Working Paper Series
Leonardo Gambacorta, Livia Pancotto,
Alessio Reghezza, Martina Spaggiari

Gender diversity in bank boardrooms
and green lending: evidence fromeuro area credit register data

No 2741 / October 2022

Disclaimer: This paper should not be reported as representing the views of the European Central Bank
(ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB.

Abstract
Do female directors on banks’ boards influence lending decisions toward less polluting firms? Byusing granular credit register data matched with information on firm-level greenhouse gas (GHG)
emission intensities, we isolate credit supply shifts and find that banks with more gender-diverse
boards provide less credit to browner companies. This evidence is robust when we differentiate
among types of GHG emissions and control for endogeneity concerns. In addition, we also
show that female director-specific characteristics matter for lending behavior to polluting firms
as better-educated directors grant lower credit volumes to more polluting firms. Finally, we
document that the “greening” effect of the female members in banks’ boardrooms is stronger incountries with more female climate-oriented politicians.

JEL classification: G01; G21; G30; Q50
Keywords: GHG emissions; Gender; Board diversity; Credit registry; Bank lending

ECB Working Paper Series No 2741 / October 20221

Non-technical summary
Climate change is a serious issue and achieving carbon neutrality is the world’s most urgent
priority. Since banks play a pivotal role in modern financial systems, they can significantly
contribute to a faster transition to a carbon-neutral economy with sustainable lending decisions.
A bank’s climate strategy and related decisions depend on the trajectory defined by the board,which in turn depends on the diversity of the board. The presence of women in banks’ boardrooms can add value along several dimensions, as explained by sociological and physiologicaltheories, as well as empirical evidence. In addition, according to previous studies, female corporate directors and women in general are more likely to care about long-term societal issues,
including climate change.
In light of the increasing relevance of climate change and the global effort in combating
this phenomenon, in this paper we investigate whether and to what extent a greater female
representation in banks’ boardrooms influences banks’ capability in “greening” the economy via
lending decisions. We do this by employing loan-level fixed-effects regressions that allow us toeffectively disentangle credit supply from credit demand and determine whether banks’ lending
choices are really driven by firms’ pollution intensity. To do this, we construct a highly granular
dataset by matching loan-level data from the euro area credit register (AnaCredit) with banklevel and firm-level data from various sources, including banks’ corporate governance variables
and firms’ greenhouse gas emissions. Given the likely effect of the Covid-19 pandemic on banks’lending patterns in 2020, we focus on the year 2019.
We find that banks with more gender-diverse boards provide more credit to greener companies. Banks with a relatively high share of female directors (i.e. above 37%) display about 10%
lower lending volumes towards firms with relatively high pollution intensity (i.e. last quartileof the distribution) compared to the other group of banks. This inverse relationship between
banks’ lending volumes and firms’ pollution intensity for boards with more female directors is
confirmed also when we differentiate among different types of emissions (i.e. direct emissions
caused by a firm’s activities and indirect emissions arising from a firm’s energy consumption
versus other indirect emissions). In addition, we also show that female director-specific characteristics matter for lending behaviour to more/less polluting firms as better-educated directors
grant lower credit volumes to more polluting firms. Finally, we document that the “greening”

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effect of the female members in banks’ boardrooms is stronger in countries with more female
climate-oriented politicians.
Our findings hold important implications for both regulators and policymakers. We underline
the crucial role played by banks in potentially driving the transition towards a greener economy. Furthermore, we identify a central contribution of female presence on boards in shaping
banks’ lending strategies in support of less polluting firms/sectors, thereby confirming the beneficial effects of more gender-diverse decision-making groups on firms’ outcomes and, in a wider
perspective, on the global economy.

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1

Introduction
Over recent years, climate change and its impact have been central to the policy debate(Bailey, 2021; Lagarde, 2021). The number of natural disasters worldwide and the associatedeconomic losses have risen sharply over the last four decades, peaking at 820 events in 2019 andrecording a total loss for the period 1980-2019 of USD 5,200 billion. Furthermore, the majorityof the extreme weather events, causing extensive devastation and economic disruption, are foundto be likely exacerbated by human activity.1
The Paris Agreement, which represents a landmark treaty on climate change signed in December 2015, aims to maintain the global average temperature below 2°C above pre-industrial
levels and the increase limited to 1.5°C.2 Moreover, according to the Paris Agreement, countries
are required to set out their plans and strategies for climate actions through nationally determined contributions (NDCs), which are regularly assessed and updated within a transparent andaccountable framework.3 Reducing pollution and the emissions of greenhouse gases (GHGs) are
key objectives to attain the main purpose of sustainable economic growth, preserving ecosystems
and biodiversity. Achieving carbon neutrality by 2050 represents the world’s most urgent priority. The rules for the Paris Agreement have been finalized at the 26th Conference of the Parties(COP 26) in November 2021, including transparency regulations for how countries report theiremissions and funding to help countries to adapt to climate change.
By relying on a unique sample of almost a million loans extracted from the analytical credit
register (AnaCredit) of the European System of Central Banks (ESCB), for the year 2019, andmatched with firm-level information on GHG emissions, we explore the potential influence of
women in the boardroom on banks’ lending strategies. To the best of our knowledge, this is
the first paper to investigate whether and to what extent a greater female representation in
the boardrooms influences banks’ capability to ”greening” the economy. In particular, we areinterested in testing whether a greater gender diversity in the boardroom can shape banks’decisions to discriminate lending between more and less polluting firms, thereby driving more
1

See www.carbonbrief.org/mapped-how-climate-change-affects-extreme-weather-around-the-world.
The Paris Agreement is a legally binding internationally treaty negotiated by 196 countries at the 21st UN
Climate Change Conference of the Parties (COP21) held in December 2015 in Paris. The Agreement has enteredinto force in November 2016.
3
Based on a revised NDC, submitted in December 2020, the EU has committed to reducing its emissions by atleast 55% by 2030, compared to 1990 (see https://www4.unfccc.int/sites/NDCStaging/pages/Party.aspx?party=
EUU).
2

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effective environmental policies. In light of the increasing relevance of climate change, also interms of financial and price stability (Pereira da Silva, 2019; Lagarde, 2021), and the global
effort in combating this phenomenon, the focus of this paper on the link between banks’ gender
diversity and sustainable lending appears to be particularly timely.
Our results indicate that banks with more gender-diverse boards provide less credit tomore polluting companies. This inverse relationship between bank lending volumes and GHG
emission intensities for boards with more female directors is confirmed also when we differentiate
among Scope 1, 2 and 3 GHG emissions. In addition, we also show that female directorspecific characteristics matter for lending behavior toward less polluting firms as better-educateddirectors grant lower credit volumes to polluting firms. Finally, we document that the “greening”
effect associated with female members in banks’ boardrooms is stronger in countries with more
female climate-oriented politicians. These results are robust when we control for potential
endogeneity concerns, such as sorting effect and sample selection biases. Given that banks moresocially responsible may be more likely to hire female directors in their boards, compared toother banks, and women may self-select into banks that are per se more socially responsible thanothers, we employ an instrumental variable (IV) approach to extract the exogenous components
from the percentage of female directors. Moreover, we employ an additional loan-level dataset,
which covers the period 2014-2018, to evaluate the robustness of our main findings when wecompare bank lending to less polluting firms in the case of a transition from a male-to-female
director within the board.
Our findings hold important implications for both regulators and policymakers. We underline
the crucial role played by banks in potentially driving the transition toward a greener economy.
Furthermore, we identify a central contribution of female presence on boards in shaping banks’lending strategies in support of less polluting firms, thereby confirming the beneficial effects ofmore gender-diverse decision-making groups on firms’ outcomes and, in a wider perspective, on
the global economy.
As a major channel of credit to the real economy, banks have the potential to play a pivotal
role in the global effort to promote green(er) projects and an effective shift toward a low-carbon
economy. In order to meet the climate targets on time, most of the transition will likely befunded by the private financial sector, including banks (De Haas and Popov, 2019). Moreover,

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increasing pressure from policymakers, investors and customers could significantly influencebanks’ lending and investing activities.4 At the same time, regulatory constraints, in terms ofincreasing capital requirements, associated with the higher risk of green energy projects (Hainet al., 2018; Brunnermeier and Landau, 2020), compared to fuel-based energy options, as well
as lower returns might hinder banks’ strategy to finance less polluting firms.5 In addition,banks need to identify - and effectively manage - emerging risks associated with climate change.
In particular, two broad categories of climate-related financial risks have been recognized: (i)the transition risks, which relate to the process of adjustments toward a low-carbon economy
and are posed, among others, by rapid (unexpected) changes in relevant policies that adversely
affect the value of financial assets and liabilities; and (ii) the physical risks, which relate to theeconomic losses arising from extreme climate-related events able to erode the value of financial
assets and/or increase that of liabilities.
A bank’s climate strategy and the commitment to align to the global sustainability agenda
strongly depend on the direction assumed by the board. Climate-related decisions often involve
sizeable investments with potentially complex and uncertain implications whose impact on different stakeholder groups can be varying (Walls et al., 2012). Therefore, a bank board mustbe sufficiently diverse to undertake effective decision-making through the sharing of a broader
range of experiences and opinions, which ensure the representation of distinct interest groups,
including financial and non-financial stakeholders. The existing literature has reported empiricalevidence on the significance of board diversity for governance outcomes, which in turn influencefinancial, social and reputational dimensions (Adams and Ferreira, 2009; Bernile et al., 2018).
Related literature. Our study informs several strands of the literature. First, we contributeto the emerging body of research that analyses the role played by the financial sector in decarbonizing the global economy, thereby addressing climate change (De Haas and Popov, 2019;
Mesonnier, 2019; Degryse et al., 2021; Reghezza et al., 2021). We add to existing literature on
gender diversity in the boardroom and corporate outcomes (Adams and Ferreira, 2009; Bernile
4
In this respect, it is worth mentioning that both the French and UK bank regulators have started to conduct
stress tests that account for climate-related risks. In 2021, the European Banking Authority (EBA) has conducted
an EU-wide pilot exercise on a sample of 29 volunteer banks from 10 countries. In the same year, the European
Central Bank (ECB) has conducted an economy-wide climate stress test on both firms and banks in the European
Union (EU), with a horizon of 30 years into the future.
5
In June 2022, the Basel Committee on Banking Supervision (BCBS) has published principles for the effective
management and supervision of climate-related financial risks. On disclosure measures, the Committee is exploring
the use of the Pillar 3 framework to promote a common disclosure baseline for climate-related financial risks.

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et al., 2018) with a specific focus on the banking industry. We provide novel evidence of the
impact of a greater female representation on the banks’ environmental decision-making process.
In this respect, we shed some light on the debated relationship between gender, climate change
and sustainable development (UNEP, 2016; Collins, 2019). Further, we extend the relatively
unexplored and still limited literature that focuses on the environmental dimension with reference to the banking sector (Thompson and Cowton, 2004). More broadly, our evidence on therole of female directors in shaping banks’ sustainability ties in with the strand of the literaturethat explores gender differences in environmentalism (Mohai, 1992; Dietz et al., 2002).
Prior empirical evidence seems to overall agree on the fact that firms with a greater female
representation on their boards tend to have - other things being equal - better financial performance (Liu et al., 2014; Post and Byron, 2015), improved firm value (Gul et al., 2011; Kim
and Starks, 2016) and greater governance quality and disclosure (Adams and Ferreira, 2009).6
The dynamics according to which women in the boardroom can add value are explained bysociological and physiological theories (Cumming et al., 2015). Both socialization and gendersocialization perspectives support the evidence of a positive impact of female directors on corporate social responsibility (CSR) because of women’s lower likelihood, compared to men, todamage the environment and their greater concerns about ethical issues (Kennedy and Kray,
2014). The social role theory (Eagly, 1987) suggests that women and men behave on the basisof stereotypes and beliefs, depending on the social role they hold. In particular, across cultures,
women are seen to be more community-minded than men and characterized by traits such as
empathy, caring and remarkable concern for others (Dobbins, 1985; Eagly and Karau, 1991;
Fondas, 1997). In this respect, women appear to be more socially oriented than their malepeers, thereby likely to be more sensitive to environmental issues. In addition, socialization cancontribute to shaping the underlying value structures, with women displaying more pronounced
beliefs about consequences for themselves, the others and the biosphere (Stern et al., 1994).
Female directors reveal a stronger orientation toward CSR, compared to male directors whoare, instead, more focused on economic performance (Ibrahim and Angelidis, 1994). Moreover,by bringing different perspectives to the table and by adopting a more participative leadershipstyle, women on boards might facilitate conversations and decisions on CSR-related tasks, being
better able to manage the relationships with various stakeholder groups (Eagly et al., 2003).
6

However, some studies point to a negative relationship between board gender diversity and firm performance/value (e.g. Adams and Ferreira, 2009; Ahern and Dittmar, 2012).

