors are nowadays more concerned about incorporating ESG risks and opportunities in their investment decisions (PricewaterhouseCoopers [PwC], 2022), from the other side. Moreover, supervisors’ concerns about bank ESG risks (see European Central Bank [ECB], 2020; Federal Reserve System [FED], 2022; Financial Stability Board [FSB], 2017 among others) jointly considered with the most recent evidence on ESG practices resulting in less risk-taking and enhanced financial stability (Galletta & Mazzù, 2023), also make it possible to argue that ESG aspects are now part of the strategy of each bank entity.

Within this context, the objective of this article is to empirically examine the relationship between ESG orientation by Eurozone banks and the probability of occurrence of a corruption scandal. To do so, we draw upon two opposing theoretical views – that is, the stakeholder view and the impression management view, proposed by Freeman (2010) and Goffman (1959), respectively – to assess whether ESG-oriented banks are genuinely concerned about anti-corruption practices or potentially involved in deceptive strategies to boost their ESG scores without adequately addressing the specific corporate governance mechanisms related to corruption events.¹

We carry out this analysis by using a wide range of sources including manually collected data and analysing a sample of 46 Eurozone banks over the 2013–2022 period. We selected this sample due to the relatively high spread of ESG practices in Europe compared to other regions (Sustainalytics, 2023), allowing us to better examine the ESG-corruption relationship compared to other contexts where ESG practices are much less developed.

Previous literature has focused on analysing both the determinants as well as the consequences of corruption events in the banking sector. As for the factors influencing the occurrence of corruption scandals, previous papers have highlighted the relevance of the state ownership of media and the level of media concentration in each country (Houston et al., 2011); the official supervisory power or the weak exercise of market discipline (Beck et al., 2006); as well as the degree of bank market power or the lack of information transparency via credit registry (Barth et al., 2009) as factors positively influencing the probability of these kinds of events. In terms of the effects of corruption in banking, it has been shown that it is associated with detrimental effects in terms of bank stability and risk-taking activities (Ali et al., 2020; Park, 2012; Toader et al., 2018 among others), while country-level characteristics, such as the level of economic development, may play a crucial role in the ultimate impact on risk (Goel & Hasan, 2011; Jalles, 2016).

In terms of ESG, the banking literature has examined the extent to which ESG disclosure in bank annual reports matters in terms of lending practices (Wang, 2023), as well as whether and how ‘ESG washing’ behaviour could be detected (Gai et al., 2023; Gambacorta et al., 2024). As in the previous case, literature on ESG has also tried to explain the effects that ESG practices may have in terms of financial and market performance (La Torre et al., 2021; Shakil et al., 2019), funding costs (Agnese, Battaglia, et al., 2023) or bank stability (Chiaramonte et al., 2022; Di Tommaso & Thornton, 2020).

We contribute to the strand of literature focusing on the entity-specific determinants of corruption events by focusing on the role of ESG orientation and, more specifically on the corporate governance aspects and bank-level characteristics that are associated to corruption events and may shape the ESG-corruption relationship. In doing so, we contribute to the literature shedding light on the emerging challenges that corporate governance models should address to achieve an overall alignment among different organisational stakeholders (Aguilera & Ruiz Castillo, 2025) by focusing on the factors associated to a lower likelihood of being involved in a corruption events, thereby going beyond the view of the ESG paradigm as a simple legitimising tool (Chen et al., 2026). Although previous studies have already analysed the relationship between corruption and ESG/Corporate Social Responsibility (CSR) orientation (Bitar et al., 2025; Hossain & Kryzanowski, 2021), they focus on country-level measures of corruption, while our bank-level manually collected data allows us to focus more in depth on entity-specific and corporate governance characteristics associated to corruption events and affecting the ESG-corruption relationship.

Broadly speaking, corruption events are strictly related to corporate governance practices (i.e., included in the ‘G’ of the ESG concept; Wu, 2005). Hence, it could be expected that good governance practices can play a relevant role in combating corruption and reducing its negative effects (Boateng et al., 2021; Fu, 2019). Moreover, the interplay with bank-level characteristics and external institutional factors could finally affect the extent to which ESG impacts the probability of a corruption event.

The reminder of this article is organised as follows: section ‘Literature Review’ provides a review of the related literature. Section ‘Theoretical Framework and Research Hypotheses’ proposes a theoretical framework and develops our research hypotheses. Section ‘Empirical Method’ describes our empirical methodology. Section ‘Results and Discussion’ shows our baseline results, additional analyses and robustness tests. Section ‘Conclusion’ concludes the article.

Literature Review

ESG in Banking

The literature on the ESG concept and its integration into bank strategies and processes to foster long-term sustainability provides a broad view of how bank management attempts to go beyond shareholders’ interests to embrace a more stakeholder-oriented business model (Cantero-Saiz et al., 2024; Galletta et al., 2022). However, while some studies shed light on the positive aspects of ESG, others have remarked certain negative consequences. Among the authors focusing on the negative side of ESG, El Khoury et al. (2023) find a negative association between ESG scores and bank profitability, casting doubts on the alignment between ESG performance and shareholders’ value creation. Along the same lines, Murè et al. (2021) show that higher levels of ESG scores by Italian banks are associated to higher probability of sanctions inflicted by the national supervisory authority. Di Tommaso and Thornton (2020) show that bank value is negatively impacted by high ESG scores, as scarce resources are diverted from profitable investments to enhance bank sustainability, highlighting the trade-off between stakeholders’ and shareholders’ views. As for the literature shedding light on the benefits of ESG in banking, Houston and Shan (2022) show that more sustainable banks tend to provide more lending to sustainable firms, thereby positively affecting borrowers’ subsequent ESG performance. Chiaramonte et al. (2022) find that bank ESG performance has a beneficial effect in terms of financial stability in the European banking industry. Cicchiello et al. (2023) provide evidence that bank ESG practices are more common in more competitive environments, as more sustainable banks have a valuable competitive advantage over their less sustainable peers. Andrieș and Sprincean (2023) find that bank ESG practices play a relevant role in reducing bank funding costs, potentially resulting in more profitable and capitalised banks.

Given the scope of the ESG concept, which includes a wide range of heterogeneous factors (Berg et al., 2022; Edmans, 2023a, 2023b), a line of research within this literature has focused on the three specific pillars of ESG. The environmental pillar is undoubtedly the most analysed (see Trahan & Jantz, 2023). Several studies highlight the benefits associated to the efforts put on fostering of the environmental pillar. For instance, Palmieri et al. (2024) show that bank default risk is mitigated for those banks that are mostly focused on the environmental pillar. El Khoury et al. (2023) find that the score of environmental pillars presents a convex relationship with bank stock market returns. According to the empirical analysis carried out by Neitzert and Petras (2022), it emerges that the environmental pillar is the main responsible of the overall beneficial ESG-risk mitigation effect in the banking industry. Some studies also focus on the social pillar of ESG. For instance, Liu et al. (2023) provide evidence of the relevant role of social and environmental pillars as key drivers to mitigate bank credit risk. Gehrig et al. (2024) show that high scores in the social pillar are associated with lower levels of systemic risk in the European banking industry. As for the studies focusing on the governance pillar, the literature is much scarcer. Among the few contributions, Aevoae et al. (2023) show that both the overall ESG score and its governance pillar have a beneficial impact on banks’ contribution to systemic risk, thereby highlighting the relevant role of corporate governance to maintain and preserve financial stability. Agnese, Cerciello, et al. (2023) find a positive relationship between ESG controversies and the governance pillar of ESG, confirming such relationship also for its sub-pillars. Given that corruption is generally considered a corporate governance failure (Boateng et al., 2021; Weismann, 2009), not only there is a gap in the literature focusing on bank corruption, but also on the broader area of the corporate governance literature.

