Enterprise Surveys data gave almost no information on the total factor productivity performances of firms belonging to the former Soviet Union area. An analysis of gender gaps at the top - firm owners and CEOs - was suggested but not yet carried out.

1. Does gender of the entrepreneur matter for firm performance?
Evidence from BEEPS panel data
Serena Boccardoa
a
Department of Economics University of Trento, via Inama 5, 38122 Trento, Italy.
Abstract
This paper analyses the relation between gender and performance in manufacturing and
services firms. In particular, it investigates whether and to which extent the gender of the
main entrepreneur a↵ects labour productivity, measured as revenues per worker, and annual
sales. Our assumption is that, once having controlled for relevant firm-level factors such as
firm size and sector, the negative performance gap of women-owned firms disappears. We
test this preposition on BEEPS standardized firm-level dataset for year 2005, since it allows
a cross-country comparison on 94 countries. Our result confirms that a negative gap in
performance between women- and men-owned firms exists, but is significantly lower when
firm-level features are added to the baseline regression. Further investigations would allow
us to control for relevant country-level factors - such as the Gender Inequality Index - and
to exploit panel data in order to control for unobservable firm-level heterogeneity.
JEL codes: F12, F14, F31, F41.
Keywords: productivity, gender, entrepreneurship.
1

2. 1. Introduction
Over the past decades, the number of women starting and owning their own businesses
has grown dramatically. Data from the most recent Global Entrepreneurship Monitor suggest
that women represent approximately one third of all new business activity and one fourth
of established business activity in countries around the globe (Amor´os and Bosma, 2014).
Literature on entrepreneurship has devoted an increasing attention to this phenomenon by
investigating, for instance, the existence of a performance gap between women- and men-
owned firms and whether conditions that support female ability to start and grow ventures
are di↵erent from those that help men. Nevertheless, empirical research on the subject is
limited and provides mixed evidences on the existence of a performance gap. Moreover,
where this gap exists, its determinants haven’t been clearly and univocally identified yet.
Mixed evidences are probably due to the fact that a causal relationship between the gender of
the entrepreneur and firm performance is extremely di cult, if not impossible, to establish,
because of fundamental di culties in disentangling personal ability from gender. Another
reasons for controversial findings lies in the fact that current studies have often investi-
gated single-country samples, that might substantially di↵er by each other. Cross-country
comparison, indeed, has often been neglected (Bardasi et al., 2011), although investigating
ine ciencies due to gender inequality is relevant for all countries, and especially developing
ones, in order to optimally exploit their human resources’ potential for economic growth.
This paper contributes to current literature by providing a cross-country analysis on the
relationship between gender of the main entrepreneur and firm performance. In other words,
it tests whether performance di↵erentials between women- and men-owned firms, if exist, are
related to the gender of the entrepreneur, or if they disappear once controlling for relevant
firm-level factors such as firm size, foreign ownership, technology, innovation and quality of
products sold.1
As indicators of firm performance we used labour productivity2
and annual
sales3
referred to fiscal year prior to the survey. In order to check for robustness, we run the
same regression on average values of labour productivity and sales calculated over the last
three years and we found similar results.
The paper proceeds as follows: Section 2 contains a review of the literature. Section 3
describes the main features of our data and provides descriptive summaries on the main
variables of interest. Section 4 shows and discuss our preliminary results. Section 5 concludes
and Section 6 elaborates on the potential for further research.
2. Background Literature
There are two di↵erent strands of literature analyzing performance in“women-lead” firms:
the first focuses on the relationship between firm productivity and presence of women in
1
Additional controls at the firm-level, in line with the determinants identified in literature, will be added
further and have been detailed in the section called “Further research”.
2
Lack of data availability do not allow us to create TFP measure nor to calculate labour productivity in
terms of value added per worker.
3
Sales revenues are measured in LCUs, values for 2005.
1

3. top positions of corporate businesses.4
A key question of this strand of literature remains
the extent to which women under-representation in senior management reflects unobserved
di↵erences in productivity, preferences, prejudice, or systematically biased beliefs about the
ability of female managers. This strand of literature mostly focus on U.S. listed companies. It
mainly uses financial markets indicators, such as stock options, profits, investments, market
value and Tobin’s Q as measures of firm performance perception. Within this category, we
can further distinguish between studies that analyze specifically the e↵ects on performance
of women in top executive positions, women in the board of directors and the introduction
of quotas in boards.
The second line of research, which is at the core of our analysis, investigates the relation-
ship between female entrepreneurs and firms’ performance. It examines the determinants
of this relationship, of self-selection of women into some businesses, and whether obstacles
faced by men di↵er by those faced by women, both in starting a business and in developing
it. This distinction is relevant since literature shows that entrepreneurs and managers have
di↵erent behavioral traits: entrepreneurs for instance want to be free to achieve and actualize
their potential, in contrast to managers (Fagenson, 1993).5
The following sections provide a summary of previous literature on both lines of research.
2.1. Firm performance and women in top managerial positions
If gender is a positive and relevant component of firm performance, then female under-
representation among executives may have important productivity and welfare implications.
This strand of literature concentrates on questions such as: does the “glass ceiling” phe-
nomenon have major implications on firms outcome? Are management practice, style, and
attitudes towards risk substantially di↵erent between men and women?
Research has highlighted that women are almost ten times less represented than men
in top positions in firms worldwide6
: Italian data show that about 26% of workers in the
manufacturing sector are women compared with only 3% of executives and 2% of CEOs
(Macis et al., 2015). As a matter of comparison, in the U.S. women are a little more than
50% of white collar workers, but they represent only 4.6% of executives (Macis et al., 2015).
Nevertheless, existing literature on the e↵ect of female leadership on firm performance is
limited and focuses mainly on financial performance indicators.7
Rare exceptions are Matsa
and Miller (2012), who looks at operating profits, Smith et al. (2006), with information on
value added and profits on a panel of Danish firms, and Rose (2007), which looks at Tobin
Q.
4
This line of research relates to literature on the “glass ceiling” theory, that investigates which barri-
ers prevent women from reaching top positions in the labor market and their consequences in terms of
performances.
5
Note that in our data, and especially in small-sized businesses, these two roles often overlap.
6
Evidence from U.S. firms is based on the Standard and Poors ExecuComp dataset, which contains
information on top executives in the S&P 500, S&P MidCap 400, and S&P SmallCap 600. A related
literature is concerned with under-representation of women at the top of the wage distribution, see for
example Albrecht et al. (2001). Both phenomena are often referred to as “glass ceiling”.
7
For example, Wolfers (2006); Albanesi and Olivetti (2009); and in the strategy literature, Ahern and
Dittmar (2012); Dezs¨o and Ross (2012); Adams and Ferreira (2007); Farrell and Hersch (2005).
2

