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Journal of Corporate Finance xxx (xxxx) xxx
Please cite this article as: Pablo de Andrés, Journal of Corporate Finance, https://doi.org/10.1016/j.jcorpn.2020.101782
Available online 21 November 2020
0929-1199/© 2020 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/).
The gender gap in bank credit access
Pablo de Andr´
es
a
, Ricardo Gimeno
b
,
*
, Ruth Mateos de Cabo
c
a
Universidad Aut´
onoma de Madrid and ECGI, Spain
b
Banco de Espa˜
na, Spain
c
Universidad CEU San Pablo, Spain
ARTICLE INFO
JEL codes:
G32
J16
L25
M13
Keywords:
Gender discrimination
Credit demand
Credit access
Credit performance
Financing
ABSTRACT
We use a sample of over 80,000 Spanish companies started by a sole entrepreneur between 2004
and 2014, and distinguish between male and female entrepreneurs demand for credit, credit
approval ratio, and credit performance. We nd that female entrepreneurs who start a business
are less likely to ask for a loan. Of the female entrepreneurs requesting a credit, the probability of
obtaining one in the founding year is signicantly lower than their male peers in the same in-
dustry. This lower credit access disappears over the subsequent years, once the company has a
track record of prots and losses. We also observe that women-led companies that receive a loan
in the founding year are less likely to default as compared to men-led companies. This superior
performance disappears for subsequent years, coinciding with the disappearance of the lower
credit access. Taking all these results together, we rule out both taste-based discrimination and
statistical discrimination in the credit industry, and point to the possible presence of double
standards which might be a consequence of implicit (unconscious) discrimination.
1. Introduction
In a market economy, the availability of and access to nance is a crucial element in the creation, development and survival of any
enterprise (Marlow and Patton, 2005). Finance is particularly critical for small and young businesses (Beck and Demirguc-Kunt, 2006).
For small businesses, bank lending is the key source of external nancing (Berger and Udell, 2002). Any potential dysfunction of the
lending channel, or any barrier or bias hindering the lending process will very negatively affect small business growth and survival,
their employees, and ultimately the whole economy.
One potential bias is that, when seeking nancing, women-led small businesses might experience greater difculties than their
men-led counterparts. This could be due to stereotypes portraying female entrepreneurs as less skilled and efcient (taste-based
discrimination); to information-related frictions that ground the credit decision on the average characteristics of the group (statistical
discrimination); or to the presence of unconscious biases (implicit discrimination / double standards). Undeniably, in any of these
cases, women-led businesses would experience tougher credit access, which would have extremely negative consequences on business
growth, employment, and the economy.
Extant research spotlights on both demand and supply sides of the credit market. From the demand side, Ongena and Popov (2016),
using a survey across several countries, nd female-owned rms apply for bank credit less often than male owned rms because they
believe their request will not be approved. Treichel (2006) report similar results. Should this fear of rejection be strong, female
* Corresponding author.
E-mail address: ricardo.gimeno@bde.es (R. Gimeno).
Contents lists available at ScienceDirect
Journal of Corporate Finance
journal homepage: www.elsevier.com/locate/jcorpfin
https://doi.org/10.1016/j.jcorpn.2020.101782
Received 7 November 2019; Received in revised form 15 October 2020; Accepted 5 November 2020
Journal of Corporate Finance xxx (xxxx) xxx
2
entrepreneurs might ask for another type of nancing, which would explain the lower level of bank credit in female-owned businesses.
Robb and Wolken (2002), Galli and Rossi (2016) and Moro et al. (2017) also report female entrepreneurs are less likely to request loans
as compared to their male counterparts.
On the supply side, Beck et al. (2018) exploit the quasi-random assignment of borrowers to loan ofcers to nd that borrowers
matched to ofcers of the opposite sex are less likely to return for a second loan. The effect is greater when ofcers have little prior
exposure to borrowers of the other gender and when ofcers have more discretion to act on their gender beliefs. However, the authors
were unable to infer about the direction of bias. That is, whether ofcers benet potential lender of the same gender or harm the other
gender. Alesina et al. (2013) nd evidence that women pay more for credit than men, although they do not nd any evidence that
female borrowers engage in more risk-taking behavior than men. The authors point to a kind of statistical discrimination that could be
the cause of these ndings; in other words, to certain risk factors that are not observable by econometricians but which are apparent to
the lender. By contrast, Ongena and Popov (2015) do not nd differences in interest rates, loan conditions and amounts, or the
likelihood of refusal, and the same outcome is reported by Moro et al. (2017) in an EU survey, or Asiedu et al. (2012) in a survey of US
Small business. Treichel (2006) report that loan refusal rates for male and female entrepreneurs are quite similar.
Extant research tries to establish a link between gender discrimination and credit access. Although it appears that female entre-
preneurs face certain barriers related to their gender when requesting nancing, it is very difcult to isolate the phenomenon, and to
infer the causality, due to the many different variables involved (Ongena and Popov, 2015; Aristei and Gallo, 2016). There is still a
clear lack of cumulative knowledge and a failure to adequately conceptualize and develop alternative theories. Yet the empirical eld
offers an opportunity to nd explanations, drawing on more and richer data sets that would allow some perspective of the relationship
between gender bias and credit access to be isolated. Efforts in this direction could help us to know the causes of this situation and
propose measures designed to amend it. This would improve the efciency of the banking system, reduce the nancial restrictions that
small businesses must face up to, and boost both economic and corporate growth.
In our paper, we deal with these problems using a very unique data set containing all bank loans requested and granted in Spain
(CIRBE, the Spanish Credit Register data set) during the period 2004–2014, and matching said database with the businesses created
each year (SABI), to see whether entrepreneurs sought a loan to begin their activity. In this way, we cover the entire population of new
small businesses that demand and use bank nancing to start their activity. These companies employ just one director and, therefore, it
is possible to know their gender. Since this data set is composed of new rms for which there is no prior nancial record -only a
business plan in a specic industry- bank ofcers must ground the credit granting decision on the basis of other readily observable
variables such as the entrepreneur’s characteristics (e.g., gender), which might be used as a proxy of the new company’s creditwor-
thiness. In this context, gender is an objective measure, which is non-modiable, and does not rely on suppositions.
Thus, using new rms without nancial or risk historical records, we respond to Alesina et al. (2013) call to avoid, or at least greatly
reduce, non-observable risk factors that are apparent to the lender but not to the econometrician. This allows us to focus the analysis on
the link between credit access and gender. Of course, we are unable to isolate the relationship completely, although we can, however,
rule out the existence of an ex-ante informational effect. Thus, information comes from the nature of the business, the capital provided
by the entrepreneur, the specic time money is requested and, the hypothesis being tested, gender. In such circumstances, the exis-
tence of any kind of gender discrimination would curb the credit granted to female entrepreneurs.