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A growing body of the literature has linked gender representation on corporate boards tospecific value-enhancing business strategies, such as greater firm innovation (Griffin et al., 2021)
and initiatives in the sphere of CSR (McGuiness et al., 2017). In recent contributions, gender
diversity has been associated with reduced cases of accounting misreporting (Garcı́a Lara et al.,2017), environmental violations (Liu, 2018) and misconduct sanctions (Arnaboldi et al., 2021).
Limited is the evidence with respect to the banking industry. Existing studies have mainly
focused on the link between gender diversity and risk-taking (Berger et al., 2014; Cardillo et al.,2020). Furthermore, recent literature investigates the link between the financial sector activity
and climate change, from the perspective of a transition to a low-carbon economy (De Haas andPopov, 2019; Reghezza et al., 2021). A number of contributions focus on the way central banks
worldwide can effectively adopt climate policies in order to meet climate targets, as well as theimplications of climate change in terms of financial stability and monetary policy (Batten et al.,2016; Campiglio et al., 2018; Bolton et al., 2020; FSB, 2020; Bernardini et al., 2021). However,to the best of our knowledge, there is a void in the existing literature on the role played bygreater gender diversity on banks’ boards as a factor with the potential to influence lending
strategies in favor of less polluting firms.
We differentiate from the extant literature by drawing on the high granularity of our data.
Recent studies on bank lending and corporate pollution mostly rely on data on large exposures(Reghezza et al., 2021) or syndicated loans (Delis et al., 2018; Degryse et al., 2021). In ourpaper, by using detailed loan-level data from the AnaCredit dataset and very granular fixedeffects, i.e. firm or industry-location-size (ILS) fixed-effects, we are able to disentangle credit
supply effects from demand effects and effectively capture cross-sectional demand of bank credit
for all the firms in our sample. While multiple bank relationships and firm fixed-effects allow
us to grasp the heterogeneity in credit demand across firms, we also include single bank-lending
relationships via ILS fixed-effects. This is essential in order to obtain a complete representation offirms’ credit demand as in most countries single-bank relationships represent a large share of theuniverse of borrowing firms (Ongena and Smith, 2001; Kysucki and Norden, 2016). Furthermore,compared to existing studies on single countries (Mesonnier, 2019; Faiella and Lavecchia, 2020),
by employing a European-wide credit register, we are able to exploit full heterogeneity acrosscountries with different national settings. We can, this way, benefit from a large variation in
cultural and institutional elements and investigate peculiarities in the “gender-green-lending”

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nexus of banks located in northern and southern euro area economies and in countries that
adopted legislative board gender quotas. Lastly, we rely on detailed corporate-level data on
GHG emissions, also distinguishing between Scope 1, 2 and 3 emissions.7 In this respect, anddifferently from related contributions (Delis et al., 2018; De Haas and Popov, 2019), we consider
the entire spectrum of GHG emissions and not only carbon dioxide (CO2) emissions. Following
this approach, we are able to draw inference on wider industry coverage and include economic
sectors that are commonly omitted, such as the agricultural one. In addition, by using firmspecific emissions data, we can conduct a very comprehensive analysis without relying on external
proxies for sustainability, such as the Environmental, Social and Governance (ESG) ratings.
The remainder of the paper is organized as follows. Section 2 develops the hypothesis central
to our study. Section 3 presents the data and methodology. Section 4 discusses the baseline
results. Section 5 provides additional analyses to explain the “gender-green-lending” nexus.
Section 6 presents the robustness tests. Section 7 concludes the paper and discusses the main
policy implications.

2

Key testable hypothesis: Board gender diversity and green(er)bank lending
We believe that gender diversity in the boardroom has the potential to influence banks’ lend-

ing behavior in favor of less polluting firms, thereby enhancing decision-making related to theenvironmental sphere. This prediction stems from prior literature, which suggests that female
directors, compared to their male peers, demonstrate a lower propensity to damage the environment, greater concerns about ethical issues and a stronger orientation toward the CSR firm’s
dimension (Liu, 2018). They are also more likely to undertake actions to mitigate perceived
risks (Schubert et al., 1999; Carter et al., 2003; Adams and Ferreira, 2009; Huang and Kisgen,2013). The evidence of a relatively lower risk appetite of women compared to men, as well
as differences in terms of judgment, are also documented in psychology and business studies(Lundeberg et al., 1994; Byrnes et al., 1999; Barber and Odean, 2001). Previous research findsthat women: (i) invest in less risky assets (Sundén and Surette, 1998); (ii) are more conservative
7

Scope 1 covers direct emissions from owned or controlled sources. Scope 2 covers indirect emissions fromthe generation of purchased electricity, steam, heating and cooling consumes by the reporting company. Scope 3
includes all other indirect emissions that occur in a company’s value chain. More details are provided in Section
3.1.

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in simulated gambles (Levin et al., 1988); (iii) and are less prone to assume risks than men(Prince, 1993). With reference to the banking industry, a number of contributions explore how
boardroom gender diversity affects bank risk-taking (Berger et al., 2014; Palvia et al., 2015;
Cardillo et al., 2020). Gender socialization and ethical theories lend support to the vision thatfemale directors might present a greater propensity to address environmental concerns, including global warming. In general, women appear more community-minded, altruistic and caring
than men (Gilligan, 1977; Eagly and Crowley, 1986) and, in relation to the environment, they
are reported to show more concern and assume more pro-environmental behaviors than males.
They are more likely to assume positions that prevent environmental risks with the potential toharm communities. Further, based on social psychological literature, gender differences in value
orientations help explain women’s greater attention to the environment. Stern et al. (1993),
based on the norm-activation model of altruism by Schwartz (1977), propose a model to explain
pro-environmental behavior and test differences between men and women. The authors findthat women hold stronger beliefs than male peers about the detrimental consequences of poor
environmental conditions for others, themselves and the biosphere and that these beliefs envisaged a more pro-environmental attitude. Stern and Dietz’s (1994) study reinforces the viewthat women have a stronger perception of the negative effects of environmental degradation.
These findings are aligned with the feminist theory that tends to suggest that women are moreconcerned about environmental issues than men (Griffin, 1978; Diamond and Orenstein, 1990).
Zelezny et al. (2000) find that women display more pronounced environmental attitudes andbehaviors than men and report higher levels of socialization to be other-oriented and socially responsible. Dietz et al. (2002)’s work underpins the importance of gender differences in altruism
as a basis for differences in environmentalism.
On a global scale, women undertake most of the decisions affecting households’ energy
consumption and these decisions in many cases appear to be mindful and prudent (Collins,2019). Women tend to have a reduced carbon footprint compared to men (Kanyama et al.,2021) and, most likely, women in leadership positions would exploit their power to select moresustainable options. In this regard, Atif et al. (2021) find a beneficial impact of the board gender
diversity on the consumption of renewable energy. These considerations, therefore, support theintuition of a positive association between a greater female representation in the boardroom
and a bank’s environmental policy and related lending strategy. In this respect, by enhancing

ECB Working Paper Series No 2741 / October 202210the collective decision-making process, board gender diversity reveals a key role in driving thelending policies of a bank.
Fighting climate change is costly but essential. On the other hand, fighting climate change
represents an opportunity of a “lifetime” (or “for a lifetime”) that the financial sector is keen
to exploit (Carney, 2020).8 By playing a pivotal role in modern financial systems, banks cansignificantly contribute to a faster transition to a carbon-neutral economy. Sustainable lending
decisions, in support of less polluting firms and industrial sectors, represent a core element of awider “greening” strategy. A bank’s climate strategy and the commitment to align its lending
portfolios to the expectations set at the international level strongly rely on the trajectory followedby the board. Board diversity can entail both benefits and costs. Diversity in a management
body can foster independence of opinions and the freedom to challenge core decisions (Westphal
and Bednar, 2005), thereby enhancing strategic decision-making (Bantel and Jackson, 1989) andeffective governance (Hillman and Dalziel, 2003). Furthermore, a sufficient degree of diversity inthe boardroom leads to a higher set of information, unique skills and expertise with the potential
to improve group decision-making (Kim and Starks, 2016).
To sum up, we contend that bank female directors are more likely to care about long-termsocietal issues, including climate change, and should therefore be more likely to promote greener
lending than their male counterparts. Furthermore, greater gender diversity is expected todrive enhanced board dynamics and decision-making in a pro-environmental vision. Hence, we
formulate the central hypothesis of our study:
H1: Banks with a greater female representation in the boardroom lend less to more polluting
firms.

3

Data and methodology

3.1

Data and sample construction

To test our hypothesis, we construct a comprehensive dataset from multiple sources. Specifically, we gather: (i) loan-level information from the AnaCredit database; (ii) firm-level dataon GHG emissions from Urgentem; (iii) banks’ corporate governance variables from Refinitiv
8

Refer to www.weforum.org/videos/christine-lagarde-says-fighting-climate-change-is-costly.

ECB Working Paper Series No 2741 / October 202211

Eikon (hereafter, Eikon); (iv) bank balance-sheet characteristics at the consolidated level fromthe ECB’s supervisory database; (v) bank ESG indicators from Eikon; (vi) firm-specific characteristics from Orbis Amadeus; and (vii) cultural/institutional variables from the European
Institute for Gender Equality (EIGE). Given the relatively limited time coverage of the AnaCredit database, for which the data collection started in September 2018, and the likely effectof the Covid-19 pandemic on banks’ lending patterns, in this study we focus on the year 2019.
Bank balance-sheet and firm-specific characteristics are taken at December 2018, given theirpotential to influence subsequent lending decisions. Furthermore, the construction of our banksample is driven by the availability of corporate governance information. In the end, we areable to consider the lending behavior of 52 banks, accounting for about 60% of banking total
assets in the euro area. The high granularity of our loan-level dataset, which covers almost amillion loans, enables us to fully exploit the heterogeneity in our sample, thereby enhancing thereliability of our inferences. In this respect, the use of confidential AnaCredit data has allowed
for a finer and more robust analysis of banks’ lending exposures.9
To classify our sample of loans into different industrial sectors, we follow the Statistical
Classification of Economic Activities in the European Community (NACE Rev. 2) codes.10 In
particular, we exploit the whole granularity of the classification, i.e. from NACE-1 to 4-digits,
based on which we are able to identify 931 industrial sectors within our dataset. Climatewarming data, measured in tonnes of GHG emissions, are gathered from Urgentem. The Urgentem Carbon Dataset covers the full spectrum of Scope 1, 2 and 3 emissions reported by morethan 6,000 global companies at a consolidated level. In addition, emissions for all other companies are inferred by Urgentem via estimation models based on industry and intensity category.
In our analysis, we use the relative GHG emissions and consider all three ”scopes”.11 In thespirit of Bolton and Kacpercyk (2021), the relative GHG emissions, which measure the carbon
intensity of a company, are expressed as tonnes of GHG equivalent divided by the company’s
9
The AnaCredit database, based on the Regulation ECB/2016/13 and developed by the ESCB, provides
monthly loan-by-loan information on credit granted by credit institutions to firms and other legal entities, coveringboth euro area and European, non-euro area, countries. For further details, refer to www.ecb.europa.eu/stats/money credit banking/anacredit/html/index.en.html.
10
NACE Rev. 2 classification is based on a hierarchical structure, which consists of first-level sections (alphabetical code), second-level divisions (2-digit numerical code), third-level groups (3-digit numerical code) and
fourth-level classes (4-digit numerical code). For additional information, please refer to https://ec.europa.eu/
eurostat/documents/3859598/5902521/KS-RA-07-015-EN.PDF
11
Based on the 1997 Kyoto Protocol, seven greenhouse gases are considered. These are (i) carbon dioxide (CO2);(ii) methane (CH4); (iii) nitrous oxide (N2O); (iv) hydrofluorocarbons (HFCs); (v) perfluorocarbons (PCFs); (vi)sulphur hexafluoride (SF6); and (vii) nitrogen trifluoride (NF3).

ECB Working Paper Series No 2741 / October 202212

revenues, expressed in EUR millions. Based on the Greenhouse Gas Protocol, GHG emissions
are classified into three groups (“scopes”), depending on the source. Scope 1 accounts for directemissions that occur from sources owned or controlled by a firm. Scope 2 covers indirect emissions associated with the purchase of electricity, steam, heating and cooling consumed by a firm.
Scope 3 comprises all other indirect emissions generated in a firm’s value chain. Table 1, among
others, reports the summary statistics for our GHG emission variables in 2019 (Panel B). Withreference to the entire spectrum of scopes (i.e. from 1 to 3), the average firm in our sample
produced 772.96 GHG relative emissions. The related distribution spans from a minimum of
126.11 GHG tonnes/millions to a maximum of 5,395.31 GHG tonnes/millions and the dispersion
around the mean is relatively large (the standard deviation is 888.31 tonnes/millions).
The decision to consider firm-level GHG emission intensities, rather than sectoral or country
breakdowns, is motivated by the substantial heterogeneity in the level of pollution across firms
within each industrial sector and country. Figure 1 shows the difference between the median
GHG emission intensity within a region (left-hand chart) and the firm-level GHG emission
intensities (right-hand chart), with the latter representing the measure used in our analysis.
As shown, the aggregate GHG emission intensity is relatively homogeneous in each region.
By contrast, firm-level GHG emissions allow capturing a greater heterogeneity in the level ofpollution caused by firms. Consequently, we add to several existing studies employing sectorlevel GHG emissions (e.g. Delis et al., 2018; Mesonnier, 2019; Faiella and Lavecchia, 2020;
Degryse et al., 2020) by combining the use of sector-level information with data on the pollution
propensities of individual firms. This enables us to also account for production processes andtechnologies, which in turn influence the level of pollution generated by each firm.
[Insert Figure 1 Here]
Panel A of Table 1 provides the summary statistics for our dependent variable. Lending, as
per the AnaCredit manual, is the outstanding amount indebted by a debtor to a creditor. Ourdata covers a large number of loans, which is reflected in a relatively large variation in the sizeof the loans. The average loan size is €648,453 and the median is €150.707. The distribution
ranges from a minimum of €25,000 to a maximum of €10,500,000.
Panel C of Table 1 reports the summary statistics for the bank corporate governance variables.