Corruption in Banking

The strand of literature analysing corruption in the banking sector has recently flourished because of the practical consequences of corruption events in terms of reputational damages and its theoretical relevance regarding the relationship with ethics, albeit there are still several relevant gaps that need to be addressed (Bahoo, 2020).

Some studies focus on the detrimental economic impact of corruption, which has been found to be associated with low levels of lending by banks (Abuzayed et al., 2024; Bermpei et al., 2021; Weill, 2011b). However, the literature is not unanimous on the negative consequences of corruption. For example, Weill (2011a) finds that bribery might incentivise banks to increase their lending activity to conniving borrowers, resulting in higher levels of lending to the real economy as a whole. Hence, this would highlight the ‘greasing effect’ role of corruption. Other scholarly research aims to detect the key factors that could help tackling and preventing corruption events in bank lending, showing that effective corporate governance, strict adherence to ethical principles as well as the country’s legal and institutional factors play a key role in explaining the levels of corruption (Azim et al., 2017; Azim & Kluvers, 2019; Barry et al., 2016; Barth et al., 2009).

Another notable negative consequence of corruption is the potentially and unsustainably high level of risk-taking by banks whose anti-corruption mechanisms are ineffective or operating in corrupted environments (Asteriou et al., 2021; Chen et al., 2015). Such high levels of bank risk-taking might result in an unstable and unsound financial system (Jenkins et al., 2023) with negative consequences for the real economy.

Other studies shed light on the association between corruption and unethical or fraudulent behaviour by bank managers including accounting manipulation strategies such as income smoothing and window dressing, thereby posing a threat on the capability of investors to make rational economic decisions based on accounting data (Nyakarimi, 2022; Ozili, 2019). The findings of these studies are in line with a broader strand of literature focusing on the relationship between corruption and accounting manipulation by non-financial firms (Lewellyn & Bao, 2017; Liu, 2016; Mazzi et al., 2019; Xu et al., 2019; Zhang & Zhang, 2023).

Given that corruption is generally considered a corporate governance failure (Boateng et al., 2021; Di Pietra & Melis, 2016; Weismann, 2009), some studies have examined the relationship between corporate governance mechanisms and corruption in banking. For instance, Toader et al. (2018) find that effective corporate governance plays a key role in preventing corruption events and, therefore, it can significantly contribute to achieve financial stability objectives. These findings are in line with the idea that corporate governance is a fundamental prerequisite to prevent and combat corruption in the banking sector (Khan et al., 2024).

While the literature focusing on the corporate governance – corruption relationship is relatively developed, the literature focusing on the relationship between the broad concept of ESG and corruption in banking is particularly scant. Although there are some studies focusing on non-financial firms (Hoang, 2022; Zhang & So, 2024), this relationship is peculiar and particularly important for banks because of the financial stability implications and potential reputational damages associated to corruption events (Sampath et al., 2018; Toader et al., 2018). Thus, further research is needed to fill this gap and analyse the interrelationships between sustainable banking, ESG and corruption in the banking sector. In addition, most studies in this strand of literature rely on country-level measures of corruption (Bermpei et al., 2021) or on corruption perception indexes (Weill, 2011b) that do not necessarily reflect actual corrupt behaviour at firm- and bank-level (Donchev & Ujhelyi, 2014). In this perspective, the use of bank-specific measures of corruption represents an important novelty that could contribute to our understanding of such a type of fraudulent practice.

Theoretical Framework and Research Hypotheses

From a theoretical perspective, there are two opposing views that can support the relationship between corruption and ESG. On the one hand, banks may sincerely be willing to implement ESG practices in their business models and act in the best interest of their stakeholders, including customers, employees and the society as a whole (Aevoae et al., 2023; La Torre et al., 2021). Hence, according to the stakeholder theory (Freeman, 2010), stakeholders may exert enough pressure on banks to push them to act responsibly from an ESG perspective, going beyond exclusive profit maximisation objectives (Agnese, Cerciello, et al., 2025; George et al., 2023). From this perspective, if banks are genuinely committed to act responsibly from an environmental and social viewpoint and implement effective and transparent governance principles for the benefit of their stakeholders, then they should also benefit from high ESG scores and, at the same time, be less likely to be involved in practices that are against ESG principles (Amiraslani et al., 2023; Drempetic et al., 2020; Pedersen et al., 2021). Specifically, given that corruption can be considered as a failure of corporate governance mechanisms (Boateng et al., 2021; Weismann, 2009), banks that are aligned to ESG principles (resulting in high ESG scores) might be less likely to be involved in corruption events, as they are genuinely concerned about effective governance practices (Annesi et al., 2025) that should be able to prevent (or at least mitigate) such a type of misconduct (He et al., 2022). Thus, following the stakeholder theory, we develop our first research hypothesis as follows:

H1A. There is a negative relationship between a bank’s ESG orientation and the probability of being involved in a corruption scandal.

However, according to the arguments of the impression management theory (Goffman, 1959), this relationship might be completely reversed. Specifically, rather than being genuinely concerned about ESG practices, banks might simply attempt to artificially increase their ESG scores (e.g., by means of sustainability disclosure), without actually implementing effective and transparent corporate governance practices (Huang et al., 2024). This idea is supported by the fact that ESG scores are generally constructed by heavily relying on ESG disclosures (Cantero-Saiz et al., 2024).² Thus, banks might engage in various misleading strategies including green-washing, social-washing and, most importantly, governance-washing (Baker et al., 2024; Giannetti et al., 2023; Yu et al., 2020) by attempting to artificially inflate their ESG scores without any impact on their probability of being involved in corruption scandals due to poor corporate governance. In doing so, banks purposedly hide negative aspects of their corporate governance and mislead their stakeholders by engaging in strategic ESG impression management strategies (Gambacorta et al., 2024).

Hence, in line with this theoretical argument, we develop our alternative research hypothesis as follows:

H1B. There is a positive relationship between a bank’s ESG orientation and the probability of being involved in a corruption scandal.

Regardless of the sign of this relationship, we contend that it should be particularly strong when it comes to the governance pillar of ESG. Given that corruption is generally considered a failure of corporate governance mechanisms (Boateng et al., 2021; Di Pietra & Melis, 2016; Weismann, 2009), the relationship between corruption events and corporate governance might be particularly strong. Specifically, reputable and professional top-managers should be more capable and willing to make efforts to prevent corruption events and develop sound anti-corruption mechanisms, being in fear of potential reputational damages (Ramakrishna Velamuri et al., 2017). Furthermore, CSR strategies involve a transparent and responsible managerial approach that is supposed to be effective in preventing and promptly tackling corruption problems (Krishnamurti et al., 2018). We do not exclude that corruption events might also have negative social or environmental consequences (Villoria et al., 2013). For instance, according to Hoang (2022), firms tend to pollute more and be less sustainable from an ESG perspective in more corrupted environments. However, such negative and possibly unintended consequences of corruption are certainly side effects if compared to the core of corruption problems, which is inextricably linked to ineffective corporate governance (Di Pietra & Melis, 2016). Thus, in light of the general disconnection between corruption and environmental/social practices, such relationship is expected to be weaker for the environmental and social pillars of ESG. Thus, we develop our second research hypothesis as follows:

H2. The relationship between the probability of being involved in a corruption scandal and the governance pillar is stronger compared to the relationship with the environmental or social pillars of ESG.