Additionally, our research strategy is not restricted to a cross-section analysis. Since the granting of a loan has an ex-post infor-
mational effects, we adopt Cabral and Mata’s (2003) dynamic approach to follow each business along its life cycle, and, especially,
whether a business requests and is granted a loan or not, as well as the business’s subsequent loan performance. This involves
continuing to match both data sets throughout the period. Computationally, this proves demanding, since we commence with around
80,000 companies which we must later track among the whole population of loan demands, loan concessions and loan defaults, month
by month over an 11-year period. However, this dynamic component of analysis is crucial vis-`
a-vis understanding the relationship
between gender and credit access, since banks obtain a kind of risk prole of companies as they grow older, a prole they lacked when
the rms set out. We think this dynamic analysis is key to correctly explaining the initial cross sections between gender and credit
access and to identifying whether this is due to potential bias by credit institutions, or whether it is a result of the lower credit quality of
entrepreneurs and their companies. To the best of our knowledge, this is the rst time this kind of analysis has been carried out.
Initially, our estimations conrm that companies run by women are less likely to ask for a loan, as previous ndings from the
literature on the demand side have shown. Therefore, we concentrate our analyses on the subset of companies that requested a loan,
effectively controlling for credit demand. On this group of companies, we estimate the likelihood of securing a loan, and nd that
female entrepreneurs suffer more nancial restrictions than their male counterparts when starting a business. This difference is
maintained for one further period (albeit marginally) and disappears after two years of activity, then remaining non-signicant over
the years. Specically, the chances of a company being granted a loan at the start are 10% lower for female than for male entre-
preneurs. After a year, this drops to roughly 6% and completely disappears after two years. This time allows banks to construct a prole
of the rm and to gather more information that was unavailable when the company was created. The fact that this probability dis-
appears after the second or subsequent years rules out the possibility that the difference stems from discrimination based on the
lender’s taste (Becker, 1957), since we would expect that adding new information about the business would not change the perceptions
derived from preferences and cultural beliefs about gender that are pervasive and persistent.
Bias might then be consistent with statistical discrimination (Phelps, 1972). Being a manifestation of information-related frictions,
this kind of bias might be expected to gradually disappear in consecutive years since, as more information is accumulated, the
importance of the borrower’s gender is attenuated. This potential statistical discrimination could be due to certain characteristics of
male and female entrepreneurs, according to which the average creditworthiness of companies owned by women would be lower than
that of their male counterparts. Such characteristics are not observable to econometricians but would be relevant when a decision is
P. de Andr´
es et al.
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3
made about awarding a loan. Should this happen, we would observe a gender gap in credit granting, but no difference in terms of the
quality of loans and the default rates, since the threshold applied to grant a credit would be the same for women as for men.
Because of this, in a third step, we track the performance of rm loans over time, nding that the probability of default is lower for
loans granted to women in the company’s foundation year and the subsequent year, and vanishes in the second and following years.
Specically, in the case of loans obtained in the rst year, the probability of default is 14% lower for companies run by women, 12%
lower in the case of credits obtained one year after the creation of company, and is not statistically different for subsequent years. This
better credit performance of companies run by women, coincident with their lower probability of obtaining a credit (10% in the rst
year, 8% one year later, and no difference in subsequent years), is not consistent with the explanation that the decision to grant credit is
based on unobservable group characteristics or is economically rational (statistical discrimination). This is especially relevant since is a
more clear evidence of credit quality than ex-ante proxy variables that are commonly used in the literature that, as Moro et al. (2017)
assess, are limited to very basic controls.
By contrast, this evidence points to the existence of double standards that stem from less intentional and rational evaluation rules in
the mind of the decision-maker that could result in implicit (unconscious) discrimination, as Bertrand et al. (2005) have proposed. The
existence of double standards/implicit discrimination in the access to credit for start-ups is a major nding which reveals and
quanties a common belief in the credit market about gender related asymmetries, as many previous papers have sought to evidence. It
also proves particularly pertinent given that in order to solve the double standard problem, unlike taste-based or statistical discrim-
ination problems, the solution lies in increasing bank ofcer awareness of the possible presence of gender bias in the credit granting
process as well as formulating explicit and objective criteria and standards to evaluate creditworthiness. In this sense, we think that by
revealing such a problem our analysis takes a rm step towards reducing or even eliminating it.
To sum up, our paper makes four main contributions. First, the Spanish CIRBE database allows us to cover the whole population of
credit demand and credit access in Spain, avoiding the inconveniences of subjective surveys, or multi-country samples where Spanish
rms represent a limited picture of the Spanish credit market that might bias the outcome (e.g. Galli and Rossi, 2016 and Stefani and
Vacca, 2013 use the ECB SAFE survey of small- and medium-size enterprises; Ongena and Popov, 2016 use the Business Environment
and Enterprise Performance Survey (BEEPS) by the World Bank and the EBRD).
Second, the study is focused on new companies, which are especially interesting since they have no previous credit history, such
that the credit score models applied to them by banks are different from those used for established rms. Thus, the ex-ante-
informational effect is widely reduced and the decision to grant a loan is based more on managerial than rm characteristics.
Among these, gender is one objective and non-modiable measure. This allows us to reduce as much as possible the risk factors non-
observable to the econometrician but observable to the lender, as Alesina et al. (2013) pointed out. This is especially relevant, since
previous literature on gender differences in credit demand typically uses already existing companies (Alesina et al., 2013; Aristei and
Gallo, 2016; Bellucci et al., 2010; Galli and Rossi, 2016; Ongena and Popov, 2015; Treichel, 2006).
Third, the comprehensive and longitudinal nature of the database also allows us to carry out a dynamic empirical approach, looking
at subsequent credit performance and tracking the credit performance of companies throughout the company life-cycle. This inno-
vative strategy avoids some of the problems of omitted variables that plague previous studies, since instead of controlling for other
variables that imperfectly capture the credit quality of the borrower we control for credit standard through credit performance. This
allows us to assess borrower credit quality in a more straightforward manner than trying to approximate it by control variables such as,
age, experience, education, personal delinquency, personal bankruptcy, legal judgments, or personal wealth, as is usually done in the
literature (Blanchower et al., 2003; Cavalluzzo and Wolken, 2005; Cole and Mehran, 2009; Coleman and Robb, 2009). This strategy is
crucial vis-`
a-vis ruling out the hypothesis that lower (more) credit access for women (men) is due to lower (higher) quality of projects,
since female-run companies behaved better than male ones in terms of credit performance (lower default rate).
Finally, and closely related to the previous point, this step by step analysis allows us not only to separate the demand from the
supply side of the market, but also to disentangle different types of potential discrimination sources, discarding the presence of taste-
based (dislike for female borrowers) and statistical discrimination (use of borrower’s gender to proxy non-observable characteristics of
credit worthiness) in favor of implicit discrimination (presence of double standards) as the only alternative to explain both the gender
gap in credit granting and in credit performance. Thus, compared to other studies on gender differences in access to credit market that
usually only explore Beckerian or/and statistical discrimination as possible causes of gender gaps (Alesina et al., 2013; Bellucci et al.,
2010; Muravyev et al., 2009), we go a step further in an attempt to disentangle the three main types of discrimination usually posed by
the literature.
