Gender bias and credit access.
Date: 2015-09-02
Institutions: European Central Bank
Subjects: aid to undertakings, credit, entrepreneur, position of women, sexual discrimination

Working Paper Series 

Steven Ongena and 
Alexander Popov 

Gender bias and credit access 

No 1822 / July 2015 

Note: This Working 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 

     This paper studies the causal effect of gender bias on access to bank credit. We extract an 
exogenous measure of gender bias from survey responses by descendants of US immigrants on 
questions about the role of women in society. We then use data on 6,000 small business firms 
from 17 countries and find that in countries with higher gender bias, female-owned firms are 
more frequently discouraged from applying for bank credit and more likely to rely on informal 
finance. At the same time, loan rejection rates and terms on granted loans do not vary between 
male and female firm owners. These results are not driven by credit risk differences between 
female-  and  male-owned  firms or  by  any  idiosyncrasies  in the  set of  countries  in  our  sample. 
Overall,  the  evidence  suggests  that  in  high-gender  bias  countries,  female  entrepreneurs  are 
more  likely  to  opt  out  of  the  loan  application  process,  even  though  banks  do  not  appear  to 
discriminate against females that apply for credit.  

  JEL classification: G21, J16, N32, Z13. 

     Keywords: Cultural bias; Gender-based discrimination; Bank credit; Female-owned firms. 

ECB Working Paper 1822, July 20151 
 
 
   
 
 
 
 
 
 
 
 
 
 
 
 
 
Non-technical summary 

In this paper, we aim to establish a causal link going from a well-defined cultural belief – 

i.e., the belief that women are inferior to men, or the gender bias – to a well-defined economic 

outcome – i.e., credit access. Specifically, we study whether, in high-gender bias countries and 

relative  to  male-owned  counterparts,  females-owned  firms:  (1)  Are  more  often  denied  bank 

credit; (2) are more often discouraged from applying for bank credit; (3) rely less on bank credit 

and  more  on  alternative  sources  of  finance,  such  as  trade  credit,  in  the  financing  of  the 

operations of their firm; and (4) are offered inferior terms on granted loans. 

To  extract  the  predetermined  component  of  the  gender  bias  across  our  sample  of 

countries,  we  employ  information  on  cultural  attitudes  of  US-born  descendants  of  European 

immigrants  embedded  in  the  General  Social  Survey.  In  particular,  we  focus  on  whether  the 

person agrees or disagrees with the claim: “It is much better for everyone involved if the man is 

the achiever outside the home and the woman takes care of the home and family.” Since social 

capital is transmitted from parents to children, the degree of the gender bias of a person born 

in the U.S. whose parents were born in Germany is a good proxy for the degree of gender bias 

of his parents, and by extension a good proxy for the predetermined component of gender bias 

in  contemporaneous  Germany.  This  measurement  strategy  allows  us  to  isolate  the  inherited 

component of the gender bias for 17 European countries. We then match our country-specific 

proxy for gender bias to information on the credit market experience by 5,905 firm-owners – 

4,193  males  and  1,712  females  –  in  these  countries,  and  test  whether  variations  in  inherited 

cultural biases are mapped into variations in credit access and in the terms on loans and credit 

lines granted by banks.  

We find that cross-country differences in gender bias explain a substantial proportion of 

the  variation  in  credit  access  by  female  firm-owners,  but  not  in  loan  rejection  rates  or  in  the 

loan  terms  of  the  ultimately  successful  loan  applications.  In  particular,  in  countries  with  a 

higher inherited gender bias and comparing female owners to male owners, the former group 

ECB Working Paper 1822, July 20152of owners: 1) More often do not apply for a loan, and 2) finance a lower portion of their firm’s 

operating expenses with bank credit and a higher portion with trade credit. However, once the 

firm  applies  for  a  loan  or  a  line  of  credit,  we  find  –  in  most  specifications  -  no  statistical 

differences between male- and female-owned firms in the probability of application denial. We 

also  find  no  differences  in  the  terms  on  granted  loans,  such  as  interest  rates,  collateral 

requirements, and the negotiation time involved. The evidence thus implies that the negative 

effect  of  gender  bias  on  credit  access  is  primarily  manifested  through  a  reluctance  of  female 

entrepreneurs  to  apply  for  formal  credit  which  is  driven  by  their  belief  that  their  credit 

application will be denied. Once the credit application is made, similar approval rates and loan 

terms suggest female-owned firms are assessed to be identical to male-owned firms, and this 

assessment is correct because their credit worthiness also turns out to be statistically similar. 

The evidence thus suggests that there is no taste-based discrimination at the level of the bank, 

i.e., it is not the case that loan officers select only the best female-owned firms while lending 

also to mediocre male-owned firms. 

The  contribution  of  our  paper  consists  in  linking  the  severity  of  the  inherited  cultural 

gender bias as it exists across countries to one specific economic outcome (credit access). Our 

results are consistent with a mechanism whereby female entrepreneurs tend to shy away from 

the  formal  credit  granting  process  in  countries  where  cultural  attitudes  favour  males,  even 

though credit markets are not actively discriminating against them. Policies in high-gender bias 

countries that enable and facilitate lending to female-owned firms are therefore called for and 

in particular and beyond doubt will enhance overall firm performance. One possibility could be 

to stimulate banks to recruit and train female loan officers that may be less subject to a gender 

bias,  and  thus  encourage  female  entrepreneurs  to  apply.  An  alternative  policy  could  be 

subsidies  to  banks  to  actively  solicit  loan  applications  from  creditworthy  female-owned  firms 

that are now for no good reason discouraged from seeking formal access to credit. 

ECB Working Paper 1822, July 20153A woman cannot be herself in the society of the present day, which is an exclusively 

masculine society, with laws framed by men and with a judicial system that judges feminine 

conduct from a masculine point of view. 

Henrik Ibsen, Ibsen’s Workshop, 1912 

1.

Introduction 

Economic research has focused intensely in recent years on the link between culture – 

which Guiso, Sapienza, and Zingales (2006) define as the customary beliefs and values that are 

transmitted  fairly  unchanged  from  generation  to  generation  –  and  economic  outcomes. 

Scholars  have  provided  strong  evidence  that  various  beliefs  and  preferences,  rooted  in  the 

cultural experience of economic agents, are related to a wide array of economic phenomena, 

ranging from growth (Knack and Keefer, 1997) and financial development (Guiso, Sapienza, and 

Zingales,  2004),  to  trade  (Guiso,  Sapienza,  and  Zingales,  2009)  and  regulation  (Aghion,  Algan, 

Cahuc, and Shleifer, 2010). 

Having established a robust correlation, the research effort has turned to identifying the 

causal  impact  of  culture  on  economic  outcomes.  Doing  so  requires  taking  two  crucial  steps. 

First,  one  needs  to  show  that  a  narrow  subset  of  the  entire  range  of  cultural  beliefs  and 

preferences  has  a  directly  measurable  impact  on  a  specific  economic  phenomenon.  Second, 

one  needs  to  show  that  not  only  the  contemporaneous  component  of  these  beliefs  and 

preferences,  but  also  their  predetermined  component,  has  an  impact  on  the  economic 

outcome in case. 

This  paper  aims  to  take  both  steps  concurrently  by  investigating  the  impact  of  a  well-

defined cultural belief – i.e., the belief that women are inferior to men, or the gender bias – on 

a well-defined economic outcome – i.e., credit access. Specifically, we study whether, in high-

gender bias countries and relative to male-owned counterparts, females-owned firms: (1) Are 

ECB Working Paper 1822, July 20154 
more often denied bank credit; (2) are more often discouraged from applying for bank credit; 

(3) rely less on bank credit and more on alternative sources of finance, such as trade credit, in 

the financing of the operations of their firm; and (4) are offered inferior terms on granted loans. 

Establishing a causal link from gender bias to credit market outcomes is at the heart of 

our  paper.  To  extract  the  predetermined  component  of  the  gender  bias  across  our  sample 

countries,  we  borrow  the  empirical  strategy  from  Algan  and  Cahuc  (2010)  who  employ 

information  on  cultural  attitudes  of  US-born  descendants  of  European  immigrants  to  extract 

measures  of  inherited  trust  and  investigate  its  effect  on  economic  growth.  We  focus  on 

answers provided by U.S. descendants of European immigrants to survey questions about the 

proper place of women in society. For instance, we compare Americans of German, Polish, or 

Russian origin whose ancestors immigrated to the US in 1960. Since it is well established that 

social  capital  is  transmitted  from  parents  to  children  (Putnam,  2000;  Guiso,  Sapienza,  and 

Zingales, 2006), the degree of the gender bias of a person born in the U.S. that has parents that 

were  born  in  Russia  is  a  good  proxy  for  the  degree  of  gender  bias  of  his  parents,  and  by 

extension a good proxy for the predetermined component of gender bias in contemporaneous 

Russia. 

This measurement strategy allows us to isolate the inherited component of the gender 

bias for 17 European countries. Our measure of inherited gender bias is clearly superior – in a 

causal  sense  –  to  similar  measures  that  are  commonly  extracted  from  contemporaneous 

surveys of cultural attitudes in these countries, such as the World Values Survey, that may be 

directly  influenced  by  the  current  experiences  of  the  surveyees  including  those  in  credit 

markets. 

Next, we match our country-specific proxy for gender bias to information on the credit 

market experience by 5,905 firm-owners – 4,193 males and 1,712 females – in these countries, 

and test whether variation in inherited cultural biases is mapped into variations in credit access 

and in the terms on loans and credit lines granted by banks. We focus on small individually- or 

ECB Working Paper 1822, July 20155family-owned  firms  to  make  sure  that  if  one  of  the  owners  of  the  firm  is  female,  she  is 

materially influential in the firm’s decisions. 

We find that cross-country differences in gender bias explain a substantial proportion of 

the  variation  in  credit  access  by  female  firm-owners,  but  not  in  loan  rejection  rates  or  in  the 

loan  terms  of  the  ultimately  successful  loan  applications.  In  particular,  in  countries  with  a 

higher inherited gender bias and comparing female owners to male owners, the former group 

of owners: 1) More often do not apply for a loan because they believe they will not get one, 

and 2) finance a lower portion of their firm’s operating expenses with bank credit and a higher 

portion with trade credit. However, once the firm applies for a loan or a line of credit, we find – 

in most specifications - no statistical differences between male- and female-owned firms in the 

probability  of  application  denial.  We  also  find  no  differences  in  the  terms  on  granted  loans, 

such as interest rates, collateral requirements, and the negotiation time involved. 

The  evidence  thus  implies  that  the  negative  effect  of  gender  bias  on  credit  access  is 

manifested through a self-selection of female entrepreneurs out of the loan application process 

rather than through higher loan denial rates or a higher price of credit. Importantly, this self-

selection is largely driven by the female firm-owners’ belief that their credit application will be 

denied. Our results are observed even when country and industry fixed effects (which capture 

important time-invariant variation within the high versus low gender-bias group), a wide range 

of  firm-specific  characteristics,  and  a  nearly  exhaustive  set  of  characteristics  capturing  the 

national business environment are included. 

Overall, our evidence is consistent with Cavalluzzo, Cavalluzzo, and Wolken (2002) and 

Muravyev,  Schäfer,  and  Talavera  (2009)  who  find  that  female-owned  firms  are  less  likely  to 

apply  for  and/or  to  obtain  bank  credit  than  male-owned  firms,  both  in  the  US  and  in  an 

international context. However, to our knowledge, our paper is the first to make the empirical 

link between gender, a specific economic outcome (credit access), and the gender bias. 

ECB Working Paper 1822, July 20156This paper’s results are robust to one potentially confounding influence. In particular, a 

higher  gender  bias  can  over  time  trigger  changes  in  the  skill  composition  of  the  labor  force 

through  the  selection  of  workers  into  education  or  employment,  or  through  migration,  for 

instance  (Mulligan  and  Rubinstein,  2008).  One  potential  implication  is  that  in  countries  with 

high  gender  bias,  female  firm-owners’  set  of  skills  required  to  run  a  firm  could  be  inferior.  If 

banks know this, they would be rationally rationing female-owned firms in the credit markets, 

and  consequently  the  line  between  statistical  and  taste-based  discrimination  would  become 

blurred.  However,  we  verify  that  there  is  no  statistical  difference  in  firm  growth  between 

female-owned and male-owned firms. Even stronger than that, we show that in high gender-

bias  countries,  there  is  no  statistical  difference  in  growth  rates  between  constrained  female-

owned  firms  and  unconstrained  male-owned  firms.  We  therefore  argue  that  our  estimates 

indeed pick up the effect of taste-based discrimination rather than the rationing of potentially 

inferior projects by banks in countries with a pronounced gender bias. 

We are clearly not the first to analyze the impact of cultural beliefs and preferences on 

economic outcomes. Putnam, Leonardi, and Nanneti (1993) show that social capital is a good 

predictor  of  government  performance  across  Italian  regions.  Guiso,  Sapienza,  and  Zingales 

show  that  trust  in  society  affects  phenomena  such  as  entrepreneurship  (2006)  and  trade 

(2009). Algan and Cahuc (2009) and Aghion, Algan, and Cahuc (2011) analyze the relationship 

between  trust  and  institutions  and  find  that,  in  general,  countries  whose  citizens  trust  each 

other less tend to put in place more restrictive institutions. Aghion, Algan, Cahuc, and Shleifer 

(2010) explain this result by arguing that distrust increases the public demand for government 

intervention  whereas  regulation  in  itself  discourages  the  formation  of  trust.  Tabellini  (2008) 

analyzes the role of culture on the per-capita-income of European regions by using institutional 

history and literacy rates as an instrument for contemporaneous trust. Finally, Barr and Serra 

(2010)  find  that  social  norms  in  the  home  country  are  related  to  immigrants’  tolerance  for 

corruption.  

ECB Working Paper 1822, July 20157Our  contribution  to  this  extant  literature  is  that  we  uncover  a  specific  mechanism 

through  which  culture  affects  economic  growth,  namely  through  the  gender  bias  and  the 

differential  access  to  credit.  To  make  this  contribution  we  borrow  the  recently  introduced 

identification strategy from Algan and Cahuc (2010). They use the cultural attitudes of U.S.-born 

descendants  of  non-U.S.  born  ancestors  to  extract  another  predetermined  component  of 

culture, i.e., trust, in the ancestral countries and in this way made an important contribution to 

this literature. 