Several different factors can play a relevant role in shaping the relationship between ESG and the probability of being involved in a corruption event (Zhang & So, 2024). In this article, we focus on three key aspects, namely: corporate governance, bank-level characteristics and country-level institutional factors. First, among the most important bank-level characteristics, previous studies have shown that size, capitalisation and profitability might have an impact on illegal and unethical practices in the banking industry (Jaafar et al., 2023). Specifically, larger banks might face more difficulties in preventing and managing corruption events because of their organisational complexity (Arnaboldi et al., 2021). Thus, size might weaken (strengthen) a potential negative (positive) association between ESG and corruption events. Capitalisation might also play a significant role as less capitalised banks are in a weaker position and face pressure to attract more investors and raise more capital (Francis & Osborne, 2012). Thus, they might be less focused on the effectiveness of anti-corruption mechanisms or even potentially involved in unethical practices to attract investors in the short run, thereby contributing to shaping the ESG – corruption relationship. A similar argument holds for profitability, as less profitable banks are pressurised to boost their financial performance. Thus, in light of the potential short-term earnings associated to unethical practices (Micewski & Troy, 2007), the ESG – corruption relationship might be less relevant for less profitable banks.

Considering the above-referred relationship between the G pillar and corruption events, corporate governance characteristics are among the most important elements to be taken into consideration (Fu, 2019; Gorshunov et al., 2021; Sena et al., 2018). For instance, effective corporate governance might be insufficient to prevent corruption events if it is not jointly combined with a sufficiently large and diverse board of directors. Specifically, a wide range of professional and soft competences are necessary to develop and maintain effective anti-corruption mechanisms, and to spread an anti-corruption culture within a bank (Ghazwani et al., 2024). In this perspective, a relatively low number of board members might imply difficulties in obtaining adequate competences necessary to achieve this objective. This argument is further reinforced by the idea that board size has been traditionally considered a key factor for the effectiveness of firms’ monitoring effectiveness in various dimensions (Germain et al., 2014), including anti-corruption mechanisms. In addition, a larger board of directors might foster more interpersonal relationships among the board members and their network of relationships, thereby leading to the development of effective and innovative approaches to prevent corruption. Another key aspect that might contribute to the development of effective anti-corruption mechanisms is board diversity. Specifically, board gender diversity has been found to be associated to more robust, effective and responsible corporate governance (Altunbaş, Gambacorta, et al., 2022; Francoeur et al., 2008). Thus, we contend that, being female board members more sensitive and empathetic (Ellwood & Garcia-Lacalle, 2015), board gender diversity might contribute to preventing corruption events and detecting potential (un)ethical paths that might push employees, managers and a wide range of other actors to be involved in corruption scandals (Manara et al., 2023). This argument is also supported by the fact the women are generally less involved in corruption events and corruption problems are less severe when they hold prominent governance positions (Swamy et al., 2001). Broadly speaking, such factors might be associated to a substitution or complementary effect between ESG on one side and internal corporate governance factors on the other. In general, theoretical arguments are not sufficient to have clear ex-ante expectations on whether these factors can strengthen the ESG-corruption relationship. Hence, this is an empirical question that should be addressed by means of our empirical analysis.

In summary, given the potential role of board size and gender diversity, we develop our third research hypothesis as follows:

H3. The relationship between the probability of being involved in a corruption scandal and ESG is moderated by corporate governance characteristics.

Bank-level characteristics may play a relevant role in shaping the ESG-corruption relationship (Bitar et al., 2025). Specifically, regarding size, larger banks may face greater exposure to corruption risks because their broader scale of operations provides stronger incentives for engagement (Baokbah & Shirodkar, 2024). This, in turn, may weaken the beneficial relationship between ESG and corruption. In addition, large banks are more complex institutions for which preventing corruption or related events might be more difficult (Arnaboldi et al., 2021). However, we cannot exclude that smaller banks, being generally characterised by weaker internal controls and anti-corruption practices, might be highly exposed to the risk of corruption (Wang et al., 2022), attenuating the beneficial effect of ESG practices in terms of reduced corruption. Capitalisation might be another key factor, as undercapitalised banks might attempt to ‘gamble for resurrection’ (Calem & Rob, 1999) and decide to be involved in risky activities potentially leading to frauds or corruption scandals in an attempt to boost profitability, hoping that their misbehaviour will remain unnoticed (Cumming et al., 2015). This may thereby weaken the positive effect of ESG practices in terms of lower levels of corruption. Similarly, banks facing difficulties in terms of financial performance for a prolonged period might engage in such risky activities to boost profitability, although more profitable banks could also exhibit a greater scale of operations (Baokbah & Shirodkar, 2024), thereby attenuating the corruption reduction effect of ESG practices. Based on these arguments, we develop our fourth research hypothesis as follows:

H4. The relationship between ESG and the probability of being involved in a corruption scandal is moderated by bank-level characteristics.

Country-level characteristics in terms of the institutional and regulatory framework might also play a key role (Boateng et al., 2021; Mazzi et al., 2019; Serra, 2006). On the one hand, a sound regulatory and institutional framework focused on combating corruption might create a more suitable environment for ESG and corporate governance to beneficially impact on the effectiveness of anti-corruption mechanisms (Lombardi et al., 2019), thereby leading to a lower probability of corruption events (i.e., resulting in a complementary role of country-level institutional factors and ESG). This hypothesis is based on the idea that the negative consequences of corruption might be less severe in stable and strong institutional environments (Gillanders, 2014; Habib & Zurawicki, 2001), thereby leading to a complementary role of institutional factors and ESG in explaining corruption levels. On the other hand, a less stringent institutional environment might push firms in general and banks in particular to develop effective ESG and corporate governance practices to avoid unsustainably high levels of corruption. Broadly speaking, in light of the strict relationship between country-level institutional factors and ESG practices (Rakestraw, 2022), the former can play a key role in shaping the ESG-corruption relationship. Based on these theoretical arguments, we develop our fifth research hypothesis as follows:

H5. The relationship between the probability of being involved in a corruption scandal and ESG is dependent upon country-level institutional factors.

Empirical Method

To carry out our empirical analysis, we focus on the European context and search for a total of 46 bank entities with available information about their ESG scores in the London Stock Exchange Group (LSEG) Data & Analytics database (previously known as Refinitiv EIKON, Thomson Reuters).³ Then, following Andrés et al. (2024), in order to classify them as corrupted or uncorrupted banks, we search for corruption scandal news in selected news publication websites. We focus on the most relevant and trustworthy economic, business and financial newspapers at European level (Reuters, Financial Times, The Economist, City A.M., Il Sole24Ore, Milano Finanza and Expansión). We searched for corruption events by using the advanced search tool provided by Google News, which allowed us to identify the year of the spread of the news of the corruption scandal.⁴ To further ensure the reliability of the information collected, we also use the Nexis Uni database and its translation services, which provide a comprehensive archive of business news (including the banking industry) and the most relevant financial news at the international level. Given that some corruption cases were borderline, we adopted a clear definition of corruption by drawing on previous studies. Specifically, we adopted the definition proposed by Pellegrini (2011), who defines corruption as

the misuse of entrusted power for private gain; it is behaviour which deviates from the formal duties of a given role because of private-regarding (personal, close family, private clique) pecuniary or status gains; or violates rules against the exercise of certain types of private regarding influence. This includes such behaviour as bribery (use of a reward to pervert the judgment of a person in a position of trust); nepotism (bestowal of patronage by reason of ascriptive relationship rather than merit); and misappropriation (illegal appropriation of public resources for private regarding uses).