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Relevant information is obtained from Eikon. The percentage of women on boards (Female
on board) ranges between 0 and 57.14%. The mean value, 32.94%, is not distant from themedian of 33.33%. In our analysis, we use a dummy variable (Board female) which takes thevalue 1 if the percentage of female members on the board is above the 75th percentile, and0 otherwise.12 We anticipate an inverse relationship between our gender variables and theamount of bank credit toward less polluting firms (“greener” firms), as motivated in Section 2.
Gender diversity in the boardroom might enhance the shared perception of green values amongindividuals, as well as fostering non-financial outcomes and thereby building environmental
strengths. A larger proportion of female directors should, ultimately, exercise more pressure on
environmental issues, thus resulting in an increased commitment toward the environment whenshaping lending decisions.
Besides the presence of female directors on the board, we account for board size (Board size),
the tenure of the board members (Board tenure), the share of independent board members(Ind board) and whether the bank has in place a system of compensation for senior executives
that is linked to CSR objectives (CSR comp). Board size is computed as the number of directors elected to the board, expressed as a logarithm. The average board size in our sample
is 15.19 directors as represented by a logarithm of 2.70. de Villiers et al. (2011) document a
positive relationship between board size and firm environmental performance, seen as a company’s capability to strategically manage its impact on the environment. The authors argue
that larger boards bring together different backgrounds, experiences and knowledge. This, inturn, increases the probability of having experts in environmental fields among the members
which can contribute to the adoption of effective green practices. A positive association between board size and environmental performance is also revealed in Walls et al. (2012) andLiu (2018). On the other hand, studies by Eisenberg et al. (1998) and Boone et al. (2007)
show that larger boards result in a lower degree of efficiency due to coordination and free-riding
issues, which can result in underestimating environmental concerns. Board tenure is employed
to control for the directors’ experience. The variable is measured as the average number of years
that each board member has been on the board. Based on the resource dependence theory, agreater human and social capital, which also factors in the length of the directorship term, can
12

The approach of using dummy variables to identify firms’ boards with a higher/lower female representation
is widely adopted in the corporate governance literature. See, for instance, Gul et al. (2011) and Liu (2018),
amongst others.

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be conducive to better addressing environmental issues, thereby influencing environmental performance (Hillman and Dalziel, 2003). Ind board captures the percentage of independent board
members. According to de Villiers et al. (2011), greater board independence is conducive tobetter corporate environmental performance. Furthermore, a larger percentage of independent
board members is commonly associated with higher monitoring incentives (Garcı́a Lara et al.,2017). CSR comp accounts for the system of executives’ compensation, which is considered
to play an important role in fostering effective environmental practices. As argued in Berrone
and Gomez-Mejia (2009), structuring the managers’ pay around environmental performance canpositively affect a firm, producing desirable outcomes for shareholders, managers and society.
Panel D of Table 1 provides the summary statistics for the bank-level controls, commonly
employed in the banking literature. Confidential information is collected from the ECB’s supervisory dataset. On average, banks have total assets (TotAss) of €647 billion. The medianvalue is €639 billion, reflecting a relatively symmetrical distribution. The ratio of total deposits
to total liabilities (Dep tl), a proxy for the stability of banks’ funding structure, is on average
76.79%. The ratio of non-performing loans to gross loans (NPL r), which captures a bank’s
asset quality, presents a mean value of 7.07%. The average profitability of the banks in our
sample, measured by the ratio of the net income to total assets (ROA), ranges between -0.49%
and 1.01%. The ratio of cash and cash equivalents to total assets (Cash ta), capturing banks’liquidity, is on average 6.99%. Banks’ business model is proxied by the ratio of fees and commissions to operating income (Fee opInc). This indicator for business diversification with respect totraditional intermediation activity ranges between 15.82% and 56.85%, presenting a relatively
large dispersion around the mean (9.32%). The average ratio of common equity tier 1 ratio(CET1 r) is 12.74%, thus reflecting the relatively high soundness of banks within our sample.
The ESGscore ranges between 37.7 and 94.11 with an average value of 75.8.
Panel E of Table 1 reports the summary statistics for the firm-level characteristics, collectedfrom Orbis Amadeus. The average size of the firm included in our sample (Firm size) is €9.56
million. The average ratio of cash holdings to current liabilities (Firm cash), a proxy for the firm
degree of short-term liquidity, is 22.16%, while the median is 9.23%. The average debt ratio,
measured as the sum of current liabilities and non-current liabilities to total assets (Firm debt),
is 73.00%. The median is 74.24%, indicating a relatively symmetrical distribution. The ratio of
earnings before interest and taxes to total assets (Firm ROA), a proxy for firm profitability, is

ECB Working Paper Series No 2741 / October 202215

on average 3.81%. The Firm WC indicator (working capital to total assets) spans from -22.93%
and 85.06%, with an average value of 24.87%. Lastly, the average Firm gearing, i.e. interest
paid to earnings before interest and taxes, is 22.67%.
Panel F of Table 1 reports summary statistics for the cultural/institutional variables collectedfrom the EIGE database. First, we assess whether cultural elements play a significant role in
influencing a bank’s lending strategy. To this end, we construct a dummy variable (South) thatassumes value 1 if the considered bank is headquartered in a southern euro area country (i.e.
Greece, Italy, Portugal and Spain), and 0 otherwise. Second, we consider whether the country
where the bank is located has adopted binding board gender quotas to promote gender balancein the top decision-making bodies. We, therefore, construct a dummy variable (Quotas), whichtakes value 1 in case a bank is located in a country with legislative gender quotas (i.e. Austria,
Belgium, France, Germany, Italy and Portugal), and 0 otherwise.13 Lastly, we consider the
potential influence of female climate-oriented political representation in national parliaments.
Specifically, we construct a dummy variable (Cpol) that takes value 1 in case a bank is locatedin a country with a proportion of female ministers and government executives dealing withenvironment and climate change above 50%, 0 otherwise.
[Insert Table 1 Here]
Table 2 describes the selected variables and provides the labels, related sources and definitions.
[Insert Table 2 Here]

3.2

Methodology
In order to explore whether and to what extent the presence of female members on boards

influence banks’ lending toward firms with lower GHG emissions, we employ loan-level fixedeffects regressions on our cross-section as they allow us to effectively disentangle credit supply
from credit demand shifts.
For identification purposes, we follow two distinct approaches. First, and in the spirit
13

The number of banks that fit in the South category is 25, while those in the category of Quotas is 30. In order
to construct the Quotas variable, we only consider countries that adopted binding gender quotas, excluding thosethat implemented softer measures. For further details, refer to eige.europa.eu/gender-statistics/dgs/data-talks/legislative-quotas-can-be-strong-drivers-gender-balance-boardrooms.

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of Khwaja and Mian (2008), we exploit multiple bank-firm relationships to control for firm
credit demand, hence firms that borrow from multiple banks and within-firm comparisons acrossbanks with more/less female representation on board. However, one shortcoming of the Khwaja
and Mian (2008) econometric identification strategy is represented by the exclusion of singlebank relationships, which are absorbed by firm fixed-effects. Since the majority of single-bankrelationships involve small and medium enterprises (SMEs), which are predominant in most
European countries, we follow the approach by Popov and Van Horen (2015), Acharya et al.
(2019) and Degryse et al. (2019) and construct ILS fixed-effects. The industry clusters are
based on 4-digit NACE codes. The location clusters are based on 5-digit postal codes and thesize clusters are built on quartiles of firms’ total assets. The inclusion of ILS fixed-effects allows
us to retain more than 300,000 additional single bank-firm relationships in our estimation. The
baseline econometric equation is specified as follows:

Lendingbj = αj,ILS + βBoard f emaleb + δGHGemissionsj +γBoard f emaleb ∗ GHGemissionsj + θXb,t−1 + τ Tb,t−1 + υZj,t−1 + bj (1)where b indicates the bank and j the firm, respectively. The reference year, t, is 2019. Ourdependent variable, Lending, is the logarithm of the outstanding amount indebted by a debtor
j to a bank b. α alternately indicates firm (j) or industry-location-size (ILS) fixed-effects,
employed to control for the heterogeneity of credit demand across firms. Board female is adummy variable that takes the value 1 if the percentage of female members on the board ofbank b at the end of 2018 is above the 75th percentile, 0 otherwise. The 75th percentile ofthe distribution corresponds to 36.82% of female directors on board and in our sample, 18(34) banks have 36.82% or more (or less) of the seats in the boardroom assigned to female
members. Selecting this threshold enables us to effectively split our sample into banks with asizeable number of female directors on the board and those with a limited representation, also
accounting for the concept of a ”consistent minority” able to make the difference in decisionmaking as suggested by the literature on the critical mass theory.14 GHGemissions is a variable
14

Based on the literature on the critical mass theory, at least three women (namely, a consistent minority)
must be seated on a firm’s board in order to be able to exert power over key decisions and add value (Kramer
et al., 2006; Torchia et al., 2011; Schwartz-Ziv, 2017; Arnaboldi et al., 2021). This matches with the choice toconstruct our key gender diversity variable as a dummy indicator taking the value 1 if the percentage of women
on banks’ boards is above 36.8% (i.e. the 75th percentile), and 0 otherwise (also considering the board average
size of 15.2 directors for our sample). This indicator is able to capture the fact that female directors tend to be

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17

that captures the emission of climate-warming gases of firm j. We employ the GHG relativeemissions, measured as tonnes over revenues (EUR millions), and separately account for (i) total
emissions, i.e. Scope 1, 2 and 3 emissions (GHGtot); (ii) Scope 1 and 2 emissions (GHG12 );and (iii) Scope 3 emissions (GHG3 ). Board female*GHGemissions, a central variable of ouranalysis, is included to test whether banks’ lending behavior toward more polluting vis-à-vis
less polluting firms is influenced by a greater/lower representation of women in the boardroom(i.e. above/below the 75th percentile).
X is a vector of lagged (end of 2018) bank corporate governance characteristics, including
Board size (i.e. the logarithm of the number of directors elected to the board), Board tenure(i.e. the average number of years that each board member has been on the board), Ind board(i.e. the percentage of independent board members) and CSR comp (i.e. a dummy to accountfor whether the compensation of senior executives is linked to CSR objectives).
T is a vector of lagged (end of 2018) bank-level controls, which comprises bank size (TotAss),measured by the logarithm of total assets, and a number of relevant ratios, such as (i) deposits tototal liabilities (Dep tl); (ii) NPLs to gross loans (NPL r); (iii) net income to total assets (ROA);(iv) cash and equivalents to total assets (Cash ta); (v) fees and commissions to operating income(Fee opInc); and (vi) CET1 capital to risk-weighted assets (CET1 r). Furthermore, we include(i) the environmental, social and governance score (ESGscore); (ii) the number of ESG-related
controversies reported in the press (ESGcontroversies); and (iii) a dummy variable equal to 1 if
a bank engages with its stakeholders, and 0 otherwise (Stakeholders).15
Z is a vector of lagged (end of 2018) firm-level characteristics that include: (i) Firm size,
measured as the logarithm of firm total assets; (ii) the ratio of cash holdings to current liabilities(Firm cash); (iii) current liabilities plus non-current liabilities to total assets (Firm debt); (iv)the ratio of earnings before interest and taxes to total assets (Firm ROA); (v) working capital tototal assets (Firm WC ); (vi) interest paid to earnings before interest and taxes (Firm gearing);and (vii) the bank-firm level amount of loan impairments identified and recognized by the bankmore influential if they attain a critical mass of three or more. An analysis of this critical threshold is reported
in Table A1 in the Appendix.
15
We include the dummy Stakeholders to account for external governance pressures as a potential substitute(or complement) for internal forces driving banks to adopt more sustainable lending practices. The indicator iscollected from Refinitiv Eikon and is based on the following information: (i) whether the bank engages with its
stakeholders; (ii) whether the bank is involving the stakeholders in its decision-making process; and (iii) whether
the bank has an established two-way communication between the company and its various stakeholders.

ECB Working Paper Series No 2741 / October 202218

over the overall loan amount granted to the firm (Loan provisions). Robust standard errors(ε bj) are two-way clustered at the bank-firm level.

4

Empirical findings
This section discusses the empirical results for the cross-sectional regression based on the

Equation (1). Tables 3 reports the main findings of our analysis, with Lending as the dependent
variable and, respectively, the inclusion of (i) the total amount of GHG emissions (columns 1
and 4); (ii) Scope 1 and 2 GHG emissions (columns 2 and 5); and (iii) Scope 3 GHG emissions(columns 3 and 6). The rationale for separately considering the different scopes is to increase
the degree of detail in investigating the lending behavior of banks in our sample toward lesspolluting. Columns 1, 2 and 3 report the estimates with the inclusion of firm fixed-effects,
whilst columns 4, 5 and 6 are those with ILS fixed-effects. Bank and firm two-way clustering
technique is adopted to adjust the robust standard errors. The model specifications control for
credit demand, bank corporate governance factors and other relevant bank- and firm-specificcharacteristics.

4.1

Board gender diversity, bank lending and firms’ GHG total emissions
Table 3 (columns 1 and 4) reports the results of our regression analysis including all the

spectrum of firms’ GHG emission intensities. The results are interesting for a number of reasons.
First, the single coefficient on the GHGtot variable for the ILS fixed-effects specification (column
4) indicates a positive and statistically significant (at the 1% level) relationship between firms’
GHG emission intensities and bank lending. This evidence reflects a general stylized fact thatfirms in more polluting industrial sectors tend to be larger firms with larger investment needs
and tend, therefore, to have more funding needs (see Figure A1 reported in the Appendix for a
simple graphical representation). However, other things being equal, banks with a % of female
directors on board below the 75th percentile of the distribution display larger lending volumes
to more polluting firms. Second, the coefficient on the interaction Board female*GHGTot isnegative and statistically significant at the 5% level for the firm fixed-effects regression (column
1) and at the 1% level for the ILS fixed-effect regression (column 4), suggesting that banks witha more female-oriented board tend to reduce their lending volumes as the level of firms’ total
GHG emissions increases, compared to the other group of banks.