The second related strand of the literature analyzes the relation between well-defined 

characteristics  of  the  credit  market  and  various  social  outcomes.  Garmaise  and  Moskowitz 

(2006) for example show that bank mergers result in substantially higher property crime rates, 

because  lower  access  to  finance  depresses  local  economic  growth  and  raises  the  relative 

benefit  of  illegal  activity.  Beck,  Levine,  and  Levkov  (2010)  show  that  bank  deregulation  is 

associated with a tighter distribution of income, an outcome that is not due to better access to 

credit for poorer households, but to an increase in the relative demand for low-skilled workers 

by business firms. While we do not explore the link between the structure of the credit market 

and  economic  outcomes,  our  work  is  naturally  related  to  these  studies  in  that  we  study  how 

gender bias is transmitted into entrepreneurship through the channel of access to bank credit. 

Our  paper  also  relates  to  the  literature  on  taste-based  discrimination  pioneered  by 

Becker (1957). This literature has focused mainly on the labor market consequences of various 

physical  attributes.  For  example,  Hammermesh  and  Biddle  (1994)  and  Mobius  and  Rosenblat 

(2006) find that physical beauty has a positive effect on earnings, while Cawley (2004) finds that 

obesity lowers the wages of white females. Persico, Postlewaite, and Silverman (2004) and Case 

and  Paxson  (2008)  establish  a  robust  effect  of  height  on  earnings,  the  latter  through  the 

channel of higher cognitive abilities. Johnston (2010) finds a substantial female wage premium 

from being blonde. 

ECB Working Paper 1822, July 20158Various  authors  have  also  looked  into  the  effect  of  discrimination  –  as  well  as  of  its 

interaction  with  various  market  developments  –  on  the  white-black  wage  gap  (Neal  and 

Johnson,  1996;  Rodgers  and  Sprigs,  1996;  Bertrand  and  Mullainathan,  2004;  Carneiro, 

Heckman, and Masterov, 2005; Charles and Guryan, 2008; Levine and Rubinstein, 2011). And, in 

a  recent  contribution,  Castilo,  Petrie,  Torero,  and  Vesterlund  (2013)  find  that  difference 

between  males  and  females  in  prices  and  rejection  rates  in  a  highly  competitive  taxi  market 

with experienced traders is due to statistical, not to taste-based discrimination.  

Conceptually most similar to our study is the research on the effect of discrimination on 

the  male-female  wage  gap  (Bayard,  Hellerstein,  Neumark,  and  Troske,  2003),  and  on  female 

market participation (Goldin and Rouse, 2000). This research in general confirms the existence 

of a sizeable earnings gender gap. For example, controlling for education, experience, personal 

characteristics, city and region, occupation, industry, government employment, and part-time 

status,  Altonji  and  Blank  (1999)  find  that  only  about  27  percent  of  the  gender  wage  gap  is 

explained by differences in observable characteristics. We argue that the difference in income 

between females and males can be related to cultural beliefs, with access to credit being one 

channel  through  which  the  gender  bias  depresses  the  earnings  of  women  in  society.  To  our 

knowledge, ours is the first paper to test for a direct link between the predetermined cultural 

component of gender discrimination and a well-defined economic outcome. 

Most  related  to  our  paper  is  the  empirical  research  on  gender  and  credit  market 

outcomes. Using US data on small businesses, Asiedu, Freeman, and Nti-Addae (2012) find that 

while race is a significant predictor of both access to credit and the cost of credit, gender is not. 

Belluci,  Borisov,  and  Zazzaro  (2010)  find  that  female  firm-owners  in  Italy  face  tighter  credit 

constraints when dealing with one individual bank, even though they do not pay higher interest 

rates.  Using  the  Italian  Credit  Registry,  however,  Alesina,  Lotti,  and  Mistrulli  (2013)  find  that 

after controlling for entrepreneurial risk, female borrowers do pay higher rates, especially when 

their  guarantor  is  a  female,  too.  Using  a  large  cross-country  sample,  Muravyev,  Schäfer,  and 

Talavera (2012) find that female firm-owners are more likely to be denied bank credit and that 

ECB Working Paper 1822, July 20159they  tend  to  pay  higher  rates  on  bank  loans,  while  for  a  number  of  Sub-Saharan  countries 

Aterido, Beck, and Iacovone (2013) in contrast find no evidence of such gender discrimination. 

The first three papers use data from one country only and so unlike ours are unable to relate 

access  to  credit  to  the  variation  in  cultural  biases  across  countries.  Relative  to  the  latter  two 

papers, we are not only able to link gender to credit outcomes in an international context, but 

also to investigate the effect of gender bias on that link. 

The  rest  of  the  paper  is  organized  as  follows.  Section  2  presents  the  data.  Section  3 

discusses our identification strategy. Our estimates of the effect of inherited cultural biases on 

credit market outcomes are presented in Section 4. Section 5 concludes. 

2.

Data 

In this section, we discuss the various data sources used in this paper. 

A.

General Social Survey 

Our  measure  of  the  inherited  gender  bias  is  constructed  using  data  on  U.S.  born 

descendants of non-U.S. born forebears. The information itself is provided by the General Social 

Survey database (GSS). The database covers the period 1972-2010. The data contains a range of 

demographic  characteristics,  such  as  age,  gender,  religion,  marital  status,  education,  and 

employment status of the respondents. Importantly for our purposes, it provides information 

on  the  birthplace  and  the  country  of  origin  of  the  respondents’  forebears  since  1977.  The 

respective  GSS  variable  “ETHNIC”  is  defined  as  the  answer  to  the  question:  “From  what 

countries or part of the world did your ancestors come?” The countries of origin cover almost 

all European countries, alongside Canada, Mexico, India, and Africa (an aggregate category). We 

use information on U.S. citizens whose ancestors came from abroad, regardless of when they 

arrived in the U.S. 

Our proxy for gender bias is derived from the variable “FEFAM” which is defined by the 

answer to following question: “It is much better for everyone involved if the man is the achiever 

ECB Working Paper 1822, July 201510outside the home and the woman takes care of the home and family.” The answers are given on 

a scale from 1 to 4. From these answers, we construct a variable for inherited gender bias which 

we denote as “Traditional gender roles”. The variable is equal to 1 if the respondent answered 

“Strongly agree” or “Agree”, and equal to 0 if the respondent answered “Disagree” or “Strongly 

disagree”.  There  are  a  total  of  20,992  responses  in  the  GSS,  of  which  8,715  (41.5  percent) 

responded  “Strongly  agree”  or  “Agree”,  and  12,277  (58.5  percent)  responded  “Disagree”  or 

“Strongly disagree”.1 6,472 of those respondents come from 17 countries that overlap with the 

countries in our firm-level dataset which we describe next. 

B.

Firm-level data 

Our  firm-level  data  come  from  the  2004/2005  wave  of  the  Business  Environment  and 

Enterprise  Performance  Survey  (BEEPS),  administered  jointly  by  the  World  Bank  and  the 

European  Bank  for  Reconstruction  and  Development  (EBRD).2  We  exclude  data  from  other 

waves of this survey as they do not provide comparable information on credit access. The 2004 

and 2005 BEEPS surveyed 9,655 firms from 27 countries in Eastern Europe and central Asia, and 

4,453 firms from 5 Western European countries, respectively. We narrow this sample down to 

17 countries for which we also have GSS data.3 In addition to that, we exclude all firms that are 

not sole proprietorships. This allows us to focus on small individually- or family-owned firms. In 

this way, we make sure that if one of the owners of the firm is female, she has actual influence 

1  Algan  and  Cahuc  (2010)  use  an  identical  strategy  to  extract  the  predetermined  component  of  trust  from  GSS 
answers on the question whether people in general can be trusted. 

2 For a detailed discussion of the data, see, e.g., Brown, Ongena, Popov, and (cid:122)e(cid:404)in (2011), Popov and Udell (2012), 
and Ongena, Popov, and Udell (2013). 

3  Unfortunately,  due  to  the  geographic  scope  of  the  survey,  we  are  missing  information  from  many  European 
countries,  such  as  the  Scandinavian  ones,  that  were  pioneers  in  women’s  rights.  We  address  this  issue  in  a 
robustness test described in Section 4.B. 

ECB Working Paper 1822, July 201511in the firm’s decisions. The number of firms that report all the information we require for this 

study ranges from 141 in Bosnia, Croatia, and Lithuania, to 763 in Poland.4 

B.1. 

Credit access 

To  measure  credit  access  by  the  firm  we  employ  four  different  measures.  We  employ 

the self-reported Share working capital financed with bank credit and the Share working capital 

financed  with  trade  credit  as  two  direct  measures  of  “past”  access  to  credit.  The  survey 

questionnaire  also  includes  three  questions  about  firm  financing  which  allow  us  to  further 

describe firms’ current access to credit. Firms are first asked if they have a loan or not. Those 

firms  without  a  loan  are  then  asked  in  Q47a  whether  they  (a)  did  not  apply  for  a  loan  or  (b) 

applied for a loan, but the application was turned down or (c) have a loan application pending.5 

Those firms that did not apply for a loan are then asked in Q47b to list the main reasons 

why they did not do so. To this question there are multiple possible answers: (a) The firm does 

not need a loan, (b) application procedures are too burdensome, (c) collateral requirements are 

too strict, (d) interest rates are too high, (e) informal payments are necessary, or (f) the firm did 

not think their application would be approved. From the above questions we establish our two 

indicators of current credit access. The variable Firm discouraged from applying for a loan is a 

dummy variable which equals 1 for those firms which did not apply for a loan, listing (b), (c), (d), 

(e), or (f) as a potential reason. For all firms which did apply for a loan, or did not apply because 

of reason (a), the variable equals 0. The variable Loan application rejected is a dummy variable 

4 The survey aimed to achieve representativeness in terms of the size of firms it surveyed: Roughly two thirds of 
the firms surveyed are “small”, i.e., they have less than 20 workers. By design the survey only covers established 
firms, i.e., firms which have been in business for at least three years. This implies that our sample does not allow us 
to examine credit access for very young or start-up firms. Moreover, our results are subject to sample selection, in 
the sense that we only observe firms which had sufficient internal or external funds to survive for at least three 
years.

5  Despite  the  care  by  the  surveyors  to  solicit  the  correct  answers,  it  is  not  entirely  impossible  that  males 
underreport past loan rejection or application discouragement more than females. Yet it seems rather unlikely to 
us that the (mis-)reporting of the shares of working capital would be gender-specific. 

ECB Working Paper 1822, July 201512which equals 1 for those firms which applied for a loan but their application was turned down, 

and  equals  0  for  those  firms  which  applied  for  a  loan  and  have  a  loan.  Firms  with  pending 

applications (i.e., less than 1 percent) are treated as missing. 

< Table 1 > 

Table 1 presents summary statistics for our indicators of past and current credit access 

by country. The table shows that there are substantial cross-country differences in credit access 

across the countries in our sample. For instance, while only 4 percent of the average Russian 

firm’s working capital is financed with bank credit, in Ireland bank credit accounts for almost a 

quarter (24 percent). The fraction of firms that are discouraged from applying for a loan varies 

between 2 percent in Ireland and 53 percent in Macedonia. The cross-country variation in loan 

rejection  rates  is  similarly  substantial,  with  1  percent  of  loan  applications  rejected  in  Bosnia 

versus 16 percent in the Czech Republic. In general, loan rejections rates appear low, suggesting 

that  in  our  sample,  restriction  in  access  to  finance  happen  mostly  in  the  dimension  of 

discouragement,  and  that  firms  apply  for  a  loan  only  when  they  are  reasonably  certain  that 

their application will not be rejected. 

The availability of data on discouraged firms is one of the main strengths of the BEEPS, 

allowing us to separate firms that did not apply for a loan because they do not need one from 

those that did not apply although they need one. The reasons for not applying for a loan vary 

widely across countries in Eastern Europe. The share of firms which do not apply because they 

do not need a loan varies from 47 percent in Yugoslavia to 81 percent in Slovenia. The share of 

firms that are discouraged by burdensome procedures varies from 2 percent in Slovakia to 16 

percent  in  Macedonia.  Whereas  only  1  percent  of  firms  in  Estonia  feel  discouraged  because 

they  anticipate  rejection,  this  share 

is  10  percent 

in  Lithuania.  In  addition  to  that, 

discouragement may signal an actual rejection if firms decide not to file an application after an 

informal conversation with the loan officer (see Duca and Rosenthal, 1993). 

ECB Working Paper 1822, July 201513B.2. 

Loan terms 

Alongside  variables  describing  various  aspects  of  credit  availability,  we  make  use  of  a 

number of variables that describe the terms of granted bank loans. Firms with outstanding bank 

credit  provide  many  details  on  their  most  recent  loan.  In particular,  firms  in  BEEPS  are  asked 

about the loan rate, the maturity, the currency denomination, the collateral requirements, and 

the time it took to negotiate the last bank loan. 

< Table 2 > 

Table  2  provides  the  sample  summary  statistics  by  country  for  the  most  relevant  loan 

characteristics. In the full sample it takes an average of almost 20 days to negotiate a loan, but 

only  8  days  in  Spain  and  more  than  a  month  in  the  Czech  Republic  and  in  Slovakia.  The 

overwhelming  majority  of  loans  in  all  countries  are  collateralized,  but  there  is  great  deal  of 

variation, with the share of collateralized loans ranging from 56 percent in Greece and Slovenia 

to  95  percent  in  Bosnia  and  Macedonia.  Collateral  itself  averages  149  percent  of  the  loan.  In 

euro  area  countries,  at  most  2  percent  of  the  loans  are  in  foreign  currency  compared  to  45 

percent in Macedonia. Finally, the nominal average annualized rate of loans is 985 basis points, 

ranging from 474 basis points in Ireland to 1,794 basis points in Romania. 

B.3. 