We included in our sample all banks involved in corruption problems that fall under this exact definition (from now on, ‘corrupted’ banks). According to this definition, we identified 22 corrupted banks, based on selected news publication websites to identify when news of these corruption problems became public domain.⁵

In order to examine the relation between ESG and corruption scandals, we estimate the following panel probit model:

\begin{array}{l} C O R R U P T_{i j t} = β_{0} + β_{1} E S G s c o r e_{i j t - 1} + β_{2} C G_{i j t - 1} \\ + β_{3} B A N K_{i j t - 1} + δ_{j} + \partial_{t} + ε_{i j t} \end{array}

(1)

where i, j and t, refer to the bank, country and year, respectively. $C O R R U P T_{i j t}$ is a dummy variable that takes value 1 when and after a specific corruption scandal for bank i becomes public and 0 otherwise. $E S G s c o r e_{i j t - 1}$ is the annual ESG score for each bank provided by LSEG Data & Analytics. $C G_{i j t - 1}$ represent our set of corporate governance variables (i.e., the size of the board of directors and its gender diversity). We also add a comprehensive set of bank-level variables ( $B A N K_{i j t - 1}$ ) based on the analysis of the extant literature that could, at least partly, explain the probability of corruption events. Although the literature analysing bank-specific determinants of corruption events is particularly scant, we draw upon the related literature focusing on other types of misconduct and related practices (Altunbaş, Polizzi, et al., 2018; Andrés et al., 2024; Nguyen et al., 2016). These variables were also collected from LSEG Data & Analytics. We include bank size – measured as the natural logarithm of total assets – the level of capitalisation, – computed as the ratio between equity and total assets – and profitability, proxied by the return on assets (ROA) ratio.⁶ Our main coefficient of interest is $β_{1}$ , which captures the effect of ESG on bank probability of being involved in corruption events.

We also re-run our regression model (1) by replacing the overall ESG score with its three pillars so that we can test our second research hypothesis regarding the relationship between the governance pillar and the probability of being involved in a corruption event. Formally, we run the following three regression models:

\begin{array}{l} C O R R U P T_{i j t} = β_{0} + β_{1} E_{i j t - 1} + β_{2} C G_{i j t - 1} \\ + β_{3} B A N K_{i j t - 1} + δ_{j} + \partial_{t} + ε_{i j t} \end{array}

(2)

\begin{array}{l} C O R R U P T_{i j t} = β_{0} + β_{1} S_{i j t - 1} + β_{2} C G_{i j t - 1} \\ + β_{3} B A N K_{i j t - 1} + δ_{j} + \partial_{t} + ε_{i j t} \end{array}

(3)

\begin{array}{l} C O R R U P T_{i j t} = β_{0} + β_{1} G_{i j t - 1} + β_{2} C G_{i j t - 1} \\ + β_{3} B A N K_{i j t - 1} + δ_{j} + \partial_{t} + ε_{i j t} \end{array}

(4)

where $E_{i j t - 1}$ , $S_{i j t - 1}$ and $G_{i j t - 1}$ are the scores of the environmental, social and governance pillars, respectively. To test our third hypothesis, we extend the baseline model by including the interaction terms between ESG and the specific bank-level characteristics so that we can examine their moderating role in shaping the ESG-corruption relationship. Finally, regarding our fourth research hypothesis, we carry out sub-sample analyses by differentiating between banks located in countries characterised by a sounder institutional framework based on their control of corruption, rule of law and regulatory quality.

In all estimates, we lag bank-level variables by one period and winsorised them at the 1% and 99% levels in order to reduce the impact of outliers. The descriptive statistics and the correlation matrix are reported in Table 1.⁷

Table 1.

Descriptive Statistics and Correlations.

Panel A: descriptive statistics
	Obs.		Mean		St. dev.		Min.		25%		Median		75%		Max.
Corruption	340		0.3705		0.4836		0		0		0		1		1
ESG score	340		64.40		17.81		16.21		54.13		67.23		77.07		95.09
Environmental	340		69.80		24.26		9.53		57.39		79.65		89.11		96.87
Social	340		67.89		17.33		19.04		59.41		70.69		80.05		96.48
Governance	340		59.48		22.66		12.55		42.54		61.88		78.28		94.35
Size	340		25.78		1.4754		21.83		24.80		25.55		27.13		28.40
Capital	340		0.1429		0.5192		0.0085		0.0504		0.0626		0.0774		3.864
ROA	340		0.0029		0.0092		–0.02		0		0		0.01		0.07
Board size	340		14.34		4.552		3		11		14		17		24
Gender	340		28.69		12.53		0		18.9		31.01		36.84		55.56
SSM bank	340		0.8264		0.3792		0		1		1		1		1
Control of corruption	340		0.9126		0.6985		–0.1339		0.2342		0.7003		1.5346		2.238
Rule of law	340		0.9770		0.6225		0.1122		0.2970		0.9782		1.5331		2.053
Regulatory quality	340		1.0504		0.4871		0.2238		0.6911		0.9429		1.5222		2.053
Total deaths for natural disasters	321		1,211.9		3,836.7		0		3		8		44		18,047
Panel B: correlations
	Corruption	ESG score	Environmental	Social	Governance	Size	Capital	ROA	Board size	Gender	SSM bank	Control of corruption	Rule of law	Regulatory quality	Total deaths for natural disasters
Corruption	1
ESG score	0.0673	1
Environmental	0.0862	0.6997***	1
Social	0.1051*	0.9024***	0.5922***	1
Governance	−0.0114	0.8525***	0.4487***	0.5813***	1
Size	0.1606***	0.4692***	0.6330***	0.4373***	0.2721	1
Capital	−0.1056*	0.1805***	0.0927*	0.1552***	0.1839***	−0.2233***	1
ROA	−0.2208***	0.0070	−0.1590***	0.0546	0.0080	−0.2034***	0.1052*	1
Board size	0.1005*	0.1983***	0.3153***	0.2481***	0.0431	0.3963***	0.0189	−0.1716***	1
Gender	−0.0103	0.3167***	0.1196**	0.3429***	0.2126***	0.1609***	0.0395	0.1255**	−0.0382	1
SSM bank	0.1586***	0.3298***	0.2731***	0.2720***	0.2847***	0.3233***	0.0521	−0.2399***	0.3232***	0.1241**	1
Control of corruption	0.0949*	0.0950*	0.1383**	0.0957*	0.0233	0.2424***	−0.0720	0.1032*	−0.1865***	0.3003***	−0.0212	1
Rule of law	0.0496	0.1046*	0.1871***	0.0926*	0.0360	0.2844***	−0.0201	0.0902*	−0.1108**	0.1824***	0.0387	0.9396***	1
Regulatory quality	0.1097**	−0.0140	0.0745	−0.0085	−0.0606	0.2324***	−0.0593	0.0959*	−0.2178***	0.2040***	−0.0432	0.9372***	0.9251***	1
Total deaths for natural disasters	−0.0383	0.1566***	0.0159	0.1616***	0.1371**	−0.0375	−0.0468	0.0845	−0.0283	0.2323***	−0.0339	−0.0484	−0.1546***	−0.1744***	1

Note: This table shows the main descriptive statistics (mean, standard deviation, median, minimum, 25th, 50th, 75th percentiles and maximum value) of the main variables of interest (Panel A) and the correlation matrix (Panel B). All the variables are defined in Table A1. ***, ** and * indicate statistical significance at 1%, 5% and 10% level, respectively.