2. Theoretical framework on gender discrimination
In credit markets, gender discrimination acts as a barrier against women’s careers when female entrepreneurs seek funding to start
a new un-established small enterprise. Access to nancial resources is key to funding the required investments and to the subsequent
protability and growth of the company. Therefore, any barrier or obstacle in the credit market might prevent the business from
opening and, even if the rm is ultimately set up, may have a lasting and negative impact on its survival and success.
Several theories have been proposed to explain differences in the way markets treat discriminated groups. Firstly, Becker (1957)
proposed taste-based discrimination, in which the market participant is willing to sacrice part of the prot in order to avoid con-
tracting the disliked member of the discriminated group out of prejudice or bias. Taste-based discrimination is originated by pref-
erences and cultural beliefs about gender that may inuence lender decisions on loan applications. Taste-based discrimination would
occur when those responsible for approving loans may inherently dislike female borrowers (prejudices) and prefer not to associate with
them, even though it may be to their own detriment in terms of lost efciency or reduced income to indulge such tastes. This would
P. de Andr´
es et al.
Journal of Corporate Finance xxx (xxxx) xxx
4
result in female borrowers being offered less credit, suffering higher denial rates, or facing higher interest rates under what are
otherwise similar circumstances to male borrowers. Becker’s theory predicts that such discriminatory practices tend to disappear with
competition among lenders, as those who prefer not to nance women businesses are no longer able to bear the higher costs.
In the case of statistical discrimination (Phelps, 1972), since borrowers’ demographic characteristics could be correlated with
unobserved characteristics of credit, the lender can use borrower gender to proxy creditworthiness. This way, if female borrowers are
on average more or less likely to default, then loan ofcers can apply to specic female-led rms the average quality of funded female-
led rms in an attempt to minimize the cost of gathering more directly relevant information about the borrower. According to Bellucci
et al. (2010), this discrimination has its origin in the lower diffusion of female-owned businesses in the economy, which provides
limited and less reliable information on these rms. This leads lenders to economize, inferring the likelihood of default on the loan,
using the average information available on the creditworthiness of current female-owned rms. As a result of the adverse selection that
stems from the difculties creditworthy female borrowers face when accessing credit (individuals from the discriminated group are
discouraged from participating in the credit market), the average quality of female rms decreases, perpetuating the difculties in
accessing credit for female borrowers in what becomes a vicious circle.
Another explanation of potential discrimination has been proposed by Bertrand et al. (2005). Underlying taste-based and statistical
No Discrimination Taste-based Discrimination
Statistical Discrimination Implicit Discrimination
Fig. 1. Types of discrimination.
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es et al.
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5
discriminations is an awareness to discriminate, either for personal motivation or because belonging to a group provides relevant
information. However, there may be a different motivation, which is less intentional and that involves greater unawareness, and which
leads to discrimination. This kind of discrimination comes from a recent body of psychological evidence that distinguishes between
explicit and implicit attitudes and how they inuence human behavior. In this way, different experiments using the Implicit Associ-
ation Test (IAT) show how implicit attitudes can inuence behavior in meaningful ways, especially in contexts of inattentiveness, time
pressure, and ambiguity. In this latter case, social psychologists argue that behavior may be more prone to implicit attitudes, and that
implicit discrimination is more likely to occur in contexts where multiple, non-racist explanations for behavior might exist (Bertrand
et al., 2005). In the context of the credit market, there might be an implicit attitude which favors granting loans to male entrepreneurs
even though the contrary has been explicitly expressed. Thus, women would be less likely to obtain credit, not as a result of rational
evaluations, but as a consequence of unintentional rules and credit scoring evaluations that would involve setting the bar higher when
evaluating women’s businesses as compared to men’s, and which are not based on economic grounds and lie outside discriminator
awareness. This implicit discrimination would be more likely in time pressure contexts, if assessment involves considerable ambiguity
(granting a loan to a rm starting up is not clear, nor is there a simple formula), and if information is insufcient (for instance, if there
are no records about the lender’s previous activity).
In this context, our work aims to identify whether there is less credit access for female entrepreneurs, and specically whether this
is due to possible economic discrimination, or the result of lower credit quality of entrepreneurs and their companies. In order to do
this, we must isolate the companies that are in fact requesting a loan, thus eliminating demand side factors. This can be done by
restricting the sample to companies which we know have applied for a loan (as in Jim´
enez et al., 2012, 2014, 2017) and therefore see,
conditional on that explicit credit demand, whether or not they were able to secure a loan.
Once we have the subsample of companies that have currently applied for a loan, we look for evidence of gender bias, testing
whether the probability of female entrepreneurs being granted a bank loan is lower than for male entrepreneurs. Should evidence of
such a gender gap in credit access exist, we will try to disentangle the type of discrimination that may be behind it.
Let us assume that CQ
i
is an unobservable random variable which represents the credit quality of entrepreneur i. When an
entrepreneur approaches a nancial institution/bank, this bank must assess the credit quality of the project by the credit scoring (CS
i
),
which can be considered an approximation to the real value of CQ
i
CQ
i
(CS
i
=CQ
i
+
ε
i
CS
i
=CQ
i
+
ε
i
). Therefore, the bank will grant the
loan if the estimated credit scoring is above a given credit standard (CS
i
*
). Thus, the probability of obtaining a loan (P[CS
i
>CS
i
*
] =P
[CQ
i
+
ε
i
>CS
i
*
]P[CS
i
>CS
i
*] =P[CQ
i
+
ε
i
>CS
i
*]) will depend on both credit quality and observation error. In the event of no
discrimination, where the credit quality, credit standard, and observation errors are equal for female and male entrepreneurs, the
probabilities of obtaining a loan should be the same (Fig. 1, top-left).
Starting with the more explicit and intentional form of discrimination, i.e., taste-based discrimination, this would imply that the
lender’s distaste towards female borrowers would incline them to behave negatively, by imposing higher credit standards on women
(CS
*W
>CS
*M
) to access formal bank credit as compared to male counterparts (Fig. 1, top-right). Moreover, given that Beckerian
discrimination stems from preferences and cultural beliefs about gender that are pervasive and persistent, we might expect that adding
new available information about the female-owned business would not change the lender’s perceptions. We would thus expect that the
lower probabilities of granting credit for female entrepreneurs would not disappear in the years following the company’s foundation.
If, on the other hand, we observe that these barriers disappear in subsequent years, we could exclude this kind of discrimination.
In the latter case, where discrimination disappears over time, we should consider subtler and more unconscious types of gender
bias. This way, if our results were consistent with statistical discrimination and if the decision to grant a loan to companies run by
women were correlated with some unobservable characteristics, the bank ofcer would judge the female applicant on the basis of her
group averages rather than solely on the basis of her own business creditworthiness. Thus, the bank ofcer would use the lower credit
quality distributions of female-owned rms (CQ
W
<CQ
M
CQ
W
<CQ
M
), since the information about them is limited compared to that of
men (e.g., due to differences in business background, motivation or less management experience) to apply the same credit standards to
both women and men, which would result in a lower likelihood of credit for women than for men. However, a second consequence
would be that, since credit standards are the same for male and female entrepreneurs, the default rate of female run companies would
be similar to that of male run companies. This would show that the decision, although discriminatory, was rational from an economic
point of view (Fig. 1, bottom-left).