4. The e↵ect, though, is still unclear: on the whole, findings show little evidence of a positive
e↵ect of female leadership on firm outcomes. However, some studies provide also positive
results, especially when women cover seats both in the board of directors and in CEOs
positions.
A possible reason for these controversial results is that current studies widely di↵er in term
of dimension, type and number of firms analyzed, country, definition of female leadership
and indicators of firm performance used. One of the first scholars focusing on this issue is
Wolfers (2006). He examined di↵erences in returns to holding stocks in female-headed and
male-headed firms using S&P index data over the period 1992-2004. By using a combination
of matching methods and OLS, he found no systematic di↵erences in performance between
the two groups. Nevertheless, the author also underlines that his results “(...) reflects the
weak statistical power of their test, rather than a strong inference” on the role of financial
markets in estimating gender gaps in performance (Wolfers, 2006). In contrast to his results,
Dezs¨o and Ross (2012), working on the same data and period, found positive results, but only
to the extent that a firm’s strategy is focused on innovation: they suggest that in innovative
contexts the informational and social benefits of gender diversity and the behaviors associated
with women in management are likely to be particularly important for managerial task.
Still, positive results have been found also by Smith et al. (2006) on a panel of large Danish
firms. He found that the proportion of women in top management jobs tend to be positively
associated with firms’ performance, but he also found that the association becomes largely
insignificant once one controls for firm fixed e↵ects.
Rather than focusing on company strategy or firm-level features, Gagliarducci and Paser-
man (2014) interestingly found out that a relevant factor for explaining performance gaps
between women- and men-led firms is the composition of the workforce: by studying the
e↵ect of the gender composition of the first two layers of management on firm and worker
outcomes on a German employer-employee panel dataset, they find that the e↵ect of female
leadership on performance gaps depends on the share of women in the second layer of the
organization. The interaction between women in various level of the organization has also
been proved to be positive for firm performance by a counterfactual experimental exercise:
a female CEO taking over a male-managed firm with at least 20% women in the workforce
increases sales per employee by about 14% more than a female CEO taking over a male-
managed firm whose workforce is composed by less than 20% women: in other words, Macis
et al. (2015) found that female CEOs alone do not have a significant impact on firm perfor-
mance, in line with the results of Wolfers (2006) and Albanesi and Olivetti (2006), but also
that when interacted with a fraction of female non-executive workers, their e↵ect is signifi-
cant and positive on three di↵erent measures of performance. On the positive e↵ect of the
contemporaneous presence of women in di↵erent layers of the organization, also Amore et al.
(2014) found that female CEOs may feel less inhibited when operating with female peers
in governance positions: by investigating medium and large family-controlled firms in Italy
between 2000 and 2010, he found that the e↵ect of the interaction of a women-dominated
board of directors with female CEO is positive and significant on firm performance. This
result is in line with Blau and Ferber (1990) and Koenig et al. (2011), who found that lone
CEO’s underperform because of the psychic costs induced by a pervasive male-oriented con-
text. Amore et al. (2014) suggested an explanation for this: the interaction may serve to
reduce the risk of communication breakdowns, improve cooperation, and facilitate informa-
3

5. tion exchange, e↵ects that should result in higher-quality board performance and thus in
more e cient managerial decision-making.
According to these findings, companies with a substantial female presence, either in
the workforce or in their boards, are likely to benefit from assigning women to leadership
positions. In a slightly di↵erent vein, Parrotta and Smith (2013) document the existence
of a negative association between female CEO and the variability of firm outcomes. Their
findings are in line with the experimental evidence that women typically exhibit higher risk
aversion than men (Croson and Gneezy, 2009; Eckel and Grossman, 2008) and that women
generally exhibit less willingness than men to engage in competitive activities and worse
performance when subject to competitive pressures (Iriberri and Rey-Biel, 2015).8
2.2. Female entrepreneurship: the determinants of performance gaps
The second line of research, which is the focus of our analysis, look at the relationship
between female entrepreneurs and firms’ performance. Empirical evidence regarding this
relationship provides mixed results. Part of the reason for mixed results lies in the fact that
these studies di↵er in the types of firms under analysis, in the definition of female enterprises
and in the main outcomes of interest: Depalo and Lotti (2013) employ the definition of
female entrepreneur given by Italian law n.215/92 and use a panel sample of medium and
large family firms collected between 2005 and 2010. This restricts the analysis only to
companies where women owned at least two thirds of total assets and covered at least two
thirds of corporate board seats. Working on this sample, they find no significant gaps in
terms of value added per worker. They used both pooled OLS and industry-year fixed e↵ects.
By using the same techniques on German establishments from 1997 to 2012, Gagliarducci
and Paserman (2014) also found that once controlled for establishment-level fixed e↵ects and
specific time trends, e↵ects on sales per worker, total employment and investment per worker
disappeared. Their definition of female-owned firm, though, was based on the fraction of
women among proprietors. Their results reveal a substantial sorting of female entrepreneurs
across establishments: small and less productive establishments that invest less, pay their
employees lower wages, but are more female friendly are more likely to be led by women.
The sorting hypothesis, also called “concentration hypothesis” by Verheul et al. (2012),
has been proved to be valid also on a larger sample of firms, covering three macro-regions:
Latin America, Eastern Europe, Central Asia and Sub-Saharan Africa. This analysis was run
by Bardasi et al. (2011), who found significant gender gaps between male- and female-owned
companies in terms of firm size, but much smaller gaps in terms of firm e ciency and growth
(except in Latin America). Bardasi et al. (2011) claim that part of the reason for performance
gap lies in the fact that women run smaller firms and that they tend to concentrate in sectors
in which firms are smaller and less e cient. On the contrary, Du Rietz and Henrekson
(2000) found evidence that female underperformance is much weaker in larger firms, but
their sample includes firms up to only 20 employees. Du Rietz and Henrekson (2000) also
used an extensive multivariate regression with a large number of firm-level controls (among
them, firm size, sector, full capacity utilization). In doing so, he also found that female
underperformance disappears for three out of four performance variables once firm-level
8
In literature, this e↵ect is definedstereotype-threat.
4

