ESG Performance and Banks’ Funding Costs
Alin Marius Andrieș
Alexandru Ioan Cuza University of Iași and Institute for Economic Forecasting, Romanian
Academy
E-mail: alin.andries@uaic.ro
Nicu Sprincean
Alexandru Ioan Cuza University of Iași and National Institute for Economic Research, Romanian
Academy
E-mail: sprincean.nicu@uaic.ro
Abstract
In this study, we explore whether and to what extent Environmental, Social, and Governance
(ESG) factors impact banks’ funding costs. Using a sample composed of 493 banks located in 39
advanced and emerging economies over the 2003-2020 period, we find that banks benefit from
incorporating ESG practices into financial decisions, enjoying lower costs of raising interestbearing liabilities (total cost of funds), as well as reduced costs of attracting deposits. All ESG
dimensions are responsible for this outcome in the case of total cost of funds, whereas for the cost
of deposits the Environmental pillar appears to have an insignificant impact, suggesting that
depositors do not value banks' environmental commitments, but rather their social performance
and corporate governance quality. Furthermore, the empirical evidence indicates that only large
banks and those located in developed countries reap the benefits of increased ESG performance in
terms of reduced financing costs.
Keywords: Banks; Funding costs; ESG ratings
JEL classification: G21; G32; M14
Acknowledgements: Andrieș acknowledges financial support from the Romanian National
Authority for Scientific Research and Innovation, CNCS – UEFISCDI – PN-III-P4-ID-PCE20200929. Sprincean acknowledges financial support from the European Social Fund, through
Operational Programme Human Capital 2014-2020, project number POCU/993/6/13/153322,
project title <<Educational and training support for PhD students and young researchers in
preparation for insertion into the labor market>>.
Electronic copy available at: https://ssrn.com/abstract=4359454
1. Introduction
Although the Environmental, Social and Governance (hereafter ESG) factors emerged since
1980’s as a way for investors to evaluate a company’s performance (Berg et al., 2022), their impact
on firms’ cost of financing has been investigated empirically only recently and is still subject to
debate. Moreover, the majority of the studies concern non-financial corporations, whereas
financial institutions have received much less attention. Recently, the European Central Bank set
the deadline as the end of 2023 for euro area banks to incorporate climate and environmental risks
into their governance, strategy, and risk management plans (ECB, 2022). In a similar fashion, the
Federal Reserve Board proposed a plan for banks with assets exceeding 100 billion USD to address
climate-related financial risks, and the six largest U.S. banks will have to assess the potential
impact of these risks on their operations in a pilot climate scenario by July 31, 2023 (Reuters,
2023).
According to the shareholder view, the mission of a company is to undertake positive net
present value projects to maximize shareholders’ wealth (Friedman, 1970). An alternative to this
perspective is the stakeholder theory put forward by Freeman (1984) which states that a firm can
succeed by doing good (Bénabou and Tirole, 2010). In this regard, a business should consider the
impact of its operations on all parties involved, i.e., the creditors, employees, governmental bodies,
customers, suppliers etc., and aim to create value for them as well and uphold their interests.
Previous evidence support the stakeholder orientation of a company and show that
ESG/Corporate Social Responsibility (CSR)1 performance is linked with better profitability
(Gangi et al., 2019), increased satisfaction of both customers and employees (Servaes and Tamayo,
2013), and less individual risk-taking (Neitzert and Petras, 2021) and systemic risk in the banking
sector (Aevoae et al., 2022).2 Murè et al. (2021) point-out that ESG represents one of the variables
that can impact the reputation of financial institutions, which in turn influences the cost of debt
(Maaloul et al., 2021). In the same vein, both financial (Francis et al., 2005; Ng and Rezaee, 2015)
and non-financial (Goss and Roberts, 2011; El Ghoul et al., 2011; Dhaliwal et al., 2014; Ng and
Rezaee, 2015; Eliwa et al., 2021; Wang et al., 2021; Azmi et al., 2021; Agnese and Giacomini,
2023; Degryse et al., 2023) disclosures are found to be value-relevant and affect the cost of capital.
1
Although ESG dimensions are employed as proxy for Corporate Social Responsibility (CSR) and used
interchangeable in the literature, Gillan et al. (2021) note that ESG is a broader term that incorporates governance
explicitly, while CSR covers it indirectly.
2
For recent reviews, see Rau and Yu (2023) and Tsang et al. (2023).
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While El Ghoul et al. (2011), Dhaliwal et al. (2014), Ng and Rezaee (2015), Eliwa et al. (2021),
Azmi et al. (2021), Agnese and Giacomini (2023) and Degryse et al. (2023)3 document a negative
association between sustainability 4 and companies’ cost of capital, Goss and Roberts (2011) show
that banks do not reward businesses that perform better in terms of CSR-related activities as
compared to their peers. However, they tend to charge higher borrowing costs for firms with CSR
concerns (Goss and Roberts, 2011) and for those that pollute more (Reghezza et al., 2022).
Moreover, Wang et al. (2021) find that CSR practices drive up the cost of equity in East Asia
pointing the economies with weak investor protection as the main cause.