Firm characteristics 

Recent empirical research by Brown, Jappelli, and Pagano (2009), Brown, Ongena, and 

Ye(cid:404)in  (2009),  Brown,  Ongena,  Ye(cid:404)in,  and  Popov (2011),  Popov  and  Udell  (2012),  and  Ongena, 

Popov, and Udell (2012) using the BEEPS data, and by Chakravarty and Xiang (2013) using the 

similar Investment Climate Survey data, has shown that firm size, ownership, activity, product 

market  competition,  accounting  standards,  bank  use  and  internal  financing,  and  obstacles  to 

doing business affect credit access and credit terms. 

Following  the  above  literature  we  relate  our  indicators  of  credit  access  to  firm-level 

indicators of firm size (Small firm, Medium firm, and Large firm), privatization history (Originally 

ECB Working Paper 1822, July 201514private),  export  activities  (Exporter),  the  number  of  local  product  market  competitors  of  the 

firm  (Competition),  access  to  government  subsidies  (Subsidized),  and  accounting  standards 

(Audited).  We  further feature  an  indicator  of  the  sector  in  which the  firm  operates  (by  SIC  1-

digit). Crucially, we incorporate information on the firm owner’s gender (Female). While we do 

not  have  data  on  tangible  assets  to  proxy  for  the  ability  of  the  firm  to  pledge  collateral,  the 

variables capturing firm size and industry of operation alleviate this problem to a large degree. 

< Table 3 > 

The  definitions  of  these  firm-level  variables  are  provided  in  the  Appendix.  Summary 

statistics for our firm-level variables are presented in Table 3. The table shows substantial cross-

country variation within our sample in terms of ownership by gender. For example, fewer than 

1  in  5  firms  in  Macedonia,  Slovakia,  and  Spain  have  a  female  owner,  while  1  in  2  in  Portugal 

does. Apart from that, the average firm in our sample is small, originally private, has no access 

to foreign product markets, receives no subsidies from local or central governments, and does 

not have its financial statements certified by an external auditor. 

C.

Country-level data 

As  in  Pistor,  Raiser,  and  Gelfer  (2000),  de  Haas  and  Lelyveld  (2006),  Giannetti  and 

Ongena  (2008),  and  Brown,  Jappelli,  and  Pagano  (2009),  we  pay  attention  to  how  access  to 

credit  may  be  determined  by  market  and  institutional  characteristics.  In  particular,  we  relate 

access  to  credit  and  loan  terms  to  foreign  ownership  in  the  banking  sector,  to  credit 

information  sharing,  and  creditors’  rights,  as  well  as  to  various  macroeconomic  and  financial 

developments. 

< Table 4 > 

Table  4  presents  summary  statistics  for  our  country-level  variables.  Unsurprisingly, 

foreign bank ownership is very low in countries with large domestic banks (like Germany and 

Spain) and very high in Eastern European countries which underwent bank privatizations in the 

ECB Working Paper 1822, July 2015151990s. Western European countries which have large and developed financial sectors and are 

euro  area  members  also  have  the  highest  private  credit/GDP  ratios,  the  highest  degree  of 

information sharing, the highest GDP per capita, and the lowest inflation. Conversely, Eastern 

European  countries  have  on  average  higher  GDP  growth  rates,  although  the  highest  growth 

rates in the sample are in Greece and Ireland. 

The  central  variable  in  our  study  is  the  country-specific  gender  bias.  The  variable  is 

calculated  using  the  answers  by  descendants  of  European  immigrants  in  the  US  to  the  GSS 

question  on  the  role  of  women  in  society  (see  Section  2.A).  We  describe  the  estimating 

procedure we employ to calculate this variable in the next section, but the general idea is that 

after controlling for a variety of demographic characteristics, we interpret the coefficient on the 

country-of-origin fixed effect as the inherited gender bias. In an OLS context, this gender bias is 

calculated  relative  to  the  rest  of  the  countries  in  the  GSS.  As  Table  4  makes  clear,  being  a 

descendant  of  parents  coming  from  the  countries  in  former  Yugoslavia  results  in  a  higher 

gender  bias,  and  being  a  descendant  of,  for  instance,  Irish  parents  results  in  a  lower  gender 

bias, than being a descendant of parents coming from the reference group. 

3.

Identification strategy 

In this section, we discuss the construction of our measure of country-specific inherited 

gender bias, as well as the identification strategy. 

A.

Inherited gender bias 

The construction of the country-specific measure of gender bias is based on the GSS. To 

simplify  things,  we  do  not  focus  on  one  particular  generation,  but  take  the  answers  of  all 

respondents in the survey, regardless of what generation immigrants they are. Thus, inherited 

ECB Working Paper 1822, July 201516trust in 2005 corresponds to the trust inherited by U.S.-born children of European immigrants, 

born at any point after 1910.6 

< Table 5 > 

Table  5  reports  the  OLS  estimates  of  the  gender  bias  for  the  full  sample.7  The  gender 

bias  of  all  other  countries  of  origin  is  used  as  the  reference  group.8  The  regression  includes 

country-of-ancestry  dummies,  whose  estimated  coefficients  are  then  used  as  the  country-

specific proxies for the gender bias. In addition to those, the regression controls for a host of 

individual-specific  characteristics:  Age,  age  squared,  education,  gender,  religion,  employment 

status, and income. The total number of observations for which data is available on all relevant 

variables is 20,992. 

The results imply that the gender bias increases with age and decreases with education. 

Males  have  a higher  gender  bias than  females. All  religious  groups have  a  higher  gender  bias 

than  atheists  (the  reference  groups),  with  the  exception  of  Jewish  respondents.  Employed, 

unemployed, and retired persons have a lower gender bias relative to the control group (which 

is comprised of inactive persons). Finally, respondents with a higher income have a significantly 

lower gender bias. 

Turning to the countries in our sample, the lowest gender bias in our sample – all else 

equal  -  is  exhibited  by  the  descendant  of  immigrants  from  Ireland,  who  are  4.1  percentage 

points  less  likely  to  agree  that  “it  is  better  if  the  man  pursues  a  professional  career  and  the 

6  Paulson  and  Osili  (2008)  show  that  the  ancestral  beliefs  of  U.S.  immigrants  decline  over  time,  suggesting  that 
gender  bias  may  be  more  pronounced  for  U.S.  citizens  who  immigrated  to  the  U.S.  more  recently.  We  perform 
tests using the latest wave of immigrants only, and our results are robust to this alternative measurement of the 
inherited gender bias. 

7 The probit regression yields similar results. 

8 An identical ranking of countries in terms of gender bias is obtained when we run the test on the 17 countries in 
the BEEPS data only, using Polish-Americans as a reference group. 

ECB Working Paper 1822, July 201517woman takes care of the house and family” than the descendants of the immigrants from the 

reference group. At the other extreme, descendants of the immigrants from Yugoslavia are 20.2 

percentage points more likely to agree with this claim than the reference country descendants. 

We match the coefficient on the Yugoslavia dummy to all five countries in the sample that by 

2005  former  Yugoslavia  had  broken  into  (i.e.,  Bosnia,  Croatia,  Macedonia,  Slovenia,  and 

Yugoslavia, comprised of Serbia and Montenegro; we later report on the strengthening effect 

on our estimates of removing Yugoslavia from the sample). We also assign the estimate on the 

Czechoslovakia dummy to both the Czech Republic and to Slovakia. 

In  general,  we  classify  the  bottom  50%  of  the  countries  as  in  terms  of  gender  bias 

relative  to  the  reference  group  as  low-gender  bias  countries.  These  countries  are  the  Czech 

Republic,  Germany,  Greece,  Ireland,  Lithuania,  Portugal,  Russia,  and  Slovakia.  Because  the 

country-specific  gender bias  includes  a  measurement error,  being  an  OLS  regression  estimate 

itself,  in  future tests,  we  pool these  countries  into the  group  of “Low  gender  bias”  countries. 

The rest of the countries we classify as the “High gender bias” countries. 

One caveat related to our approach is that even if we have a good proxy for inherited 

gender  bias,  the  correlation  between  inherited  values  and  contemporaneous  economic 

outcomes can be interpreted as a causal relation only if the two variables are not codetermined 

by common factors. It is possible that changes in inherited trust and in credit access are driven 

by changes in institutions that disadvantage women. For example, if the legal system excludes 

women  from  the  inheritance  of  property,  female  entrepreneurs  will  be  less  likely  to  have 

tangible collateral to pledge against bank credit, and this phenomenon may affect both gender 

bias and credit access. To address this issue, in robustness tests we calculate the gender bias 

from  the  earliest  possible  sub-sample  of  immigrants  to  the  US.  This  does  not  change  the 

ranking of countries in terms of gender bias, and hence the main results of the paper. 

ECB Working Paper 1822, July 201518B.

Empirical strategy 

In  order  to  tease  out  the  causal  effect  of  culture  on  credit  market  outcomes,  we 

evaluate  the  effect  of  gender  on  credit  market  outcomes  accounting  for  gender  bias.  For 

simplicity  of  illustration,  yet  without  loss  of  generality,  assume  that  countries  can  be  divided 

into  those  with  a  high  gender  bias  and  those  where  society  does  not  derive  disutility  from 

treating  females  as  equal  to  males.  We  create  a  dummy  variable  equal  to  1  if  the  US 

descendants of emigrants from the respective country have a higher gender bias than the US 

descendants of emigrants from the reference group, and to 0 if they have a lower gender bias. 

Then, we estimate the following equations: 

Credit

_

access

(cid:32)

(cid:69)
1

Female

isc

(cid:152)

Bias

c

(cid:14)

(cid:69)
2

Female

isc

(cid:14)

(cid:69)
3

X

isc

(cid:14)

(cid:69)
4

(cid:14)(cid:39)
sc

(cid:72)
isc

isc

                (1) 

and 

Loan

_

term

(cid:32)

(cid:69)
1

Female

isc

(cid:152)

Bias

c

(cid:14)

(cid:69)
2

Female

isct

(cid:14)

(cid:69)
3

X

isc

(cid:14)

(cid:69)
4

Y
sc

(cid:14)(cid:39)(cid:14)
(cid:69)
5

sct

(cid:72)

isct

      (2) 

isct

In model (1), we evaluate the effect of the gender of the owner of firm i in sector s in 

country c on the probability of being rejected or discouraged in credit markets, as well as on the 

share of capital financed from bank versus non-bank sources, in high relative to low gender bias 

countries.9  The  equation  controls  for  firm  (X)  characteristics,  and  it  includes  interactions  of 

sector  and  country  fixed  effects  (

sc(cid:39) ).  Including  firm  characteristics  is  crucial  since  female-

owned  firms  may  also  be  smaller,  and  so  the  effect  of  gender  on  credit  we  measure  may  be 

contaminated  by  the  effect  of  size.  The  fixed  effects  are  also  enormously  important  as  they 

control for any market-sector unobservables that are common across all firms. For example, it 

is  possible  that  there  is  disproportionate  concentration  of  female  entrepreneurs  in  industries 

9 In the case of our dummy variables for credit access, we employ a probit regression, but the results are robust to 
using a logit model. 

ECB Working Paper 1822, July 201519which use more trade credit and less bank credit for technological reasons. While this “female-

run  agriculture  vs.  male-run  manufacturing”  story  may  be  more  applicable  to  developing 

economies than to the countries in our sample, we wish to fully account for such possibilities, 

and the comprehensive matrix of country and sector fixed effects does exactly that. 

In  model  (2),  we  evaluate  the effect of  gender on  the terms  of  granted  loans  (days to 

negotiate the loan, collateral, currency denomination, loan rate), in high relative to low gender 

bias countries. Although there is only one loan per firm, this regression has a panel component 

because loans were received at different points in time (between 1998 and 2005), which allows 

us  to  control  for  the  time-varying  component  of  the  various  country-level  developments  (Y). 

The regressions also include different combinations of country, industry, and time fixed effects. 

In  the  two  equations, 

isc(cid:72)   and 

isct(cid:72) ,  respectively,  are  i.i.d.  error  terms  composed  of  a 

person-specific  idiosyncratic  shock  and any unobservable  sector-country (sector-country-year) 

fixed  effects.  We  cluster  the  standard  errors  by  country  to  allow  for  arbitrary  within-country 

correlations in the errors. 

In  these  specifications, 

2(cid:69)   measures  the  causal  impact  of  being  female  on  access  to 

credit or on the terms of granted loans, respectively, and

1 (cid:69)(cid:69) (cid:14)
2

 measures the effect of being 

female  on  credit  market  outcomes  or  on  the  terms  of  granted  loans  in  countries  with  high 

gender bias. The direct effect of gender bias, which is common to everyone in the respective 

group of countries, is subsumed in the country fixed effects. The matrix of country and industry 

fixed effect interactions accounts for the possibility that a “female-run agriculture vs. male-run 

manufacturing”-type  separation  is  more  likely  to  take  place  in  countries  with  a  high  gender 

bias. 

ECB Working Paper 1822, July 2015204.

A.

Results 

Inherited cultural bias, gender, and credit market outcomes 

We now turn to the correlation between gender and credit market outcomes, in high vs. 

low gender bias countries. The idea of this section is to test the following hypotheses: a) Firm-

owners’  decision  whether  to  apply  for  credit  are  affected  by  their  gender  and  by the  cultural 

environment, and b) bankers’ decisions to ration female-owned firms that are similar to male-

owned firms in terms of creditworthiness are driven by a gender bias. While Hertz (2011) and 

Muravyev,  Schäfer,  and  Talavera  (2012)  provide  evidence  that  banks  discriminate  against 

female  corporate  loan  applicants,  we  want  to  test  whether  such  an  effect  can  be  linked  to 

gender bias in society. We also aim to test whether there is an informal mechanism whereby 

female firm-owners are more often discouraged from applying for formal credit in high gender 

bias countries. 