6. controls are added.9
Overall, these results show that the underperformance hypothesis of
female-owned firms is rejected once firm-level features such as firm age, size and capacity
utilization are taken into account.
Other studies show evidences of negative gender gap. Two di↵erent groups of expla-
nations have been proposed for it. The first concerns factors exogenous to the individual
running the company: they are barriers related to additional di culties that women might
face in obtaining credit, in cultivating business networks, in dealing with government and
other o cials and to existing cultural norms that restrict the mobility of women or exclude
them from a male-dominated arena. Proxies for these kind of obstacles are country-specific
indicators on female political participation, fertility rates, female literacy rates, etc.(Aidis
et al., 2007) and will be examined further.
The second explanation refers to the existence of individual characteristics, motivation
and preferences of women as entrepreneurs: according to this hypothesis, women are more
risk adverse than men so their performance is lower (Masters and Meier, 1988), or they opt for
smaller business because of a desire to better accommodate their family needs (Jianakoplos
and Bernasek, 1998; Barber and Odean, 2001; Dohmen et al., 2005; Kepler and Shane,
2007).10
Other explanations for the negative gap refer to barriers women face immediately at the
entry into entrepreneurship, especially in accessing credit. Low access to credit, then, might
indirectly a↵ect firm performance: di culties in obtaining a loan have been identified as
the main driver of poor performance by Bardasi et al. (2011) and Muravyev et al. (2008).
According to Bardasi et al. (2011) what is more relevant for women is the cost of collateral,
higher in regions where female feel more constrained than men to obtain formal financing.
Muravyev et al. (2008), instead, found that female firms - defined as those firms where
women are major shareholders and managers at the same time - are less likely to obtain a
loan than their male counterparts and, conditional on obtaining it, they face higher interest
rates and have to pledge higher collateral than men. Both studies are based on a sub-sample
of BEEPS entrepreneurial ventures for year 2005.
On the reasons behind low access to credit, Bardasi et al. (2011) emphasize the role
of unobservable individual characteristics, such as creditworthiness, ability and motivation,
human capital, experience and education; Verheul et al. (2012), instead, claim that the main
reason for lower access to credit among women is endogenous to their preferences: they tend
to concentrate in some sector, such as services, which need less capital and have fewer market
growth opportunities while banks typically lend on the basis of hard assets, such as plant
and equipment (of which service businesses have few).
The literature reviewed so far mainly considering the relationship between female en-
trepreneurs and firm’s productivity measures by sales per worker, investment per workers
and value added per worker. A set of empirical analyses consider other firms’ performance:
Du Rietz and Henrekson (2000), using data on Swedish firms, looks at firms’ profitability
and their work did not find any gender di↵erential; Bosma et al. (2004) considers survival
9
But it is important to notice that their performance variables are all dummies based on survey questions,
not size-related performance indicators.
10
This literature does not distinguish between women in entrepreneurship and women in top executive
positions so it is strongly related to personal characteristics of the manager discussed in the above paragraph.
5

7. Table 1: Number of firms in BEEPS 2005 by country and gender
Country Male Female Country Male Female Country Male Female
Albania 32 8 Germany 113 23 Morocco 753 64
Angola 171 43 Greece 55 10 Namibia 74 26
Argentina 466 230 Guatemala 564 145 Nicaragua 478 227
Armenia 173 17 Guinea 105 30 Niger 14 1
Bangladesh 756 12 Guyana 80 53 Oman 38 1
Belarus 21 8 Honduras 474 131 Panama 135 101
Benin 117 10 Hungary 134 77 Paraguay 191 158
Bolivia 193 144 India 3,239 288 Peru 233 108
Bosnia and Herze 21 6 Indonesia 27 5 Philippines 212 123
Botswana 48 64 Ireland 86 46 Poland 247 106
Brazil 1,253 236 Jamaica 33 13 Portugal 32 22
Bulgaria 23 5 Jordan 287 50 Romania 174 66
Burkina Faso 26 9 Kazakhstan 127 58 Russian Federati 54 15
Burundi 75 27 Kenya 113 6 Rwanda 35 22
Cambodia 15 2 Korea, Rep. 93 12 Senegal 91 5
Cameroon 39 26 Kyrgyz Republic 25 9 Slovak Republic 18 1
Cape Verde 16 9 Lao PDR 46 118 Slovenia 19 4
Chile 879 334 Latvia 16 5 South Africa 284 27
Colombia 299 315 Lebanon 65 22 Spain 66 28
Costa Rica 91 163 Lesotho 19 4 Swaziland 55 14
Croatia 24 6 Lithuania 82 48 Syrian Arab Repu 146 4
Czech Republic 49 11 Madagascar 143 49 Tajikistan 37 5
Dominican Republ 99 11 Malawi 0 25 Tanzania 309 59
Ecuador 502 154 Malaysia 460 34 Thailand 570 79
Egypt, Arab Rep. 672 209 Mali 59 3 Turkey 745 123
El Salvador 549 273 Mauritania 68 11 Uganda 302 80
Eritrea 17 2 Mauritius 101 13 Ukraine 76 28
Estonia 14 7 Mexico 770 250 Uruguay 179 128
Ethiopia 209 0 Moldova 70 25 Uzbekistan 19 5
Gambia, The 28 5 Mongolia 73 55 Vietnam 420 110
Georgia 13 8 Montenegro 16 3 Zambia 39 8
Total 20,478 5,723
Note: Table reports the composition of our sample for 94 selected countries in terms of gender of the
entrepreneur: (1) “Female” firms definition includes firms having at least a woman among the owners; (2)
“Male” firms otherwise. Our elaboration on BEEPS Standardized data 2005.
probabilities of Dutch business and found male-businesses to survive longer than their fe-
male counterparts; similarly, Lohmann and Luber (2004) shows that in Germany only 42%
of self-employed women remain self-employed after 5 years, while the corresponding rate for
male entrepreneurs is 63%. Other studies show that female-owned enterprises do not under-
perform in terms of employment creation (Fischer et al., 1993; Chaganti and Parasuraman,
1996) or survival rates (Kalleberg and Leicht, 1991; Br¨uderl and Preisend¨orfer, 1998).
Our paper strongly relates to the work of Bardasi et al. (2011) and it extends it considering
firms belonging to a larger number of region and to 94 countries.
3. Data Description
The Business Environment and Enterprise Performance Survey (BEEPS) standardized11
dataset 2005 is an extensive firm-level database produced by the World Bank and the Eu-
11
Standardized data is country data that has been matched to a standard set of questions. This format
allows cross-country comparisons and analysis but sacrifices those country-specific survey questions which
6