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19

To investigate whether the effect on bank lending is economically meaningful, we plot in
Figure 2 the estimated relationship between GHG emissions (x-axis) and the log of lending
volumes (y-axis). The coefficients are taken from the specification in column 4 of Table 3. In
particular, the left-hand chart of Figure 2 reports the estimated log of lending volumes at different levels of GHG emissions between the categories of banks with a % of female directors below
and above the 75th percentile, whilst the right-hand chart reports the estimated difference inlending volumes at different levels of GHG emissions between the two groups of banks. For
the selection of the GHG levels, we rely on the descriptive statistics and select the 5th (245
GHG tonnes/revenues), 25th (300 GHG tonnes/revenues), 50th (564 GHG tonnes/revenues),
75th (847 GHG tonnes/revenues) and 95th (1,386 GHG tonnes/revenues) percentiles of the distribution. As shown in the left-hand chart of Figure 2, lending volumes to firms with a levelof GHG emissions equal to and/or below 847 tonnes/revenues (75th percentile) are not statistically different for the two groups of banks at the 95% confidence level (the related confidence
intervals overlap). This evidence suggests that banks with a greater female representation in
the boardroom tend to grant lending volumes to low and mid-polluting firms comparable tothose granted by banks with a greater male representation. However, we find that banks withmore female board members lend less to firms with a level of GHG emissions equal to and/or
above 1,386 tonnes/revenues (95th percentile), i.e. highly polluting firms. As an illustration of
the different effects in the two groups of banks, in the right-hand chart of Figure 2, banks with
an above-75th percentile of female directors display about 10% lower lending volumes towardfirms with 1,386 tonnes (last quartile) of relative GHG emissions compared to the other groupof banks. Our findings point to a key role played by female directors in shaping banks’ lending
strategies, on the one hand, and greater consideration for the environmental dimension withina more gender-diverse board, on the other hand.
[Insert Figure 2 Here]Among bank-specific controls (columns 1-6), we document a negative association betweenbank ROA and lending volumes. We also find a negative and statistically significant (at the
1% level) relationship between bank liquidity (Cash ta) and bank lending. ESGcontroversies
displays a positive and statistically significant association with our dependent variable.
Regarding firm-specific controls, significant associations are documented for size, liquidity,

ECB Working Paper Series No 2741 / October 202220

debt, profitability and gearing proxies (columns 4-6). Arguably, larger and more profitable firms
tend to borrow more than smaller firms, which is reflected in the positive and highly statistically significant relationship between our dependent variable and Firm size and Firm ROA,
respectively. Reasonably, more leveraged firms (as captured by Firm debt and Firm gearing)are shown to receive more bank lending. Working capital (Firm WC ) and Loan provisions do
not appear to have a significant impact on the volumes of funds granted by banks.
Among banks’ corporate governance factors (columns 1 to 6), our findings reveal a positiveand economically significant impact of board size on the volumes of bank lending. Finally, we
find that the coefficient on CSR comp is negative and highly statistically significant (at the 1%
level), suggesting that, ceteris paribus, banks that link their senior executives’ compensation to
CSR objectives grant lower volumes of credit.

4.2

Board gender diversity, bank lending and firms’ Scope 1&2 GHG emissions
Columns 2 and 5 of Table 3 report the results for the regression analysis that accounts

only for firms’ Scope 1 and 2 GHG emissions (GHG12). Scope 1 emissions are those caused
directly by a firm’s activities, while Scope 2 emissions include indirect emissions arising from afirm’s energy consumption. These two categories of emissions can be measured relatively easily,referring to the firm’s utility bills and fuel expenses.
Similarly to the results presented in columns 1 and 4, we find a positive and statisticallysignificant relationship between GHG12 and the logarithm of lending volumes (column 5), indicating that banks with a % of female directors below the 75th percentile lend more to firms
with higher levels of Scope 1 and 2 relative GHG emissions. The interaction terms are, once
again, negative and statistically significant at the 5% (column 2) and at the 1% levels (column5), suggesting that banks with more female directors on the board reduce their lending volumes
as GHG emissions increase, compared to the other banks.
Figure 3 (left-hand chart) compares the estimates of the log of lending volumes at different levels of Scope 1 and 2 relative GHG emissions for both banks with a % of female
directors above and below the 75th percentile. For the selection of the GHG levels, we relyon the descriptive statistics and select the 5th (10 GHG tonnes/revenues), 25th (14 GHG

ECB Working Paper Series No 2741 / October 202221

tonnes/revenues), 50th (27 GHG tonnes/revenues), 75th (52 GHG tonnes/revenues) and 95th
(232 GHG tonnes/revenues) percentiles of the related distribution. The right-hand chart reports the estimated difference in lending volumes at different levels of GHG relative emissions
for the two groups of banks. While lending volumes increase for banks with fewer female directors (solid blue line), this pattern is reversed for banks with more female members on theboard. For GHG values equal and/or below the 75th percentile (52 GHG tonnes/revenues), we
do not find a statistically significant difference between the two groups of banks (the confidence
intervals overlap). However, for GHG values equal and/or above the 95th percentile (232 GHG
tonnes/revenues) the difference is statistically significant, indicating that banks with a greater
female representation showcase lower lending volumes to more polluting firms. As displayedin the right-hand chart, institutions with an above 75th percentile of female directors display
12.5% lower lending volumes to firms with 232 tonnes of Scope 1 & 2 GHG emissions per million
revenues.
[Insert Figure 3 Here]

4.3

Board gender diversity, bank lending and firms’ Scope 3 GHG emissions
Columns 3 and 6 of Table 3 display the results for the regression analysis that accounts only

for firms’ Scope 3 GHG emissions (GHG3). This category, among the three, is the most difficult
to be measured, given that it covers indirect, value chain and product-related emissions (notcaptured in Scope 2) and includes both upstream and downstream emissions. Scope 3 emissions
often represent a corporate’s largest GHG impact (GHG Protocol) and should be carefully
tracked and assessed. Based on the Protocol, there are 15 categories of Scope 3 emissions andoften firms’ disclosure is limited to those deemed to be material. While the level of regulation
is increasing, there is still room for a certain degree of case-by-case interpretation. However,
the increased transparency in terms of reporting, fostered by the recommendations stemmingfrom international authorities, such as the Task Force on Climate-Related Financial Disclosure
set by the Financial Stability Board (FSB), contributes to enhancing banks’ awareness andcommitment to reducing the carbon emissions they fund.16
Our results confirm a beneficial effect associated with a larger proportion of female directors
in the boardroom on bank lending toward firms generating higher levels of climate-warming
16

See www.fsb-tcfd.org/.

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22

emissions. The relationship between GHG3 and our dependent variable (Lending) is positiveand statistically significant, reflecting a direct link between firms’ GHG emission intensities
and bank lending. The interaction term (Board female*GHG3 ) is negative and statisticallysignificant at the 1 and 5% levels, across all model specifications, suggesting that banks withmore female directors on the board reduce their lending volumes as GHG relative emissions
increase, compared to their peers. The graphical evidence in Figure 4 displays similarities withthat reported for all spectrums of GHG emissions (Figure 2). It appears that greater attention to
the environmental dimension associated with bank lending is paid by banks with more womenin leadership positions. From a wider perspective, this evidence assumes a specific relevanceif considering the contribution of climate-related risks, within the banking sector, in terms ofoverall financial stability.
[Insert Table 3 Here][Insert Figure 4 Here]

5

Inside the ”gender-green-lending” nexus
In this section, we discuss some additional analyses that explain the “gender-green-lending”

nexus. First, we try to understand whether there is a difference in banks’ lending behavior
depending on the country where the bank is located. In particular, we aim at evaluating if thebeneficial effect of a more gender-diverse board, in terms of lending toward less polluting firms, islinked to the geographical area where the bank operates. In this respect, different cultural factors
could potentially enhance or hinder the influence of female directors on greener banks’ lending
strategies. To test this hypothesis we enrich Equation (1), by including a dummy variable Souththat takes value 1 if the bank is located in a southern euro area country (i.e. Greece, Italy,
Portugal and Spain), and 0 otherwise. The rationale underlying this geographical selection liesin the historical predominance of the so-called “male-breadwinner” model in southern Europe(Gonzalez et al., 1999; Pfau-Effinger, 2004). Based on this theoretical model, there exists a
clear hierarchical and patriarchal division of labor and power within the nuclear family, where
only male members participate in the labor market, being the family providers, and women aremostly confined to the role of housewives.

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Second, we are interested in understanding how and to what extent the female climateoriented representation in the national political settings plays a relevant role in influencing
banks’ lending decisions in favor of less polluting firms. To test this hypothesis, we include in
Equation (1) a dummy variable Cpol equal to 1 if a bank is located in a country with a proportionof female ministries and government executives dealing with environment and climate change
above 50% and 0 otherwise. The politicians’ gender might have implications for national policy
outcomes, such as the provision of services in the health (Bhalotra and Clots-Figueras, 2014) or
education (Clots-Figueras, 2012) sphere, also including climate change initiatives. Mavisakalyan
and Tarvedi (2019), based on a cross-country analysis, argue that female political representation
positively contributes to the adoption of more stringent climate change actions, also leading tolower CO2 emissions.
Third, we aim to test whether the adoption of legislative gender quotas to promote thegender-equality in decision-making bodies has relevance to the “gender-green-lending” nexus
we identified in our baseline analysis. To this end, we construct and include in our crosssectional regression a dummy variable Quotas that takes value 1 if a bank is located in a country
with legislative gender quotas (Austria, Belgium, Germany, France, Italy and Portugal), and 0
otherwise. The significant under-representation of women in corporate boards worldwide isa timely topic of great interest to policymakers, practitioners and academics (Reding, 2012;
OECD, 2015; Terjesen and Sealy, 2016). Assessing the implications associated with the adoptionof gender quotas is a core part of the current debate on how to improve gender equality indecision-making positions. To date, the evidence is mixed. Ahern and Dittmar (2012) documenta negative impact of quotas on the value and performance of Norwegian firms.17 A similar
evidence is shown in Matsa and Miller (2013) and Greene et al. (2020). Other studies reveal
a relatively neutral impact of gender quotas on firms’ value (Eckbo et al., 2022) or a beneficial
impact in terms of the labor market for directors (Ferreira et al., 2019). Terjesen and Sealy(2016) argue that female directors positively impact social capital within the board, acting as
knowledge brokers between others. The related discussion also involves the banking industry(Cardillo et al., 2020).
17

In 2003, Norway was the first country in the world to introduce binding gender quotas for all publicly tradedand state-owned companies.

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5.1

The role of cultural factors
In columns 1 and 4 of Table 4, we report the results of the analysis including the Southdummy in Equation (1). The estimated coefficient on the double interaction term (Board female*
GHGTot) is negative and statistically significant in the ILS fixed-effects regression (column 4) although it loses statistical significance in the firm fixed-effects regression (column 1). However,when we turn our attention to the triple interaction term (Board female*GHGTot*South), we
can infer that the discussed inverse relationship between more gender-diverse boards and lending
toward less polluting firms does not hold for banks located in southern euro area countries. The
related coefficients are positive and, in the ILS fixed-effects specification, statistically significant
at the 10% level, suggesting that banks with more female directors on boards and located insouthern euro area countries lend more to more polluting firms, compared to banks located inother areas. This evidence might be explained by a predominance of the ”male-breadwinner”
model in southern Europe, which places women in the role of housewives, mostly excluded
from the labor market. This collective cultural approach would tend to undermine female
empowerment within the organization and in the top-level decision-making positions.

5.2

The role of female climate-oriented politicians

In columns 2 and 5 of Table 4, we report the results for the cross-sectional regression analysis,
based on Equation (1), with the inclusion of the Cpol dummy variable (equal to 1 if a bankis located in a country with more than 50% of female ministries and government executives
dealing with environment and climate change, and 0 otherwise). The triple interaction term
(Board female*GHGTot*Cpol) is negative (columns 2 and 5) and highly statistically significant.
This evidence suggests that banks with more female representation on board and that are locatedin countries with a share of female climate-oriented politicians above 50% tend, ceteris paribus,
to lend lower volumes of funds to more polluting firms. This result is in line with the literature
analysing the relationship between women’s political empowerment and CO2 emissions, which
points to a negative and statistically significant correlation between the number of ministerial
positions held by women and changes in CO2 emission (Ergas and York, 2012). Either becauseof the greener orientation of women, which could be crucial for successful environmental policymaking or because of the general effect that more gender equality at the political level might haveon the way people perceive the environment, a higher number of female ministers and climate-

ECB Working Paper Series No 2741 / October 202225

government executives contribute to promoting gender equality and environmental instances,which in turn eases the task of bank female directors in achieving better environmentally-related
results.