Sustainability can affect the cost of financing through several channels. First, companies
with long-term commitment to sustainability exhibit greater information transparency which
results in less information asymmetry between the contracting parties (Hong and Kacperczyk,
2009; Dhaliwal et al., 2014), leading to a higher demand for banks’ stocks or bonds (El Ghoul et
al., 2011). Being socially conscious induces a lower exposure to litigation risks, such as those
related to employee benefits or environmental pollution, which can have a negative impact on
future cash flows and impair the ability to fulfil outstanding obligations (Hong and Kacperczyk,
2009; Ge and Liu, 2015). Consequently, investors require a lower rate of return, whereas
depositors in banks are willing to accept lower deposit rates. Second, ESG-focused firms have a
stronger relationship with stakeholders, which generates moral capital (Godfrey et al., 2009). They
can also benefit from a greater potential to attract customers, employees, and investors, take
advantage of business opportunities tied to societal trends, avert regulatory intervention or
environmental-related penalties (Edmans and Kacperczyk, 2022), and can identify a variety of
risks, including strategic, operational, reputational, regulatory and financial with potential impact
on future performance and enterprise value (Ng and Rezaee, 2015). Galletta et al. (2023) document
that ESG-focused banks benefit from a reduction of their reputational risk. Third, as shown in
previous studies (e.g., Neitzert and Petras, 2021), ESG ratings manifest a beneficial impact on
companies’ idiosyncratic risk being a good risk-management approach. As a result, companies
with higher ESG scores enjoy reduced costs of raising funds as compared to their riskier
counterparts (El Ghoul et al., 2011; Agnese and Giacomini, 2023).
3
Degryse et al. (2023) find that green banks offered cheaper loans for environmental conscious firms only after the
ratification of the Paris Agreement in 2015.
4
Sustainability and ESG performance are used interchangeably in this paper.
Electronic copy available at: https://ssrn.com/abstract=4359454
In this paper, we examine the role of banks’ sustainability performance, i.e., the integration
of ESG issues into financial decisions, on their funding costs. Using a sample composed of 493 in
an international setting for a period spanning from 2003 to 2020, we document that an enhanced
aggregate ESG rating is significantly associated with lower financing costs measured by the
interest expense over interest-bearing liabilities (total cost of funds) and by the the ratio of interest
expense on bank deposits to total deposits (cost of deposits). We find supporting evidence that all
pillars that compose the ESG score are responsible for this outcome in the case of costs of raising
interest-bearing liabilities, whereas for the cost of deposits the Environmental pillar appears to
have an insignificant impact. Further, we show that only large banks and those from advanced
countries benefit from transparency in the dissemination of ESG information in terms of funding
costs reduction. The findings are robust across a variety of models and specifications, and across
different sample structures. Our work relates to that of Azmi et al. (2021) highlighting no
significant impact of ESG practices on the cost of debt for banks from emerging markets, and with
Agnese and Giacomini (2023) investigating the role of sustainability on the cost of bond issuance
by banks in the primary market in which the authors find that ESG-oriented banks from the
European Union display cheaper funding costs, with the Governance pillar driving the results.
Our work adds to existing literature in multiple ways. First, we extend the studies that
analyze the impact of sustainability orientation of financial institutions on the cost of funds. Only
few works focus on financial institutions, with inconclusive results (El Ghoul et al., 2013; Azmi
et al., 2021; Agnese and Giacomini, 2023). Financial institutions, especially banks, play an
important role in the financial system by providing loans to the real economy and by performing
maturity transformation (Berger et al., 2020). Inefficient management of a bank or excessive risktaking could lead to a bailout using taxpayers’ money, hence the attention from the government,
media, and academia (Wu and Shen, 2013). Second, our analysis focuses on the cost of debt capital
performed separately for the overall cost of funds and deposit funding costs, which is distinct from
the approach taken by most scholars who examine the impact of ESG performance on either the
cost of equity or the cost of debt, which can vary greatly among banks due to differences in interest
rates, debt maturity, and structure (Levine et al., 2021), without disentangling the role of deposits.
Third, we contribute on the debate of the relevance of the stakeholder theory and find empirical
evidence in favor of negative association between non-financial information disclosure and the
cost of capital in banks.
Electronic copy available at: https://ssrn.com/abstract=4359454
The rest of the paper is organized as follows: Section 2 describes the data and methodology
employed, Section 3 presents the results and performs robustness checks, and Section 4 offers
conclusions.
2. Sample, data, and empirical strategy
2.1 Sample and data
Our dataset contains 493 publicly listed banks from 39 emerging and advanced economies
covering 2003-2020 period. We started with all financial institutions included in the Refinitiv ESG
Global Banking Services 5 index and selected only banks (712) based on Thomson Reuters
Business Classification. Due to the availability of the data concerning ESG scores, bank-, banking
system- and macroeconomic-level explanatory variables, we ended up with 493 banks with an
average size of 152 billion USD at the end of 2020. Bank-specific characteristics are retrieved
from the Refinitiv Eikon, whereas other control variables are obtained from the International
Monetary Fund (IMF) and World Bank (WB).
Following prior work (e.g., Aevoae et al., 2022; Agnese and Giacomini, 2023) we employ
the ESG score, as well as its three dimensions, i.e., Environmental (encompassing Emissions,
Innovation, and Resource use sub-pillars), Social (incorporating Community, Human rights,
Product responsibility, and Workforce sub-pillars) and Governance (containing CSR strategy,
Management, Shareholders sub-pillars) pillars, from the Refinitiv Asset4 database. These ten
categories are derived from self-reported information disclosed by the banks and are weighted
according to the number of issues they encompass to get the ESG Score. A higher ESG score
indicates better sustainability performance. In addition, Refinitiv also reports the ESG Combined
Score that considers 23 ESG controversy topics related to business ethics, anti-competition
behaviors, accounting and reporting practices etc. ESG scandals are found to cause severe damage
to a company's reputation and financial performance (Aouadi and Marsat, 2018).