8. 0 1,000 2,000 3,000
Other transport equipment
Auto and auto components
Other manufacturing
Paper
Non−metallic and plastic materia
Wood and furniture
Chemicals and pharmaceutics
Electronics
Metals and machinery
Beverages
Food
Garments
Leather
Textiles
Gender refers to main firm’s owner
Our elaboration on BEEPS 2005
Number of observations by industry sector
sum of male sum of female
Figure 1: Figure reports n.observations by industry sector and gender of the main en-
trepreneur. Sectors classification based on standard ISO codes. Source: Our elaboration
on BEEPS standardized data 2005.
ropean Bank for Reconstruction and Development (EBRD) for examining the quality of the
business environment in di↵erent regions. Interviews cover topics ranging from firm financing
to labour, corruption and infrastructure. Only registered firms are included in the sample,
which is based on national registry collected firms, representative of the manufacturing and
service sectors. The sectoral contribution to “manufacturing” versus “services” is determined
by their relative contribution to GDP. In each country, the sample is stratified by size, sector
and geographic region, using simple random sampling. All survey variables refer to the fiscal
year before the interview took place.
One of the main strengths of these data is that they are collected homogeneously across
countries, allowing for cross-country comparison of results. However, weaknesses include the
presence of a very small sample in some countries and the numerous missing answers to some
variables of interest (e.g. intermediate goods) which considerably limited the construction
of our the dependent variables.
For our analysis, we restrict our attention to the 2005 cross-section since it is the newest
wave containing a representative sample for our variable of interest, defined as “Gender of
the principal owner of the firm”.12
The BEEPS standardized dataset originally contained
71,789 firms ranging across all economic activities from 94 countries for the year 2005. Once
we dropped observations having missing values on our variables of interest, we were left with
cannot be matched. The standardization process requires that certain compromises are made in order to
match some of the variables. One of the compromise has been to consider interviews occurring in di↵erent
years as belonging to the same questionnaire. This is the reason why we controlled for country-year fixed
e↵ect although it is a cross-sectional dataset.
12
A peculiarity of this wave is that its questionnaire also reports whether the manager/director coincides
with the owner or not, although this information has not yet been used in our analysis.
7

9. 26,201 firms. Table 1 describes the composition of our sample across countries in terms of
gender: women-run firms are almost 22% of the total sample and they concentrate mainly in
Argentina, Brazil, Chile, Colombia, Egypt, El Salvador, Nicaragua, India and Mexico. These
are also the most populous countries in terms of firms interviewed. Only a few countries of
the sample belong to the EU area and they show very few observations. In all countries, the
number of male-owned firms prevails over women-owned, with Ethiopia showing observations
only for male-owned firms.
Figure 1 shows instead the sample distribution by industry sector: male-owned firms
predominate in all sectors, while women-owned are concentrated mainly in Garments and
Food. The most male-dominated sector in relative terms is Electronics and overall the
presence of women is very limited with respect to that of men. This suggest that almost
all sector are male-dominated, although this distribution does not take into account the
dimension of firms observed.
Aggregate summary statistics on that are shown in Table 2 which summarizes average
values, median and number of observations for three variables of interest, considering the
whole sample in 2005: labour productivity, annual sales and total employment, which is our
proxy for firm size (all variables are in log form). Labour productivity has been built as a ratio
between annual sales and total employment. Table 2 shows that the number of observations
for labour productivity is less than those in sales and employment. This is because for some
firms either data on sales or on total employment were missing. Nevertheless, it is worth
noticing that although both aggregate means for sales and for employment are lower for
female than for men, average labour productivity for women-owned firms is slightly higher
that that of men: it seems that women-lead firms, although less numerous, are relatively
more productive (in terms of sales per permanent worker). This descriptive evidence might
be probably driven by firms belonging to the Latin and Caribbean regions, since in this area
female labour productivity di↵erentials are positive and the number of firms interviewed was
very high, as Table3 shows.
In Table 3, the disaggregation of firms in five macro-regions based on their geographical
location13
shows that in African and Middle Eastern countries there are positive di↵erentials
for gender on all productivity indicators but this result is observed on a relative low number
of firms. European and Central Asian countries, instead, show negative di↵erentials on all
variables of interest. Labour productivity di↵erentials for women are negative only in the
ECA region, driven by sales and number of employees.
While aggregate data in Table 2 showed slight positive di↵erentials in favor of women-
owned firms, considering the whole frequency distributions as in Figure 2 of male-owned firms
seem to dominate for all our variables. Indeed, male-owned Epanechnikov kernel densities
are shifted to the right with respect of those of female-owned.14
.
13
AFR = Africa Region countries; EAP= East Asian and Pacific countries; ECA= European and Central
Asian countries; LCR= Latin and Caribbean Region; MNA= Middle East and Northern African countries
14
Note that Stata calculates and uses by default the optimal width
8