5.3

The role of gender quotas
In columns 3 and 6 of Table 4, we report the findings with the inclusion of the dummy

variable Quotas that takes value 1 if a bank is located in a country with legislative gender
quotas, and 0 otherwise. Both the double (Board female*GHGTot) and the triple interaction
terms (Board female*GHGtot*Quotas) are lacking statistical significance (the sum of the twocoefficients is statistically significant), suggesting that there are no differences in terms of lending
to less polluting firms between banks operating in countries with/without legislative gender
quotas. In this respect, gender quotas do not appear to play a relevant role in enhancing
female influence on banks’ lending strategies, within a pro-environmental vision. As suggested
by Ahern and Dittmar (2012), this evidence could be explained by a lack of work and leadershipexperience of post-quota female directors, compared to male counterparts. Furthermore, as
argued in Terjesen and Sealy (2016), it might be the case that post-quota female directors are“busier” because simultaneously sit on multiple boards, thereby being less capable to exert theirpower. Lastly, a problem in terms of female directors’ legitimacy in quota-mandating countries(Tienari et al., 2009) might undermine women’s influence on the board and on strategic decisions.
[Insert Table 4 Here]

5.4

The role of female directors’ specific characteristics: Age, education &backgroundIn this section, we aim to shed some light on the effects coming from specific characteristics

of female directors in our sample. To this end, we deepen our investigation by looking at three
aspects that differentiate the women on the banks’ boards. Specifically, we consider their (i) age;(ii) level of education; and (iii) background. We, therefore, include three additional variables in
the model specification presented in Section 3.2 (Equation (1)) and, alternatively, interact themwith the Board female indicator. The first inclusion is Age, which measures the average age ofthe female directors on the board. The second one is PhD, which accounts for the number offemale directors holding a doctoral degree. The third one is Academic, which indicates whether

ECB Working Paper Series No 2741 / October 202226

a female director holds (or has held) an academic position. For the latter variable, we construct
a dummy that takes value 1 if the director is an academic, 0 otherwise. All information is
manually gathered from the banks’ annual reports.
Table 5 provides the results of our investigation. Although age is found to largely explain thevariance in moral judgment, with older individuals displaying higher moral reasoning (Ruegger
and King, 1992), we do not find any relationship between Age and lending to less polluting firms
as the coefficient on the interaction term Board female*GHGTot*Age is negative and lacking
statistical significance.
In exploring the impact of the directors’ level of education on banks’ lending to less polluting
firms, we find that better-educated female directors, i.e. holding a doctoral degree, positively
influence the collective decisions toward greener borrowers. The coefficient on the triple interaction Board f emale*GHGT ot*P HD is negative and statistically significant at the 10% (column
2) and 5% levels (column 5). The educational level is seen to impact managers’ cognitive skills
and value system, which in turn influence a firm’s strategic decisions (Hitt and Tyler, 1991).
Furthermore, the managers’ educational background can affect a firm’s degree of innovativeness
and, thus, a firm’s strategic address (Hambrick and Mason, 1984). More educated managers,
with greater environmental awareness, due to an enhanced capability to develop and leverage alarger breadth of understanding, may be able to put higher green pressure on firms than theirless-educated peers (Rest and Narvaez, 1994; Diamantopoulos et al., 2003). Our findings appear
to corroborate the evidence that more educated female directors can effectively exert greaterpressure on decision-making toward greener options.
Finally, given the central role played by universities in driving societal changes and pushingfor a more sustainable future (Cortese et al., 2003; Wang et al., 2013), it is reasonable to expectthat directors with an academic position might be more responsive to younger generations’concerns, including those related to the environment. However, our findings do not support
the hypothesis of greater attention to the environment of female directors with an academic
background. The coefficient on the Board female*GHGTot*Academic term is lacking statisticalsignificance (columns 3 and 6).
[Insert Table 5 Here]

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6Robustness checks to account for endogeneity

6.1

Sorting effectIt is well known that corporate governance studies may suffer from endogeneity problems(Coles et al., 2012). Consequently, in this section, we control for the so-called sorting effect,
namely for the possibility that reverse causality drives our results. Indeed, banks more socially
responsible may be more likely to hire female directors on their boards than other banks. In
addition, women may self-select into banks that are per se more socially responsible than others.
Lastly, given that we suggest that women are more risk-averse than their male peers, it is also
plausible that women tend to select boards of less risk-taking banks.
We account for these endogeneity concerns by employing the instrumental variable (IV)approach and estimate the regressions using a two-stage least squares (2SLS) framework to
extract the exogenous component from the percentage of female directors. The main challenge
in using 2SLS is the identification of exogenous IVs that are not directly correlated with thedependent variable. We, therefore, need to identify a source of exogenous variation in our main
variable of interest. To this end, we employ the ratio of female participation in the workforce
at the country level (Womenpart) as our instrumental variable. We borrow the idea about
this instrument from Huang and Kisgen (2013) and Chen et al. (2017), who suggest that thegreater the female participation in the workforce, the higher is the probability for a bank (orfirm) to find talented female candidates from a larger pool of contenders. By contrast, there isno evidence suggesting that female participation in the workforce of a country influences banklending volumes. The IV econometric identification is specified as follows:Stage1 : E[Board f emaleb |W omenpart, Controls] =Φ(W omenpartk , Controlsb,t−1 )(2)Stage2 : Lendingbj = αj,ILS + βF itted Board f emaleb + δGHGtotj +γBoard f emaleb ∗ GHGT otj + θXb,t−1 + τ Tb,t−1 + υZj,t−1 + bj(3)

In the first stage of Equation (2), we rely on a probit model and regress our main variable of

ECB Working Paper Series No 2741 / October 2022

28

interest (Board female) on the instrumental variable (Womenpart), as well as on the bank- andcorporate governance-specific characteristics used throughout the paper to capture the probability of having an above-75th percentile of females on board. In the second stage, we introduce
the fitted values of Board female from the stage 1 regression into Equation (3) and regress our
main dependent variable (Lending) on the same set of variables employed in Equation (1).
Column 1 of Table 6 reports the results of the first-stage regression. In line with therequirements for a valid instrument, Womenpart is statistically significant at the 1% level andpositively correlated with the probability of having an above-75th percentile of females in thebank boardroom, suggesting the validity of the IV. Moreover, the instrument employed is strongas per the Kleibergen-Paap, Cragg-Donald test statistics (Cragg and Donald, 1993; Stock and
Yogo, 2005). Columns 2 and 3 of Table 6 display the results for the second-stage regression,
which makes use of the predicted probability from the first-stage regression (Fitted Board female)to estimate the banks’ lending behavior to less polluting firms. The results are in line withthose obtained for the baseline regression (Table 3), indicating an inverse relationship between ahigher percentage of female directors and lending volumes to more polluting firms, which further
corroborates our main findings.
[Insert Table 6 Here]

6.2
6.2.1

Sample selection biases
Large exposures

As a second robustness check, we account for the possible existence of sample selection biases.
In the previous analyses, we show that the presence of female directors in the boardroom hasa significant effect on shaping banks’ lending decisions toward less polluting firms. However,
the average lending amount granted by the banks in the credit register sample is relatively
small (€648,453) and reflects a composition oriented to SMEs. While credit register data are
fundamental to assessing the effect of female members in bank boards for a broader spectrumof borrowing firms - in contrast to studies that focus on specific loan categories (Delis et al.,2018; Reghezza et al., 2021) - it may be the case that female directors’ ”voice” on a greener
lending behavior is less heard when loans are large and directed to international corporations.
This could be due to a lower female’s perception of self-efficacy and confidence (Lenney, 1977;

ECB Working Paper Series No 2741 / October 202229

Barber and Odean, 2001), compared to men, in undertaking complex financial decisions (Endres
et al., 2008). With this additional test, we, therefore, aim to understand whether the greening
effect of female directors holds regardless of the category of loans and related board decisions.
To tackle this concern, we employ an additional loan-level dataset which is collected under thelarge exposures regime.18 An exposure to a single borrower or a connected group of borrowers is
considered to be a large exposure when, before the application of credit risk mitigation measuresand exemptions, is equal to or higher than 10% of an institution’s eligible capital or has a value
equal to or higher than €300 million (Article No.393 of the Capital Requirements Regulation,
CRR).19 As for AnaCredit, we match the large exposure dataset with bank corporate governance variables, bank-specific characteristics and GHG emissions. Our large exposures sample
covers 40 large banks and 124 large corporations over the period 2014-2018, for a total of 2,270
observations. The econometric identification we employ in this analysis is very similar to that
presented in Equation (1) and is specified as follows:

Lendingbjt = αjt + βBoard f emalebt +γBoard f emalebt ∗ GHG12jt + θXbt + τ Tbt + bjt

(4)

As in the baseline specification, we use as a dependent variable the logarithm of bank lending
volumes (Lending). In addition, we define a dummy Board female p75 equal to 1 for those bankswith a percentage of female directors above the 75th percentile of the distribution, 0 otherwise.
We capture firms’ GHG emission intensities by weighting Scope 1 and 2 emissions over firm total
assets (GHG12 ). Differently from credit register data, the large exposure dataset enables us to
exploit a panel dataset as the time series spans from 2014 to 2018. Table A2 in the Appendix
reports the summary statistics for the variables employed in this further analysis. X is a vectorof bank-level controls, which includes bank size (TotAss), measured by the logarithm of total
assets, and some relevant ratios, such as (i) deposits to total liabilities (Dep tl ); (ii) NPLs to
18

Information on euro area bank large exposures data to individual counterparties is taken from the Supervisory
Reporting (COREP 27-31), which requires banks to report to the Single Supervisory Mechanism (SSM) detailed
information on their large exposures since 2014. Introduced in the EU in 2014, the regime aims to ensure thatrisks arising from large exposures are reduced by limiting the maximum loss a bank can incur in the event of asudden counterparty’s failure.
19
The large exposure dataset encompasses detailed information on the exposures (e.g. instruments) and reporting entities, which allows us to link the large-exposure dataset to the complementary data source. The
large-exposure templates used here are reported at the highest level of consolidation and also, form the most
relevant group sub-structures, at the individual level.

ECB Working Paper Series No 2741 / October 202230

gross loans ratio (NPL r); (iii) net income to total assets (ROA); (iv) cash and equivalents tototal assets (Cash ta) and the common equity tier 1 ratio (CET1 r). T is a vector of lagged bankcorporate governance characteristics, including Board size (i.e. the logarithm of the number of
directors elected to the board), Board tenure (i.e. the average number of years that each board
member has been on the board) and CSR comp (i.e. a dummy variable to account whether thecompensation of senior executives is linked to CSR objectives). Given that the large exposures
dataset only covers large corporations that borrow from multiple banks (firms have on average
about 4 large exposure loans), we follow the approach of Khwaja and Mian (2008) and include
borrower*time fixed-effects to control for the time-varying heterogeneity in credit demand acrossfirms. Robust standard errors are two-way clustered at the bank-firm level.
Columns 1 and 2 of Table 7 display the results based on Equation (4). As in the baseline
results, we find an inverse and statistically significant relationship between the lending volumesof banks with more female directors and GHG12 relative emissions (at the 5 and 1% levels,
respectively). This evidence suggests that our main results are not driven by the sample composition and they are even more robust when considering larger loans and a longer sample period.
The magnitude of the effect is also economically meaningful. As an illustration, banks with apercentage of female directors on board above the 75th percentile lend about 1.2% less to firmsthat generate 285 tonnes of GHG emissions/million assets (i.e. firms in the 75th percentile of
the GHG emissions distribution), in comparison to the other banks.
6.2.2

Transition effects in bank boardroom composition: From male-to-female director

As a third robustness check, we control for the possibility that psychological traits and/or
risk-aversion of women in the bank boardroom may not differ from those of their male peers. The
key rationale underlying our analysis contends that female directors are more environmentallyoriented than men due to their psychological traits and/or a different level of risk-aversion. For
instance, Adams and Funk (2012), by using data from the European Social Survey (ESS) andsurvey data on directors’ psychological traits show that women in the boardroom can be very
different from women in the general population. Specifically, they argue that female directors areboth less tradition-oriented than women in the population at large and less tradition-oriented
than male directors. Their analysis suggests that it is important to consider the choices women

ECB Working Paper Series No 2741 / October 202231

might face to attain their positions before making assumptions about the preferences of womenin corporate leadership positions.
To account for this possibility, we follow the approach of Huang and Kisgen (2013) andFaccio et al. (2016) and compare banks’ lending to less polluting corporations before and after
a transition from a male to a female director with a control group of banks that face a femaleto-male transition. Specifically, we define a dummy equal to 1 if in the bank boardroom one
or more male directors are replaced by one or more female directors (male-to-female). This
dummy is time-varying and allows for the inclusion of bank fixed-effects: the same bank canface a male-to-female transition in one year and a female-to-male transition in a different year.
In contrast to previous studies (see, for instance, Huang and Kisgen, 2013), we do not use an
indicator (dummy) variable at t+1 to capture the effect on the outcome variable (Lending) after
the executive transition as decisions to grant new large exposure loans and/or the renewal ofexisting ones are commonly revised at the board level several times within the same year (EBA).
Consequently, a female replacing a male director can immediately affect the decision on whether
the large exposure loan is granted, extended or renewed.
The results of this additional test are presented in columns 3 and 4 of Table 7. The coefficient that captures the transition from male-to-female (interaction male-to-female*GHG12 ) is
negative and statistically significant at the 5% level, thus indicating that in years where a maledirector is replaced by a female director, lending volumes to more polluting/large corporations
are lower. This evidence should further strengthen the core hypothesis of this paper, whichsees female directors more inclined, than their male peers, to account for the risks (and related
implications) arising from climate change.
[Insert Table 7 Here]
6.2.3

Banks with a strong climate agenda and strategic election of female members

A final interesting aspect to explore is whether banks with strong climate agenda strategically elect female members to their boards to facilitate the change. Of course, that selection
is likely endogenous. However, one could think of some visible changes in the way banks commit to the agenda. For example, Kacperczyk and Peydró (2021) show that banks committing
to Science Based Targets Initiative (SBTi) subsequently alter the provision of credit. Simi-