Finally, the more subtle and automatic type of discrimination —implicit discrimination— would lead credit ofcers to uncon-
sciously underestimate (E[
ε
W
] <E[
ε
M
]) the credit quality of new women-led companies when considering the entrepreneur’s credit
scoring. This implicit bias of the credit quality of women’s startups would, at the same time, lead us to observe a lower percentage of
loans granted to new businesses owned by women, and a lower default ratio among new women-led businesses that were granted loans
compared to their male counterparts. Although these two facts may seem counterintuitive, since fewer women-led rms comply with
bank loan requirements, one would expect no differences in performance compared to their male counterparts. They may, as argued in
the case of statistical discrimination, occur simultaneously in the presence of implicit discrimination. This way, although bank ofcers
consciously believe they are applying the same standards to both men and women-led companies, since implicit discrimination leads
them to unconsciously underestimate the credit quality of their business, they would be implicitly setting higher bars to evaluate
women-led businesses as compared to men-led businesses. This results in a double standard that gives rise to lower default ratios
among women’s start-ups who faced greater difculties when seeking to secure a loan (Fig. 1, bottom-right).
3. Data
Our empirical analysis matches two separate databases. Sabi is a comprehensive database that includes ofcial register information
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Journal of Corporate Finance xxx (xxxx) xxx
6
for almost all Spanish companies. Through Sabi, we rst identify companies created in Spain between 2004 and 2014, and to select
those with a solo entrepreneur and at least one employee in the rst year of the company’s life (Table 1). The former condition allows
us to identify the “gender of a company”; the latter excludes companies with no real activity (i.e., many companies are created to
protect real estate assets for scal purposes). Both conditions produce a database of 84,586 companies, with 17,726 of them run by a
female entrepreneur (21%) (see Fig. 2). As 91% of the Spanish companies in the database have either multiple directorships or no
economic activity, our sample is 9% of the population. As shown in Figs. 3 and 4, there are differences across regions and industries in
terms of male or female leadership of these companies.
As a second step, we merge this sample with the Spanish Credit Register run by the Spanish Central Bank (CIRBE). CIRBE is a
comprehensive and condential database of all banks and credit institutions operating in Spain. These institutions must le monthly
reports of all outstanding loans (with an outstanding amount of at least 6000
€
) owed by Spanish individuals and corporations, as well
as their performing situation (normal, doubtful, in arrears, and write-off). The high level of banking sector penetration in the Spanish
economy and the very low level of the 6000
€
hurdle implies that the database covers an extremely high proportion of Spanish lending
activity. The second key point of this database is that when a bank or credit institution is assessing fresh credit for a company or
individual, it sends an information request to CIRBE (with the written consent of the rm or individual) concerning the credit situation
of the individual/rm. CIRBE keeps records of all these information requests, which can be identied as loan applications from an
individual/rm (e.g., Jim´
enez et al., 2012, 2014, 2017).
1
Fig. 5 tracks all 84,586 companies’ demand for credit since their founding.
The demand for credit ranges between 30% and 50% for the year the companies are created, and steadily decreases to around 15–20%
once companies get older. As depicted, companies’ demand for loans diminishes with time, since other nancing sources become
available.
Once we identify credit demand, we match loan applications to actual loans. In order to do so, we track the rm’s outstanding loans
(according to CIRBE) in the month the credit was requested and the following three months. We consider a loan is approved when the
bank has increased the outstanding loans to that rm. We consider a loan is rejected when we do not observe that increase.
2
This
identication procedure is the same as used by Jim´
enez et al. (2012, 2014, 2017). Fig. 6 shows the proportion of rms that, having
asked for a loan, are given one. As can be seen, the proportion of credit access depends to a great extent on the economic situation, with
a proportion of credit access in the year of the rm’s creation of around 75–80% pre crisis, to below 55% in 2012–2013. There is a
reduction in the likelihood of obtaining a loan once the company gets older, but there are also differences depending on the credit
cycle.
Due to large differences in credit access and credit demand depending both on the company’s life cycle and the economic cycle, all
analyses are performed separately for each year of the company life cycle, and we add year xed effects to account for the economic
cycle.
The nal step of our analysis requires tracking the credit performance of the loans granted to those companies. Using the CIRBE
database, we follow each loan for the available horizon, identifying whether the loan has been considered doubtful, has gone into
arrears, or if the bank has given up trying to recover the loan and has written it off at any moment after having awarded the credit.
Fig. 7 depicts the default ratios.
4. Analysis
4.1. Credit demand
First, we explore differences in credit demand between female and male entrepreneurs. In order to do so, we estimate a logit model
(Eq. (1)) where the dependent variable is a dummy variable that is equal to 1 if the company asked for a loan in a given year, and
0 otherwise. The independent variable is gender (female). Control variables are time (year), rm industry (2-digit industry level),
region (50 provinces and two regions) and rm size (the equity of the rm at the beginning of the year).
PDemandiy =1=fβy⋅Femalei+γy⋅lnEquityiy +δyear +ϑIndustry +ϑProvince +
ν
iy(1)
As shown in the estimation of Eq. (1) in Table 2, companies run by a female entrepreneur are about 10% less likely to request a loan
than a rm run by a male in the year the company is created (Table 3, column 1). It is worth noting that this gender gap in credit
demand remains even years after the company’s creation (Columns 2–11 in Table 3), where women-led companies are between 10%
and 25% less likely to ask for a bank loan. This result conrms the hypothesis that female entrepreneurs are less likely request a loan.
There are several possible explanations for this outcome. The most obvious explanation is to consider that women are more risk averse
than men (Jianakoplos and Bernasek, 1998; Sunden and Brian, 1998), and so are less likely to look for a more leveraged company.
However, an alternative explanation, in line with Schubert et al. (1999) and Ongena and Popov (2016), is that women would not ask
for a loan because they anticipate (correctly or incorrectly) that they are less likely to be given it. We will explore this in the following
section.
1
Although data protection laws do not allow us to study individuals, these laws do not affect rms, which are the subject of this study.
2
To construct this variable, we will look at the debt increase of this company with that bank between the month previous to the consultation and
the three months after consultation. If there is an increase in the sum of loans and stand-by credit in any of those months, we consider the loan to
have been approved (approval =1) while if there is no such increase, we deem the request to have been rejected (approval =0).
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Journal of Corporate Finance xxx (xxxx) xxx
7
4.2. Credit access
Once we isolate the rms that indeed asked for a bank loan in a given year, we then look at whether those companies were suc-
cessful in obtaining a loan. Thus, we restrict our analysis to the subsample of rms that have asked for a loan in the previous step of the
analysis in order to isolate the supply from the demand side, and again use a logit model, in this case with the dependent variable a
dummy variable that is equal to 1 if the rm has secured a loan, and 0 otherwise. As we did in the previous model, we separate the
analysis depending on the age of the rm, using a different logit regression for each year since rm’s creation. Thus, as happens with
logit models on the credit demand, this is equivalent to estimating a single model with xed effects not only on the year, industry and
region (province), but also on the rm age, as well as interactions of the latter with all the other variables and xed effects.