10. Table 2: Descriptive statistics by gender
Gender Lab. Prod. Sales Empl.
Male 5.70 9.15 3.44
(5.18) (8.74) (3.22)
20,170 20,210 20,263
Female 5.77 9.02 3.22
(4.89) (8.34) (3.00)
5,628 5,632 5,673
Total 5.71 9.12 3.39
(5.12) (8.70) (3.18)
25,798 25,842 25,936
Note: Table reports mean values, median (in parenthesis) and n.observations for the main indicators of firm
performance (in log form). (1) Labour Productivity is defined as sales per employee; (2) Sales refers to fiscal
year prior to the survey and is measured in thousands of LCUs. (3) Employment is defined as average n.
workers in the year prior to the survey. Definition of “Female ” includes firms having at least a woman
among the owners. Definition of “Male ” otherwise. Our elaboration on BEEPS Standardized data 2005.
4. Results
By exploiting cross-sectional data for year 2005 we test for the existence of a productivity
gap between female- and male-owned firms in terms of labour productivity and annual sales.
We perform a linear regression model, where the dependent variable is a proxy for firm
performance expressed in log form (either labour productivity or sales) and the main regressor
is a dummy representing the gender of the main owner. This specification enables us to
investigate how di↵erences in performance are related to gender. Note that Year fixed e↵ect
are inserted although we are using a cross-section because BEEPS standardized dataset 2005
contains interviews collected in previous years. Our baseline regression model is the following
ln Yf,c = c + ↵Dfemown
f,c + dc,t + ds + "f,c (1)
where Yf , c, s is a proxy for a firm’s performance, either labour productivity, measured by
total sales per employee or annual sales. Dfemown
f,c is a dummy which equals 1 if the owner
is female and 0 otherwise. Therefore, coe cient ↵ measures how women-owned di↵er with
respect to the baseline (men-owned firms). To account for heterogeneity across countries, we
introduce country-year fixed e↵ect (dc,t). Industry fixed-e↵ects (ds) are also included to allow
for peculiar features of each sector. Standard errors are clustered at the firm-level, although
for robustness we can also cluster them by country, industry and country-industry-year levels.
As a robustness check, we run a second specification
ln Yf,c = c+↵Dfemown
f,c + 1Sizef,c + 2Agef,c + 3fof,c + 4Techf,c + 5Qualf,c +dc,t +ds +"f,c
(2)
where we add to the baseline model further firm-level controls such as firm size, proxied by the
(log) of number of permanent employees in previous fiscal year, firm age and three dummies
9

11. Table 3: Descriptive statistics by region
AFR
Gender Lab. Prod. Sales Empl.
Male - mean 7.73 10.78 3.05
p50 (7.72) (10.49) (2.77)
N 2,529 2,535 2,546
Female - mean 7.66 10.83 3.18
p50 (7.83) (10.74) ( 2.89)
N 574 575 573
EAP
Gender Lab. Prod. Sales Empl.
Male - mean 5.84 9.51 3.65
p50 (5.85) (9.16) (3.40)
N 3,195 3,205 3,204
Female - mean 6.06 9.48 3.38
p50 (5.89) (9.31) (3.14)
N 656 654 657
ECA
Gender Lab. Prod. Sales Empl.
Male - mean 3.34 6.60 3.23
p50 (3.46) (6.42) (3.16)
N 2,548 2,557 2,564
Female - mean 3.17 6.13 2.92
p50 (3.25) (5.99) (2.83)
N 771 770 783
LCR
Gender Lab. Prod. Sales Empl.
Male - mean 5.62 9.05 3.40
p50 (4.79) (8.29) (3.22)
N 7,349 7,354 7,371
Female - mean 5.94 9.12 3.16
p50 (4.93) (8.26) (3.00)
N 3,113 3,118 3,141
MNA
Gender Lab. Prod. Sales Empl.
Male - mean 4.54 8.14 3.59
p50 (4.09) (7.88) (3.40)
N 1,928 1,934 1,947
Female - mean 4.55 8.38 3.83
p50 (3.70) (7.73) (3.58)
N 344 344 348
Note: Table reports mean values, median (in parenthesis) and N. observations for the main indicators of
firm performance (in log form) observed in 94 selected countries grouped by region: AFR = Africa Region
countries; EAP= East Asian and Pacific countries; ECA= European and Central Asian countries; LCR=
Latin and Caribbean Region ; MNA= Middle East and Northern African countries. Our elaboration on
BEEPS Standardized data 2005.
10

12. 0.05.1.15.2
0 5 10 15
Ln(Labour Productivity)
Woman−lead Man−lead
Gender refers to main firm’s owner
Our elaboration on BEEPS 2005
0.05.1.15
0 5 10 15 20
Ln(Sales)
Woman−lead Man−lead
0.1.2.3.4
1 2 3 4 5 6
Ln(Employment)
Woman−lead Man−lead
Figure 2: Figures report density distribution of three main indicators of firm performance by gender (in
log form): (1) Labour Productivity is defined as sales per employee; (2) Sales refers to fiscal year prior to
the survey and is measured in thousands of LCUs. (3) Employment is defined as average n. workers in the
year prior to the survey. Definition of “Female” includes firms having at least a woman among the owners.
Definition of “Male” otherwise. Our elaboration on BEEPS Standardized data 2005.
accounting respectively for: foreign ownership, defined as more than a half of proprietorship
owned abroad (fo); quality of internal processes defined by the ISO qualification (qual);
technology (tech) proxied by the development/upgrading of a major product line or by the
introduction of new technology in the last three years. Controlling for these factors allow
us to partially account for potential omitted variables that might influence productivity
variables. The results of both specifications are shown in Table 4. All coe cients are
highly significant and show evidence of a negative performance of female-owned firms with
respect to male ones (our baseline). In terms of percent change, we found that female-
owner dummy negatively a↵ects labour productivity of 12,5%: it lowers the gap in expected
value of labour productivity for female by 12,5% with respect to men, Column (1). The
percentage gap is approximately 22% for gender di↵erentials on performance gaps in annual
sales (Column (3)). As we expected, though, control-factors contribute to explain this gap:
indeed, Column (2) and (4) show that once they are added to the regression, the magnitude
of ↵ is reduced in both specifications. Robustness check (See Section 7) regressions show
similar results. It is worth noticing, though, that in robustness check regressions while our
11