Results and Discussion

ESG-Corruption Relationship and the Role of the Three ESG Pillars

To test our first research hypothesis, we first examine the basic relationship between ESG scores and the probability of corruption scandals by running regression model (1). The results reported in the first column of Table 2 show a negative and statistically significant coefficient for the ESG score, suggesting that banks more focused on ESG practices have a lower probability of being involved in corruption scandals. This result supports our first research hypothesis H1A regarding the negative relationship between ESG and the probability of being involved in a corruption event. This baseline finding turns out to be in line with the stakeholder theory (Freeman, 2010), while the impression management theory (Goffman, 1959) seems to play a less prominent role in explaining the ESG-corruption relationship, thereby highlighting a genuine interest by banks to go beyond profit maximisation objective and pursue a more comprehensive goal in terms of corporate social responsibility for the benefit of a wider range of stakeholders and the environment (Carnevale & Drago, 2024; Houston & Shan, 2022). These findings are also confirmed in the second column of Table 2 in which we include also corporate governance controls (i.e., Board Size as a measure of the size of the board of directors; and Gender representing the proportion of women in the board).

Table 2.

ESG and Corruption Events.

Dependent variable: corrupt	(1)	(2)	(3)	(4)	(5)
ESG score	−0.0095* (–1.70)	−0.0099* (–1.75)
Environmental			−0.0003 (–0.03)
Social				−0.0055 (–1.00)
Governance					−0.0080** (–2.05)
Size	0.2157*** (3.12)	0.2706*** (3.53)	0.2007*** (2.62)	0.2328*** (3.26)	0.2511*** (3.62)
Capital	−0.1824 (–0.57)	−0.0771 (–0.66)	−0.1899* (–1.75)	−0.1404 (–1.31)	−0.0822 (–0.72)
ROA	−0.4891*** (–4.79)	−0.4532*** (–3.15)	−0.4606*** (–3.20)	−0.4484*** (–3.12)	−0.4661*** (–3.17)
Board size		0.0286 (1.46)	0.0242 (1.24)	0.0281 (1.42)	0.0246 (1.23)
Gender		−0.0262*** (–3.31)	−0.0263*** (–3.33)	−0.0259*** (–3.27)	−0.0269*** (–3.41)
Year dummies	Yes	Yes	Yes	Yes	Yes
Country-year dummies	Yes	Yes	Yes	Yes	Yes
Observations	340	340	340	340	340
Banks	46	46	46	46	46
Pseudo-R²	0.1640	0.2055	0.1992	0.2010	0.2088
Wald-test (p-value)	0.00	0.00	0.00	0.00	0.00

Note: The present table analyses the relationship between bank ESG scores (including its three pillars) and the probability of being involved in a corruption event. Variable definitions are provided in Appendix 1.

***

,** and * indicate statistical significance at 1%, 5% and 10% level, respectively.

As for our second research hypothesis regarding the individual components of ESG, columns (3) to (5) of Table 2 show that the overall result of the ESG-corruption relationship is mainly driven by the governance pillar, as the fifth column of Table 2 reports a negative and statistically significant coefficient. This result is in line with hypothesis H2 and with the idea that corruption events can be considered as a corporate governance failure (Boateng et al., 2021; Di Pietra & Melis, 2016; Weismann, 2009) as ineffective corporate governance is associated to a higher probability of corruption events. In contrast, the scores of the environmental and social pillars (columns (3) and (4), respectively) enter the regression with a statistically insignificant coefficient, remarking that the results seem to be driven by the G pillar of ESG mainly.

As regards the corporate governance and additional bank-level control variables, we obtain statistically significant coefficients for the variables Gender, Size and ROA. These results show that larger banks, as long as they can be considered more complex organisations, may face more difficulties difficult in avoiding any type of involvement in corruption or related events (Arnaboldi et al., 2021). In addition, more profitable banks seem to be less likely to be involved in corruption scandals as they face less pressure to boost financial performance by means of potentially unethical activities. Finally, in line with the literature remarking the benefits of board gender diversity (Haque, 2017), the negative coefficient obtained for the Gender variable suggests that gender diversity also plays a key role in reducing the probability of being involved in a corruption scandal (Cumming et al., 2015).

The Role of Bank-Level Characteristics and the Institutional Framework

We next examine if the relationship between ESG and the probability of a corruption scandal is ultimately dependent on the features of banks’ internal corporate governance and individual bank-level characteristics. In other words, we aim to test the existence of potential complementary or substitution effects between banks’ ESG orientation and their features in terms of traditional characteristics (size, capitalisation and profitability) as well as in terms of corporate governance aspects such as board size and board gender diversity. Such results are reported in Table 3. As can be seen, the overall negative relationship with ESG remains in four out of five estimates (columns (1), (3), (4) and (5)), supporting the robustness of our baseline findings.

Table 3.

ESG and Corruption Events: The Role of Bank-Level Characteristics.

Dependent variable: corrupt	(1)	(2)	(3)	(4)	(5)
ESG score	−0.0431*** (–2.67)	−0.0429*** (–3.61)	−0.1490* (–1.69)	0.0088 (1.12)	−0.0108* (–1.90)
ESG score × Board size	0.0024** (2.25)
ESG score × Gender		0.0012*** (3.11)
ESG score × Size			0.0054 (1.58)
ESG score × Capital				−0.3143*** (–3.08)
ESG score × ROA					0.0108 (1.39)
Board size	−0.1192* (–1.70)
Gender		−0.1027*** (4.07)
Size			−0.0987 (–0.40)
Capital				26.43*** (3.11)
ROA					−1.103** (–2.19)
Year dummies	Yes	Yes	Yes	Yes	Yes
Country-year dummies	Yes	Yes	Yes	Yes	Yes
Observations	340	340	340	340	340
Banks	46	46	46	46	46
Pseudo-R²	0.2172	0.2276	0.2010	0.2384	0.2114
Wald-test (p-value)	0.00	0.00	0.00	0.00	0.00

Note: The present table analyses the moderating role of corporate governance characteristics and bank-level features on the relationship between bank ESG scores (including its three pillars) and the probability of being involved in a corruption event. Variable definitions are provided in Appendix 1.

***

,** and * indicate statistical significance at 1%, 5% and 10% level, respectively.

We analyse the moderating role of bank corporate governance characteristics, examined in columns (1) and (2), the results are consistent with the existence of a substitution effect between ESG and two corporate governance characteristics, namely board size and board gender diversity, and highlighting the key relationship between corruption and corporate governance (Fu, 2019; Gorshunov et al., 2021; Sena et al., 2018). This result suggests that larger and more gender diverse boards are associated to a weaker ESG-corruption relationship, supporting the idea that these characteristics might be associated to stronger corporate governance mechanisms that are sufficient to significantly reduce the probability of corruption events, thereby creating a substitution effect with ESG orientation.

The results for the impact of specific bank-level characteristics, namely Size, Capital and ROA are shown in columns (3) to (5). It emerges that the level of capitalisation (Capital) plays a relevant role in shaping the ESG-corruption relationship. We obtain a negative and statistically significant coefficient indicating that poorly capitalised banks might face pressure to raise capital to comply with regulatory requirements (Ben-David et al., 2019) and they could be more likely to be involved in unethical practices to regain a sufficient level of capitalisation, thereby compromising the beneficial ESG-corruption relationship. The interaction term with Size shows a positive coefficient, suggesting that the lower probability of a bank being involved in a corruption event is counteracted by bank size. We should be cautious, however, with this result as the coefficient for the interaction term ESG*Size is not statistically significant at conventional levels. Finally, the relationship between ESG and the probability of a corruption event does not seem to be affected by bank profitability (ROA).