In the rst year of a company’s existence, there are no records on its protability, so banks and credit institutions judge potential
credit performance based on entrepreneur characteristics (of which gender is one of the most evident), the collateral the rm can
provide (García-Posada and Mora-Sanguinetti, 2014), type of business, and local market where the loan is requested. To control for
collateral, we include the rm’s equity that considers the entrepreneur’s initial capital (Table 3). Business and market conditions are
accounted for by industry and regional dummy variables.
PCredit Accessiy =1Demandiy =1=fβy⋅Femalei+γy⋅lnEquityiy +δyear +ϑIndustry +ϑProvince +
ν
iy(2)
Table 4 depicts results from estimating Eq. (2). As shown, among newly founded companies, a female entrepreneur reduces the
Table 1
Descriptive statistics.
Year Female Credit demand Credit access Number of obs.
2004 19.6% 31.1% 75.7% 9476
2005 18.6% 29.4% 76.7% 8850
2006 18.8% 40.7% 78.6% 9997
2007 18.4% 46.9% 77.9% 7073
2008 20.0% 40.5% 70.0% 7080
2009 22.2% 42.8% 62.5% 6750
2010 23.4% 42.5% 63.5% 6990
2011 23.1% 45.6% 61.9% 4150
2012 22.5% 34.0% 54.2% 8155
2013 23.1% 36.9% 53.2% 9366
2014 22.9% 44.5% 62.0% 6699
Fig. 2. Number of companies and proportion of them led by a woman, by year of creation.
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Fig. 3. Number of companies and proportion of them led by a woman, by province.
Fig. 4. Number of companies and proportion of them led by a woman, by industry.
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odds of receiving a loan by roughly 10%. One plausible cause is that given the lack of perfect information to analyze the characteristics
of the individual entrepreneur asking for the loan, gender can be used to proxy unobservable specic and differential group char-
acteristics (statistical discrimination). Nevertheless, as the company gets older and there is more information available on rm per-
formance (Prot & Loss accounts), banks and credit institutions no longer need to check on difcult to gather individual information.
Characteristics such as gender no longer prove to be a relevant factor. As can be seen in Table 4, in the year the company is created
(column 1), women entrepreneurs are less likely to secure a loan than their male counterparts. This continues the year after the
company’s creation (column 2), although the effect is only marginally signicant. It then subsequently dissipates (columns 3 to 11).
These results clearly rule out the existence of taste-based discrimination in the credit industry given that, in the second year,
differences in credit access between men and women companies disappear and remain insignicant the following years. This might
Fig. 5. Number of companies requesting a loan, by year of creation.
Fig. 6. Proportion of rms obtaining a loan, by year of creation.
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come from the lack of information on a specic person, which leads to the average group quality being applied. This might be lower for
female-owned rms, due to them being less experienced or committed to the company, or even because they are less inclined towards
risk taking. None of these characteristics are observable to us, but would imply that credit demand was of a lower quality. It would then
be economically justiable to reduce credit supply to these groups (statistical discrimination). However, the lower probability of
women-led companies obtaining a loan might not be based on rational evaluations, but due to unintentional rules and credit scoring
evaluations. This would imply that higher bars have been set to evaluate female-run businesses compared to male-run businesses
(double standards) and that decisions are not based on economic foundations and lie outside discriminator awareness (implicit
discrimination). This is the key point of the following estimations.
4.3. Credit performance
We now try to disentangle which type of discrimination (statistical or implicit double standards) might lie behind the lower
likelihood of women entrepreneurs getting a loan by looking at the future performance of those loans. In the case of statistical
discrimination, the decision to award credit would be rational, and based on the differential average characteristics of male and female
entrepreneurs. Thus, future credit performance would be independent of borrower gender, since the credit rating would merely have
been taking proper account of such characteristics. However, if implicit double standards are prevalent, the decision to discriminate a
group would not be based on rational reasoning vis-`
a-vis the credit quality of the company, and would thus be creating an unconscious
double standard that penalizes women. In this latter situation, the higher implicit standards required for women will lead to their credit
performing better. We therefore specify a logit model on credit performance for the subsample of companies who obtained a loan (Eq.
(3)). The dependent variable will be equal to one if we observe that, in the future, the bank classies the credit obtained in a given year
of the life’s company either as doubtful, in arrears or written-off, and equal to zero if it has never been in that position. Independent
variables are entrepreneur gender, original leverage ratio, and xed effects by year, industry and province.
PNon Performingiy =1Credit Accessiy =1=fβy⋅Femalei+γy⋅DebtEquityiy +δyear +ϑIndustry +ϑProvince +
ν
iy(3)
Eq. (3) estimations are shown in Table 5, where each column represents the estimation of loan performance for the credit obtained
in each year since company’s creation. In the case of the credits obtained in the founding year, we observe that the probability of the
credit going into default is 14% less likely in the case of loans given to women-led companies. Had the level of credit worthiness used
for women and men been the same, we would not expect such an outcome. However, since women-led companies show better credit
performance, it implies that the credit quality was higher than men-led companies who also obtained a loan in the year the rm was
founded. Moreover, these women-owned companies with better credit performance are the same ones who had a tougher time
obtaining credit in their foundation year (indeed, they were 10% less likely to obtain a credit when requesting it). This would be
Fig. 7. Proportion of credit default, by year of company creation.
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Table 2
Logit regression on the probability of a rm seeking a loan in a given year after the creation of the rm.
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)
Credit Demand
Year 0
Credit
Demand Year
1
Credit
Demand Year
2
Credit
Demand Year
3
Credit
Demand Year
4
Credit
Demand Year
5
Credit
Demand Year
6
Credit
Demand Year
7
Credit
Demand Year
8
Credit
Demand Year
9
Credit
Demand Year
10
Female CEO −0.099***** −0.090*** −0.159*** −0.184*** −0.192*** −0.190*** −0.243*** −0.288*** −0.230*** −0.132*** −0.199*
(0.018) (0.023) (0.028) (0.033) (0.036) (0.040) (0.045) (0.052) (0.061) (0.076) (0.107)
Equity 0.084*** 0.064*** 0.082*** 0.122*** 0.142*** 0.139*** 0.166*** 0.192*** 0.174*** 0.183*** 0.152***
(0.005) (0.006) (0.006) (0.007) (0.008) (0.009) (0.010) (0.012) (0.013) (0.017) (0.023)
Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Industry FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Province FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
# observations 81,640 56,234 42,354 33,456 28,841 24,292 19,999 15,685 11,833 7468 3757
Pseudo R2 0.0290 0.0224 0.019 0.0236 0.0287 0.0302 0.0376 0.0417 0.0423 0.0536 0.0536
LR stat 3162*** 1654*** 1006*** 967.4*** 992.4*** 861.0*** 861.5*** 738.4*** 560.3*** 443.6*** 232.4***
Odds ratio
Female
CEO
0.906 0.914 0.853 0.832 0.825 0.827 0.784 0.750 0.795 0.877 0.819
Standard errors in parentheses. The dependent variable is a dummy variable if the company has asked for a bank loan. Each column represents the years to have elapsed since the creation of the rm (from
year 0 to year 10). We control by rm industry (2 digits CNAE), year, province and rm size (the log of the equity). Sample range from companies created in 2004 to those created in 2014.