   A.1. Inherited cultural bias, gender, and credit access 

We  first  investigate  the  determinants  of  credit  access.  Table  6  reports  the  empirical 

estimates  from  Model  (1).  We  start  by  zooming  in  on  the  sources  of  financing  of  working 

capital.  We  focus  on  bank  credit  and  on  trade  credit.  Prior  work  on  the  pecking  order  in 

corporate  finance  suggests  that  trade  credit  is  the  most  expensive  source  of  finance  for  the 

firm,  and  it  is  only  used  when  no  other  source  of  external  finance  (such  as  bank  credit)  is 

accessible  (e.g.,  Petersen  and  Rajan,  1994;  Petersen  and  Rajan,  1997;  Harhoff  and  Koerting, 

1998).  We  make  two  salient  assumptions,  i.e.,  that  female  firm-owners  have  the  same 

preferences  over  the  pecking  order  and  that  they  face  the  same  probability  of  success  when 

applying for trade credit. Then, female owners having relatively lower access to cheaper bank 

credit  and  having  to  rely  relatively  more  on  more  expensive  trade  credit,  especially  in  high 

gender bias countries, would be one manifestation of taste-based discrimination. 

< Table 6 > 

ECB Working Paper 1822, July 201521Column  (1)  investigates  the  determinants  of  bank  credit  finance  and  column  (2)  the 

determinants of trade credit finance. Consistent with the prior literature we find that, all else 

equal, small firms and sole proprietorships are more credit constrained, potentially indicating a 

lower  ability  to  tap  alternative  capital  markets;  firms  that  export  part  of  their production  are 

less constrained in terms of both types of finance, potentially signalling the willingness of banks 

and customers/suppliers to lend to firms with higher growth prospects; and audited firms are 

less credit constrained, implying gains from the reduction of informational opacity in terms of 

credit market access. 

Turning to our primary variable of interest - the interaction term of “Traditional gender 

roles”  with  “Female  owner”  –  we  find  that  a  stronger  gender  bias  in  the  country  moderately 

decreases  the  share  of  working  capital  that  is  financed  with  bank  credit  in  firms  with  female 

owners  (column  (1)).  This  effect  is  not  statistically  significant.  At  the  same  time,  female  firm-

owners appear to finance a smaller portion of their capital expenditures using bank loans, with 

this effect being significant across the board. 

The  estimate  in  column  (2)  implies  that  a  strong  gender  bias  sharply  (and  statistically 

significantly)  increases  the  share  financed  with  trade  credit.  Numerically,  the  estimated 

coefficient  implies  that  if  a  firm  with  a  female  owner  would  move  from  Ireland  to  Spain,  for 

example, trade credit financing would increase by 1.6 percentage points more than if the owner 

of  the  firms  was  a  male.  Given  that  the  mean  share  of  working  capital  financed  with  trade 

credit in the sample is only 6.4 percent, moving to a low-gender bias country would decrease 

reliance on informal credit by 25%. 

In the next two columns, we investigate the determinants of being completely shut out 

from the credit markets, either because the firm was discouraged from applying (column (3)) or 

because it applied but its application was rejected (column (4)). We confirm the sign of the firm 

covariates; namely, small firms are both rejected and discouraged more often, while exporters 

and audited firms are less often credit constrained. Importantly, we find further evidence for a 

ECB Working Paper 1822, July 201522link  between  gender  bias  and  credit  access.  Column  (3)  shows  that  female  owners  are  more 

likely  to  be  discouraged  from  applying  for  a  loan  in  countries  with  high  gender  bias. 

Numerically,  moving  from  Ireland  to  Spain  increases  discouragement  by  female  owners  by  6 

percentage points more than male owners. Once again, this result is economically significant: 

given  that  the  mean  probability  of  discouragement  across  all  firms  is  23  percent,  a  female 

entrepreneur  in  a  low-gender  bias  country  would  be  26%  less  likely  to  be  discouraged  from 

applying for bank credit than in a high-gender bias country, ceteris paribus.10  

To  get  an  idea  of  the  aggregate  effect,  consider  the  following  back-of-the-envelope 

calculation. In 2012, there were about 2.5 mln. SMEs in Spain.11 According to the BEEPS, 19% of 

these are female-owned, or 475,000. Out of these, 61% have enough internal funds to finance 

their  investment  needs.  Of  the  39%  female-owned  firms  with  a  strictly  positive  need  of  bank 

credit,  20%  are  discouraged  from  applying  for  a  loan,  which  amounts  to  37,000  firms  in  the 

aggregate.  Our  estimates  imply  that  if  Spain  had  as  low  gender  bias  as  Ireland,  an  additional 

2200  female-owned  firms  would  be  applying  for  bank  credit  each  year.  Of  course,  this  is  a 

conservative calculation which disregards the fact that with lower gender bias, there could be 

many more female-owned firms to begin with.  

Finally,  the  estimate  of  the  coefficient  on  the  interaction  term  in  column  (4)  is  not 

statistically  significant  (although  economically  relevant,  too).  This  implies  that  the  banking 

sector  in  high-gender  bias  countries  does  not  discriminate  against  female  firm-owners  that 

actually  apply  for  lines  of  credit.  To  take  once  again  the  example  of  Spain,  our  evidence 

suggests that if a female-owned SME with a positive need of bank credit were to apply for a line 

10 Female owners in strong gender bias countries may be discouraged from applying for a loan, not because they 
correctly anticipate rejection due to taste-based discrimination, but because they “internalize” the gender bias and 
feel less confident applying per se. As obtaining trade credit is also often a matter of requesting and negotiating it 
(yet  we  observe  trade  credit  to  increase)  we  do  not  think  such  a  lack  of  confidence  necessarily  applies  to  the 
female owners in our sample. 

11 See the European Commission’s “Enterprise and industry SBA Factsheet 2012: Spain”. 

ECB Working Paper 1822, July 201523of  credit,  it  would  face  the  same  application  approval  rate  (98%)  as  a  male-owned  SME.  We 

conclude that in high-gender bias countries, female entrepreneurs appear to be more likely to 

opt out of the loan application process, even though banks do not discriminate against females 

that end up applying for credit. 

A.2. Inherited cultural bias, gender, and loan terms 

Next we assess if the inherited cultural bias has an impact on the loan terms firms with 

female  owners  obtain  (versus  firms  owned  by  males).  We  extend  the  framework  in  Alesina, 

Lotti,  and  Mistrulli  (2013)  who  reject  the  hypothesis  that  banks  do  not  price  discriminate 

against females. However, we go beyond previous studies that focused on loan rates, by also 

looking at the effect of the firm owner’s gender on the time it takes to negotiate the loan, on 

the  loan’s  collateral,  and  on  its  currency  denomination.  Crucially,  we  compare  these  effects 

across high and low gender bias countries. 

For a start, we find that some of the variables which matter for credit access (as in Table 

6) also have an impact on the terms of granted loans. In particular, small firms pay higher rates 

on bank loans (column (5)), but these loans are less likely to require collateral (column (2)) and 

take  a  shorter  time  to  negotiate  (column  (1)).  On  the  contrary,  exporters  are  charged  lower 

rates by their bank (column (5)) and are more likely to have a loan in foreign currency (column 

(4)), potentially in order to reduce exchange rate risk vis-à-vis their export destination. 

In terms of some of the time-varying country-level variables included in this regression, 

loan  conditions  are  easier  to  negotiate  banks  in  countries  with  better  protection  of  creditors 

rights  (column  (1)),  and  banks  charge  on  average  higher  rates  in  countries  with  higher 

restrictions on new business entry (column (5)). 

< Table 7 > 

Turning  to  the  variables  of  interest,  in  sharp  contrast  to  our  earlier  findings  on  credit 

access, the estimated coefficients on the interaction term of the “Traditional gender roles” and 

ECB Working Paper 1822, July 201524“Female  owner”  is  statistically  insignificant  in  all  but  one  case  when  a  loan  term  is  the 

dependent variable. For example, female-owned firms are no more likely to be charged higher 

rates  on  loans,  both  in  high  and  in  low  gender  bias  countries  (column  (1)).  This  finding  is 

somewhat  at  odds  with  Alesina,  Lotti,  and  Mistrulli  (2013)  who  detect  price  discrimination 

against female entrepreneurs in Italy. We also find that in high-gender bias countries, female- 

and male-owned firms do not differ in the time it takes to negotiate a loan (column (1)) or in 

collateralization conditions (columns (2) and (3)), either.12 

This  collection  of  null  results  implies  that  in  high-gender  bias  countries  there  is  no 

difference in the terms on loans between male- and female-owned firms. Our data thus provide 

no  evidence  of  discrimination  by  banks  against  female  firm-owners  in  high-gender  bias 

countries,  both  in  the  dimension  of  loan  rejection  rates  and  in  the  dimension  of  terms  on 

granted  loans.  However,  credit  access  by  female-owned  firms  is  statistically  lower  in  high-

gender  bias  countries because  female entrepreneurs  are  more  likely  to  opt  out  of the formal 

credit granting process. Consequently, female-owned firms are more likely to resort to informal 

(and potentially more expensive) credit, such as trade credit. The evidence so far thus implies 

that  if  our  results  are  indeed  due  to  the  operation  of  a  cultural  bias,  this  bias  works  through 

discouraging  females  from  seeking  credit  access  rather  than  through  banks  actively  denying 

them such access. 

B.

Robustness 

There  are  potentially  a  number  of  non-trivial  yet  surmountable  problems  with  our 

empirical analysis so far. To begin with, one of the identifying assumptions related to models (1) 

and  (2)  is  that  female-  and  male-owned  firms  seeking  access  to  finance  are  similar  in  all 

12 Model (2) can alternatively be written as a two-stage model, whereby in the first stage firms select themselves 
into or out of the loan application process, and in the second stage are offered loan terms by the bank (see Popov 
and Udell, 2012, for an example of such empirical strategy). We have run such a version of the empirical model, 
and the main results are qualitatively unchanged (results available upon request).

ECB Working Paper 1822, July 201525respects  except  for  the  owner’s  gender.  This  assumption  is  intrinsically  questionable.  For 

example,  if  female  entrepreneurs  are  indeed  discriminated  against,  they  are  likely  to  end  up 

running smaller firms. This may bias our results even if we formally control for firm size, if the 

distribution  of  firm  sizes  would  suffer  from  an  insufficient  overlap  across  the  two  groups. 

Second, our dummy for gender bias is constructed such that countries with gender bias close to 

0,  according  to  the  estimates  in  Table  5  (Greece,  Germany,  and  Portugal),  are  split  in  two 

different groups. 

The third problem is related to sample size. In particular, our measure of gender bias is 

calculated  across  a  limited  number  of  countries;  we  have  only  17  countries  in  the  BEEPS 

dataset. Furthermore, only 12 countries of origin in the GSS dataset which we use to calculate 

the proxy for gender bias used in the empirical tests. Part of this exercise relies on assigning one 

measure of gender bias to both the Czech Republic and to Slovakia, and one measure of gender 

bias  to  five  countries  from  former  Yugoslavia  (Bosnia,  Croatia,  Macedonia,  Serbia  and 

Montenegro, and Slovenia). Such extrapolation may not be fully appropriate if these countries 

exhibited large cultural differences. 

We  begin  by  addressing  the  first  problem,  namely,  that  female-owned  firms  may  be 

systematically different from male-owned ones. To control for that possibility, we borrow from 

the  propensity  score  literature  pioneered  by  Rosenbaum  and  Rubin  (1983).  We  first  run  a 

logistic  regression  of  the  female  dummy  on  the  rest  of  the  firm-level  variables  described  in 

Table 3. We also include country and industry fixed effects in the regression. Panel A of Table 8 

clearly  indicates  that  the  two  types  of  firms  indeed  differ  across  a  number  of  dimensions.  In 

particular, female-owned firms are statistically more likely than male-owned firms to be small 

and originally private rather than privatized, as well as to face a less competitive environment. 

< Table 8 > 

We use the set of statistically significant variables from this regression to calculate the 

propensity score. Next, we choose for each female-owned firm a male-owned one that is most 

ECB Working Paper 1822, July 201526similar  based  on  this  set  of  observable  characteristics,  and  re-run  model  (1)  on  the  new, 

reduced  sample  derived  from  this  propensity  score  matching  procedure.  In  the  resulting 

sample,  female-  and  male-owned  firms  are  comparable  based  on  observable  firm 

characteristics.  Panel  B  of  Table  8  indicates  that  our  main  results  are  not  weakened  by  this 

alternative procedure. Female-owned firms are still more likely to be discouraged and to resort 

to informal sources of finance, such as trade credit, in high gender bias countries. They are also 

less  likely  to  have  their  loan  application  rejected,  and  this  time  the  result  is  marginally 

significant.  

We  address  the  second  problem  in  two  different  ways.  First,  we  replace  the  dummy 

variable  for  gender  bias  that  we  have  used  so  far  with  the  continuous  variable  estimated  in 

Table 5. While the latter is clearly subject to measurement error, its advantage is that it does 

not  separate  in  different  groups  medium-bias  countries  that  are  very  close  to  each  other  in 

terms of numerical estimates (e.g., Germany and Greece). The estimates of these regressions 

are  reported  in  Table  9,  Panel  A,  and  they  imply  that  our  main  results  are  robust  to  this 

alternative specification. In Panel B, we address the same problem by simply dropping the three 

medium-bias  countries  (Germany,  Greece,  and  Portugal).  The  estimates  continue  to  suggest 

that in high-gender bias countries, female firm owners are more likely to be discouraged from 

applying  for  bank  credit  and  to  resort  to  informal  finance.13  Numerically,  for  example,  the 

estimate  reported  in  column  (3)  implies  that  females  are  statistically  more  likely  to  be 

discouraged from applying than men in countries where the continuous gender bias variable is 

higher  than  -0.02,  a  groups  which  includes  12  countries  rather  than  the  8  countries  in  the 

treatment group in our tests so far.  

< Table 9 > 

13 In unreported regressions we exclude the 5 countries of former Yugoslavia that were given the same value for 
gender bias in the empirical procedure in Table 5; the magnitude of the effect of gender bias on discouragement 
increases by 50% and becomes significant at the 1% level.