2.2 Empirical strategy
To test whether ESG performance influence banks’ funding costs we estimate the
following model:
𝐶𝑜𝑠𝑡𝑖𝑗,𝑡 = 𝛽0 + 𝛽1 × 𝐸𝑆𝐺𝑖𝑗,𝑡−1 + 𝛽2 × 𝑿𝑖𝑗,𝑡−1 + 𝛽3 × 𝒁𝑗,𝑡 +𝜆𝑖 + 𝜔𝑡 + 𝜀𝑖𝑗,𝑡
5
Ticker LA43GLBG.
Electronic copy available at: https://ssrn.com/abstract=4359454
(1)
where 𝐶𝑜𝑠𝑡𝑖𝑗,𝑡 is the funding costs of bank i from country j in year t computed alternatively as
Interest expense over Interest-bearing liabilities (total cost of funds) and Interest expense on bank
deposits over Total deposits (cost of deposits) in line with Levine et al. (2021), 𝐸𝑆𝐺𝑖𝑗,𝑡−1 is the
ESG Score, ESG Combined Score and the Environmental, Social and Governance pillars,
respectively for bank i in year t-1, 𝑿𝑖𝑗,𝑡−1 is a vector of lagged bank-level factors that are discussed
in the literature to influence banks’ financing costs, i.e., Size (natural logarithm of total assets),
Lending Activities (total loans/total assets), Capitalization (common equity/total assets), Credit
Risk Ratio (non-performing loans/total loans), Profitability (net income/common equity), and
Income Diversification (non-interest income/total revenue) (Ellul and Yerramilli, 2013; Levine et
al., 2021; Azmi et al., 2021; Agnese and Giacomini, 2023), 𝒁𝑗,𝑡 is a vector of banking system and
macroeconomic control variables, i.e., Bank Concentration, Financial Institutions Index, Real
GDP Growth, Yield to capture monetary policy stance (3-month government bond yield), and
Slope to account for the impact of the unconventional monetary policy measures (difference
between 10-year government bond yield and 3-month government bond yield) (Alessandri and
Nelson, 2015; Levine et al., 2021; Azmi et al., 2021). 𝜆𝑖 are bank fixed effects to capture bankspecific time-invariant heterogeneity and mitigate omitted variables bias, and 𝜔𝑡 are year fixed
effects to account for aggregate time shocks common to all banks. Finally, 𝜀𝑖𝑗,𝑡 represents the error
term corresponding to bank i from country j in year t. We use cluster-robust standard errors to
correct for any form of heteroskedasticity and autocorrelation in the residuals. Following
Gambacorta and Shin (2018) all bank-level control variables are lagged one year to mitigate
possible endogeneity issues. To reduce the potential influence of outliers, we winsorize all
variables between 1 st and 99th percentiles.
3. Empirical findings
3.1 Descriptive statistics
In Table A1 in the Appendix, we report the descriptive statistics of the variables used in the
empirical analysis. As we can note, the average cost of funds was 1.79% ranging from 0.09% to
9.40%, with a standard deviation of 1.87%, whereas the cost of deposits is situated between 0.04%
and 10.02%, with a mean of 1.49% and a standard deviation of 1.83%.
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Regarding the ESG score, it ranges from a minimum of 7.57 to a maximum of 88.31, with
a mean of 45.53 and a standard deviation of 21.09. The ESG Combined Score has slightly lower
values, with an average of 42.91 and a standard deviation of 18.70, with a minimum value of 7.57
and a maximum value of 84.73.
3.2 Empirical findings
The benchmark results exhibited in Tables 1 and 2 show a negative and statistically significant at
the 1% level association between the cumulative effect of Environmental, Social and Governance
practices (ESG Score) and total cost of funds (interest expense/interest-bearing liabilities, Table 1,
Model (1)) and cost of deposits (interest expense on bank deposits/total deposits, Table 2, Model
(1)), respectively. The results remain consistent when we employ the ESG Combined Score, which
accounts for specific controversies at the bank level derived from the international media sources,
but with a reduced statistical significance. Thus, strong sustainability practices lead to lower
fundings costs for banks, providing important evidence in favor of stakeholder theory. The results
bear economic significance too: a one standard deviation increase in the ESG Score is linked with
a decrease of 6.83% of a standard deviation in total cost of funds, whereas an increase of one
standard deviation in the ESG Score leads to a decrease of 7.82% in the standard deviation of the
cost of deposits. This is valid for all pillars of the ESG Score in the case of costs of interest-bearing
liabilities and especially for the Social factor (social responsibility). Our results are consistent with
those of Agnese and Giacomini (2023) who find a decrease in the cost of bond issuance for banks
with strong ESG performance, but attribute it solely to the Governance pillar. Similarly, Eliwa et
al. (2021) and Raimo et al. (2021) reach the same conclusion for non-financial corporations in
terms of cost of debt financing.