13. Table 4: Regression on two main firm performance indicators in year 2005
Dep. Var. (1) (2) (3) (4)
ln Lab. Prod. ln Lab. Prod. ln Sales ln Sales
Female owner -0.134⇤⇤⇤
-0.111⇤⇤⇤
-0.243⇤⇤⇤
-0.117⇤⇤⇤
(0.017) (0.018) (0.029) (0.018)
Size 0.102⇤⇤⇤
1.064⇤⇤⇤
(0.007) (0.008)
Firm age 0.057⇤⇤⇤
0.068⇤⇤⇤
(0.010) (0.011)
FO 0.365⇤⇤⇤
0.409⇤⇤⇤
(0.035) (0.037)
Tech 0.094⇤⇤⇤
0.099⇤⇤⇤
(0.017) (0.018)
Qual 0.341⇤⇤⇤
0.355⇤⇤⇤
(0.024) (0.025)
Country-Year FE Yes Yes Yes Yes
Sector FE Yes Yes Yes Yes
N.Obs. 25,779 20,221 25,823 20,275
Adj. R2
0.859 0.871 0.714 0.891
Note: Table reports results of a OLS regression of two main indicators of firm performance, for female and
male owners. Specifications include: without (1) and with (2) firm-level controls. Baseline category is male
owner. See the Section3 for further explanation on country-year FE. Size is defined by the average n. of
workers in the year prior to the survey. FO is a dummy for foreign ownership; similarly, Tech is a dummy
for technology advancement and Qual is a dummy for ISO certification. Robust standard errors clustered at
firm-level are reported in parenthesis below the coe cients. Asterisks denote significance levels (***: p<1%;
**: p<5%; *: p< 10%). Our elaboration on BEEPS Standardized data 2005.
dependent variables are calculated as averages of labour productivity and sales over the last
three years, the dummy variable for female owner refers to last fiscal year only because data
did not allow us to check whether proprietorship changed over the three years considered
for robustness. Overall, results are in line with evidences from previous literature: limited
evidence of underperformance of female enterprises exists, on both productivity variables
considered, but its magnitude is lower when controlling for firm-level factors. In the following
of this research, we would like to test to which extent these productivity gaps between female
and male enterprises are reduced once the level of gender inequality in the country where
the firm is located is taken into account.
5. Conclusion
The result obtained is in line with evidences from previous literature: a limited evidence
of the female underperformance hypothesis exists, but firm-level characteristics - e.g. size -
contribute to explain it. Robustness check on average values for our dependent variables over
12

14. last three year confirms the result. Nevertheless, the cross-sectional nature of the analysis
does not make it possible to establish causality. Moreover, panel data would have allowed us
to overcome the problem of firm-level heterogeneity but data availability limited us to use a
cross-section. Further analysis are required to improve these conclusions.
6. Further research
By now, our focus has been limited to female entrepreneurial performances in relation to
the gender of the main owner. The next step is to test further hypothesis, such as :
a) underperformance of women-owned firm is driven by country-level factors, rather than
the gender of the main owner? And is the e↵ect of female owner significant and relevant when
interacted with a country-level variable? Inequality-adjusted human development indexes
might turn out to be relevant in this regard, especially the Gender Inequality Index (GII). In
particular, some of their components, (e.g. share of seats in parliament, maternal mortality
ratio, percentage of female labour force participation) might a↵ect the relationship between
gender of the owner and firm performance more than others.
This might be true since female entrepreneurial performances are also influenced by dif-
ferences across countries in terms of female freedom to work and travel due to traditional
family and religious norms and by other important institutions which impact female en-
trepreneurship, such as equal legal rights, access to education, networks, technology, capital,
social norms, values, and expectations(Terjesen and Elam, 2012). Furthermore, the overall
business environment in terms of laws, regulations, and business stability will a↵ect busi-
nesses ability to thrive and grow (Terjesen and Elam, 2012). Thus, we will test our initial
assumptions including into the regression a large set of country-level indicators, including
the Gender Inequality Index (GII) and the Female Entrepreneurship Index (FEI), in or-
der to assess the extent of the impact of external conditions on the relationship between
gender and firm performance. In particular, we expect the GII to be significant per se on
firm performance, and its magnitude to be lower in countries where gender inequality is lower.
b) the positive e↵ect of an interaction between female CEOs and female owners (it is pos-
sible to test this assumption only on wave 2009 since it is the only one in BEEPS containing
both variables). Indeed, findings from previous literature suggest evidences of a positive
e↵ect of the joint presence of women in various positions inside the organization.
BEEPS data allow us to test the interaction of female owner with:
- the number of part-time and full-time female workers;
- the number of female permanent workers in non-production functions;
- the percentage of female in senior management;
- the cases where CEO and owner coincide (wave 2005 only).
Moreover, current analysis can be expanded further in the following directions:
i) functional forms explaining the relationship between performance and gender of the main
owner better than simple OLS regression;
ii) standard errors clustered at the country- rather than firm-level;
13