ECB Working Paper 1822, July 201527While  the  procedure  in  Table  9  goes  some  way  in  addressing  the  concern  that  our 

results  may  be  contaminated  by  credit  market  experience  in  medium-bias  countries,  it  only 

exacerbates the third problem which is related to the small size of our sample of countries. To 

that end, we next resort to the Global Entrepreneurship Monitor (GEM) to extract alternative 

measures of male and female entrepreneurial tendencies. The GEM collects survey data from 

current  and  potential  entrepreneurs  in  67  countries,  ranging  from  Western  Europe  to  Sub-

Saharan Africa. While there are no questions related to access to finance, there are questions 

on entrepreneurial intentions summarized, by gender for each country. In particular, one of the 

questions in the survey asks non-entrepreneurs: “Do you intend to start a business in the next 

three  years?”  We  hypothesize  that  if  potential  female  entrepreneurs  are  less  likely  to  start  a 

business in the future – relative to male counterparts – in high gender bias countries, this could 

be interpreted as corroborative evidence to the one we have presented so far. 

There are 28 countries which are present both in the GEM as a country of residence, and 

in the GSS as a country of origin. We repeat on this larger sample our test aimed at extracting 

exogenous  measures  of  inherited  gender  bias  (See  Section  3.A.).14  This  allows  us  to  extract 

measures  of  inherited  gender  bias  for  each  of  the  28  countries,  as  the  coefficient  on  the 

country-of-origin dummy. Next, we calculate the variable Intention to start (cid:258)(cid:3)(cid:271)(cid:437)(cid:400)(cid:349)(cid:374)(cid:286)(cid:400)(cid:400)(cid:853)(cid:3)(cid:1104)(cid:3)(cid:894)(cid:68)(cid:258)(cid:367)(cid:286)(cid:3)

– Female) as the difference in average entrepreneurial intentions for males and for females, for 

each country. This difference ranges from 0.02 in Sweden (where 12% of males intend to start a 

business  in  the  next  three  years  and  10%  of  females  do)  to  15%  in  Romania  (where  35%  of 

males  intend  to  start  a  business  in  the  next  three  years  and  20%  of  females  do).  We  then 

correlate  this  measure  of  relative  entrepreneurial  tendencies  to  our  measure  of  inherited 

gender bias. 

14 We now include the following countries, in addition to the ones included in the initial test reported in Table 5: 
Austria, Belgium, Denmark, Finland, France, Italy, Japan, Mexico, Netherlands, Norway, Sweden, Switzerland, and 
the UK. The Czech Republic and Lithuania are not included in the GEM. 

ECB Working Paper 1822, July 201528< Table 10 > 

In Table 10, we report the estimates from this test. Column (1) suggests that gender bias 

alone  explains  about  21%  of  the  variation  in  entrepreneurial  intentions  between  males  and 

females. The correlation itself is statistically significant at the 1% level. In column (2), we add 

the  rest  of  the  country-level  variables  employed  in  Table  7.  The  male-female  gap  in 

entrepreneurial tendencies widens with GDP growth and with good institutions for protection 

of  creditors  and  for  enforcement  of  contracts,  and  it  is  larger  in  countries  with  fewer  entry 

barriers. Crucially, the male-female gap is still positively and statistically significantly (at the 5% 

level)  correlated  with  our  measure  of  gender  bias  derived  from  the  GSS.  Encouragingly,  the 

limited  set  of  country-level  variables  included  in  the  regression  has  a  very  high  explanatory 

power (R-squared of 0.68), suggesting that the correlation between gender bias and the male-

female gap in entrepreneurial tendencies is unlikely to be driven by an omitted factor. 

C.

Interpretation of the results 

Our results so far indicate that credit access is far more problematic for female than for 

male firm-owners in countries where the gender bias is strong. This is especially true in the case 

of discouragement from applying for bank credit. Loan rejection rates and loan terms, on the 

other hand, are not different between female- and male-owned firms. 

Do  these  findings  imply  that  there  is  taste-based  discrimination  against  female 

entrepreneurs  in  countries  with  high  gender  bias?  This  question  crucially  depends  on  the 

nature  of  discouragement  and  on  differences  in  ability  across  genders  in  those  countries. 

Focusing  on  the  first  question,  out  of  the  five  possible  reasons  for  discouragement,  only  one 

deals with beliefs about access (“the firm did not think their application would be approved”), 

while two deal with beliefs about credit terms that would be offered if the firm were to apply 

for a bank loan (“collateral requirements are too strict” and “interest rates are too high”). It is 

possible  that  in  high-gender  bias  countries,  female  entrepreneurs  are  less  wealthy,  in  which 

ECB Working Paper 1822, July 201529case  their  being  discouraged  by  adverse  credit  terms  would  be  a  rational  response  to  their 

inability to pledge collateral rather than a sign of discrimination. 

< Table 11 > 

To address this issue, we split the reasons for discouragement in the four categories for 

which  there  are  sufficient  number  of  answers  (this  procedure  leads  to  the  exclusion  of 

discouragement due to the fact that “it is necessary to make informal payments to the bank”), 

and  re-estimate  Model  (3).  The  estimates  in  Table  11  indicate  that  the  discouragement  of 

female  entrepreneurs  is  driven  by  the  belief  that  their  application  would  not  be  approved 

rather  than  by  their  perception  of  adverse  credit  terms.  This  finding  strongly  points  in  the 

direction of a belief-based “abstention” from partaking in the process to obtain credit. To the 

extent  that  this  result  may  reflect  a  mechanism  related  to  the  fear  of  social  stigma,  it  is 

reminiscent  of  microfinance  studies  which  have  found  a  tendency  of  female  heads  of 

households to underreport debt relative to males (e.g., Karlan and Zinman, 2010). 

Turning  to  the  question  of  differences  in  ability  across  genders,  if  male  and  female 

entrepreneurs are indeed of equal ability, then our results point to taste-based discrimination 

before  the  loan  application  stage  where  females  are  mostly  discouraged  from  applying,  and 

resort to informal finance, such as trade credit. At the loan application stage itself, the financial 

system does not seem to be discriminating, resulting in similar terms on granted loans.15 

Alternatively, men and women could differ in other salient ways. For example, women 

tend  to  be  more  risk-averse  (Powel  and  Ansic,  1997;  Jianakoplos  and  Bernasek,  1998).  More 

recent experimental evidence has suggested that gender-specific risk behaviour may be due to 

15 Once access to credit through a non-discriminatory loan officer is possible, potentially because the loan officer 
herself  is  a  female  (Beck,  Behr,  and  Madestam,  2012),  similar  loan  terms  suggest  that  female-owned  firms  are 
deemed to be similar to male-owned firms. However, we do not have information on the loan officer to investigate 
this possibility further. 

ECB Working Paper 1822, July 201530 
differences  in  opportunity  sets  rather  than  risk  attitudes  (Schubert,  Brown,  Gysler,  and 

Brachniger,  1999),  implying  that  women  may  be  particularly  more  risk-averse  in  high-gender 

bias countries. In addition, women tend to dislike competition (Niederle and Vesterlund, 2007). 

These considerations might explain our result that female entrepreneurs are more likely to be 

discouraged from applying for a loan, especially in high-gender bias countries. In addition, they 

also imply that in countries with a high gender bias, where women are discriminated against, 

only  the  most  capable  and  competitive females start  a  business  and  therefore  can  survive  to 

the loan application stage. In this case, the average female entrepreneur would be more skilled 

than the average male entrepreneur. If so, then equal loan conditions would also point to taste-

based discrimination, this time at the loan approval stage. 

However,  female  entrepreneurs  could  differ  from  male  entrepreneurs  in  ways  which 

would  explain  our  results  absent  taste-based  discrimination.  For  example,  if  women  are 

discriminated  against,  their  inputs  (such  as  education  and  training)  might  be  of  lower  quality 

than  those  of  men.  Klumpp  and  Su  (2013)  argue  that  the  belief  that  the  human  capital 

distribution exhibits less variation across females can be self-fulfilling, resulting in fewer “star” 

females and leading to a “glass ceiling” effect. Female firm owners might therefore face lower 

access to credit not because of direct discrimination at that stage, but because of differential 

skill  accumulation  leading  to  lower  expected  growth  or  higher  expected  risk.  Consistent  with 

this idea, Cavaluzzo and Cavaluzzo (1998) and Blanchflower, Levine, and Zimmerman (2003) do 

not  find  significant  evidence  of  gender  based  discrimination  in  the  US  credit  market  once 

proxies for firm risk are taken into account. The firm-level variables and interactions of sector 

and  country  dummies  we  have  included  in  our  tests  so  far  only  imperfectly  control  for  firm 

growth or risk. 

To  investigate  this  point  more formally,  we now  analyse  the difference  in  firm  growth 

across  the  dimensions  of  gender,  gender  bias,  and  access  to  credit.  In  Table  12,  we  run  a 

ECB Working Paper 1822, July 201531version  of our  main  tests  where the  independent  variable  is realised  firm  sales  growth  in  the 

past three years.16 

< Table 12 > 

Column  (1)  suggests  that  conditioning  on  firm-level  observables  and  on  country  and 

industry  fixed  effects,  there  are  no  differences  between  male-owned  and  female  owned  in 

terms of sales growth. If anything, female-owned firms tend to grow faster, but the difference 

is  not  significant  in  the  statistical  sense.  In  addition,  the  evidence  in  Column  (2)  implies  that 

female-owned and male-owned firms exhibit similar growth rates in high-gender bias countries 

as  well.  This  goes  somewhat  against  the  idea  of  statistical  discrimination  in  high-gender  bias 

countries whereby access to finance reflects the inferior skills of female entrepreneurs due to 

gender discrimination which starts early on in their lives. 

However, it could still be that only the most capable females become entrepreneurs, so 

their  firms  should  grow  faster,  but  because  they  are  constrained,  their  firms  grow  as  fast  as 

male-owned ones. To that end, in the next two columns we perform even stricter tests where 

we construct a dummy variable equal to 1 if the firm is female-owned and it does not have a 

bank  loan  because  it  was  rejected  or  was  discouraged  from  applying,  and  to  0  if  the  firm  is 

male-owned  and  its  loan  application  was  approved.  Essentially,  we  are  comparing  the 

performance of otherwise identical firms which vary both in the dimension of gender and credit 

experience. The evidence suggests that we cannot reject the null hypothesis that the two types 

of firms grow at different rates, not even in high-gender bias countries (Column (4)). The fact 

that constrained female-owned firms grow as fast as unconstrained male-owned firms suggests 

that there is no evidence to females having inferior skills to males in high-gender bias countries. 

16 Our results are qualitatively unchanged when we repeat the tests for the logarithm of sales growth, in order to 
account for outliers. 

ECB Working Paper 1822, July 201532Therefore, it is reasonable to conclude that the evidence in Tables 6-11 is suggestive of credit 

barriers to female entrepreneurs in countries with a high gender bias. 

Of  course,  we  have  no  way  of  fully  eliminating  alternative  mechanisms  related  to 

unobservable differences between male and female entrepreneurs. One such mechanism can 

be  related  to  spatial  mismatch.  For  example,  Rosenthal  and  Strange  (2012)  show  that female 

entrepreneurs  can  be  less  networked  than  male  counterparts,  and  they  may  face  greater 

domestic burdens, leading them to choose locations that are farther from agglomeration and 

are associated with shorter commuting distances. With such mechanism in place in gender bias 

countries where domestic burdens may be particularly high, female entrepreneurs may be less 

experienced in dealing with banks, and as a result be more reluctant to apply for credit. While 

we cannot entirely rule out this possibility, we think that this mechanism may not be as strong 

in most countries in our sample as these have fairly low birth rates, a recent communist past of 

gender equality when it comes to labor participation, and wide availability of child crèches. 

5.

Conclusion 

We  analyse  differential credit  access  and  terms across  17  countries for female-  versus 

male-owned firms using responses from 5,905 small family firms in the 2004/2005 wave of the 

Business Environment and Enterprise Performance Survey (BEEPS). We find that in high-gender 

bias countries, firms owned by females have lower access to bank credit than otherwise similar 

firms owned by males. This phenomenon is fully explained by the fact that in high-gender bias 

countries,  female  entrepreneurs  are  more  likely  to  opt  out  of  the  formal  credit  granting 

process, largely because they believe their loan application will be denied. In contrast, there are 

no  statistical  differences  between female-  and male-owned  firms  in  loan  rejection  rates  or  in 

the terms on granted loans, such as rates or collateral required by the bank. We also find that 

female-owned  firms  do  not  underperform  male-owned  firms  in  terms  of  sales  growth,  even 

when  not  obtaining  credit  or  when  based  in  high  gender-bias  countries,  alleviating  concerns 

that our results are driven by statistical discrimination. 

ECB Working Paper 1822, July 201533Our findings suggest taste-based gender discrimination that limits credit access of firms 

owned by females through the mechanism of self-selection out of the credit granting process. 

Once  the  credit  application  is  made,  similar  approval  rates  and  loan  terms  suggest  female-

owned firms are assessed to be identical to male-owned firms, and this assessment is correct 

because  their  credit  worthiness  also  turns  out  to  be  statistically  similar.  The  evidence  thus 

suggests that there is no taste-based discrimination at the level of the bank, i.e., it is not the 

case that loan officers select only the best female-owned firms while lending also to mediocre 

male-owned firms. 

The contribution of our paper therefore consists in linking the severity of the inherited 

cultural  gender  bias  as  it  exists  across  countries  to  one  specific  economic  outcome  (credit 

access).  Our  results  are  consistent  with  taste-based  gender  discrimination  whereby  female 

entrepreneurs  tend  to  shy  away  from  the  formal  credit  granting  process  in  countries  where 

cultural  attitudes  favour  males,  even  though  credit  markets  are  not  actively  discriminating 

against  them.  One  potentially  effective  policy  aimed  at  addressing  this  issue  is  to  stimulate 

banks to recruit and train female loan officers that may be viewed as less subject to a gender 

bias (Beck, Behr, and Guettler, 2013), which in turn could encourage female entrepreneurs to 

apply for loans or lines of credit.  