Finally, we also examine whether and to what extent country-level institutional factors may affect the relationship between ESG orientation and the probability of being involved in a corruption event. Hence, we perform subsample analyses by differentiating between those countries that are above and below the median of three key country-level variables measuring institutional quality, namely: Control of Corruption, Rule of Law and Regulatory Quality. The results reported in Table 4 (Panel A) show that there is a strong negative and statistically significant association between ESG scores and the probability of corruption in countries characterised by high levels (i.e., above or on the median) of Control of Corruption, Rule of Law and Regulatory Quality (columns (1), (3) and (5), respectively). In contrast, in countries characterised by low levels of institutional quality (i.e., below the median of the three aforementioned country-level variables) the ESG-corruption relationship turns out to be positive and significant at 10% confidence level (columns (2) and (6)) or positive and statistically insignificant (column (4)), suggesting that the beneficial ESG-corruption relationship is either reversed or inexistent. These findings support the relevance of country-level factors as key determinants of corruption levels (Boateng et al., 2021; Serra, 2006), and support our fifth research hypothesis, as it emerges that the strength and even the sign of the basic ESG-corruption relationship depend upon country-level institutional factors.

Table 4.

ESG and Corruption Events: The Role of Country-Level Characteristics.

	High control of corruption	Low control of corruption	High rule of law	Low rule of law	High regulatory quality	Low regulatory quality
Panel A: ESG score
Dependent variable: corrupt	(1)	(2)	(3)	(4)	(5)	(6)
ESG score	−0.0378*** (–3.29)	0.0138* (1.77)	−0.0381*** (–3.39)	0.0088 (1.24)	−0.0314*** (–2.75)	0.0133* (1.66)
Observations	170	170	173	167	180	160
Banks	27	25	26	25	38	26
Pseudo-R²	0.3208	0.3845	0.3015	0.3619	0.2828	0.3386
Wald-test (p-value)	0.00	0.00	0.00	0.00	0.00	0.00
Panel B: environmental dimension
Dependent variable: corrupt	(1)	(2)	(3)	(4)	(5)	(6)
Environmental	−0.0132* (–1.80)	0.0236*** (3.82)	−0.0145** (–2.07)	0.0205*** (3.32)	−0.0131* (–1.96)	0.0240*** (3.38)
Observations	170	170	173	167	180	160
Banks	27	25	26	25	38	26
Pseudo-R²	0.2854	0.4165	0.2680	0.3924	0.2577	0.3678
Wald-test (p-value)	0.00	0.00	0.00	0.00	0.00	0.00
Panel C: social dimension
Dependent variable: corrupt	(1)	(2)	(3)	(4)	(5)	(6)
Social	−0.0216* (–1.93)	0.0133* (1.72)	−0.0183* (–1.75)	0.0085 (1.15)	−0.0173* (–1.68)	0.0144* (1.84)
Observations	170	170	173	167	180	160
Banks	27	25	26	25	38	26
Pseudo-R²	0.2889	0.3833	0.2647	0.3614	0.2557	0.3412
Wald-test (p-value)	0.00	0.00	0.00	0.00	0.00	0.00
Panel D: governance dimension
Dependent variable: corrupt	(1)	(2)	(3)	(4)	(5)	(6)
Governance	−0.0208*** (–3.06)	0.0036 (0.61)	−0.0224*** (–3.19)	0.0004 (0.08)	−0.0182*** (–2.87)	0.0000 (0.02)
Observations	170	170	173	167	180	160
Banks	27	25	26	25	38	26
Pseudo-R²	0.3149	0.3745	0.2987	0.3572	0.2793	0.3297
Wald-test (p-value)	0.00	0.00	0.00	0.00	0.00	0.00
Year dummies	Yes	Yes	Yes	Yes	Yes	Yes
Country-year dummies	Yes	Yes	Yes	Yes	Yes	Yes

Note: The present table analyses the role of country-level institutional factors on the relationship between bank ESG scores (including its three pillars) and the probability of being involved in a corruption event by differentiating between countries that are above or below the median of three key institutional quality variables (i.e., Control of Corruption, Rule of Law and Regulatory Quality). Variable definitions are provided in Appendix 1.

***

,** and * indicate statistical significance at 1%, 5% and 10% level, respectively.

In addition, Panels B, C and D of Table 4 show that such results hold for the three pillars of ESG. Specifically, the regression coefficients are always negative and significant at 10% level or higher in columns (1), (3) and (5) (i.e., when country-level institutional variables are above or on the median), while they are positive, although not always statistically significant, in columns (2), (4) and (6) (i.e., when country-level institutional variables are below the median). These results further confirm our fourth research hypothesis.

Robustness and Additional Analyses

The Role of Centralised Supervision

Among the institutional factors that could play a relevant role in shaping the ESG – corruption relationship, banking supervision is a key element worthy of investigation. At the European level, previous studies have shown that the European Banking Union and the associated establishment of the Single Supervisory Mechanism (SSM) has significantly contributed to strengthening the European banking system by making supervised entities more stable (Avignone et al., 2021; Cuadros-Solas et al., 2025), capitalised (Fiordelisi et al., 2017), transparent (Altunbaş, Polizzi, et al., 2022) and sound from a corporate governance perspective (Arrigoni & Rivolti, 2022). The SSM regulatory architecture sees the European Central Bank as the main supervisory authority for those banks that have been considered as ‘significant’: such banks are supervised directly by the ECB, while less significant entities remain under the supervision of national competent authorities. In light of the establishment of the Banking Union, we might expect that SSM centralised supervision could play a role in shaping the beneficial ESG – corruption relationship.

In order to carry out this analysis, we define a differences-in-differences (DID) approach in order to test if when and after a bank has moved from the domestic supervisory authority to the supranational framework has an implication on the effect of bank ESG orientation and the probability of being involved in a corruption event. Before running the DID analysis, in Table 5, we show the differences between the two groups of banks (i.e., banks supervised through the SSM, or SSM banks for short, and nationally supervised banks, or non-SSM banks for short) by means of t-tests. As can be seen, we do not find any statistically significant difference between the two groups of banks before the implementation of the SSM, thereby highlighting the high level of comparability between the two groups of treated (i.e., SSM-supervised banks) and non-treated (i.e., non-SSM-supervised banks). Moreover, we report statistically significant differences in terms of corruption probability, ESG scores (including its three pillars) and certain bank-level characteristics (i.e., Size, ROA and Board Size) after the implementation of the SSM, providing preliminary evidence that the Banking Union might have had a relevant effect on the variables examined in our empirical analysis.

Table 5.

T-Test for SSM and Nationally Supervised Banks.

Variable	Panel A: before SSM			Panel B: after SSM
Variable	SSM banks	Non-SSM banks	T-test	SSM banks	Non-SSM banks	T-test
Corruption dummy	0.1304	0.1250	−0.06	0.4635	0.2272	−2.94***
ESG score	63.04	56.84	−1.23	70.31	59.42	−4.37***
Environmental	80.66	71.16	−1.60	72.38	58.12	−3.90***
Social	62.14	60.01	−0.42	73.83	65.16	−3.72***
Governance	60.75	51.15	−1.63	65.47	54.22	−3.21***
Size	26.19	26.04	−0.43	26.00	24.45	−6.81***
Capital	0.2043	0.1608	−0.25	0.1315	0.0903	−0.53
ROA	−0.0869	0.10	0.8279	0.3090	0.8181	4.13***
Board size	17.13	15.15	−1.58	14.48	11.38	−4.82***
Gender	21.42	21.38	0.50	33.27	32.28	−0.53

Note: This table presents the t-test results for the comparison between the banks supervised through the Single Supervised Mechanism (SSM) -directly supervised by the ECB- and nationally supervised (non-SSM) banks on selected bank-level characteristics before and after the implementation of the SSM. *** indicate statistical significance at 1% level.