***
p <0.01.
**
p <0.05.
*
p <0.10.
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tantamount to saying there were double standards for companies seeking loans depending on the gender of the director, with it being
tougher for women-run companies. This is consistent with implicit discrimination, but not with statistical discrimination, since, in the
latter case, we should not observe any difference in credit performance.
For subsequent years, the results are parallel to those observed in credit demand. In the case of loans obtained one year after
company’s creation, ventures run by women are 12.4% less likely (marginally signicant) to go into default (they were 6.3% less likely
to obtain a loan, also a marginally signicant difference). However, for subsequent years, the likelihood of going into default is not
statistically signicant between rms run by women or men, in line with what happened at the moment the loan was granted.
5. Robustness exercises
In order to conrm the validity of our results, we have run several robustness exercises. All of them are in line with previous
analyses.
5.1. Construction vs. commerce
The main driver of our results is that in the rst year of a rm, the lack of hard data force banks to assess company credit worthiness
using qualitative judgments, while in the following years, credit scoring can be derived from models fed by Prot & Loss and Balance
Sheet variables.
To further reinforce this intuition, we extracted two subsamples for industrial groups, one of which is construction and real estate
developers. In this case, although banks lack information on the companies, they can rely on third party appraisals of the projects that
are then used as collateral for the projects. The other subgroup is commerce and restaurants. These companies generally rent their
locations and lack collateral, such that banks are not able to use quantitative models for their projects, and qualitative analysis remains
the most relevant factor. The results of the previous analysis on both sectors are shown in Table 6.
In the case of Construction and Real Estate Developers, we do not observe any difference between rms run by female and male
directors, even for the rst year of the company. By contrast, for Commerce and Restaurants, the effect for the rst years is even clearer
than for the whole sample, with lower credit access and higher credit performance for female-run companies. This result reinforces the
idea that qualitative credit assessment is the root of implicit discrimination.
5.2. Alternative performance measures
We also use alternative measures of performance as an additional robustness exercise. In section 4.3, we use whether the company
has gone into default in the following years, without limits. It might be argued that the longer the period used to assess performance,
the more additional effects might appear inuencing this measure. As an initial robustness, we have changed the denition of non-
performing to check only for the creditworthiness in the following two years after securing the loan. We use this lag because it is
when there is a peak in the new defaults after loans are granted. Results are shown in Table 7.
Another way to look at performance is through quantitative loss in the event of loan default. We use a Tobit model (Tobin, 1958), as
in Eq. (4), where the dependent variable is the amount of the loan that has defaulted. This variable is obviously censored (Loss =0) for
cases in which no default has occurred. The estimation results for these regressions are shown in Table 8.
Loss
iy
=0 if Default
iy
=0.
Lossiy =βy⋅Femalei+γy⋅DebtEquityiy +δyear +ϑIndustry +ϑProvince +
ν
iy (4)
We also estimate a Cox hazard model (Cox, 1972) for the likelihood of going into default at a given moment. This duration model
where the variable explained is the time to default gives a hazard function for a given time and variables as shown in Eq. (5). The
estimated parameters are shown in Table 9.
Table 3
Initial equity of rms in the year of their creation.
Log (equity) Log (equity) - Male Log (equity) - Female
#obs. Mean Std.Dev. #obs. Mean Std.Dev. #obs. Mean Std.Dev.
2004 9320 1.933 1.419 7489 1.954 1.413 1831 1.848 1.440
2005 8667 2.012 1.507 7055 2.014 1.482 1612 2.004 1.613
2006 9776 1.972 1.493 7938 1.978 1.480 1838 1.950 1.551
2007 6955 2.025 1.511 5677 2.052 1.513 1278 1.909 1.495
2008 6989 1.962 1.483 5599 1.995 1.498 1390 1.829 1.413
2009 6680 1.896 1.389 5195 1.936 1.416 1485 1.756 1.227
2010 6948 1902 1394 5321 1.959 1.436 1627 1.713 1.227
2011 4128 2.062 1.512 3173 2.101 1.545 955 1.933 1.392
2012 8164 1.907 1.456 6294 1.942 1.488 1817 1.797 1.348
2013 9364 1.908 1.441 7167 1938 1.451 2149 1.814 1.411
2014 6696 1.965 1.490 5139 2006 1.536 1533 1.835 1.327
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Table 4
Logit model on the probability of getting a loan when requested.
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)
Credit Access
Year 0
Credit Access
Year 1
Credit Access
Year 2
Credit Access
Year 3
Credit Access
Year 4
Credit Access
Year 5
Credit Access
Year 6
Credit Access
Year 7
Credit Access
Year 8
Credit Access
Year 9
Credit Access
Year 10
Female CEO −0.102***** −0.065* −0.005 −0.007 −0.064 0.042 −0.012 −0.111 −0.126 −0.057 −0.293
(0.032) (0.037) (0.047) (0.056) (0.063) (0.070) (0.081) (0.097) (0.111) (0.144) (0.208)
Equity 0.025*** −0.009 0.010 0.026 0.017 0.019 0.001 −0.003 −0.002 0.055 −0.008
(0.009) (0.009) (0.011) (0.012) (0.014) (0.016) (0.018) (0.020) (0.024) (0.031) (0.044)
Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Industry FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Province FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
# observations 28,762 20,421 13,707 10,142 8240 6620 5167 3930 2893 1796 957
Pseudo R2 0.0174 0.0133 0.0152 0.0160 0.0220 0.0209 0.0232 0.0298 0.0310 0.0664 0.0808
LR stat 626.1*** 367.7*** 288.3*** 224.5*** 249.9*** 189.6*** 164.0*** 156,9*** 122.6 163.1*** 107.0
Odds Ratio
Female CEO
0.903 0.937 0.995 0.993 0.938 1.043 0.988 0.895 0.882 0.945 0.746
Standard errors in parentheses. The dependent variable is a dummy variable that equals 1 if the company obtains a bank loan, and 0 otherwise. Each column represents the years to have elapsed since the
creation of the rm (from year 0 to year 10). We control by rm industry (2 digits CNAE), year, region (province), and rm size (the log of the equity). The sample ranges from companies created in 2004 to
those created in 2014.
***
p <0.01.
**
p <0.05.
*
p <0.10.
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Table 5
Logit model on the probability of going into arrears after getting a bank loan.