[Table 1 goes here]
[Table 2 goes here]
Our results could be explained by the following mechanisms: Banks can exhibit
environmentally conscious behavior both internally and in their relations with clients and business
partners (Bătae et al., 2021).They contribute to environmental issues through (i) efficient use of
resources, (ii) funding environmentally friendly projects, and (iii) lending less to customers that
Electronic copy available at: https://ssrn.com/abstract=4359454
cause environmental damage (Gangi et al., 2019). Giannarakis et al. (2018) state that climate
change disclosure is an useful instrument in reducing information asymmetry for both shareholders
and stakeholders. This reduction in information asymmetry would ultimately attract more
depositors (Azmi et al., 2021) and decrease the cost of financing. The purpose of the social
performance is to showcase companies’ involvement with stakeholders (e.g., customers,
employees, non-governmental organizations etc.), cross-industry partnerships and efforts towards
community development, and is a tool in differentiating from their competitors and enhancing the
public's view of their operations (Gangi et al., 2019; Bătae et al., 2021), thus gaining moral capital
(Godfrey et al., 2009). As a consequence, increased level of social transparency translates into
lower cost of capital (Dhaliwal et al., 2014). Corporate governance mechanisms, in light of agency
theory, plays an important role in aligning the interests of managers and shareholders (Grove et
al., 2011). Effective corporate governance practices lower perceived risk and reduce information
asymmetry, leading to a decrease in the cost of capital for companies (Ng and Rezaee, 2015).As
concerning the cost of attracting deposits, we document that depositors do not value environmental
commitments of banks, but rather their social performance and corporate governance quality.
The estimated coefficients of control variables are consistent with previous literature on
the factors affecting banks' funding costs (e.g., Arnould et al., 2022).Thus, banks’ total cost of
funds is reduced by a funding structure that is mainly based on deposits, increased reliance on nontraditional sources of income, and larger GDP growth. On the other hand, well-developed financial
institutions, more concentrated banking systems, and higher interest rates positively impact the
cost of raising interest-bearing liabilities. As for the cost of attracting deposits, only larger share
of deposits in total liabilities has a negative effect, while factors such as size, bank concentration,
financial institutions index, and the yield on a 3-month government bond contribute to an increase
in the cost of deposits.
3.3 Robustness tests
To test the robustness of our fundings, we perform several robustness checks. First, we employ
alternative estimation models. In Tables 3 and 4, Model (1), we include country fixed effects to
account for country-level unobserved heterogeneity (bank fixed effects are dropped from the
output because of collinearity). To control for any form of cross-sectional dependence, we estimate
a regression with Driscoll and Kraay standard errors (Driscoll and Kraay, 1998) in Model (2). In
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this framework, the error term is presumed to be heteroskedastic, exhibiting autocorrelation up to
a certain lag, and correlated between panels. This method can handle unbalanced panels and have
better sample properties than other estimators, performing well even when the number of crosssection units exceeds the time dimension (Hoechle, 2007).
[Table 3 goes here]
[Table 4 goes here]
Furthermore, in Model (3) we employ a dynamic estimator, i.e., the bias-corrected fixed
effects (BCFE) developed by Everaert and Pozzi (2007) to capture the potential autoregressive
structure of funding costs. The bias correction is applied through an iterative bootstrap algorithm
and yields higher efficiency compared to Generalized Method of Moments (GMM) estimators
(Everaert and Pozzi, 2007). Overall, the results maintain their significance regardless of the
estimation method, both static and dynamic.
Second, to make sure that results are not driven by the sample structure which is dominated
by the US banks6, we exclude banks headquartered in the United States. As shown in Tables 5 and
6, Model (1), the main findings remain consistent. Next, we split the sample between advanced
and emerging economies according to the IMF classification and perform the estimation from Eq.
(1) separately. As presented in Tables 5 and 6, Models (2) and (3), only banks situated in advanced
countries benefit from a sustainable behavior in terms of fundings costs reduction, whereas the
results for those with the headquarter in emerging economies are found to be statistically
insignificant, supporting the evidence of Azmi et al. (2021). Companies that operate in emerging
markets face a number of challenges that affect their ESG performance given weak regulations,
poor corporate governance, and lack of transparency (Khanna and Palepu, 2000).
[Table 5 goes here]
[Table 6 goes here]
6
Similarly, the sample used in Poursoleyman et al. (2023) is dominated by the US corporations: among 5410 firms,
2825 are domiciled in the United States.
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Large and small banks can be affected differently by their ESG disclosures given the fact
that larger banking institutions benefit from the too-big-to-fail status (Anginer et al., 2018) and
have a greater ability to diversify and gain access to capital markets as opposed to their smaller
peers (Shim, 2003). We find that only large banks7 that are oriented towards ESG disclosure enjoy
smaller costs of raising interest-bearing liabilities (Models (4) and (5)), whereas depositors value
banks’ sustainability practices regardless of their size.
4. Conclusion
This paper examines the nexus between banks' Environmental, Social, and Governance (ESG)
practices and their funding costs in an international context, analyzing data from 493 banks across
39 countries from 2003 to 2020. We find that incorporating ESG practices into banks' financial
decision-making has a beneficial impact on the cost of raising interest-bearing liabilities (total cost
of funds) and the cost of deposits, providing further empirical evidence in favour of stakeholder
theory. We further decompose the impact along individual ESG factors and show that all pillars
contribute to cost reduction for interest-bearing liabilities, and only social performance and
corporate governance quality matter for depositors. Moreover, the results indicate that only large
banks and those located in developed countries benefit from increased transparency in the
reporting of ESG information in terms of reduced funding costs.. Our findings bear important
policy implications in terms of non-financial disclosure and banks’ funding costs as higher costs
of raising capital are passed through into higher lending rates, thus reducing the credit availability
to the real economy.