15. iii) additional measures of productivity, e.g. labour productivity defined as total sales over
number of employees rather than number of permanent workers only; current analysis in-
deed was limited by the shortage of data on e.g. intermediate goods costs, which would
have allowed us to build more precise measures of performance such as TFP (Total Factor
Productivity). We had to limit our analysis to labour productivity because data did not
contain measures of value-added except for a limited sub-sample of firms;
iv) additional measures of firm-level controls: e.g. percentage of senior management’s time is
spent in dealing with requirements imposed by government regulations; percent of domestic
sales; percentage of working capital from local banks are hidden factors that might a↵ect
performance gaps according to literature;
v) additional robustness check: productivity growth di↵erentials over time can also be inves-
tigated in relationship to change in country-level determinants, thus overcoming the limited
availability of panel data;
vi) demographic variables other than gender contained in the dataset and referred to firm
owner (personal assets, highest level of education and years of experience) can help disen-
tangling personal characteristics from gender.
In addition to the above:
a) the creation of a “female concentration index” in line with Bardasi et al. (2011) and
defined as “the ratio between the percentage of women entrepreneurs in a specific sector and
the average percentage of women entrepreneurs in the whole country” can be useful to build
a gender dummy at the sectorial level which account for female presence in a given industry-
sector over a given threshold; therefore, dummies for gender presence at three di↵erent levels
(firm, sector and country) could be exploited for carrying on a multilevel analysis;
b) merging the newest BEEPS wave (2013) would allow expanding the analysis on most
recent data. Panel data (BEEPS 2002-05-09) also contain our variables of interest, but only
on a small sub-sample of firms. Though, conditional on data availability, it is still possible to
conduct cross-sectional analysis on di↵erent waves (2002, 2005, 2009 and 2013) and compare
the results.
14

16. 7. References
Adams, R. B. and Ferreira, D. (2007). A theory of friendly boards, The Journal of Finance
62(1): 217–250.
Ahern, K. R. and Dittmar, A. K. (2012). The changing of the boards: The impact on
firm valuation of mandated female board representation, Quarterly Journal of Economics
127(1): 137–197.
Aidis, R., Welter, F., Smallbone, D. and Isakova, N. (2007). Female entrepreneurship in
transition economies: the case of Lithuania and Ukraine, Feminist Economics 13(2): 157–
183.
Albanesi, S. and Olivetti, C. (2006). Gender and dynamic agency: Theory and evidence on
the compensation of female top executives, Boston University-Department of Economics-
Macroeconomics Working Papers Series WP2006-061 .
Albanesi, S. and Olivetti, C. (2009). Home production, market production and the gender
wage gap: Incentives and expectations, Review of Economic dynamics 12(1): 80–107.
Albrecht, J., Bjorklund, A. and Vroman, S. (2001). Is there a glass ceiling in sweden?
Amore, M. D., Garofalo, O. and Minichilli, A. (2014). Gender interactions within the family
firm, Management Science 60(5): 1083–1097.
Amor´os, J. E. and Bosma, N. (2014). Global entrepreneurship monitor 2013 global report,
Recovered on February 28: 2014.
Barber, B. M. and Odean, T. (2001). Boys will be boys: Gender, overconfidence, and
common stock investment, Quarterly journal of Economics pp. 261–292.
Bardasi, E., Sabarwal, S. and Terrell, K. (2011). How do female entrepreneurs perform?
evidence from three developing regions, Small Business Economics 37(4): 417–441.
Blau, F. D. and Ferber, M. A. (1990). Women’s work, women’s lives: A comparative economic
perspective, Technical report, National Bureau of Economic Research.
Bosma, N., Van Praag, M., Thurik, R. and De Wit, G. (2004). The value of human and social
capital investments for the business performance of startups, Small Business Economics
23(3): 227–236.
Br¨uderl, J. and Preisend¨orfer, P. (1998). Network support and the success of newly founded
business, Small business economics 10(3): 213–225.
Chaganti, R. and Parasuraman, S. (1996). A study of the impacts of gender on business
performance and management patterns in small businesses, Entrepreneurship Theory and
Practice 21: 73–75.
Croson, R. and Gneezy, U. (2009). Gender di↵erences in preferences, Journal of Economic
literature pp. 448–474.
15

17. Depalo, D. and Lotti, F. (2013). Che genere di impresa? di↵erenziali di performance tra
imprese maschili e femminili (firms and gender: Performance di↵erentials between male
and female firms), Di↵erenziali di performance tra imprese maschili e femminili (Firms
and Gender: Performance Di↵erentials between Male and Female Firms)(June 21, 2013).
Bank of Italy Occasional Paper 1(184).
Dezs¨o, C. L. and Ross, D. G. (2012). Does female representation in top management improve
firm performance? a panel data investigation, Strategic Management Journal 33(9): 1072–
1089.
Dohmen, T. J., Falk, A., Hu↵man, D., Sunde, U., Schupp, J. and Wagner, G. G. (2005). Indi-
vidual risk attitudes: New evidence from a large, representative, experimentally-validated
survey.
Du Rietz, A. and Henrekson, M. (2000). Testing the Female Underperformance Hypothesis,
Small Business Economics 14(1): 1–10.
Eckel, C. C. and Grossman, P. J. (2008). Men, women and risk aversion: Experimental
evidence, Handbook of experimental economics results 1: 1061–1073.
Fagenson, E. A. (1993). Personal value systems of men and women entrepreneurs versus
managers, Journal of Business Venturing 8(5): 409–430.
Farrell, K. A. and Hersch, P. L. (2005). Additions to corporate boards: the e↵ect of gender,
Journal of Corporate finance 11(1): 85–106.
Fischer, E. M., Reuber, A. R. and Dyke, L. S. (1993). A theoretical overview and extension
of research on sex, gender, and entrepreneurship, Journal of business venturing 8(2): 151–
168.
Gagliarducci, S. and Paserman, M. D. (2014). The E↵ect of Female Leadership on Estab-
lishment and Employee Outcomes: Evidence from Linked Employer-Employee Data, IZA
Discussion Papers 8647, Institute for the Study of Labor (IZA).
Iriberri, N. and Rey-Biel, P. (2015). Let’s (Not) Talk about Sex: The E↵ect of Information
Provision on Gender Di↵erences in Performance under Competition, Working Papers 583,
Barcelona Graduate School of Economics.
Jianakoplos, N. A. and Bernasek, A. (1998). Are women more risk averse?, Economic inquiry
36(4): 620.
Kalleberg, A. L. and Leicht, K. T. (1991). Gender and organizational performance: Determi-
nants of small business survival and success, Academy of management journal 34(1): 136–
161.
Kepler, E. and Shane, S. (2007). Are male and female entrepreneurs really that di↵erent?,
Technical report, US Small Business Administration, O ce of Advocacy.
16