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larly, banks committing to the SBTi may be more willing to reduce credit to more polluting
firms. Committing to a climate agenda might be correlated to the number of females in banks’boardrooms, given that more responsible banks, which pay more attention to the environmental
sphere, might also be the ones hiring more female directors. To control for this possibility, andfollowing Kacperczyk and Peydró (2021), we hand-collected information about the participationof banks in the SBTi and create a dummy variable SBTi equal to 1 for those banks that joined
the initiative, and 0 otherwise.20 In our sample, SBTi assumes the value 1 for 10 banks, and
0 for 42 banks. The results from the inclusion of the dummy variable SBTi reported in Table
A3 of the Appendix indicate a positive and statistically significant relationship between SBTiand bank lending, suggesting, ceteris paribus, that banks that joined the initiative lend more tofirms compared to banks that did not join it. More importantly for our identification strategy,
the inclusion of the dummy variable SBTi does not affect our coefficients of interest.21

7

Conclusions
This study aims to investigate the impact of gender diversity on banks’ boards on lending

decisions toward less polluting firms. While existing academic papers explore the role of gender
diversity in banks’ decision-making (Berger et al., 2014; Owen and Temesvary, 2018; Cardillo
et al., 2020; Arnaboldi et al., 2021), there is a lack of evidence about the impact of gender
diversity in banks’ boards in shaping lending strategies in favor of greener options. Given theextremely relevance of climate change and the increasing attention on how to combat its effects,
this paper represents a timely and significant contribution to the extant literature, of interest
to policymakers, academics and practitioners.
Using granular credit register data matched with GHG firms’ emissions, as well as an extensive range of bank- and firm-specific information, we show that banks with more gender-diverse
boards lend less to more polluting firms. This ”greening” effect is robust to a variety of additional analyses and tests, including those to rule out endogeneity concerns. An investigation of
the female-director specific characteristics suggests that better-educated directors pay greater
20

Data have been collected from: https://sciencebasedtargets.org/companies-taking-action.
We also checked whether banks made their board more gender diverse following the commitment to SBTi.
On average, banks committing to the SBTi increased the share of female board members by 2 percentage points
within the 2 years following the commitment. This provides additional supporting evidence to our main findingsthat highlight the key relevance of female directors for banks’ greener lending strategies
21

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33

attention to the environment, thereby granting lower credit volumes to browner firms. We also
document that the “greening” effect associated with female members in banks’ boardrooms isstronger in countries with more female climate-oriented politicians
Our results have important implications for policymakers. Policies that envisage a larger
percentage of women at the bank management level not only have an impact on gender diversity
imbalances but allow for more efficient fulfillment of environmental objectives. However, ourresults do not investigate the potential trade-off between the environmental results achieved byfemales in the bank boardroom and the corporate financial performance and risk objectives.
Is the achievement of climate objectives also in the interest of bank shareholders? Additional
research is needed to study the alignment (or potential misalignment) between the climaterelated benefits and the financial repercussions that might stem the deployment of green lending
strategies. Recent empirical evidence shows that the stock market values carbon emissions,as investors require higher compensation for holding the stocks of more polluting companies(Bolton and Kacpercyk, 2021a; Bolton and Kacpercyk, 2021b). The increasing cost of equity
for companies with higher emissions can be regarded as an alternative system of decentralized
taxation and a way to pass the problem to financial markets. Our study offers an alternative
view, as also banks can do their part through their lending decisions, and with the help of agreater presence of females in bank boardrooms.

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34

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Ruegger, D., King, E.W. (1992). A study of the effect of age and gender upon student
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Schubert, R., Brown, M., Gysler, M., Brachinger, H.W. (1999). Financial decision-making:
Are women really more risk-averse? American Economic Review, 89, 381-385.
Schwartz-Ziv, M. (2017). Gender and board activeness: The role of a critical mass. Journalof Financial and Quantitative Analysis, 52, 751-780.
Sundén, A.E., Surette, B.J. (1998). Gender differences in the allocation of assets inretirement savings plans. American Economic Review, 88, 207-211.
Terjesen, S., Sealy, R. (2016). Board gender quotas: Exploring ethical tensions from A

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multi-theoretical perspective. Business Ethics Quarterly, 26, 23-65.
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ECB Working Paper Series No 2741 / October 2022

43

Figure 1. Comparison of GHG emissions
This figure displays the difference between the median of the sectoral GHG emission intensities within aregion (left-hand chart) and the firm-level GHG emission intensities (right-hand chart). GHG emissions
are relative to firm’s revenues in logarithm.

Figure 2. Estimated relationship between GHG emissions (Scope 1, 2 & 3) and bank lending
The left-hand chart plots the estimated relationship between GHG emissions (Scope 1, 2 & 3) and lending
for banks with a below-75th percentile of female directors in the boardroom (blue solid line) and bankswith an above-75th percentile of female directors in the boardroom (yellow dashed line). The right-handchart plots the estimated difference in bank lending at different levels of GHG emissions for banks with abelow-75th percentile of female directors on the board and banks with an above-75th percentile of female
directors on the board. The grey bands represent the 95% confidence intervals. In both charts, the y-axis
refers to the estimated logarithm of lending volume whilst the x-axis indicates the GHG emissions overfirm revenues. The coefficients are taken from the specification in column 4 of Table 3.

ECB Working Paper Series No 2741 / October 2022

44

Figure 3. Estimated relationship between GHG emissions (Scope 1 & 2) and bank lending
The left-hand chart plots the estimated relationship between GHG emissions (Scope 1 & 2) and lending
for banks with a below-75th percentile of female directors in the boardroom (blue solid line) and bankswith an above-75th percentile of female directors in the boardroom (yellow dashed line). The right-handchart plots the estimated difference in bank lending at different levels of GHG emissions for banks with abelow-75th percentile of female directors on the board and banks with an above-75th percentile of female
directors on the board. The grey bands represent the 95% confidence intervals. In both charts, the y-axis
refers to the estimated logarithm of lending volume whilst the x-axis indicates the GHG emissions overfirm revenues. The coefficients are taken from the specification in column 5 of Table 3.

Figure 4. Estimated relationship between GHG emissions (Scope 3) and bank lending The
left-hand chart plots the estimated relationship between GHG emissions (Scope 3) and lending for bankswith a below-75th percentile of female directors in the boardroom (blue solid line) and banks with an
above-75th percentile of female directors in the boardroom (yellow dashed line). The right-hand chartplots the estimated difference in bank lending at different levels of GHG emissions between banks with abelow-75th percentile of female directors on the board and banks with an above-75th percentile of female
directors on the board. The grey bands represent the 95% confidence intervals. In both charts, the y-axis
refers to the estimated logarithm of lending volume whilst the x-axis indicates the GHG emissions overfirm revenues. The coefficients are taken from the specification in column 6 of Table 3.

ECB Working Paper Series No 2741 / October 2022

45

ECB Working Paper Series No 2741 / October 2022

46

South (dummy)
Gender quotas (dummy)
Cpol (dummy)

Firm size (log total assets)
Firm size (€ml)
Liquidity ratio (%)
Debt (%)
Firm ROA (%)
Working capital (%)
Gearing ratio (%)
Loan provisions (%)

Bank size (log total assets)
Bank size (€bn)
Deposits (%)
NPLs (%)
ROA (%)Cash (%)
Fees and commissions (%)
CET1 ratio (%)
ESGscore
ESGcontroversies
Stakeholders

Female on board (%)
Board Female (dummy)
Board size (level)
Board size (log)
CSR compensation (dummy)
Board tenure (years)
Ind board (%)

Scope 1-3 relative GHG (%)
Scope 1 and 2 relative GHG (%)
Scope 3 relative GHG (%)

Lending (log)
Lending (€)

Min

Max

p25

MedianPanel A. Dependent Variable
910,895
10.12
16.16
11.06
11.92
910,895
25,00010,500,000
63,593
150,707
Panel B: GHG emission variables
5,395.31
300.57564.76
910,895
126.11
910,8955.47
928.3714.96
27.22
910,895
107.85
4,529.76
283.06540.92
Panel C: Bank corporate governance variables
0.00
57.14
30.00
33.33
910,895
910,895
0.00
1.00
0.00
0.00
910,895
9.00
21.00
14.00
15.00
910,895
2.19
3.04
2.63
2.70
910,895
0.00
1.00
0.00
0.00
910,895
1.76
12.15
3.43
5.48
910,895
16.66
100.0050.00
68.42
Panel D: Bank-specific variables
910,895
23.78
28.23
25.80
27.18
910,895
21.30
1,830.00
160.00639.00910,895
44.41
96.24
71.14
79.26
910,895
1.20
45.52
3.19
3.77
910,895
-0.49
1.01
0.27
0.55
910,895
0.72
14.62
4.05
7.85
910,895
15.82
56.85
32.39
37.58
910,895
11.02
18.35
11.75
12.05
918,895
37.68
94.11
65.88
77.24
918,895
0.00
1.00
1.00
1.00
910,895
0.61
100.0041.35
77.77
Panel E: Firm-level variables
910,895
11.42
18.91
14.11
14.31
910,895
0.09
163
1.34
1.64
910,895
0.03
411.003.68
9.23
910,895
14.19
148.4065.66
74.24
910,895-28.23
30.84
2.19
3.54
910,895-22.93
85.06
12.10
20.86
910,895-151.87
194.51
9.48
15.22
910,895
0.00
89.74
0.11
0.38
Panel F: Cultural/Institutional variables
910,895
0.00
1.00
0.00
1.00
910,895
0.00
1.00
0.00
1.00
910,895
0.00
1.00
0.00
0.00

Obs

Table 1. Summary statistics
This table reports the summary statistics for the variables employed in the empirical analysis.

32.94
0.28
15.19
2.70
0.48
5.64
65.17

36.82
1.00
17.00
2.83
1.00
7.88
76.47

1.00
1.00
0.00

15.39
4.83
13.59
83.41
5.01
35.17
34.11
1.28

0.73
0.56
0.14

14.72
9.56
22.16
73.00
3.81
24.87
22.67
5.52

26.64
647.00
76.79
7.07
0.48
6.99
39.09
12.74
75.75
0.98
70.97

772.9660.80
707.10847.4152.36
763.8727.51
887.00
82.07
7.52
0.64
8.87
44.20
13.54
87.19
1.00
100

12.15
648,453

Mean

12.98
435,160

p75

0.44
0.49
0.35

1.42
26.20
53.13
20.46
6.90
21.59
43.30
15.51

1.19
571.00
10.17
7.47
0.30
3.29
9.39
1.49
13.83
0.11
29.92

8.22
0.45
2.42
0.16
0.49
2.60
18.19

888.31134.88769.651.39
1,614,857

SD

Table 2. Variables, definitions and sources
Variable (label)
Dependent variable
Lending (Lending)
GHG emission variables
Scope 1-3 GHG relative emiss (GHGtot)
Scope 1-2 GHG relative emiss (GHG12 )Scope 3 GHG relative emiss (GHG3 )Bank corporate governance variables
Female on board (Female board)
Board Female (Board female)
Board size (Board size)
CSR compensation (CSR comp)Board tenure (Board tenure)
Independent Board Members (Ind board)
Bank-specific variables
Bank size (TotAss)
Deposits (Dep tl)
NPLs (NPL r)
ROA (ROA)Cash (Cash ta)
Fees and commissions (Fee opInc)
capitalization (CET1 r)
ESG score (ESGscore)
ESG controversies (ESGcontroversies)
Stakeholder engagement (Stakeholders)Firm-specific variables
Firm size (Firm ta)
Liquidity ratio (Firm cash)
Debt (Firm debt)
Firm ROA (Firm ROA)Working capital (Firm WC )Gearing ratio (Firm gearing)
Loan Provisions (Loan provisions)Cultural/Institutional variables
South (South)
Gender quotas (Quotas)

Climate politicians (Cpol)

Definition

SourceOutstanding amount (in logarithm) indebted by a debtor to a creditor

AnaCredit

Sum of Scope 1, 2 and 3 GHG emissions to firm’s revenues
Sum of Scope 1 and 2 GHG emissions to firm’s revenues
Scope 3 GHG emissions to firm’s revenues

UrgentemUrgentemUrgentem

Percentage of women on board
Dummy variable equal to 1 for banks with a percentage of female onboard above the 75th percentile, 0 otherwise
Number of members on bank board
Dummy variable equal to 1 if the senior executive’s compensation islinked to CSR targets, 0 otherwise
Average number of years each board member has been on the board
Percentage of independent directors to overall directors

Refinitiv Eikon
Authors’ calculation
Refinitiv Eikon
Refinitiv Eikon
Refinitiv Eikon
Refinitiv Eikon

Logarithm of bank total assets
Ratio of customer deposits to total liabilities
Ratio of non-performing loans to gross loans
Ratio of net income to total assets
Ratio of cash and cash equivalent to total assets
Ratio of fees and commissions to operating income
Ratio of common equity tier1 capital to risk-weighted assets
Environmental, social and governance score
Number of ESG-related controversies reported in the press
Dummy variable equal to 1 if a bank engages with its stakeholders, 0
otherwiseSupervisory data
Supervisory data
Supervisory data
Supervisory data
Supervisory data
Supervisory data
Supervisory data
Refinitiv Eikon
Refinitiv Eikon
Refinitiv Eikon

Logarithm of firm total assets
Ratio of cash and cash equivalent to current liabilities
Ratio of current liabilities + non-current liabilities to total assets
Ratio of earnings before interest and taxes to total assets
Ratio of working capital to total assets
Ratio of interest paid to earnings before interest and taxes
Bank-firm level amount of loan impairments identified and recognizedby
the bank over the overall loan amount granted to the firm