7
We define large and small banks based on the median size of total assets in USD as of 2020.
Electronic copy available at: https://ssrn.com/abstract=4359454
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Electronic copy available at: https://ssrn.com/abstract=4359454
Table 1. Results for the base model: total cost of funds.
Dependent: Total Cost of Funds
ESG Score (t-1)
ESG Combined Score (t-1)
Environmental (t-1)
Social (t-1)
(1)
-0.0060***
(0.0019)
(2)
(3)
(4)
(5)
-0.0038**
(0.0016)
-0.0022*
(0.0012)
-0.0048***
(0.0017)
Governance (t-1)
-0.0021**
(0.0009)
Size (t-1)
0.0307
0.0140
-0.0032
0.0231
-0.0080
(0.0701)
(0.0705)
(0.0691)
(0.0693)
(0.0716)
Lending Activities (t-1)
0.0014
0.0017
0.0013
0.0013
0.0014
(0.0035)
(0.0036)
(0.0035)
(0.0035)
(0.0035)
Funding Structure (t-1)
-0.0176***
-0.0178***
-0.0179***
-0.0179***
-0.0179***
(0.0045)
(0.0045)
(0.0045)
(0.0045)
(0.0045)
Capitalization (t-1)
-0.0037
-0.0038
-0.0039
-0.0033
-0.0050
(0.0133)
(0.0132)
(0.0132)
(0.0133)
(0.0131)
Credit Risk Ratio (t-1)
0.0060
0.0062
0.0059
0.0060
0.0054
(0.0074)
(0.0076)
(0.0077)
(0.0075)
(0.0076)
Profitability (t-1)
0.0013
0.0014
0.0012
0.0012
0.0013
(0.0023)
(0.0023)
(0.0023)
(0.0023)
(0.0023)
Income Diversification (t-1)
-0.0094***
-0.0093***
-0.0090***
-0.0094***
-0.0092***
(0.0027)
(0.0027)
(0.0027)
(0.0027)
(0.0027)
Bank Concentration
0.0069**
0.0070**
0.0065**
0.0068**
0.0071**
(0.0029)
(0.0029)
(0.0029)
(0.0029)
(0.0029)
Financial Institutions Index
1.9214**
2.0282**
1.9374**
1.9304**
2.0046**
(0.8721)
(0.8800)
(0.8790)
(0.8707)
(0.8732)
Real GDP Growth
-0.0305*
-0.0294*
-0.0294*
-0.0308**
-0.0280*
(0.0156)
(0.0157)
(0.0158)
(0.0157)
(0.0157)
Yield
0.2536***
0.2547***
0.2561***
0.2552***
0.2523***
(0.0267)
(0.0269)
(0.0270)
(0.0268)
(0.0268)
Slope
0.0108
0.0119
0.0156
0.0125
0.0107
(0.0241)
(0.0240)
(0.0239)
(0.0240)
(0.0240)
Constant
0.8762
1.0384
1.3824
0.9790
1.4400
(1.3793)
(1.3753)
(1.3591)
(1.3970)
(1.3858)
Observations
3257
3257
3257
3257
3257
Banks
493
493
493
493
493
Countries
39
39
39
39
39
R-squared
0.6451
0.6436
0.6429
0.6445
0.6430
Bank FE
Yes
Yes
Yes
Yes
Yes
Country FE
No
No
No
No
No
Year FE
Yes
Yes
Yes
Yes
Yes
Note: This table reports the results for the base model described in Eq. (1). Robust standard errors clustered at the
bank level in parentheses. ***, **, and * denote statistical significance at the 1%, 5% and 10% level, respectively.
Electronic copy available at: https://ssrn.com/abstract=4359454
Table 2. Results for the base model: cost of deposits.
Dependent: Cost of Deposits
ESG Score (t-1)
ESG Combined Score (t-1)
Environmental (t-1)
Social (t-1)
(1)
-0.0068***
(0.0026)
(2)
(3)
(4)
(5)
-0.0046**
(0.0020)
-0.0020
(0.0013)
-0.0047**
(0.0023)
Governance (t-1)
-0.0032**
(0.0013)
Size (t-1)
0.2047**
0.1888**
0.1627*
0.1902**
0.1688*
(0.0822)
(0.0843)
(0.0885)
(0.0831)
(0.0871)
Lending Activities (t-1)
0.0017
0.0021
0.0016
0.0016
0.0017
(0.0037)
(0.0037)
(0.0038)
(0.0038)
(0.0037)
Funding Structure (t-1)
-0.0145*
-0.0147*
-0.0149*
-0.0148*
-0.0147*
(0.0083)
(0.0084)
(0.0085)
(0.0084)
(0.0084)
Capitalization (t-1)
-0.0253
-0.0253
-0.0256
-0.0250
-0.0267
(0.0172)
(0.0172)
(0.0172)
(0.0174)
(0.0170)
Credit Risk Ratio (t-1)
-0.0030
-0.0028
-0.0034
-0.0032