18. Koenig, A. M., Eagly, A. H., Mitchell, A. A. and Ristikari, T. (2011). Are leader stereo-
types masculine? a meta-analysis of three research paradigms., Psychological bulletin
137(4): 616.
Lohmann, H. and Luber, S. (2004). Trends in self-employment in germany: di↵erent types,
di↵erent developments, The Reemergence of Self-Employment. A comparative study of
self-employment dynamics and social inequality pp. 36–74.
Macis, M., Schivardi, F., Moro, A. and Flabbi, L. (2015). Do Female Executives Make a
Di↵erence? The Impact of Female Leadership on Gender Gaps and Firm Performance,
2015 Meeting Papers 392, Society for Economic Dynamics.
Masters, R. and Meier, R. (1988). Sex di↵erences and risk-taking propensity of entrepreneurs,
Journal of small business management 26(1): 31.
Matsa, D. A. and Miller, A. R. (2012). A female style in corporate leadership? evidence
from quotas, Evidence from Quotas (December 13, 2012). American Economic Journal:
Applied Economics, Forthcoming .
Muravyev, A., Schaefer, D. and Talavera, O. (2008). Entrepreneurs’ gender and financial
constraints: evidence from international data, Discussion Papers 11, Kyiv School of Eco-
nomics.
Parrotta, P. and Smith, N. (2013). Female-led firms: Performance and risk attitudes, Tech-
nical report, IZA Discussion Paper.
Rose, C. (2007). Does female board representation influence firm performance? the danish
evidence, Corporate Governance: An International Review 15(2): 404–413.
Smith, N., Smith, V. and Verner, M. (2006). Do women in top management a↵ect firm
performance? a panel study of 2,500 danish firms, International Journal of Productivity
and Performance Management 55(7): 569–593.
Terjesen, S. and Elam, A. (2012). Women entrepreneurship: a force for growth, International
Trade Forum, number 2 in 2, International Trade Centre, p. 16.
Verheul, I., Thurik, R., Grilo, I. and van der Zwan, P. (2012). Explaining preferences
and actual involvement in self-employment: Gender and the entrepreneurial personality,
Journal of Economic Psychology 33(2): 325–341.
Wolfers, J. (2006). Diagnosing discrimination: Stock returns and ceo gender, Journal of the
European Economic Association 4(2-3): 531–541.
17

19. Appendix A: definition of variables
Table A1: Variables description
Variable Wording of survey questions and answers’ codes
Female owner
QUESTION: Is the principal owner male? Yes=1 No= 2
Dummy variable (reversed) Female Owner: Yes=1 No=0
Sales
QUESTION: Total sales one year ago in thousands of LCUs. Size
QUESTION: Average n. of permanent workers one year ago.
Ln. Empl. mean
QUESTION: Average n. of permanent workers one year ago.
QUESTION: Average n. of permanent workers two year ago.
QUESTION: Average n. of permanent workers three year ago.
Age
QUESTION: In what year did your firm begin operations in this country?
Foreign Ownership
QUESTION: Which of the following best describes the largest shareholder or owner in your firm?
1)Individual
2)Family
3)Domestic company
4)Foreign company
5)Bank
6)Investment fund
7)Managers of the firm
8)Employees of the firm
9)Government or government agency
10) Other (Specify)
Quality
QUESTION: Has your firm received ISO (e.g. 9000, 9002 or 14,000) certification?
Yes=1 ; No=2
Innovation
QUESTION: Has your company undertaken any of the following initiatives in the last three years?
1) Developed a major new product line: Yes=1 ; No=2
2) Upgraded an existing product line: Yes=1 ; No=2
3) Introduced new technology that has substantially changed
the way that the main product is produced: Yes=1 ; No=2
Fem empl
QUESTION: Average percentage of permanent female workers one year ago.
Fem empl: variable (reversed)
QUESTION: What percent of the senior management is male?
Perc time
QUESTION: What percentage of senior management’s time is spent in dealing with requirements
imposed by government regulations?
Perc dom sales
QUESTION: What percent of your establishment?s sales are sold domestically?
Note: The table reports the questions in the BEEPS standardized 2005 questionnaire used to construct our
variables of interest. Moreover, it reports useful variables for extending the analysis further as explained in
latest section.
18

20. Appendix B: Robustness check
Table B1: Robustness check: regression on two main performance indicators averaged over
the last 3 years.
(1) (2) (3) (4)
ln Lab. Prod. ln Lab. Prod. ln Sales ln Sales
Female owner -0.149⇤⇤⇤
-0.129⇤⇤⇤
-0.260⇤⇤⇤
-0.129⇤⇤⇤
(0.020) (0.020) (0.030) (0.020)
Size mean 0.075⇤⇤⇤
1.075⇤⇤⇤
(0.008) (0.008)
ln age 0.057⇤⇤⇤
0.057⇤⇤⇤
(0.011) (0.011)
fo 0.382⇤⇤⇤
0.382⇤⇤⇤
(0.040) (0.040)
tech 0.121⇤⇤⇤
0.121⇤⇤⇤
(0.019) (0.019)
qual 0.355⇤⇤⇤
0.355⇤⇤⇤
(0.028) (0.028)
Country-Year FE Yes Yes Yes Yes
Sector FE Yes Yes Yes Yes
N.Obs. 26,182 20,731 26,201 20,731
Adj. R2
0.826 0.839 0.682 0.873
Standard errors in parentheses
⇤
p < 0.10, ⇤⇤
p < 0.05, ⇤⇤⇤
p < 0.01
Note: Table reports results of a OLS regression on the means of two main indicators of firm performance over
3 last fiscal years, for female and male owners and controlling for firm-level factors in the second specification.
Baseline category is male owner. See the Section3 for further explanation on country-year FE. Dummies
are assumed to be time-invariant (see Appendix A for further details). Size is defined by the average n. of
workers in the three years prior to the survey. FO is a dummy for foreign ownership; similarly, Tech is a
dummy for technology advancement and Qual is a dummy for ISO certification. Robust standard errors
clustered at firm-level are reported in parenthesis below the coe cients. Asterisks denote significance levels
(***: p<1%; **: p<5%; *: p< 10%). Our elaboration on BEEPS Standardized data 2005.
19