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ECB Working Paper 1822, July 201541Table 1. Credit access, by country 

Country 

# firms 

Share working 
capital financed 
with bank credit 

Share working 
capital financed 
with trade credit 

Firm discouraged 
from applying  
for a loan 

Loan 
application 
rejected 

Bosnia 
Croatia 
Czech Republic 
Germany 
Greece 
Hungary 
Ireland 
Lithuania 
Macedonia 
Poland 
Portugal 
Romania 
Russia 
Slovakia 
Slovenia 
Spain 
Yugoslavia 

141 
141 
253 
778 
498 
462 
395 
141 
153 
763 
370 
393 
434 
142 
132 
501 
208 

Total 

5,905 

0.18 
0.15 
0.07 
0.13 
0.10 
0.12 
0.24 
0.10 
0.04 
0.06 
0.08 
0.12 
0.04 
0.06 
0.15 
0.12 
0.08 

0.11 

0.04 
0.06 
0.06 
0.11 
0.12 
0.05 
0.09 
0.13 
0.06 
0.04 
0.04 
0.06 
0.07 
0.02 
0.03 
0.00 
0.05 

0.06 

0.17 
0.10 
0.36 
0.10 
0.11 
0.25 
0.02 
0.24 
0.53 
0.42 
0.31 
0.25 
0.42 
0.15 
0.08 
0.14 
0.39 

0.23 

0.01 
0.02 
0.16 
0.08 
0.01 
0.03 
0.02 
0.05 
0.04 
0.09 
0.03 
0.12 
0.08 
0.03 
0.01 
0.02 
0.03 

0.05 

Note: This table presents statistics on firm credit access, by country. Share working capital financed with bank 
credit  is  the  proportion  of  the  firm’s  working  capital  that  has  been  financed  by  borrowing  from  private 
commercial banks in the past 12 months. Share working capital financed with trade credit is the proportion of 
the firm’s working capital that has been financed with trade credit from suppliers or customers in the past 12 
months. Firm discouraged from applying for a loan is a dummy equal to 1 if the firm did not apply for a bank 
loan because it thought it felt discouraged by credit market conditions, and equal to 0 otherwise (all dummies 
will have these two values; so the “equal to 0 otherwise” will no longer be included below). Loan application 
rejected is a dummy equal to 1 if the firm applied for credit and had its loan application rejected. See Appendix 
for variable definitions and data sources. 

ECB Working Paper 1822, July 201542 
 
 
 
 
 
Table 2. Loan-level variables 

Days to 
negotiate loan 

Loan not 
collateralized 

Collateral as 
% of loan 

Loan in foreign 
currency  

Annualized 
loan rate 

18.48 
27.02 
32.91 
18.72 
17.42 
22.53 
16.12 
27.30 
22.00 
22.77 
10.99 
21.21 
19.80 
35.67 
14.22 
8.22 
22.40 

19.52 

0.05 
0.25 
0.18 
0.06 
0.44 
0.06 
0.37 
0.19 
0.05 
0.20 
0.41 
0.06 
0.11 
0.23 
0.44 
0.28 
0.07 

0.20 

203.48 
160.36 
122.20 
123.69 
132.72 
168.04 
158.22 
135.15 
198.05 
153.18 
120.12 
153.12 
148.91 
144.31 
157.24 
134.79 
193.36 

148.85 

0.01 
0.29 
0.03 
0.02 
0.04 
0.21 
0.02 
0.33 
0.45 
0.12 
0.00 
0.38 
0.13 
0.02 
0.22 
0.01 
0.17 

0.11 

10.30 
8.13 
10.04 
7.74 
7.35 
13.22 
4.74 
5.93 
11.37 
13.06 
7.02 
17.94 
18.07 
8.07 
6.48 
6.51 
13.66 

9.85 

Country 

Bosnia 
Croatia 
Czech Republic 
Germany 
Greece 
Hungary 
Ireland 
Lithuania 
Macedonia 
Poland 
Portugal 
Romania 
Russia 
Slovakia 
Slovenia 
Spain 
Yugoslavia 

Total 

Note: This table presents statistics on firm financing, by country. Days to negotiate loan is the number of days it 
took to negotiate the loan with the bank from the day of application. Loan not collateralized is a dummy equal 
to 1 if the financing of the loan did not require collateral. Collateral as % of loan is the value of the collateral 
required  as  a  percentage  of  the  loan  value.  Loan  in  foreign  currency  is  a  dummy  equal  to  1  if  the  loan  is 
denominated in foreign currency. Annualized loan rate is the loan’s annual rate of interest. See Appendix for 
variable definitions and data sources. 

ECB Working Paper 1822, July 201543 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Country 

Female owner 

Small firm 

Large firm 

Originally private 

Exporter 

Competition 

Subsidized 

Audited 

Table 3. Firm-level explanatory variables 

Bosnia 
Croatia 
Czech Republic 
Germany 
Greece 
Hungary 
Ireland 
Lithuania 
Macedonia 
Poland 
Portugal 
Romania 
Russia 
Slovakia 
Slovenia 
Spain 
Yugoslavia 

Total 

0.26 
0.20 
0.22 
0.20 
0.25 
0.40 
0.41 
0.26 
0.18 
0.34 
0.50 
0.28 
0.28 
0.18 
0.35 
0.19 
0.25 

0.29 

0.67 
0.82 
0.87 
0.85 
0.83 
0.81 
0.86 
0.74 
0.82 
0.87 
0.91 
0.72 
0.75 
0.80 
0.92 
0.88 
0.82 

0.83 

0.04 
0.06 
0.03 
0.06 
0.06 
0.02 
0.03 
0.04 
0.06 
0.02 
0.04 
0.04 
0.07 
0.03 
0.02 
0.06 
0.06 

0.04 

0.83 
0.80 
0.92 
0.99 
0.99 
0.88 
0.99 
0.79 
0.84 
0.95 
0.99 
0.85 
0.86 
0.93 
0.86 
0.97 
0.89 

0.93 

0.32 
0.30 
0.22 
0.13 
0.17 
0.31 
0.29 
0.32 
0.27 
0.21 
0.10 
0.20 
0.13 
0.27 
0.42 
0.14 
0.25 

0.21 

0.62 
0.51 
0.45 
0.00 
0.00 
0.65 
0.58 
0.64 
0.65 
0.65 
0.00 
0.41 
0.22 
0.82 
0.68 
0.00 
0.56 

0.35 

0.01 
0.15 
0.06 
0.13 
0.09 
0.15 
0.08 
0.06 
0.02 
0.08 
0.04 
0.05 
0.01 
0.06 
0.10 
0.03 
0.05 

0.08 

0.45 
0.35 
0.27 
0.47 
0.46 
0.70 
0.94 
0.36 
0.24 
0.30 
0.74 
0.35 
0.35 
0.43 
0.20 
0.27 
0.24 

0.44 

Note: This table presents firm-level statistics, by country. All firms in the final sample are individually- or family-owned. Female owner is a dummy equal to 1 if 
the principal owner (or one of the principal owners) is a female. Small firm is a dummy equal to 1 if the firm has less than 20 employees. Large firm is a dummy 
equal to 1 if the firm has more than 100 employees. Originally private is a dummy equal to 1 if the firm was founded as a private company rather than through 
privatization of a previously state-owned one. Exporter is a dummy equal to 1 if the firm has access to foreign markets. Competition is a dummy equal to 1 if 
the firm faces “fairly”, “very”, or “extremely” strong competition. Subsidized is a dummy equal to 1 if the firm has received subsidies during the last 3 years 
from the central or local government. Audited is a dummy equal to 1 if the firm employs external auditing services. Omitted category for firm size is Medium 
firm. See Appendix for variable definitions and data sources. 

ECB Working Paper 1822, July 201544 
 
 
 
 
 
 
 
Table 4. Country-level explanatory variables 

Traditional 
gender roles 

Private credit 
to GDP 

Bank 
concentration 

Information 
sharing 

Log GDP per 
capita 

GDP growth  Creditors’ 

rights 

Contract 
enforcement 

Inflation 

Entry time 

Bosnia 
Croatia 
Czech Republic 
Germany 
Greece 
Hungary 
Ireland 
Lithuania 
Macedonia 
Poland 
Portugal 
Romania 
Russia 
Slovakia 
Slovenia 
Spain 
Yugoslavia 

Total 

0.202 
0.202 
-0.028 
-0.008 
0.018 
0.111 
-0.041 
-0.027 
0.202 
0.029 
-0.001 
0.109 
-0.036 
-0.028 
0.202 
0.088 
0.202 

0.046 

16.38 
50.73 
32.00 
115.44 
59.16 
40.34 
125.52 
22.48 
19.39 
26.92 
137.24 
13.04 
19.86 
33.35 
43.97 
108.62 
19.37 

61.85 

0.53 
0.63 
0.60 
0.68 
0.93 
0.65 
0.56 
0.78 
0.79 
0.59 
0.88 
0.64 
0.22 
0.83 
0.67 
0.87 
------ 

0.67 

0.74 
0.00 
1.92 
6.00 
4.00 
4.04 
5.00 
3.00 
2.05 
2.76 
4.00 
3.88 
0.00 
2.81 
2.90 
5.00 
0.00 

3.43 

8.46 
9.36 
9.85 
10.28 
10.06 
9.65 
10.51 
9.36 
8.76 
9.44 
9.88 
8.96 
9.34 
9.61 
9.98 
10.22 
8.64 

9.71 

1.17 
3.06 
1.78 
0.62 
4.58 
1.98 
4.40 
3.28 
0.93 
1.80 
-0.39 
3.02 
3.38 
1.85 
1.93 
3.12 
2.15 

2.28 

3.29 
4.18 
7.00 
8.00 
3.00 
7.00 
8.00 
5.00 
7.00 
8.00 
3.00 
7.00 
3.00 
9.00 
5.00 
6.00 
7.00 

6.16 

6.83 
5.61 
6.63 
4.03 
8.19 
3.35 
5.15 
2.10 
5.21 
9.98 
5.77 
5.37 
2.81 
6.02 
14.25 
5.15 
9.40 

6.04 

2.82 
3.77 
2.59 
1.50 
3.23 
5.23 
2.90 
1.91 
1.51 
3.24 
3.03 
17.36 
19.26 
4.91 
3.99 
3.10 
14.43 

5.59 

63 
22 
17 
18 
38 
16 
13 
26 
15 
31 
7 
9 
29 
27 
60 
47 
23 

25 

Note: The table summarizes various country-level characteristics used in the empirical analysis. Traditional gender roles is the estimate of the inherited views on 
the role of women in society by U.S. descendants of European immigrants (see Table 5 for details). Foreign bank share denotes the assets held by all foreign 
banks as a share of the assets of all commercial banks.  Private credit to GDP denotes the ratio of private credit by deposit money banks and other financial 
institutions to GDP. Bank concentration denotes the assets held by the three largest banks as a share of the assets of all commercial banks. Information sharing 
denotes the maximum of an index for private credit bureaus and an index for public credit registers. Log GDP per capita denotes the natural logarithm of per 
capita GDP. GDP growth denotes annual growth in gross domestic product per capita, average over the past three years. Creditors’ rights denotes the degree of 
protection  of  creditors’  rights.  Contract  enforcement  denotes  the  number  of  days  it  takes  to  enforce  a  legal  contract.  Inflation  denotes  annual  inflation, 
averaged over the past three years. Entry time denotes the time in days necessary to register a new business. See Appendix for variable definitions and data 
sources. 

ECB Working Paper 1822, July 201545 
 
 
 
 
 
 
 
 
 
Table 5. Country of origin and inherited gender bias 

Traditional gender roles 

Coefficient 
(1) 

Standard error 
(2) 

Demographic characteristics 

Age 
Age squared 
Education 
Male 
Catholic 
Protestant 
Jewish 
Muslim 
Other 
Employed 
Unemployed 
Retired 
Income category 

Country of origin 

Czechoslovakia 
Germany 
Greece 
Hungary 
Ireland 
Lithuania 
Poland 
Portugal 
Romania 
Russia 
Spain 
Yugoslavia 

Observations 
R-squared 

0.003** 
0.004*** 
-0.029*** 
0.096*** 
0.116*** 
0.140*** 
0.027 
0.173*** 
0.121*** 
-0.107*** 
-0.096*** 
-0.073*** 
-0.009*** 

-0.028 
-0.008 
0.018 
0.111** 
-0.041*** 
-0.027 
0.029* 
-0.001 
0.109** 
-0.036* 
0.088** 
0.202** 

20,992 
0.16 

(0.001) 
(0.001) 
(0.001) 
(0.007) 
(0.011) 
(0.010) 
(0.026) 
(0.040) 
(0.018) 
(0.009) 
(0.020) 
(0.014) 
(0.001) 

(0.031) 
(0.009) 
(0.052) 
(0.049) 
(0.011) 
(0.068) 
(0.018) 
(0.059) 
(0.053) 
(0.023) 
(0.033) 
(0.084) 

Note: The dependent variable captures the views on the role of women in society inherited by US immigrants 
from the period 1935-2000. Traditional gender roles is measured from the answer to the question: “It is much 
better  for  everyone  involved  if  the  man  is  the  achiever  outside  the  home  and the  woman  takes  care of  the 
home  and  family.”  The  answers  are  given  on  a  scale  from  1  to  4,  which  correspond  to  “Strongly  agree,” 
“Agree,” “Disagree,” and “Strongly disagree.” The gender bias indicator is equal to 1 if the respondent agrees 
or strongly agrees, and equal to 0 if the respondent disagrees or strongly disagrees. The reference group in 
religion  is  “Atheist”.  The  reference  group  in  employment  is  “Inactive”.  Data  come  from  the  General  Social 
Survey.  White  (1980)  robust  standard  errors,  clustered  at  the  country  level,  are  reported  in  parentheses, 
where *** indicates significance at the 1% level, ** at the 5% level, and * at the 10% level. 