To examine this aspect more in depth, we carry out an econometric analysis by including an interaction term between the ESG score (and its three pillars) and a dummy variable (SSM) defined as an indicator that equals 1 when and after a bank becomes part of the SSM framework and 0 otherwise (i.e., if the bank is directly supervised by the ECB). The results reported in Table 6 show that all interaction terms are positive and statistically significant at 5% confidence level or higher. Hence, we show that the beneficial ESG – corruption relationship is weaker for those banks that are directly supervised by the ECB within the SSM, thereby highlighting the existence of a substitution effect between centralised supervision and ESG practices on the decreased probability of being involved in a corruption scandal. In other words, ESG practices are particularly beneficial for banks supervised at national level, for which supervisory interventions are less standardised and potentially less strict compared to SSM-supervised banks (Avignone et al., 2021). In contrast, ESG practices are less helpful for SSM-supervised banks as their supranational supervision already aims at ensuring the effectiveness of bank-specific and corporate governance mechanisms (Arrigoni & Rivolti, 2022) that can potentially reduce the probability of being involved in a corruption scandal. These findings provide further support to our fourth research hypothesis on the key role of institutional factors is shaping the ESG-corruption relationship.

Table 6.

ESG and Corruption Events: The Role of Centralized Supervision.

Dependent variable: corrupt	(1)	(2)	(3)	(4)
ESG score × SSM	0.0116*** (2.79)
Environmental × SSM		0.0089*** (2.65)
Social × SSM			0.0110*** (2.71)
Governance × SSM				0.0109** (2.54)
SSM	−0.1541 (–0.50)	−0.0831 (–0.29)	−0.2296 (–0.71)	0.0088 (0.03)
ESG score	−0.0111* (–1.85)
Environmental		−0.0086* (–1.78)
Social			−0.0032 (–0.56)
Governance				−0.0147*** (–2.78)
Bank-level controls	Yes	Yes	Yes	Yes
Year dummies	No	No	No	No
Country-year dummies	Yes	Yes	Yes	Yes
Observations	340	340	340	340
Banks	46	46	46	46
Pseudo-R²	0.0948	0.0922	0.0973	0.0968
Wald-test (p-value)	0.00	0.00	0.00	0.00

Note: The present table analyses the moderating role of the Single Supervisory Mechanism (SSM) banking supervision on relationship between bank ESG scores (including its three pillars) and the probability of being involved in a corruption event. Variable definitions are provided in Appendix 1.

***

,** and * indicate statistical significance at 1%, 5% and 10% level, respectively.

Endogeneity

Although in all the above reported analyses we consider the lagged values of all the explanatory variables in order to mitigate reverse causality concerns, we cannot exclude that corruption events are endogenously determined in our regression models. To address this problem, we now apply an Instrumental Variables method (IV) by instrumenting our main variable of interest (i.e., the ESG score). An appropriate instrument should impact our dependent variable (Corrupt) only through the ESG score, without any direct impact on the probability of being involved in a corruption event. We use the number of deaths caused by natural disasters at country-level to instrument the ESG score. We collect these data from the EM-DAT platform,⁸ which provides data on over 26,000 records of natural disasters including biological, climatological, geophysical, hydrological and meteorological events at worldwide level. The argument behind the use of this instrument is that while natural disasters have a strong impact on ESG practices in general and on the environmental pillar in particular (Huang et al., 2022), they are not expected to directly affect the probability of being involved in a corruption scandal. In the first stage of our 2SLS approach, we regress the total number of deaths caused by natural disasters (Total Deaths for Natural Disasters) on the ESG score. In the second stage, we use the fitted values of the first-stage regression ( $\hat{E S G S c o r e}$ ) as a regressor in a model in which the dependent variable is our Corrupt dummy.

From the results reported in Table 7, it emerges that, in the first stage of the 2SLS, the Total Deaths for Natural Disasters are significantly associated with the ESG score (column (1)) as well as with its three pillars (columns (3), (5) and (7)), thereby highlighting the validity of our instrument. As for the second stage of our IV approach, it emerges that $\hat{E S G S c o r e}$ is negatively and significantly associated to our Corrupt dummy in three out of four regressions (columns (2), (4) and (8)). Thus, these results allow us to identify a causal relationship according to which higher levels of ESG scores lead to a lower probability of being involved in a corruption scandal, thereby supporting the robustness of our baseline results.

Table 7.

ESG and Corruption Events: Endogeneity Concerns.

Dependent variable	1st stage: ESG score (1)	2nd stage: corrupt (2)	1st stage: environmental (3)	2nd stage: corrupt (4)	1st stage: social (5)	2nd stage: corrupt (6)	1st stage: governance (7)	2nd stage: corrupt (8)
Total deaths for natural disasters	0.0003*** (4.89)		0.0004*** (2.83)		0.0002* (1.96)		0.0005*** (2.68)
$\hat{E S G S c o r e}$		−0.1626* (–1.89)		−0.1515* (–1.89)		0.2599* (1.89)		−0.1145* (–1.89)
Bank-level controls	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year dummies	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Country-year dummies	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Observations	321	321	321	321	321	321	321	321
Banks	45	45	45	45	45	45	45	45
R ²	0.5494	−	0.5051	−	0.5205	−	0.3481	0.2090
Pseudo-R²	−	0.2090	−	0.2090	−	0.2090	−	−
Wald-test (p-value)	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00

Note: The present table shows the results of the two-stage-least-square (2SLS) regression in which we regress the ESG score on the number of deaths caused by natural disasters (first stage) and our Corrupt dummy on the fitted values of the fist-stage regression (second stage). Variable definitions are provided in Appendix 1.

***

and * indicate statistical significance at 1% and 10% level, respectively.

Other Robustness Tests

We run a battery of additional robustness tests to further confirm our empirical results. First, we use an alternative dependent variable. Specifically, we replace ESG scores with ESG controversies scores collected from the (LSEG) Data & Analytics database, widely used in the banking literature (e.g., Agnese, Battaglia, et al., 2023). This score is calculated based on 23 ESG controversy topics including business ethics and fraud controversies. Our results reported in column (1) of Table 8 show that also the ESG controversies score enters the regression with a negative and statistically significant coefficient, thereby supporting the robustness of our baseline models.

Table 8.

ESG and Corruption Events: Robustness.

Dependent variable: Corrupt	Alternative explanatory variable (1)	Logit model (2)	Excluding Italy (3)	Excluding GIPSI countries (4)
ESG controversies	−0.0109*** (–3.50)
ESG score		−0.0164* (–1.65)	−0.0358*** (–4.30)	−0.0417** (–2.21)
Observations	340	340	250	108
Number of banks	46	46	32	18
Pseudo-R²	0.2235	0.2088	0.2215	0.4357
Wald-test (p-value)	0.00	0.00	0.00	0.00

Note: The present table shows the results of four robustness models in which: (i) we substitute ESG scores with ESG controversies scores; (ii) we use logit regression rather than probit regression; (iii) we exclude the most represented country in our sample (Italy); (iv) we exclude banks located in GIPSI (Greece, Italy, Portugal, Spain and Ireland) countries. Variable definitions are provided in Appendix 1.

***

,** and * indicate statistical significance at 1%, 5% and 10% level, respectively.