Orbis Amadeus
Orbis Amadeus
Orbis Amadeus
Orbis Amadeus
Orbis Amadeus
Orbis Amadeus
AnaCredit

Dummy variable equal to 1 if a bank is located in a southern euro area
country (Greece, Italy, Portugal and Spain), 0 otherwise
Dummy variable equal to 1 if a bank is located in a country with legislative gender quotas (Austria, Belgium, France, Germany, Italy andPortugal), 0 otherwise
Dummy variable equal to 1 if a bank is located in a country with apercentage above 50% of female ministries and government executives
dealing with environment and climate change, 0 otherwise

Authors’ calculation
European Institute for Gender
Equality
European Institute for Gender
Equality

ECB Working Paper Series No 2741 / October 2022

47

Table 3. Baseline results (Total, Scope 1&2 and Scope 3 GHG emissions)This table reports the results of the baseline regression that accounts for all three scopes of GHG emissions. Lending,the dependent variable, is the logarithm of the outstanding amount indebted by a debtor to a creditor as per
the AnaCredit definition. Banks’ corporate governance variables, balance-sheet controls, ESG indicators and firmlevel characteristics are included. For more details on the variables’ construction and sources, refer to Table 2.
ILS indicates industry*location*size fixed-effects. All variables are winsorized at the 1st and 99th percentiles. Robust standard errors clustered at the bank-firm level are reported in parentheses. ∗∗∗ p<.01, ∗∗ p<.05, ∗ p<.1.
(1)
(2)
(3)
(4)
(5)
(6)
VARIABLES
Lending
Lending
Lending
Lending
Lending
Lending
Board female
GHGTot

0.02343(0.087)0.01423(0.091)0.02565(0.087)GHG120.04221(0.062)0.00004***(0.000)

GHG3
Board female*GHGTot
Board female*GHG12-0.00002**(0.000)Board female*GHG3
L.TotAss
L.Dep tl
L.NPL r
L.ROA
L.Cash ta
L.Fees opinc
L.CET1 r
L.ESGscore
L.ESGcontroversies
L.Stakeholders
L.Firm ta-0.00008**(0.000)-0.00004***(0.000)0.15292***(0.043)0.00867**(0.004)0.00627(0.005)-0.36944***(0.101)-0.03054***(0.010)0.00713**(0.003)0.02135(0.019)0.00073(0.002)0.00400***(0.001)-0.19729(0.188)0.15308***(0.043)0.00867**(0.004)0.00630(0.005)-0.36975***(0.101)-0.03049***(0.010)0.00714**(0.003)0.02126(0.019)0.00071(0.002)0.00401***(0.001)-0.19701(0.188)-0.00002**(0.000)0.15293***(0.043)0.00867**(0.004)0.00627(0.005)-0.36939***(0.101)-0.03054***(0.010)0.00713**(0.003)0.02137(0.019)0.00073(0.002)0.00400***(0.001)-0.19719(0.188)0.76225***(0.160)-0.14370***(0.044)0.03582***(0.013)0.00303(0.002)0.76214***(0.160)-0.14365***(0.044)0.03585***(0.013)0.00304(0.002)0.76215***(0.160)-0.14368***(0.044)0.03582***(0.013)0.00303(0.002)0.08322(0.052)0.00368(0.005)0.00210(0.004)-0.28973**(0.111)-0.02578***(0.008)0.00470(0.003)0.03524*(0.019)0.00257(0.002)0.00309***(0.001)-0.36510**(0.165)0.58446***(0.022)0.00056***(0.000)0.00770***(0.000)0.00518***(0.001)-0.00006(0.000)0.00031***(0.000)0.06535(0.068)0.62778***(0.165)-0.12538***(0.042)0.01978(0.015)0.00364*(0.002)607,4450.7329YesNo
52
236,478
Bank-firm

607,4450.7329YesNo
52
236,478
Bank-firm

607,4450.7330YesNo
52
236,478
Bank-firm

910,895
0.6341
NoYes
52
539,928
Bank-firm

910,895
0.6340NoYes
52
539,928
Bank-firm

910,895
0.6341
NoYes
52
539,928
Bank-firm

L.Firm ROA
L.Firm WC
L.Firm gearing
Loan provisions

L.Board tenure
L.Ind board
Observations
R-squared
Firm FEILS FE
N.Banks
N.Firms
Cluster

ECB Working Paper Series No 2741 / October 2022

-0.00022***(0.000)0.00004***(0.000)0.08327(0.052)0.00369(0.005)0.00211(0.004)-0.28945**(0.111)-0.02582***(0.008)0.00471(0.003)0.03530*(0.019)0.00259(0.002)0.00309***(0.001)-0.36460**(0.165)0.58431***(0.022)0.00057***(0.000)0.00771***(0.000)0.00519***(0.001)-0.00006(0.000)0.00031***(0.000)0.06545(0.068)0.62719***(0.165)-0.12521***(0.042)0.01983(0.015)0.00362*(0.002)

L.Firm debt

L.CSR comp

0.00014**(0.000)0.04463(0.062)-0.00004***(0.000)0.08333(0.052)0.00370(0.005)0.00211(0.004)-0.28941**(0.111)-0.02582***(0.008)0.00471(0.003)0.03532*(0.019)0.00258(0.002)0.00308***(0.001)-0.36434**(0.165)0.58435***(0.022)0.00057***(0.000)0.00771***(0.000)0.00519***(0.001)-0.00006(0.000)0.00031***(0.000)0.06546(0.068)0.62699***(0.164)-0.12514***(0.042)0.01985(0.015)0.00362*(0.002)

L.Firm cash

L.Board size

0.02724(0.066)48

Table 4. The ”gender-green-lending” nexus: The role of cultural and institutional
factors

This table reports the results of the regression model specification that also accounts for cultural and institutional variables (South, Cpol and Quotas). Lending, the dependent variable, is the logarithm of the outstanding amount indebted
by a debtor to a creditor as per the AnaCredit definition. Banks’ corporate governance variables, balance-sheet controls,
ESG indicators and firm-level characteristics are included. For more details on the variables’ construction and sources,refer to Table 2. ILS indicates industry*location*size fixed-effects. All variables are winsorized at the 1st and 99th percentiles. Robust standard errors clustered at the bank-firm level are reported in parentheses. ∗∗∗p<.01, ∗∗p< .05, ∗p< .1.
(1)
(2)
(3)
(4)
(5)
(6)
VARIABLES
Lending
Lending
Lending
Lending
Lending
Lending
Board female
GHGTotBoard female*GHGTot
SouthBoard female*SouthSouth*GHGTot
Board female*GHGTot*South
Cpol

0.05493(0.087)o.10509(0.081)-0.25913*(0.132)-0.00002(0.000)0.17786(0.162)-0.00136(0.115)-0.00000(0.000)0.00001(0.000)0.00000(0.000)0.00001(0.000)Board female*Cpol
Cpol*GHGTot
Board female*GHGTot*Cpol
Quotas

0.02793(0.102)-0.15384(0.095)0.00008***(0.000)-0.00010***(0.000)Board female*Quotas
Quotas*GHGTot
Board female*GHGTot*Quotas
Observations
R-squared
Firm FEILS FEBank controls
Governance controls
Firm controls
N.Banks
N.Firms
Cluster

607,4450.7332
Yes
No
Yes
Yes
Absorbed
52
236,478
Bank-firm

ECB Working Paper Series No 2741 / October 2022

607,4450.7331
Yes
No
Yes
Yes
Absorbed
52
236,478
Bank-firm

0.09939(0.072)0.00004***(0.000)-0.00005***(0.000)0.14010(0.152)-0.10760(0.127)-0.00001(0.000)0.00003*(0.000)-0.08256(0.089)0.35482*(0.181)-0.00000(0.000)-0.00002(0.000)607,4450.7331
Yes
No
Yes
Yes
Absorbed
52
236,478
Bank-firm

910,895
0.6342
No
Yes
Yes
Yes
Yes
52
539,928
Bank-firm

0.05128(0.077)0.00004***(0.000)-0.00001(0.000)0.19269*(0.103)-0.15745(0.117)0.00002(0.000)-0.00006***(0.000)910,895
0.6342
No
Yes
Yes
Yes
Yes
52
539,928
Bank-firm-0.06992(0.177)0.00003(0.000)-0.00003(0.000)-0.14050(0.090)0.19291(0.201)0.00002(0.000)-0.00001(0.000)910,895
0.6342
No
Yes
Yes
Yes
Yes
52
539,928
Bank-firm49

Table 5. The ”gender-green-lending” nexus: The role of female directors’ specificcharacteristics

This table reports the results of the regression model specification that also accounts for female directors’ specific characteristics (Age, PhD, Academic). Lending, the dependent variable, is the logarithm of the outstanding amount indebted
by a debtor to a creditor as per the AnaCredit definition. Banks’ corporate governance variables, balance-sheet controls,
ESG indicators and firm-level characteristics are included. For more details on the variables’ construction and sources,refer to Table 2. ILS indicates industry*location*size fixed-effects. All variables are winsorized at the 1st and 99th percentiles. Robust standard errors clustered at the bank-firm level are reported in parentheses. ∗∗∗p<.01, ∗∗p<.05, ∗p<.1.
(1)
(2)
(3)
(4)
(5)
(6)
VARIABLES
Lending
Lending
Lending
Lending
Lending
Lending
Board female
GHGTotBoard female*GHGTot
Age
Board female*Age
GHGTot*Age
Board female*GHGTot*Age
PhD

3.4953***(1.164)0.27559**(0.1179)0.24601***(0.113)0.00011(0.000)-0.0058(0.007)-0.0598***(0.019)0.00000**(0.000)-0.00000(0.000)-0.00000(0.000)-0.00002(0.000)Board female*PhD
GHGTot*PhD
Board female*GHGTot*PhD
Academic

0.02884(0.037)-0.14320***(0.037)0.00000(0.000)-0.00001*(0.000)Board female*Academic
Academic*GHGTot
Board female*GHGTot*Academic
Observations
R-squared
Firm FEILS FEBank controls
Governance controls
Firm controls
N.Banks
N.Firms
Cluster

607,4450.7335
Yes
No
Yes
Yes
Absorbed
52
236,478
Bank-firm

ECB Working Paper Series No 2741 / October 2022

607,4450.7337
Yes
No
Yes
Yes
Absorbed
52
236,478
Bank-firm

2.6719**(1.093)-0.00002(0.000)0.00004(0.000)-0.00503(0.007)-0.0454**(0.018)0.00000(0.000)-0.00000(0.000)0.08970(0.073)-0.31139**(0.122)0.00000(0.000)0.00001(0.000)607,4450.7334
Yes
No
Yes
Yes
Absorbed
52
236,478
Bank-firm

910,895
0.6345
No
Yes
Yes
Yes
Yes
52
539,928
Bank-firm

0.1891(0.1204)
0.00002*(0.000)-0.00001(0.000)0.00510(0.032)-0.07915*(0.043)0.00001***(0.000)-0.00002**(0.000)910,895
0.6344
No
Yes
Yes
Yes
Yes
52
539,928
Bank-firm

0.1863(0.122)0.00003**(0.000)-0.00004**(0.000)0.06201(0.066)-0.21196*(0.122)0.00001(0.000)0.00001(0.000)910,895
0.6343
No
Yes
Yes
Yes
Yes
52
539,928
Bank-firm

50

Table 6. Robustness check: Instrumental variable regressions

This table reports the results of the instrumental variable (IV) regressions. Lending, the dependent variable, is the logarithmof the outstanding amount indebted by a debtor to a creditor as per the AnaCredit definition. W omenpart is the ratio of
female participation in the workforce at the country level. Banks’ corporate governance variables, balance-sheet controls,
ESG indicators and firm-level characteristics are included. For more details on the variables’ construction and sources, refer
to Table 2. ILS indicates industry*location*size fixed-effects. All variables are winsorized at the 1st and 99th percentiles.
Robust standard errors clustered at the bank or the bank-firm level are reported in parentheses. ∗∗∗p<.01, ∗∗p<.05, ∗p< .1.
(1)
(2)
(3)
VARIABLES
Board Female
Lending
Lending
WomenpartFitted Board female

0.2857***(0.0007)-2.7976***(0.040)-0.5251***(0.0005)
0.0267***(0.0002)1.3490***(0.064)0.01115***(0.007)0.0429***(0.002)0.4078***(0.009)0.1372***(0.001)
0.0953***(0.001)-0.0001**(0.0000)
0.1428***(0.0420)0.0094**(0.0039)0.0045(0.0057)-0.3783***(0.1115)-0.0342***(0.0119)0.0083**(0.0036)
0.0226(0.0186)0.0012(0.0021)0.0038***(0.0011)-14.2913***(0.250)12.9715***(0.206)-1.7900***(0.0098)-0.2633(0.0036)0.8087***(0.1702)-0.1552***(0.0494)
0.0383***(0.0140)0.0040(0.0140)0.1710(0.1265)0.0001***(0.0000)-0.0002***(0.0000)
0.1101**(0.0431)0.0086*(0.0048)0.0040(0.0043)-0.1513(0.1086)-0.0194**(0.0092)
0.0058(0.0055)
0.0165(0.0180)0.0174(0.0187)
0.0307***(0.001)0.58433***(0.022)0.00058***(0.000)0.00770***(0.000)0.00520***(0.001)-0.00005(0.000)0.00030***(0.000)0.06463(0.069)0.62695***(0.165)-0.12515***(0.042)0.01980(0.016)0.00363*(0.002)