-0.0038
(0.0103)
(0.0105)
(0.0105)
(0.0104)
(0.0104)
Profitability (t-1)
-0.0022
-0.0020
-0.0023
-0.0023
-0.0021
(0.0031)
(0.0031)
(0.0031)
(0.0031)
(0.0031)
Income Diversification (t-1)
-0.0036
-0.0036
-0.0033
-0.0036
-0.0034
(0.0039)
(0.0039)
(0.0039)
(0.0039)
(0.0039)
Bank Concentration
0.0100***
0.0101***
0.0096***
0.0099***
0.0104***
(0.0035)
(0.0035)
(0.0035)
(0.0035)
(0.0035)
Financial Institutions Index
2.7573***
2.8787***
2.7930***
2.7760***
2.8392***
(0.6838)
(0.6859)
(0.7021)
(0.6995)
(0.6766)
Real GDP Growth
-0.0227
-0.0216
-0.0215
-0.0229
-0.0199
(0.0201)
(0.0200)
(0.0201)
(0.0203)
(0.0198)
Yield
0.2517***
0.2530***
0.2540***
0.2533***
0.2493***
(0.0333)
(0.0335)
(0.0333)
(0.0331)
(0.0336)
Slope
0.0158
0.0169
0.0206
0.0178
0.0141
(0.0309)
(0.0308)
(0.0307)
(0.0308)
(0.0310)
Constant
-3.3385**
-3.2048*
-2.7176
-3.1304*
-2.8034
(1.6297)
(1.6553)
(1.7548)
(1.6449)
(1.7311)
Observations
3217
3217
3217
3217
3217
Banks
490
490
490
490
490
Countries
39
39
39
39
39
R-squared
0.4949
0.4934
0.4918
0.4934
0.4936
Bank FE
Yes
Yes
Yes
Yes
Yes
Country FE
No
No
No
No
No
Year FE
Yes
Yes
Yes
Yes
Yes
Note: This table reports the results for the base model described in Eq. (1). Robust standard errors clustered at the
bank level in parentheses. ***, **, and * denote statistical significance at the 1%, 5% and 10% level, respectively.
Electronic copy available at: https://ssrn.com/abstract=4359454
Table 3. Robustness tests using different estimation models: total cost of funds.
Dependent: Total Cost of Funds
ESG Score (t-1)
Size (t-1)
Lending Activities (t-1)
Funding Structure (t-1)
Capitalization (t-1)
Credit Risk Ratio (t-1)
Profitability (t-1)
Income Diversification (t-1)
Bank Concentration
Financial Institutions Index
Real GDP Growth
Yield
Slope
(1)
Country FE
-0.0051***
(0.0015)
-0.0303
(0.0274)
0.0037*
(0.0023)
-0.0218***
(0.0037)
0.0128
(0.0099)
0.0064
(0.0064)
0.0025
(0.0024)
-0.0090***
(0.0018)
0.0074**
(0.0030)
2.1909**
(0.8640)
-0.0331**
(0.0153)
0.2456***
(0.0259)
0.0083
(0.0239)
(2)
Driscol-Kraay
-0.0060***
(0.0016)
0.0307
(0.1126)
0.0014
(0.0023)
-0.0176***
(0.0026)
-0.0037
(0.0130)
0.0060
(0.0072)
0.0013
(0.0021)
-0.0094**
(0.0035)
0.0069
(0.0049)
1.9214
(1.1376)
-0.0305*
(0.0147)
0.2536***
(0.0211)
0.0108
(0.0180)
Dependent (t-1)
Constant
(3)
BCFE
-0.0037***
(0.0012)
-0.0561
(0.0583)
0.0032*
(0.0019)
-0.0086***
(0.0024)
-0.0071
(0.0074)
-0.0032
(0.0068)
0.0001
(0.0016)
0.0049**
(0.0022)
0.0018
(0.0017)
-0.4222
(0.5194)
-0.0276*
(0.0152)
0.1830***
(0.0234)
-0.0101
(0.0218)
0.6239***
(0.0238)
2.9718***
0.8762
(1.0740)
(1.9180)
Observations
3257
3257
3146
Banks
493
493
446
Countries
39
39
39
R-squared
0.6436
0.6451
Bank FE
No
Yes
Yes
Country FE
Yes
No
No
Year FE
Yes
Yes
Yes
Note: This table reports the results for the robustness tests. Robust standard errors clustered at the bank level in
parentheses for Model (1), Driscol and Kraay standard errors in parentheses for Model (2), and bootstrapped standard
errors in parentheses for Model (3). ***, **, and * denote statistical significance at the 1%, 5% and 10% level,
respectively.
Electronic copy available at: https://ssrn.com/abstract=4359454
Table 4. Robustness tests using different estimation models: cost of deposits.