ECB Working Paper 1822, July 201546 
 
 
 
 
 
 
 
 
 
 
 
Table 6. Culture, gender, and credit access 

Share working capital  
financed with bank 
credit 
(1) 

Share working capital  
financed with trade 
credit 
(2) 

Firm discouraged  
from applying for a 
loan 
(3) 

Loan application 
rejected 
(4) 

Traditional gender roles  (cid:117)  
Female owner 
Female owner 

Small firm 

Large firm 

Originally private 

Exporter 

Competition 

Subsidized 

Audited 

Fixed effects 
Observations 
R-squared 

-0.006 
(0.011) 
-0.027*** 
(0.006) 
-0.046** 
(0.017) 
0.026 
(0.019) 
0.029** 
(0.012) 
0.032** 
(0.013) 
0.025** 
(0.010) 
0.064*** 
(0.021) 
0.021*** 
(0.007) 

5,307 
0.10 

0.016** 
(0.008) 
0.003 
(0.007) 
-0.006 
(0.010) 
0.017 
(0.020) 
-0.006 
(0.012) 
0.019*** 
(0.005) 
0.005 
(0.006) 
0.016* 
(0.019) 
-0.023* 
(0.014) 
Country (cid:117) Industry 
5,307 
0.09 

0.184** 
(0.083) 
-0.018 
(0.055) 
0.614*** 
(0.130) 
-0.109 
(0.172) 
-0.120 
(0.104) 
-0.345*** 
(0.094) 
-0.074 
(0.108) 
-0.754*** 
(0.124) 
-0.291*** 
(0.110) 

2,817 
0.20 

0.208 
(0.180) 
0.124 
(0.140) 
0.262*** 
(0.080) 
-0.260 
(0.325) 
0.036 
(0.217) 
-0.302* 
(0.170) 
-0.129 
(0.083) 
-0.550** 
(0.240) 
-0.297** 
(0.117) 

1,837 
0.13 

Note:  This  table  presents  regression  results  of  indicators  of  credit  market  experience  by  firms  on  firm-level 
characteristics.  All  firms  in  the  final  sample  are  individually  owned  or  family  owned.  The  dependent  variable  is 
Share  working  capital  financed  with  bank  credit  (column  (1)),  Share  working  capital  financed  with  trade  credit 
(column (2)), Firm discouraged from applying for a loan (column (3)), and Loan application rejected (column (4)). 
Traditional gender roles is a country-level measure of inherited views on the role of women in society estimated in 
Table 5. Female owner is a dummy equal to 1 if the principal owner (or one of the principal owners) is a female. 
Small firm is a dummy equal to 1 if the firm has less than 20 employees. Large firm is a dummy equal to 1 if the 
firm has more than 100 employees. Originally private is a dummy equal to 1 if the firm was founded as a private 
company  rather  than  privatized.  Exporter  is  a  dummy  equal  to  1  if  the  firm  does  not  have  access  to  foreign 
markets.  Competition  is  a  dummy  equal  to  1  if  the  firm  faces  fairly,  very,  or  extremely  strong  competition. 
Subsidized is a dummy equal to 1 if the firm has received subsidies during the last 3 years from the central or local 
government. Audited is a dummy equal to 1 if the firm employs external auditing services. Omitted category for 
firm  size  is  Medium  firm.  See  Appendix  for  variable  definitions  and  data  sources.  All  regressions  include  fixed 
effects  as  specified.  White  (1980)  robust  standard  errors,  clustered  at  the  country  level,  are  reported  in 
parentheses, where *** indicates significance at the 1% level, ** at the 5% level, and * at the 10% level. 

ECB Working Paper 1822, July 201547 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 7. Culture, gender, and loan terms 

Log days to 
negotiate 
loan 
(1) 

Loan not 
collateralized 
(2) 

Log 
collateral as 
% of loan 
(3) 

Loan in 
foreign 
currency 
(4) 

Annualized 
loan rate 
(5) 

0.041 
(0.091) 
-0.124* 
(0.068) 
-0.238*** 
(0.071) 
0.083 
(0.136) 
0.099 
(0.092) 
-0.089* 
(0.046) 
-0.040 
(0.062) 
0.153*** 
(0.048) 
-0.015 
(0.057) 
-0.002 
(0.005) 
0.195 
(0.489) 
0.070* 
(0.036) 
0.063 
(0.638) 
0.025 
(0.025) 
-0.194** 
(0.084) 
0.184*** 
(0.059) 
-0.007 
(0.006) 
-0.018 
(0.022) 

-0.101 
(0.157) 
0.080 
(0.067) 
0.248** 
(0.115) 
0.055 
(0.128) 
-0.195* 
(0.116) 
-0.021 
(0.100) 
-0.194 
(0.153) 
-0.143 
(0.155) 
0.130 
(0.135) 
0.005 
(0.005) 
0.185 
(0.540) 
0.034 
(0.032) 
0.390 
(0.823) 
-0.001 
(0.033) 
0.068 
(0.102) 
-0.016 
(0.169) 
0.006 
(0.012) 
0.057 
(0.102) 

-0.057 
(0.040) 
0.006 
(0.019) 
0.021 
(0.032) 
0.012 
(0.051) 
0.043 
(0.039) 
0.052 
(0.034) 
0.033 
(0.022) 
-0.037** 
(0.016) 
-0.028 
(0.034) 
0.004** 
(0.002) 
-0.077 
(0.139) 
0.025 
(0.018) 
-0.359 
(0.279) 
0.020** 
(0.009) 
-0.055 
(0.041) 
-0.010 
(0.039) 
0.004 
(0.003) 
0.020** 
(0.008) 
Country (cid:117) Industry 
Year 

0.388* 
(0.220) 
-0.398* 
(0.206) 
-0.014 
(0.119) 
0.319* 
(0.178) 
0.118 
(0.160) 
0.349*** 
(0.101) 
0.016 
(0.070) 
-0.375** 
(0.118) 
0.094 
(0.122) 
0.003 
(0.015) 
1.230 
(1.025) 
0.065 
(0.093) 
-2.547 
(2.621) 
0.037 
(0.052) 
-0.194 
(0.338) 
-0.296 
(0.275) 
-0.012 
(0.012) 
-0.258 
(0.306) 

-0.185 
(0.259) 
-0.063 
(0.172) 
0.691** 
(0.238) 
-0.921* 
(0.491) 
0.124 
(0.385) 
-0.740** 
(0.359) 
0.344 
(0.342) 
-0.179 
(0.444) 
-0.009 
(0.310) 
0.032 
(0.036) 
0.392 
(1.929) 
-0.070 
(0.147) 
1.281 
(3.591) 
-0.030 
(0.093) 
0.410 
(0.712) 
-0.475 
(0.343) 
0.079 
(0.075) 
0.169* 
(0.094) 

2,158 
0.21 

2,199 
0.15 

1,708 
0.17 

2,114 
0.23 

2,119 
0.59 

Traditional gender roles  (cid:117)  
Female owner 
Female owner 

Small firm 

Large firm 

Originally private 

Exporter 

Competition 

Subsidized 

Audited 

Private credit to GDP 

Bank concentration 

Information sharing 

Log GDP per capita 

GDP growth 

Creditors’ rights 

Contract enforcement 

Inflation 

Entry time 

Fixed effects 

Observations 
R-squared 

Note: This table presents regression results of loan terms on firm-level and country-level characteristics. All firms 
in the final sample are individually owned or family owned. The dependent variable is the logarithm of Days to 
negotiate loan (column (1)), Loan not collateralized (column (2)), the logarithm of Collateral as % of loan (column 
(3)), Loan in foreign currency (column (4)), and Nominal interest rate (column (5)).  Traditional gender roles is a 
country-level measure of inherited views on the role of women in society estimated in Table 5. Female owner is a 

ECB Working Paper 1822, July 201548 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
dummy equal to 1 if the principal owner (or one of the principal owners) is a female. Small firm is a dummy equal 
to  1  if  the  firm  has  less  than  20  employees.  Large  firm  is  a  dummy  equal  to  1  if  the  firm  has  more  than  100 
employees. Originally private is a dummy equal to 1 if the firm was founded as a private company rather than 
privatized. Exporter is a dummy equal to 1 if the firm does not have access to foreign markets. Competition is a 
dummy equal to 1 if the firm faces fairly, very, or extremely strong competition. Subsidized is a dummy equal to 1 
if  the  firm  has  received  subsidies  during  the  last  3  years  from  the  central  or  local  government.  Audited  is  a 
dummy equal to 1 if the firm employs external auditing services. Omitted category for firm size is Medium firm. 
Foreign bank share denotes the assets held by all foreign banks as a share of the assets of all commercial banks. 
Private credit to GDP denotes the ratio of private credit by deposit money banks and other financial institutions 
to  GDP.  Bank  concentration  denotes  the  assets  held  by  the  three  largest  banks  as  a  share  of  the  assets  of  all 
commercial banks. Information sharing denotes the maximum of an index for private credit bureaus and an index 
for  public  credit  registers.  Log  GDP  per  capita  denotes  the  natural  logarithm  of  per  capita  GDP.  GDP  growth 
denotes annual growth in gross domestic product per capita, average over the past three years. Creditors’ rights 
denotes the degree of protection of creditors’ rights. Contract enforcement denotes the number of days it takes 
to  enforce  a  legal  contract.  Inflation  denotes  annual  inflation,  averaged  over  the  past  three  years.  Entry  time 
denotes  the  time  in  days  necessary  to  register  a  new  business.  See  Appendix  for  variable  definitions  and  data 
sources. All regressions include fixed effects as specified. White (1980) robust standard errors, clustered at the 
country level, are reported in parentheses, where *** indicates significance at the 1% level, ** at the 5% level, 
and * at the 10% level.  

ECB Working Paper 1822, July 201549 
 
 
 
 
 
 
 
 
 
 
Table 8. Culture, gender, and credit access: Propensity score matching results 

Panel A. Choosing variables for the estimation of the propensity score 

Small firm 

Large firm 

Originally private 

Exporter 

Competition 

Subsidized 

Audited 

Fixed effects 
Observations 
R-squared 

Female owner 

0.423*** 
(0.113) 
0.254 
(0.204) 
0.271* 
(0.144) 
-0.044 
(0.089) 
-0.168** 
(0.084) 
-0.078 
(0.132) 
-0.001 
(0.075) 
Country (cid:117) Industry 
5,367 
0.09 

Panel B. Second stage  

Share working capital  
financed with bank 
credit 
(1) 

Share working capital  
financed with trade 
credit 
(2) 

Firm discouraged  
from applying for a 
loan 
(3) 

Loan 
application 
rejected 
(4) 

Traditional gender roles  (cid:117)  
Female owner 
Female owner 

Firm controls 
Fixed effects 
Observations 
R-squared 

-0.014 
(0.012) 
-0.023*** 
(0.005) 

4,167 
0.10 

0.016* 
(0.009) 
0.005 
(0.008) 

0.172** 
(0.089) 
0.008 
(0.056) 

Yes 
Country (cid:117) Industry 

4,167 
0.08 

2,088 
0.18 

0.316* 
(0.167) 
0.075 
(0.139) 

1,307 
0.12 

Note:  This  table  presents  regression  results  of  indicators  of  credit  market  experience  by  firms  on  firm-level 
characteristics. All firms in the final sample are individually owned or family owned. The dependent variable in 
Panel A is Female owner. The dependent variable in Panel B is Share working capital financed with bank credit 
(column (1)), Share working capital financed with trade credit (column (2)), Firm discouraged from applying for 
a  loan  (column  (3)),  and  Loan  application  rejected  (column  (4)).  Traditional  gender  roles  is  a  country-level 
measure of inherited views on the role of women in society estimated in Table 5.  Female owner is a dummy 
equal to 1 if the principal owner (or one of the principal owners) is a female. Small firm is a dummy equal to 1 if 
the firm has less than 20 employees. Large firm is a dummy equal to 1 if the firm has more than 100 employees. 
Originally private is a dummy equal to 1 if the firm was founded as a private company rather than privatized. 
Exporter is a dummy equal to 1 if the firm does not have access to foreign markets. Competition is a dummy 
equal to 1 if the firm faces fairly, very, or extremely strong competition. Subsidized is a dummy equal to 1 if the 
firm has received subsidies during the last 3 years from the central or local  government. Audited is a dummy 
equal  to  1  if  the  firm  employs  external  auditing  services.  Omitted  category  for  firm  size  is  Medium  firm.  See 
Appendix  for  variable  definitions  and  data  sources.  All  regressions  include  fixed  effects  as  specified.  White 
(1980) robust standard errors, clustered at the country level, are reported in parentheses, where *** indicates 
significance at the 1% level, ** at the 5% level, and * at the 10% level. 

ECB Working Paper 1822, July 201550 
 
 
 
 
 
 
 
 
 
 
 
 
Table 9. Culture, gender, and credit access: Robust measures of gender bias 

Panel A. Continuous measure of gender bias 

Share working capital  
financed with bank 
credit 
(1) 

Share working capital  
financed with trade 
credit 
(2) 

Firm discouraged  
from applying for a 
loan 
(3) 

Loan 
application 
rejected 
(4) 

Traditional gender roles  (cid:117)  
Female owner 
Female owner 

-0.048 
(0.085) 
-0.027*** 
(0.005) 

0.108** 
(0.055) 
0.004 
(0.007) 

0.761* 
(0.406) 
0.015 
(0.058) 

1.914 
(1.587) 
0.120 
(0.143) 

Firm controls 
Fixed effects 
Observations 
R-squared 

Yes 
Country (cid:117) Industry 

5,307 
0.10 

5,307 
0.09 

2,817 
0.20 

1,837 
0.13 

Panel B. Excluding medium-bias countries  
Share working capital  
financed with trade 
credit 
(2) 

Share working capital  
financed with bank 
credit 
(1) 

Firm discouraged  
from applying for a 
loan 
(3) 

-0.003 
(0.011) 
-0.030*** 
(0.005) 

0.017* 
(0.010) 
0.003 
(0.009) 

0.187** 
(0.082) 
-0.025 
(0.048) 

Yes 
Country (cid:117) Industry 

Loan 
application 
rejected 
(4) 

0.213 
(0.226) 
0.125 
(0.197) 

3,916 
0.11 

3,916 
0.08 

2,190 
0.19 

1,352 
0.15 

Traditional gender roles  (cid:117)  
Female owner 
Female owner 

Firm controls 
Fixed effects 
Observations 
R-squared 

Note:  This  table  presents  regression  results  of  indicators  of  credit  market  experience  by  firms  on  firm-level 
characteristics.  All  firms  in  the  final  sample  are  individually  owned  or  family  owned.  In  Panel  A,  Gender  bias 
denotes the continuous measure of gender bias. In Panel B, Germany, Greece, and Portugal are dropped from 
the  tests.  The  dependent  variable  is  Share  working  capital  financed  with  bank  credit  (column  (1)),  Share 
working capital financed with trade credit (column (2)), Firm discouraged from applying for a loan (column (3)), 
and  Loan  application  rejected  (column  (4)).  Traditional  gender  roles  is  a  country-level  measure  of  inherited 
views on the role of women in society estimated in Table 5. Female owner is a dummy equal to 1 if the principal 
owner  (or  one  of  the  principal  owners)  is  a  female.  All  other  variables  from  Table  6  are  included  in  the 
regressions (coefficients are not reported for brevity). See Appendix for variable definitions and data sources. 
All regressions include fixed effects as specified. White (1980) robust standard errors, clustered at the country 
level, are reported in parentheses, where *** indicates significance at the 1% level, ** at the 5% level, and * at 
the 10% level. 