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)
Non
Performing
Year 0
Non
Performing
Year 1
Non
Performing
Year 2
Non
Performing
Year 3
Non
Performing
Year 4
Non
Performing
Year 5
Non
Performing
Year 6
Non
Performing
Year 7
Non
Performing
Year 8
Non
Performing
Year 9
Non
Performing
Year 10
Female CEO −0.139** −0.133* −0.027 −0.036 −0.054 0.021 0.093 −0.083 0.148 0.181 −1.156
(0.068) (0.073) (0.085) (0.098) (0.116) (0.131) (0.156) (0.188) (0.230) (0.309) (0.737)
Debt/equity 0.105*** 0.093*** 0.106*** 0.052** 0.030 0.035 −0.107*** −0.048 −0.156*** −0.227*** −0.533***
(0.016) (0.016) (0.025) (0.021) (0.024) (0.028) (0.034) (0.039) (0.048) (0.061) (0.120)
Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Industry FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Province FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
# observations 23,789 15,973 10,784 7994 6282 4866 3559 2671 1812 1009 387
Pseudo R2 0.1016 0.0827 0.0748 0.0646 0.0591 0.0568 0.0592 0.0753 0.1044 0.1373 0.2374
LR stat 1421.8*** 945.8*** 639.4*** 423.4*** 287.1*** 202.7*** 151.0*** 145.7*** 132.7*** 100.5** 66.0***
Odds ratio
Female
CEO
0.861** 0.876* 0.973 0.965 0.947 1.021 1.097 0.920 1.159 1.199 0.315
Standard errors in parentheses. The dependent variable is a dummy variable that indicates whether the company has gone into arrears at any moment after the loan was obtained. Each column represents
the years to have elapsed since the creation of the rm (from year 0 to year 10). We control by rm industry (2 digits CNAE), year, region (province), and rm leverage (the log of debt over equity). The
sample ranges from companies created in 2004 to those created in 2014.
***
p <0.01.
**
p <0.05.
*
p <0.10.
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Table 6
Logit models on the probability of demanding a loan, obtaining one and going into arrears for construction and commerce companies.
Construction and Real Estate Developers Commerce and Restaurants
Year 0 Year 1 Year 2 Year 3 Year 4 Year 0 Year 1 Year 2 Year 3 Year 4
Credit Demand
Female CEO −0.054 −0.100* −0.074 −0.197** −0.205** −0.171*** −0.104** −0.260*** −0.237*** −0.229***
(0.055) (0.060) (0.069) (0.082) (0.091) (0.037) (0.041) (0.049) (0.056) (0.061)
# observations 13,412 11,431 9157 7426 6228 18,313 15,803 12,180 9841 8569
OR Female CEO 0.948 0.905* 0.929 0.821** 0.814** 0.843*** 0.901** 0.771*** 0.789*** 0.795***
Credit Access
Female CEO −0.138 −0.059 −0.097 −0.064 −0.026 −0.210*** −0.144** 0.084 0.064 −0.098
(0.087) (0.088) (0.104) (0.124) (0.141) (0.061) (0.067) (0.084) (0.096) (0.105)
# observations 5952 4808 3589 2623 1996 7176 5605 3904 2997 2550
OR Female CEO 0.894 1.022 0.977 0.926 0.988 0.811*** 0.866** 1.088 1.066 0.907
Non-Performing Loans
Female CEO −0.197 −0.134 −0.268 −0.308 0.148 −0.298** −0.326** −0.150 −0.371* −0.237
(0.138) (0.136) (0.170) (0.213) (0.268) (0.130) (0.138) (0.159) (0.198) (0.239)
# observations 5216 4654 2903 1881 1259 6849 4822 3034 2155 1570
OR Female CEO 0.821 0.875 0.765 0.735 1.160 0.743** 0.722** 0.861 0.690* 0.789
Standard errors in parentheses.
Credit Demand models are logit models where the dependent variable is a dummy variable that indicates whether the company has asked for a bank loan. Credit Access models are also logit models where
the dependent variable is a dummy variable that equals 1 if the company obtains a bank loan, and 0 otherwise. Finally, Non-Performing models are logit models where the dependent variable is a dummy
variable indicating whether the company has gone into arrears at any moment after the loan was obtained. Two subsamples have been used. On the left are the models with Construction and Real Estate
Developer models (CNAE Industry codes 41, 42, 43, 68), while the right-hand side models use Commerce and Restaurant companies (CNAE Industry codes: 45, 46, 47, 55, 56). Each column represents a
separate model for the years to have elapsed since the creation of the rm (from year 0 to year 4). We control by year, region (province), rm size (log equity) for credit demand and access models, and rm
leverage (the log of debt over equity) for non-performing models. The sample spans companies created between 2004 and 2014.
***
p <0.01.
**
p <0.05.
*
p <0.10.
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Table 7
Logit models on the probability of going into arrears two years after obtaining a bank loan.
Arrears
Year 0 Year 1 Year 2
Female CEO −0.184** −0.140* −0.0852
(0.082) (0.078) (0.096)
Debt/equity −0.622*** −0.882*** −2.440***
(0.207) (0.280) (0.545)
Year FE Yes Yes Yes
Province FE Yes Yes Yes
Industry FE Yes Yes Yes
# observations 22,058 15,867 9912
LR stat 438.86*** 407.07*** 434.52***
Standard errors in parentheses. The dependent variable is a dummy variable that indicates whether the company has gone into
arrears in the following two years after the loan was obtained. Each column represents the years to have elapsed since the
creation of the rm (from year 0 to year 2). We control by rm industry (2 digits CNAE), year, region (province), and rm
leverage (the log of debt over equity). The sample spans companies created between 2004 and 2014.
***
p <0.01.
**
p <0.05.
*
p <0.10.
Table 8
Tobit models on the amount of money lost for each bank loan granted.
Loan Loss
Year 0 Year 1 Year 2
Female CEO −1.332** −1.258* −1.178
(0.679) (0.681) (0.814)
Debt/equity −7.215*** −7.103*** −14.99***
(1.62) (1.88) (3.45)
Year FE Yes Yes Yes
Province FE Yes Yes Yes
Industry FE Yes Yes Yes
# observations 21,331 15,537 9875
LR stat 1268.8*** 946.6*** 650.9***
Standard errors in parentheses. The dependent variable is the amount of money the bank fails to receive after granting a loan.
Tobit model censored at zero for loans where there has been no loss. Each column represents the years to have elapsed since the
creation of the rm (from year 0 to year 2). We control by rm industry (2 digits CNAE), year, region (province), and rm
leverage (the log of debt over equity). The sample spans companies created between 2004 and 2014.
***
p <0.01.
**
p <0.05.
*
p <0.10.
Table 9
Cox Regression on the hazard of default.
Hazard model
Year 0 Year 1 Year 2
Female CEO −0.362*** −0.089 −0.249
(0.137) (0.117) (0.155)
Debt/equity −0.823*** −0.686* −1.901**
(0.355) (0.409) (0.746)
Year FE Yes Yes Yes
Province FE Yes Yes Yes
Industry FE Yes Yes Yes
# observations 20,593 14,556 9130
LR stat 121.39*** 192.90*** 278.74*
Standard errors in parentheses. Cox regression hazard model where the dependent variable is the time to default since the loan
is granted, censored for cases where there has been no default. Each column represents the years to have elapsed since the
creation of the rm (from year 0 to year 2). We control by rm industry (2 digits CNAE), year, region (province), and rm
leverage (the log of debt over equity). The sample spans companies created between 2004 and 2014.