Dependent: Cost of Deposits
ESG Score (t-1)
Size (t-1)
Lending Activities (t-1)
Funding Structure (t-1)
Capitalization (t-1)
Credit Risk Ratio (t-1)
Profitability (t-1)
Income Diversification (t-1)
Bank Concentration
Financial Institutions Index
Real GDP Growth
Yield
Slope
(1)
Country FE
-0.0054**
(0.0024)
-0.0568*
(0.0312)
0.0047**
(0.0021)
-0.0196***
(0.0058)
0.0057
(0.0144)
-0.0052
(0.0092)
-0.0003
(0.0031)
-0.0059**
(0.0025)
0.0111***
(0.0036)
3.2544***
(0.6675)
-0.0266
(0.0200)
0.2504***
(0.0323)
0.0264
(0.0305)
(2)
Driscol-Kraay
-0.0068***
(0.0021)
0.2047
(0.1194)
0.0017
(0.0020)
-0.0145***
(0.0025)
-0.0253
(0.0164)
-0.0030
(0.0073)
-0.0022
(0.0029)
-0.0036
(0.0049)
0.0100*
(0.0053)
2.7573***
(0.7961)
-0.0227
(0.0304)
0.2517***
(0.0284)
0.0158
(0.0265)
Dependent (t-1)
Constant
(3)
BCFE
-0.0040***
(0.0013)
0.1297*
(0.0691)
0.0041**
(0.0017)
0.0007
(0.0029)
-0.0242***
(0.0082)
-0.0081
(0.0054)
-0.0003
(0.0015)
0.0048**
(0.0020)
0.0010
(0.0014)
-0.5170
(0.4427)
-0.0233*
(0.0136)
0.1689***
(0.0240)
-0.0008
(0.0307)
0.6291***
(0.0280)
1.1931
-3.3385
(1.0444)
(2.1812)
Observations
3217
3217
3085
Banks
490
490
444
Countries
39
39
39
R-squared
0.4881
0.4949
Bank FE
Yes
Yes
Yes
Country FE
No
No
No
Year FE
Yes
Yes
Yes
Note: This table reports the results for the robustness tests Robust standard errors clustered at the bank level in
parentheses for Model (1), Driscol and Kraay standard errors in parentheses for Model (2), and bootstrapped standard
errors in parentheses for Model (3). ***, **, and * denote statistical significance at the 1%, 5% and 10% level,
respectively.
Electronic copy available at: https://ssrn.com/abstract=4359454
Table 5. Robustness tests using different sample structures: total cost of funds.
(1)
(2)
(3)
(4)
(5)
No U.S.
Dependent: Total Cost of Funds banks
Advanced Countries Emerging Markets Large Banks Small Banks
ESG Score (t-1)
-0.0062**
-0.0061***
0.0001
-0.0060***
-0.0026
(0.0026)
(0.0018)
(0.0045)
(0.0021)
(0.0022)
Size (t-1)
-0.1164
0.1027
-0.3470*
0.0383
-0.1522
(0.0848)
(0.0736)
(0.2011)
(0.0893)
(0.1466)
Lending Activities (t-1)
0.0001
0.0002
0.0001
0.0007
0.0055
(0.0046)
(0.0034)
(0.0069)
(0.0038)
(0.0036)
Funding Structure (t-1)
-0.0182***
-0.0129***
-0.0295***
-0.0189***
0.0016
(0.0056)
(0.0042)
(0.0086)
(0.0051)
(0.0046)
Capitalization (t-1)
-0.0151
-0.0089
-0.0167
-0.0037
-0.0289*
(0.0190)
(0.0113)
(0.0425)
(0.0162)
(0.0162)
Credit Risk Ratio (t-1)
0.0003
0.0138*
-0.0273
0.0033
-0.0103
(0.0079)
(0.0071)
(0.0207)
(0.0079)
(0.0123)
Profitability (t-1)
0.0028
-0.0007
0.0109
0.0007
-0.0008
(0.0029)
(0.0020)
(0.0098)
(0.0024)
(0.0039)
Income Diversification (t-1)
-0.0121***
-0.0025
-0.0456***
-0.0103***
-0.0018
(0.0034)
(0.0023)
(0.0100)
(0.0029)
(0.0034)
Bank Concentration
0.0105***
0.0070**
-0.0033
0.0079***
0.0003
(0.0031)
(0.0029)
(0.0084)
(0.0029)
(0.0238)
Financial Institutions Index
1.9748**
3.4024***
-2.8409
2.0342**
0.1897
(0.8938)
(0.9934)
(1.9662)
(0.8935)
(4.0618)
Real GDP Growth
-0.0374**
0.0248**
-0.1201***
-0.0332**
-0.0824**
(0.0173)
(0.0097)
(0.0250)
(0.0164)
(0.0410)
Yield
0.2608***
0.2922***
0.2105***
0.2516***
0.3235**
(0.0296)
(0.0287)
(0.0403)
(0.0274)
(0.1258)
Slope
0.0152
0.0014
0.0217
0.0009
0.1518
(0.0252)
(0.0317)
(0.0449)
(0.0245)
(0.1467)
Constant
3.9442**
-2.7490*
13.5526***
0.8945
3.8910
(1.6653)
(1.4304)
(4.0275)
(1.7972)
(3.0422)
Observations
1906
2618
639
2447
810
Banks
203
409
84
247
246
Countries
38
22
17
38
21
R-squared
0.6024
0.7480
0.5636
0.6591
0.6252
Bank FE
Yes
Yes
Yes
Yes
Yes
Country FE
No
No
No
No
No
Year FE
Yes
Yes
Yes
Yes
Yes
Note: This table reports the results for the base model described in Eq. (1). Robust standard errors clustered at the
bank level in parentheses. ***, **, and * denote statistical significance at the 1%, 5% and 10% level, respectively.
Electronic copy available at: https://ssrn.com/abstract=4359454
Table 6. Robustness tests using different sample structures: cost of deposits.
(1)
(2)
(3)
(4)
(5)
No U.S.