Second, we use an alternative econometric approach based on logit regression. Although logit and probit models generally display similar results (Comelli, 2016), we check whether our baseline results obtained by means of a panel probit regression hold when using a panel logit model. The results shown in column (2) of Table 8 confirm that our results are qualitatively unchanged, supporting the robustness of our results.

Finally, we perform two subsample analyses by excluding banks located in Italy and in the so called GIPSI (i.e., Greece, Italy, Portugal, Spain and Ireland) countries. We exclude Italy because it is the most represented country in our sample, and we aim to rule out the possibility that our results are mainly driven by these banks. The results reported in column (3) of Table 8 support the robustness of our baseline model. We exclude also GIPSI countries because previous research has shown that these countries are characterised by relatively high levels of corruption (Andrés et al., 2024) that could potentially drive our baseline findings. From column (4), it emerges that our results hold, thereby supporting once again the robustness of our baseline models.⁹

Conclusion

In this article, we analyse the relationship between bank ESG orientation and the probability of being involved in a corruption event. We examine a sample of 46 Eurozone banks over the 2013–2022 period through probit regression by using data collected from multiple sources including LSEG Data & Analytics, World Bank Group Worldwide Governance Indicators and manually collected data on bank involvement in corruption events that became public domain. We also carry out a more detailed analysis by assessing the impact of each pillar of ESG and the importance of corporate governance characteristics and country-level institutional factors in shaping this basic relationship.

Our findings are that there is a beneficial ESG-corruption relationship as higher levels of ESG orientation are negatively associated with the probability of being involved in a corruption scandal. Digging deeper on this relationship, it emerges that this basic result is mainly driven by the governance pillar in line with the idea that corruption events can be considered as a failure of corporate governance mechanisms (Di Pietra & Melis, 2016). We also examine the role of individual bank-level characteristics and internal corporate governance features as well as country-level institutional factors in shaping the ESG-corruption relationship. We find a substitution effect between ESG and specific corporate governance characteristics (i.e., board size and board gender diversity). Moreover, regarding the role of the institutional framework, it emerges that the beneficial ESG-corruption relationship is stronger in countries characterised by higher levels of regulatory quality, rule of law and control of corruption. In an additional analysis, we also show that the centralised supervision performed by the European Central Bank by means of the SSM leads to a weaker ESG-corruption relationship as harmonised and strict supervision pushes banks to adopt sound corporate governance practices (Arrigoni & Rivolti, 2022) that can potentially improve anti-corruption mechanisms, regardless of bank ESG orientation. Our results also pass a comprehensive battery of robustness tests to address endogeneity and sample selection concerns as well as using alternative variables and econometrical approaches.

This article may have important theoretical and practical implications. From a theoretical perspective, we show that banks that are more oriented towards ESG practices tend to be genuinely concerned about the specific corporate governance mechanism related to anti-corruption systems, in line with the expectation of the stakeholder theory (Freeman, 2010), as it emerges that banks are going beyond their traditional profit maximisation objective for the benefit of a wide range of stakeholders and embracing a more sustainable business model, which results in an actual effort to prevent corruption and avoid its negative financial and non-financial consequences. Thus, in general, banks do not engage in deceptive strategies posited by the impression management theory (Goffman, 1959), that would result in a disassociation between their ESG score and their attention towards the prevention of corruption events. From a practical perspective, our results are highly relevant in terms of bank management and banking supervision as we show that banks that are not aligned with ESG practices are generally more likely to be involved in corruption events. Given that such a type of scandals could result in a ‘domino effect’ with potential negative impact on depositors’ and customers’ trust on the banking system, bank supervisors can leverage on this finding to monitor more closely banks that are not aligned with ESG practices. On the one hand, such closer monitoring should be implemented regardless of the soundness of the institutional framework, as it seems that it does not have a strong impact on the ESG-corruption nexus.

On the other hand, corporate governance characteristics should be taken into consideration in light of their substitution effect with bank ESG orientation, as well as country-level institutional factors that can shape such beneficial ESG-corruption relationship.

This article does not come without limitations. While the manual data collection represents a value added and makes our dataset unique, it forces us to limit our analysis to a relatively small sample of 46 banks. Future research could analyse larger samples to shed additional light on the relevance of country-level factors in shaping the ESG-corruption relationship.

Figure 1.

Bank corruption cases per country.

Footnotes

Appendix 1

Table 10.

Sample of Banks.

Bank	Country
BAWAG Group AG	Austria
Erste Group Bank AG	Austria
Raiffeisen Bank International AG	Austria
Kbc Groep NV	Belgium
Bank of Cyprus Holdings PLC	Cyprus
TCS Group Holding PLC	Cyprus
Aktia Bank Abp	Finland
Nordea Bank Abp	Finland
BNP Paribas SA	France
Credit Agricole SA	France
Societe Generale SA	France
Aareal Bank AG	Germany
Commerzbank AG	Germany
Deutsche Bank AG	Germany
Deutsche Pfandbriefbank AG	Germany
ProCredit Holding AG & Co KGaA	Germany
Alpha Services and Holdings SA	Greece
EUROBANK	Greece
National Bank of Greece SA	Greece
Piraeus Bank	Greece
Aib Group PLC	Ireland
Bank of Ireland Group PLC	Ireland
Permanent TSB Group Holdings PLC	Ireland
Banca Generali SpA	Italy
Banca IFIS SpA	Italy
Banca Mediolanum SpA	Italy
Banca Monte dei Paschi di Siena SpA	Italy
Banca Popolare Di Sondrio SpA	Italy
Banco BPM	Italy
Bper Banca SpA	Italy
Credito Emiliano SpA	Italy
FinecoBank Banca Fineco SpA	Italy
Intesa San Paolo	Italy
Mediobanca Banca di Credito Finanziario SpA	Italy
Unicredit	Italy
Illimity Bank SpA	Italy
ABN Amro Bank NV	Netherlands
ING Groep NV	Netherlands
Banco Comercial Portugues	Portugal
Nova Ljubljanska Banka dd Ljubljana	Slovenia
BBVA	Spain
Banco Santander SA	Spain
Banco de Sabadell SA	Spain
Bankinter SA	Spain
Caixabank SA	Spain
Unicaja Banco SA	Spain

Authors’ Note

The views and opinions expressed are solely those of the authors and do not necessarily reflect those of the European Union, nor can it be held responsible for them.

ORCID iDs

Pablo de Andrés

Salvatore Polizzi

Enzo Scannella

Nuria Suárez

Funding

The authors disclosed receipt of the following financial support for the research, authorship and/or publication of this article: The authors acknowledge participants at the 2025 FEBS Conference, 2025 ACEDE Conference, 2024 Wolpertinger Annual Conference and at the EUMODNEXT international congress. We also thank the funding provided by the European Union within EUMODNEXT EUAF program under the grant agreement No 101101627. P. de Andrés, E. Scannella and N. Suárez acknowledge the financial support from the Spanish Ministry of Economy and Competitiveness (Project PID2023-149010NB-I00). S. Polizzi and E. Scannella acknowledge the funding provided by the Santander Financial Institute (SANFI) – research project PRJ-1853 (RiMaDiTERiBa) entitled ‘Risk Management and Disclosure of Traditional and Emerging Risks in Banking: New Insights from Quantitative and Qualitative Computer-Assisted Content Analysis Methodologies’ (XV Convocatoria ‘Ayuda SANFI a la Investigación para Jóvenes Investigadores’). S. Polizzi acknowledges the funding provided by the European Union – NextGenerationEU, Mission 4, Component 2, in the framework of the GRINS – Growing Resilient, INclusive and Sustainable – project (GRINS PE00000018 – CUP B73C22001260006).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Notes

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