***
p <0.01.
**
p <0.05.
*
p <0.10.
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λ(t|X) = λ0(t)⋅exp.βy⋅Femalei+γy⋅DebtEquityiy +δyear +ϑIndustry +ϑProvince(5)
All three approaches produce the same result we observed for the default models of Eq. (3) in section 4.3. That is, rms run by
women perform better than those run by men for loans obtained in the year the rm was created, although there is no performance
difference for latter years.
5.3. Panel data models
As a nal robustness analysis, we have taken advantage of the fact that we are observing the same companies in different years. This
allows us to estimate panel data models for all three logit models (credit demand, access, and default). In these cases, we include an
interaction variable to single out the cases of companies run by women in the year the company was set up. Results for both xed and
random rm effects are shown in Table 10.
The likelihood of securing a loan is lower for companies run by a woman in the rst year, although those who do obtain one are less
likely to default. However, the main caveat of panel models (especially for xed effects) for this analysis is that several rms must be
dropped from the analysis due to lack of variability in the outcome within rms.
6. Conclusions
This paper examines the presence of discrepancies in credit demand, credit access and credit performance by rms run by female
and male directors. We conrm that women-led companies are less likely than men-led companies to ask for a loan. This result is
compatible both with the potential higher risk aversion of women as well as self-exclusion due to an anticipation by the female
administrator of a lower probability of obtaining a loan, which makes them desist from initially seeking one.
We observe that women-led companies who ask for a loan in the founding year are less likely to obtain a loan (that is, even after
controlling for their lower credit demand). However, this credit access gap disappears after the second year the rm is created. One
possible explanation for this outcome is that when the company started out, the lack of information on its nancial record led the
lender to use the personal characteristics of the director, such as gender, to proxy their new company creditworthiness in an attempt to
minimize the cost of gathering more directly relevant information about the borrower’s business plan. In that context, if women-led
companies have, on average, less creditworthiness than those men-led companies, whatever the reason (women entrepreneurs may be
younger, have less business experience or may prioritize their work-life balance), women would face a handicap that reduces the
likelihood of getting a bank loan. However, once the company has run for long enough, banks may apply more formal credit scoring
models using balance sheet and prot and loss accounts. Personal characteristics then become less relevant, until borrower gender is no
longer a factor in loan decision making.
As we nd no bias for older rms, we can rule out the presence of Beckerian taste-based discrimination, since having its roots in
prejudices and cultural beliefs does not tend to disappear in the presence of additional nancial information. However, the lower
proportion of credit access among younger rms run by women might be caused by two alternative explanations, a rational one (i.e.,
statistical discrimination), or an unconscious and less intentional one (i.e., implicit double standards). Through our analysis of credit
performance, we have been able to disentangle these two alternative explanations. Our results show that women-led companies who
experienced tougher credit access in the founding year and are less likely to go into default, which would point to some kind of non-
rational bias. Indeed, this result signals the existence of an implicit double standard rather than to statistical discrimination.
Table 10
Panel data models on credit demand, credit access and credit default.
Credit Demand Credit Access Credit Default
Woman*Start 0.081 *** −0.139 *** −0.155 *** −0.111 *** −2.346 *** −0.331 *
(0.027) (0.074) (0.041) (0.445) (0.179)
Woman*Start+1 0.084 *** −0.141 *** −0.060 −0.046 −1.431 *** −0.239
(0.029) (0.071) (0.043) (0.420) (0.205)
Equity 0.152 *** 0.146 *** 0.067 *** 0.012 **
(0.003) (0.014) (0.005)
Debt −6.988 −0.004
(1.10) (0.007)
Firm Effects Fixed Random Fixed Random Fixed Random
Year Effects Yes Yes Yes Yes Yes Yes
Observations 193,706 282,535 48,445 90,575 2129 62,384
Firms 35,226 66,316 14,201 43,735 614 39,290
Wald/LR Test 2211.64 *** 7226.88 *** 2411.05 *** 5256.83 *** 123.3 *** 844.32 ***
Credit Demand models are logit models where the dependent variable is a dummy variable that indicates whether the company has asked for a bank
loan. Credit Access models are also logit models where the dependent variable is a dummy variable that equals 1 if the company obtains a bank loan,
and 0 otherwise. Finally, Credit Default models are logit models where the dependent variable is a dummy variable indicating whether the company
has gone into arrears at any moment after the loan was obtained. Women*Start is the interaction between companies with a female CEO and the year
the company was created. Women*Start +1 is the interaction between companies with a female CEO and one year after the creation of the company.
Wald Test results are reported for Firm Fixed Effect Logit regressions, and Likelihood Ratio Test for Firm Random Effect Logit regressions.
P. de Andr´
es et al.
Journal of Corporate Finance xxx (xxxx) xxx
18
These results have two main consequences. Firstly, implicit double standards suggest that credit allocation among start-ups (i.e.,
young rms) is not efcient. Correcting this bias would imply that women-led companies would be more likely to grow in the initial
years of the rm and so not lag behind men-led companies for the rest of the company’s life. Our ndings suggest that nancial in-
stitutions could improve the quality of their asset (credit) portfolio if they corrected such unconscious bias. Secondly, contrary to taste-
based and statistical discrimination, implicit discrimination can be more easily corrected. Once actors acknowledge the existence of
implicit discrimination, they are likely to correct it voluntarily since this discrimination goes against their own interests. In the case of
credit scoring for entrepreneurs, one likely cause of implicit discrimination is that credit scoring methodologies (both qualitative and
quantitative) are calibrated with the most common group (in this case, male entrepreneurs), but the relationship between the en-
trepreneur’s characteristics and the credit quality of the company they run might differ between women entrepreneurs and men
entrepreneurs (e.g., due to different risk appetites observed in credit demand). Therefore, we advise banks to review their credit
scoring processes to explore whether the interaction of the traditional factors/variables and gender would improve credit performance.
Apart from the factual result concerning the situation in the credit market, the paper also contributes in the methodology used, in
that it disentangles different types of discrimination by separating credit market supply and demand factors. Once these are isolated,
we were able to identify not only whether there is some kind of discrimination, but also the actual type, by looking at performance. The
same procedure could be used for other situations, such as hiring decisions, or any other evaluation process.
Acknowledgements
The authors beneted from the helpful comments of Steven Ongena, Sergio Mayordomo, Bel´
en Nieto, Gonzalo Rubio, Tano Santos,
Almudena Sevilla, Siri Terjesen, and participants attending research seminars at -Banco de Espa˜
na, Zaragoza, Murcia and CEU Uni-
versities, as well as conferences of EURAM, ICGS, Academy of Management and EFiC. This research has been funded by (i) FEF-IEAF
with the Research Award Antonio Dionis Soler 2019; (ii) the Spanish Government (AEI/FEDER-UE) (ref. FEM2017-83006-R) (iii)
USPCEU-Mutua Madrile˜
na (060516-USPMM-02/17) (iv) Spanish Government (ref. ECO2015-65826-P) (v) Spanish Government (ref.
ECO2017-85356-P).
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