Advanced
Emerging
Large
Small
Dependent: Cost of Deposits
Banks
Countries
Markets
Banks
Banks
ESG Score (t-1)
-0.0073**
-0.0037**
-0.0096
-0.0064**
-0.0055**
(0.0035)
(0.0016)
(0.0089)
(0.0028)
(0.0025)
Size (t-1)
0.0722
0.2195***
-0.2849
0.1948*
0.0315
(0.0906)
(0.0694)
(0.3723)
(0.1110)
(0.1090)
Lending Activities (t-1)
0.0005
0.0024
-0.0105
0.0029
-0.0029
(0.0048)
(0.0039)
(0.0090)
(0.0033)
(0.0039)
Funding Structure (t-1)
-0.0174*
-0.0085**
-0.0330
-0.0145
0.0034
(0.0104)
(0.0036)
(0.0272)
(0.0094)
(0.0044)
Capitalization (t-1)
-0.0493*
-0.0140
-0.0952*
-0.0365*
-0.0236*
(0.0266)
(0.0141)
(0.0567)
(0.0209)
(0.0128)
Credit Risk Ratio (t-1)
-0.0099
0.0179**
-0.1049***
-0.0051
-0.0074
(0.0107)
(0.0073)
(0.0369)
(0.0108)
(0.0087)
Profitability (t-1)
-0.0007
-0.0034
-0.0028
-0.0016
-0.0002
(0.0041)
(0.0027)
(0.0165)
(0.0030)
(0.0037)
Income Diversification (t-1)
-0.0047
-0.0014
-0.0145
-0.0062*
-0.0018
(0.0055)
(0.0035)
(0.0159)
(0.0036)
(0.0035)
Bank Concentration
0.0132***
0.0137***
-0.0116
0.0105***
0.0146
(0.0037)
(0.0033)
(0.0104)
(0.0035)
(0.0204)
Financial Institutions Index
2.9993***
3.5635***
5.4732
2.8923***
-0.9921
(0.7353)
(0.6501)
(4.1759)
(0.7252)
(4.8877)
Real GDP Growth
-0.0297
0.0120
-0.1114***
-0.0283
-0.0071
(0.0228)
(0.0117)
(0.0357)
(0.0214)
(0.0266)
Yield
0.2547***
0.3020***
0.2041***
0.2441***
0.2871***
(0.0374)
(0.0299)
(0.0454)
(0.0337)
(0.0985)
Slope
0.0117
0.0491*
0.0119
0.0103
0.0145
(0.0337)
(0.0275)
(0.0533)
(0.0329)
(0.0934)
Constant
-0.4500
-6.0168***
10.4185
-3.2975
3.7161
(1.8940)
(1.3067)
(8.0221)
(2.3203)
(3.2465)
Observations
1866
2589
628
2413
804
Banks
200
407
83
246
244
Countries
38
22
17
38
19
R-squared
0.4551
0.6917
0.3732
0.5037
0.7973
Bank FE
Yes
Yes
Yes
Yes
Yes
Country FE
No
No
No
No
No
Year FE
Yes
Yes
Yes
Yes
Yes
Note: This table reports the results for the base model described in Eq. (1). Robust standard errors clustered at the
bank level in parentheses. ***, **, and * denote statistical significance at the 1%, 5% and 10% level, respectively.
Electronic copy available at: https://ssrn.com/abstract=4359454
APPENDIX
20
Electronic copy available at: https://ssrn.com/abstract=4359454
Table A1. Summary statistics.
Variables
Mean
Total Cost of Funds (%)
1.7874
Cost of Deposits (%)
1.4929
ESG Score
45.5281
ESG Combined Score
42.9086
Environmental
36.9115
Social
45.3618
Governance
52.6377
Size
17.7039
Lending Activities (%)
64.5490
Funding Structure (%)
75.0453
Capitalization (%)
9.1945
Credit Risk Ratio (%)
2.8147
Profitability (%)
9.4482
Income Diversification (%) 27.8359
Bank Concentration (%)
50.9017
Financial Institutions Index 0.7908
Real GDP Growth (%)
1.4192
Yield (%)
1.9671
Slope (%)
1.1541
St. dev.
1.8682
1.8264
21.0929
18.7048
32.9335
23.9534
22.5234
1.9478
13.5948
19.2486
3.5985
3.8433
8.1369
14.1523
16.8004
0.1705
3.1557
2.8819
1.1410
p25
0.5200
0.3286
29.0600
28.9500
1.9700
26.3700
34.8400
16.3315
56.5213
63.8994
6.4641
0.7376
6.3400
17.5590
39.2830
0.7297
0.5841
0.0790
0.3540
Median
1.0700
0.7734
42.1200
41.0500
25.5300
41.6300
54.3400
17.7939
66.6723
80.5136
9.1004
1.5757
9.8150
26.3206
42.4171
0.8840
2.2557
1.3910
0.8610
p75
2.2600
1.9002
62.9200
56.8700
68.0600
64.2400
70.7400
18.9202
74.0395
90.5178
11.3979
3.1382
13.5650
36.9114
61.4688
0.8963
2.9189
2.3810
1.8080
Min
0.0900
0.0365
7.5700
7.5700
0.0000
2.7100
6.0300
12.7190
25.4716
18.2669
2.4562
0.0089
-32.1300
0.8770
28.0704
0.2590
-8.4425
-0.9000
-2.6170
Max
9.4000
10.017
88.3100
84.7300
94.6700
92.5700
93.7300
21.5012
91.5989
99.4086
23.6986
22.0036
31.8700
68.1527
99.5394
0.9782
9.0215
18.7970
5.3980
Obs.
3257
3217
3251
3251
3251
3251
3251
3257
3255
3257
3257
3236
3252
3257
3257
3257
3257
3257
3257
Note: This table presents the descriptive statistics of the variables used in the empirical analysis, winsorized at the 1 st and 99th percentiles.
21
Electronic copy available at: https://ssrn.com/abstract=4359454