1,853,303

607,445
Yes
No

910,895
No
Yes

Bank-firmBank-firm

GHGTotFitted Board female*GHGTot
L.TotAss
L.Dep tl
L.NPL r
L.ROA
L.Cash ta
L.Fees opInc
L.CET1 r
L.ESGscore
L.ESGcontroversies
L.Firm ta

0.1821(0.1204)

L.Firm cash
L.Firm debt
L.Firm ROA
L.Firm WC
L.Firm gearing
Loan provisions
L.Board size
L.CSR compL.Board tenure
L.Ind board
Observations
Firm FEILS FE
Kleibergen-Paap rk LM statistic
Cragg-Donald Wald F-statistic
Stock-Yogo weak ID test critical values at 10% IV size
Cluster

ECB Working Paper Series No 2741 / October 2022

42.576
1.1e+05
16.87
Bank

51

Table 7. Sample selection bias: Large exposures and transition effects

This table reports the results for the regression analysis that considers the large exposure dataset.
Lending,
the dependent variable, is the logarithm of the outstanding amount indebted by a debtor to a creditor as per
the AnaCredit definition.
The male-to-female variable accounts for the replacement, over time, of male directors by female directors.
Banks’ corporate governance variables and balance-sheet controls are included.
For
more details on the variables’ construction and sources, refer to Table 2.
All variables are winsorized at the
1st and 99th percentiles. Robust standard errors clustered at the bank level. ∗∗∗p <.01, ∗∗p< .05, ∗p< .1.
(1)
(2)
(3)
(4)
VARIABLES
Lending
Lending
Lending
Lending
Board female

0.0223(0.078)0.0994(0.084)-0.0004**(0.000)-0.0004***(0.000)0.5865***(0.096)0.0072(0.008)0.0278(0.036)0.0757(0.201)-0.0056(0.022)-0.0129(0.0338)
2,270
0.426
Yes
No
No
40
124
Bank-firm

Male-to-Female
Board female*GHG12
Male-to-Female*GHG12
TotAssDep tl
NPL r
ROACash ta
CET1 rObservations
R-squared
Firm*time FE
Bank FE
Governance controls
N.Banks
N.Firms
Cluster

ECB Working Paper Series No 2741 / October 2022

0.1831**(0.075)0.1852***(0.066)

0.5725***(0.087)0.0063(0.009)0.0297(0.035)0.0143(0.221)-0.0157(0.021)-0.0067(0.0291)-0.0003**(0.000)-0.4768(0.434)-0.0233(0.017)0.0442**(0.019)-0.1394(0.113)-0.0381(0.022)
0.0665***(0.021)-0.0003**(0.000)-0.5259(0.597)-0.0265(0.0203)
0.0582**(0.026)-0.1477(0.102)-0.0381*(0.020)0.0620***(0.019)

2,270
0.430
Yes
No
Yes
40
124
Bank-firm

2,263
0.460
Yes
Yes
No
33
123
Bank-firm

2,263
0.471
Yes
Yes
Yes
33
123
Bank-firm

52Appendix A
Figure A1. Firm size and GHG relative emissions by NACE industry code
This figure displays the relationship between the average firm total assets (y-axis) and the average GHG
total emissions to revenues by the 2-digit NACE industry code.

ECB Working Paper Series No 2741 / October 2022

53

Table A1. Baseline results using alternative threshold values

This table reports the results of the baseline regression model specification by using alternative threshold values.
Lending, the dependent variable, is the logarithm of the outstanding amount indebted by a debtor to a creditor
as per the AnaCredit definition. Banks’ corporate governance variables, balance-sheet controls, ESG indicators andfirm-level characteristics are included. For more details on the variables’ construction and sources, refer to Table
2. ILS indicates industry*location*size fixed-effects. All variables are winsorized at the 1st and 99th percentiles.
Robust standard errors clustered at the bank-firm level are reported in parentheses. ∗∗∗p<.01, ∗∗p< .05, ∗p< .1.
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
VARIABLES
Lending
Lending
Lending
Lending
Lending
Lending
Lending
Lending(90th perc)(90th perc)(75th perc)(75th perc)(66th perc) (66th perc) (50th perc) (50th perc)
Board female
GHGTotBoard female*GHGTot
Observations
R-squared
Firm FEILS FEBank controls
Governance controls
Firm controls
N.Banks
N.Firms
Cluster

0.0234(0.086)-0.0001**(0.000)607,4450.7329
Yes
No
Yes
Yes
Absorbed
52
236,478
Bank-firm

0.0422(0.061)0.00003***(0.000)-0.0003***(0.000)910,895
0.6341
No
Yes
Yes
Yes
Yes
52
539,928
Bank-firm

0.0234(0.087)-0.00002**(0.000)607,4450.7329
Yes
No
Yes
Yes
Absorbed
52
236,478
Bank-firm

ECB Working Paper Series No 2741 / October 2022

0.0422(0.062)0.00004***(0.000)-0.00004***(0.000)910,895
0.6341
No
Yes
Yes
Yes
Yes
52
539,928
Bank-firm

0.0067(0.273)-0.00001*(0.000)607,4450.7329
Yes
No
Yes
Yes
Absorbed
52
236,478
Bank-firm

0.0433(0.282)0.00002***(0.000)-0.00001*(0.000)910,895
0.6340
No
Yes
Yes
Yes
Yes
52
539,928
Bank-firm

0.0427(0.060)0.0602(0.041)0.00003**-0.0000(0.000)607,4450.7330
Yes
No
Yes
Yes
Absorbed
52
236,478
Bank-firm-0.0000(0.000)910,895
0.6341
No
Yes
Yes
Yes
Yes
52
539,928
Bank-firm

54

Table A2. Descriptive statistics for the dataset on the large exposures

Lending (log)
Lending (€ml)
GHG12 (tonnes)
GHG12 (tonnes/millions)Board size (log)
TotAss (€bn)
Dep tl (%)
ROA (%)Cash ta (%)
NPL r (%)
CET1 r (%)
Female board (%)
Board size (level)
CSR comp (%)
Board tenure
Observations

(1)
Mean
18.378269
17,300,000
230.8127.65
1141.08
61.06
0.31
7.09
4.47
12.10
35.95
15.84
0.44
5.92
2417

ECB Working Paper Series No 2741 / October 2022

(2)
SD
1.63
462
34,600,000
451.870.67
520.55
12.10
0.38
3.81
3.73
1.56
9.95
3.59
0.50
2.18

55

Table A3. Baseline results with the inclusion of the dummy SBTi

This table reports the results of the baseline regression that accounts for banks with a greater climate agenda.
Lending, the dependent variable, is the logarithm of the outstanding amount indebted by a debtor to a creditor
as per the AnaCredit definition. Banks’ corporate governance variables, balance-sheet controls, ESG indicators andfirm-level characteristics are included. For more details on the variables’ construction and sources, refer to Table 2.
ILS indicates industry*location*size fixed-effects. All variables are winsorized at the 1st and 99th percentiles. Robust standard errors clustered at the bank-firm level are reported in parentheses. ∗∗∗ p<.01, ∗∗ p<.05, ∗ p<.1.
(1)
(2)
(3)
(4)
(5)
(6)
VARIABLES
Lending
Lending
Lending
Lending
Lending
Lending
Board female
GHGTot0.01517(0.084)0.0539(0.088)0.01742(0.084)GHG12

0.02339(0.057)0.00003***(0.000)

GHG3
Board female*GHGTot
Board female*GHG12-0.00001**(0.000)Board female*GHG3
L.TotAss
L.Dep tl
L.NPL r
L.ROA
L.Cash ta
L.Fees opinc
L.CET1 r
L.ESGscore
L.ESGcontroversies
L.Stakeholders
L.Firm ta-0.00003**(0.000)-0.00003***(0.000)0.13527***(0.043)0.00952**(0.006)0.00190(0.006)-0.41060***(0.105)-0.0419***(0.014)0.00589**(0.002)0.03949*(0.020)0.00261(0.002)0.00318***(0.000)-0.28876(0.213)0.13550***(0.043)0.00952**(0.003)0.00194(0.006)-0.41085***(0.105)-0.04140***(0.014)0.00590**(0.002)0.03932*(0.020)0.00258(0.002)0.00319***(0.000)-0.28812(0.213)-0.00002**(0.000)0.13527***(0.043)0.00952**(0.003)0.00189(0.006)-0.41064***(0.105)-0.04150***(0.014)0.00588**(0.002)0.03952*(0.020)0.00261(0.002)0.00318***(0.000)-0.28869(0.213)0.85122***(0.186)-0.14932***(0.047)
0.03690***(0.012)0.00339(0.002)0.07976*(0.047)0.85076***(0.186)-0.14924***(0.047)
0.03693***(0.012)0.00340(0.002)0.07945(0.047)0.85115***(0.186)-0.14930***(0.047)
0.03691***(0.012)0.00339(0.002)0.07980*(0.047)0.06568(0.048)0.00502(0.005)0.00126(0.004)-0.35205***(0.112)-0.035205***(0.010)0.00317(0.002)0.05466***(0.020)0.00452**(0.001)0.00207**(0.000)-0.43490**(0.166)
0.58432***(0.022)0.00056***(0.000)0.00770***(0.000)0.00519***(0.000)-0.00004(0.000)0.00031***(0.000)0.06726(0.068)0.73450***(0.170)-0.12817***(0.043)0.02307(0.015)0.00373*(0.001)0.10999***(0.0397)

607,4450.7331
YesNo
52
236,478
Bank-firm

607,4450.7331
YesNo
52
236,478
Bank-firm

607,4450.7331
YesNo
52
236,478
Bank-firm

910,895
0.6344
NoYes
52
539,928
Bank-firm

910,895
0.6343NoYes
52
539,928
Bank-firm

910,895
0.6344
NoYes
52
539,928
Bank-firm

L.Firm ROA
L.Firm WC
L.Firm gearing
Loan provisions

L.Board tenure
L.Ind board
SBTi
Observations
R-squared
Firm FEILS FE
N.Banks
N.Firms
Cluster

ECB Working Paper Series No 2741 / October 2022

-0.00022***(0.000)0.00004***(0.000)0.06570(0.048)0.00504(0.005)-0.00126(0.004)-0.35184***(0.112)-0.03811***(0.010)0.00318(0.002)0.05475***(0.020)0.00454**(0.001)0.00206**(0.000)-0.43447**(0.166)
0.58418***(0.022)0.00056***(0.000)0.00771***(0.000)0.00520***(0.000)-0.00004(0.000)0.00031***(0.000)0.06737(0.068)0.73404***(0.170)-0.12800***(0.043)0.02312(0.015)0.00371*(0.001)0.11014***(0.0397)

L.Firm debt

L.CSR comp

0.00014***(0.000)0.02579(0.057)-0.00004***(0.000)0.06576(0.048)0.00504(0.005)0.00125(0.004)-0.35180**(0.112)-0.035180***(0.010)0.00318(0.002)0.05477***(0.020)0.00454**(0.001)0.00206**(0.000)-0.43421**(0.166)
0.58422***(0.022)0.00056***(0.000)0.00771***(0.000)0.00520***(0.000)-0.00004(0.000)0.00031***(0.000)0.06738(0.068)0.73384***(0.170)-0.12792***(0.043)0.02314(0.015)0.00371*(0.001)0.11014***(0.0397)

L.Firm cash

L.Board size

0.00802(0.062)56

Acknowledgements
We thank two anonymous referees for very useful comments and suggestions. We are also indebted to comments received fromcolleagues in the Directorate General Macroprudential Policy and Financial Stability of the European Central Bank (ECB) and Monetaryand Economic Department of the Bank for International Settlements (BIS). We also thank participants at the: ALTIS Graduate School
Business and Society - Università Cattolica del Sacro Cuore; Centre for Responsible Banking Finance (CRBF) – University of
St.Andrews, School of Management; Essex Finance Centre (EfiC) Research Seminar – Essex Business School; 2022 International
Finance and Banking Society (IFABS) conference – University of Naples Federico II; Strathclyde Business School – research seminarseries; Sustainable Finance and Governance conference – University of Bath; 11th Financial Engineering Banking Society (FEBS)
conference – Climate change, Regulatory Responses and Sustainable Finance; University of Technology Sydney research seminar;
University of Sydney research seminar; University of New South Wales research seminar; 2022 European Economic Association (EEA);
2022 Wolpertinger conference for very useful comments.
This paper should not be reported as representing the views of neither the ECB nor the BIS. The views expressed are those of theauthors and do not necessarily reflect those of the ECB and/or BIS.
Leonardo Gambacorta
Bank for International Settlements, Basel, Switzerland; Centre for Economic Policy Research, London, United Kingdom;
email: Leonardo.Gambacorta@bis.org
Livia Pancotto
University of Strathclyde, Glasgow, United Kingdom; email: livia.pancotto@strath.ac.uk
Alessio Reghezza
University of Genoa, Genoa, Italy; European Central Bank, Frankfurt am Main, Germany;
email: Alessio.Reghezza@ecb.europa.eu
Martina Spaggiari
European Central Bank, Frankfurt am Main, Germany; email: Martina.Spaggiari@ecb.europa.eu© European Central Bank, 2022
Postal address 60640 Frankfurt am Main, Germany
Telephone+49 69 1344 0
Websitewww.ecb.europa.eu
All rights reserved. Any reproduction, publication and reprint in the form of a different publication, whether printed or produced
electronically, in whole or in part, is permitted only with the explicit written authorisation of the ECB or the authors.
This paper can be downloaded without charge from www.ecb.europa.eu, from the Social Science Research Network electronic library orfrom RePEc: Research Papers in Economics. Information on all of the papers published in the ECB Working Paper Series can be foundon the ECB’s website.
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