ECB Working Paper 1822, July 201551 
 
 
 
 
 
 
 
 
Table 10. Gender bias and entrepreneurship: GEM data 

Intention to start a business, (cid:1104) (Male – Female) 

(1) 

0.189*** 
(0.066) 

28 
0.21 

(2) 

0.122** 
(0.059) 
-0.003 
(0.008) 
0.019 
(0.021) 
0.002 
(0.004) 
-0.018 
(0.010) 
0.421* 
(0.223) 
0.010*** 
(0.002) 
0.007*** 
(0.002) 
0.002* 
(0.001) 
-0.108*** 
(0.028) 
28 
0.68 

Traditional gender roles 

Private credit to GDP 

Bank concentration 

Information sharing 

Log GDP per capita 

GDP growth 

Creditors’ rights 

Contract enforcement 

Inflation 

Entry time 

Observations 
R-squared 

Note: This table presents regression results of loan terms on firm-level and country-level characteristics. All 
firms in the final sample are individually owned or family owned. The dependent variable is the difference in 
the share of male and female non-entrepreneurs who declared an intention to become entrepreneurs in the 
next three years, from the Global Entrepreneurship Monitor (GEM). Traditional gender roles is a country-level 
measure of inherited views on the role of women in society estimated in Table 5, for all countries in both the 
GSS and the GEM. Private credit to GDP denotes the ratio of private credit by deposit money banks and other 
financial institutions to GDP. Bank concentration denotes the assets held by the three largest banks as a share 
of the assets of all commercial banks. Information sharing denotes the maximum of an index for private credit 
bureaus  and  an  index  for  public  credit  registers.  Log  GDP  per  capita  denotes  the  natural  logarithm  of  per 
capita GDP. GDP growth denotes annual growth in gross domestic product per capita, average over the past 
three  years.  Creditors’  rights  denotes  the  degree  of  protection  of  creditors’  rights.  Contract  enforcement 
denotes the number of days it takes to enforce a legal contract. Inflation denotes annual inflation, averaged 
over  the  past  three  years.  Entry  time  denotes  the  time  in  days  necessary  to  register  a  new  business.  See 
Appendix  for  variable  definitions  and  data  sources.  All  regressions  include  fixed  effects  as  specified.  White 
(1980)  robust  standard  errors,  clustered  at  the  country  level,  are  reported  in  parentheses,  where  *** 
indicates significance at the 1% level, ** at the 5% level, and * at the 10% level.  

ECB Working Paper 1822, July 201552 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 11. Culture, gender, and credit access: Disentangling the components of discouragement 

Application 
procedures too 
burdensome 
(1) 

Collateral 
requirements 
too strict 
(2) 

Interest rates 
too high 
(3) 

Did not think 
application would 
be approved 
(5) 

Traditional gender roles  (cid:117)  
Female owner 
Female owner 

Firm controls 
Fixed effects 
Observations 
R-squared 

0.043 
(0.208) 
-0.066 
(0.090) 

1,568 
0.17 

0.090 
(0.186) 
0.140 
(0.124) 

0.169 
(0.186) 
0.037 
(0.109) 

Yes 
Country (cid:117) Industry 
1,428 
0.19 

1,378 
0.19 

0.679** 
(0.349) 
-0.182 
(0.302) 

1,450 
0.19 

Note: This table presents regression results of various reasons for firms’ being discouraged from applying for 
a loan even though they need one. All firms in the final sample are individually owned or family owned. The 
dependent variable is Application procedures too burdensome (column (1)), Collateral requirements too strict 
(column (2)), Interest rates too high (column (3)), and Did not think application would be approved (column 
(4)).  Traditional gender  roles  is a country-level  measure of inherited views on the role  of women in  society 
estimated  in  Table  5.  Female  owner  is  a  dummy  equal  to  1  if  the  principal  owner  (or  one  of  the  principal 
owners)  is  a  female.  All  other  variables  from  Table  6  are  included  in  the  regressions  (coefficients  are  not 
reported  for  brevity).  See  Appendix  for  variable  definitions  and  data  sources.  All  regressions  include  fixed 
effects  as  specified.  White  (1980)  robust  standard  errors,  clustered  at  the  country  level,  are  reported  in 
parentheses, where *** indicates significance at the 1% level, ** at the 5% level, and * at the 10% level. 

ECB Working Paper 1822, July 201553 
 
 
 
 
Table 12. Culture, gender, credit access, and firm performance 

(1) 

(2) 

(3) 

(4) 

Sales growth 

Female owner 

Traditional gender roles  (cid:117)  Female owner 

Constrained female vs unconstrained male 

Traditional gender roles  (cid:117)  
Constrained female vs unconstrained male 
Firm controls 
Fixed effects 
Observations 
R-squared 

0.629 
(0.742) 

1.459 
(1.360) 
-1.725 
(1.356) 

-1.709 
(1.999) 

Yes 
Country (cid:117) Industry 
1,251 
0.11 

3,173 
0.06 

3,173 
0.06 

-1.173 
(3.735) 
-1.098 
(4.493) 

1,251 
0.11 

Note: This table presents regression results of indicators of credit market experience by firms on firm-level 
characteristics. All firms in the final sample are individually owned or family owned. The dependent variable 
in all columns is  Sales growth. Female owner is a dummy equal to 1 if the principal owner (or one of the 
principal owners) is a female. Traditional gender roles is a country-level measure of inherited views on the 
role  of  women  in  society  estimated  in  Table  5.  Constrained  female  vs  unconstrained  male  is  a  dummy 
variable equal to 1 if the principle owner (or one of the principal owners) is a female and does not have a 
bank  loan  because  she  was  rejected  or  was  discouraged  from  applying,  and  is  equal  to  0  if  the  principle 
owner (or one of the principal owners) is a male whose loan application was approved. All other variables 
from  Table  6  are  included  in  the  regressions  (coefficients  are  not  reported  for  brevity).  See  Appendix  for 
variable definitions and data sources. All regressions include fixed effects as specified. White (1980) robust 
standard  errors,  clustered  at  the  country  level,  are  reported  in  parentheses,  where  ***  indicates 
significance at the 1% level, ** at the 5% level, and * at the 10% level. 

ECB Working Paper 1822, July 201554 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Appendix 
Variables: Definitions and sources 

Variable Name 

Definition 

Source 

Firm characteristics 

Female 

Small firm 

Medium firm 

Large firm 

Dummy = 1 if the principal owner (or one of the principal owners) of the firm is a female. 

BEEPS 2004/05  

Dummy = 1 if firm has less than 20 employees. 

Dummy = 1 if the firm has between 20 and 100 employees. 

Dummy = 1 if firm has more than 100 employees. 

BEEPS 2004/05 

BEEPS 2004/05 

BEEPS 2004/05 

Originally private 

Dummy = 1 if firm was founded as a private company rather than through privatization. 

BEEPS 2004/05 

Exporter 

Competition 

Subsidized 

Audited 

Sales growth 

Constrained female vs 
unconstrained male 

Dummy = 1 if the firm has access to foreign markets. 

Dummy = 1 if the firm faces fairly, very, or extremely strong competition. 

Dummy = 1 if the firm has received subsidies during the last 3 years from the central or 
local government. 
Dummy = 1 if the firm employs external auditing services. 

The percentage change in sales in the past three years. 

Dummy = 1 if the principle owner (or one of the principal owners) is a female who does not 
have a bank loan because she was rejected or was discouraged from applying, and = 0 if 
the principle owner (or one of the principal owners) is a male whose loan application was 
approved. 

Credit access 

Share working capital financed 
with bank credit 

The proportion of the firm’s working capital that has been financed by borrowing from 
private commercial banks in the past 12 months. 

Share working capital financed 
with trade credit 
Loan application rejected 

The proportion of the firm’s working capital that has been financed with trade credit from 
suppliers or customers in the past 12 months. 
Dummy = 1 if the firm applied for credit and had its loan application rejected. 

Firm discouraged from applying  
for a loan 

Dummy = 1 if a firm that declared positive demand for credit did not apply for a bank loan 
because it was discouraged by adverse credit market conditions. Self-reported reasons for 

BEEPS 2004/05  

BEEPS 2004/05 

BEEPS 2004/05 

BEEPS 2004/05 

BEEPS 2004/05 

BEEPS 2004/05 

BEEPS 2004/05  

BEEPS 2004/05  

BEEPS 2004/05 

BEEPS 2004/05 

ECB Working Paper 1822, July 201555discouragement include: (a) application procedures are too burdensome, (b) collateral 
requirements are too high, (c) interest rates are too high, (d) informal payments are 
necessary, or (e) the firm did not think their application would be approved. 

Application procedures too 
burdensome 

Dummy = 1 if a firm that declared positive demand for credit was discouraged from 
applying for credit because application procedures are too burdensome. 

Collateral requirements too strict 

Dummy = 1 if a firm that declared positive demand for credit was discouraged from 
applying for credit because collateral requirements are too strict. 

Interest rates too high 

Dummy = 1 if a firm that declared positive demand for credit was discouraged from 
applying for credit because interest rates are too high. 

Did not think application would be 
approved 

Dummy = 1 if a firm that declared positive demand for credit was discouraged from 
applying for credit because it did not think that its application would be approved. 

BEEPS 2004/05 

BEEPS 2004/05 

BEEPS 2004/05 

BEEPS 2004/05 

Loan terms 

Days to negotiate loan 

The number of days it took to negotiate the loan with the bank from the day of application.  BEEPS 2004/05  

Loan not collateralized 

Dummy = 1 if the financing of the loan did not require collateral. 

Collateral as % of loan 

The value of the collateral required as a percentage of the loan value. 

Loan in foreign currency 

Dummy = 1 if the loan is denominated in foreign currency. 

Annualized loan rate 

The loan’s annual rate of interest. 

BEEPS 2004/05 

BEEPS 2004/05 

BEEPS 2004/05 

BEEPS 2004/05 

Traditional gender roles 

Private credit 

Country variables 

The inherited views on the role of women in society by U.S. descendants of European 
immigrants. It is measured from the answer to the question: “It is much better for everyone 
involved if the man is the achiever outside the home and the woman takes care of the home 
and family.” The answers are given on a scale from 1 to 4, which corresponds to “Strongly 
agree,” “Agree,” “Disagree,” and “Strongly disagree.” The gender bias indicator is equal to 
1 if the respondent agrees or strongly agrees, and to 0 if the respondent disagrees or 
strongly disagrees. 
The ratio of private credit by deposit money banks and other financial institutions to GDP.  WB FDSD 

GSS 

Bank concentration 

The assets held by the three largest banks as a share of the assets of all commercial banks. 

Information sharing 

The maximum of an index for private credit bureaus and an index for public credit 
registers. 

WB FDSD 

WB DBD 

ECB Working Paper 1822, July 201556GDP growth 

Creditors’ rights  

Degree of protection of creditors’ rights. 

Annual growth in gross domestic product per capita, average over the past three years. 

PWT 6.3 

Contract enforcement 

The number of days it takes to enforce a legal contract. 

Inflation 

Entry time 

Annual inflation, average over the past three years. 

Time in days necessary to register a new business. 

Intention to start a business, (cid:1104) 
(Male – Female) 

The difference in the share of male and female non-entrepreneurs who declared an 
intention to become entrepreneurs in the next three years. 

WB DBD 

WB DBD 

EBRD TR 

WB DBD 

GEM 

Note:  The  Table  uses  the  following  sources:  Business  Environment  and  Enterprise  Performance  Survey  (BEEPS),  2005;  General  Social  Survey  (GSS); 
European Bank for Reconstruction and Development Transition Report (2000-2005 EBRD TR); World Bank Financial Development and Structure Database 
by Thorsten Beck, Asli Demirguc-Kunt, and Vojislav Maksimovic, 2010 (WB FDSD); Penn World Tables (PWT 6.3); World Bank Doing Business database 
(WB DBD); Global Entrepreneurship Monitor (GEM). 

ECB Working Paper 1822, July 201557 
Acknowledgements 
We would like to thank Thorsten Beck, Allen Berger (FIRS discussant), Andre Guttler, Byeongju Jeong, Stepan Jurajda, and seminar 
participants at CERGE and at the 2013 FIRS (Dubrovnik) for useful discussion. The opinions expressed herein are those of the authors 
and do not necessarily reflect those of the ECB or the Eurosystem. 

Steven Ongena 
University of Zurich, SFI and CEPR; Institute for Banking and Finance, Zürich, Switzerland; 
e-mail: steven.ongena@bf.uzh.ch  

Alexander Popov 
European Central Bank; Frankfurt am Main, Germany; 
e-mail: alexander.popov@ecb.int  

© European Central Bank, 2015  

Postal address 
Telephone 
Internet 

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www.ecb.europa.eu 

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This paper can be downloaded without charge from www.ecb.europa.eu, from the Social Science Research Network electronic library at 
http://ssrn.com or from RePEc: Research Papers in Economics at https://ideas.repec.org/s/ecb/ecbwps.html.  
Information on all of the papers published in the ECB Working Paper Series can be found on the ECB’s website, 
http://www.ecb.europa.eu/pub/scientific/wps/date/html/index.en.html. 

ISSN 
ISBN 
DOI 
EU catalogue number 

1725-2806 (online) 
978-92-899-1635-6 
10.2866/875691 
QB-AR-15-062-EN-N