Does gender of the entrepreneur matter for firm performance?

Evidence from BEEPS panel data

Serena Boccardoa

aDepartment of Economics University of Trento, via Inama 5, 38122 Trento, Italy.

Abstract

This paper analyses the relation between gender and performance in manufacturing andservices firms. In particular, it investigates whether and to which extent the gender of themain entrepreneur a↵ects labour productivity, measured as revenues per worker, and annualsales. Our assumption is that, once having controlled for relevant firm-level factors such asfirm size and sector, the negative performance gap of women-owned firms disappears. Wetest this preposition on BEEPS standardized firm-level dataset for year 2005, since it allowsa cross-country comparison on 94 countries. Our result confirms that a negative gap inperformance between women- and men-owned firms exists, but is significantly lower whenfirm-level features are added to the baseline regression. Further investigations would allowus to control for relevant country-level factors - such as the Gender Inequality Index - andto exploit panel data in order to control for unobservable firm-level heterogeneity.

JEL codes: F12, F14, F31, F41.Keywords: productivity, gender, entrepreneurship.

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1. Introduction

Over the past decades, the number of women starting and owning their own businesseshas grown dramatically. Data from the most recent Global Entrepreneurship Monitor suggestthat women represent approximately one third of all new business activity and one fourthof established business activity in countries around the globe (Amoros and Bosma, 2014).Literature on entrepreneurship has devoted an increasing attention to this phenomenon byinvestigating, 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 venturesare di↵erent from those that help men. Nevertheless, empirical research on the subject islimited 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 ofthe entrepreneur and firm performance is extremely di�cult, if not impossible, to establish,because of fundamental di�culties in disentangling personal ability from gender. Anotherreasons 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-countrycomparison, indeed, has often been neglected (Bardasi et al., 2011), although investigatingine�ciencies due to gender inequality is relevant for all countries, and especially developingones, 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 therelationship 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, arerelated to the gender of the entrepreneur, or if they disappear once controlling for relevantfirm-level factors such as firm size, foreign ownership, technology, innovation and quality ofproducts sold.1 As indicators of firm performance we used labour productivity2 and annualsales3 referred to fiscal year prior to the survey. In order to check for robustness, we run thesame regression on average values of labour productivity and sales calculated over the lastthree years and we found similar results.

The paper proceeds as follows: Section 2 contains a review of the literature. Section 3describes the main features of our data and provides descriptive summaries on the mainvariables of interest. Section 4 shows and discuss our preliminary results. Section 5 concludesand 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

1Additional controls at the firm-level, in line with the determinants identified in literature, will be addedfurther and have been detailed in the section called “Further research”.

2Lack of data availability do not allow us to create TFP measure nor to calculate labour productivity interms of value added per worker.

3Sales revenues are measured in LCUs, values for 2005.

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top positions of corporate businesses.4 A key question of this strand of literature remainsthe extent to which women under-representation in senior management reflects unobserveddi↵erences in productivity, preferences, prejudice, or systematically biased beliefs about theability of female managers. This strand of literature mostly focus on U.S. listed companies. Itmainly uses financial markets indicators, such as stock options, profits, investments, marketvalue and Tobin’s Q as measures of firm performance perception. Within this category, wecan further distinguish between studies that analyze specifically the e↵ects on performanceof women in top executive positions, women in the board of directors and the introductionof 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 determinantsof this relationship, of self-selection of women into some businesses, and whether obstaclesfaced by men di↵er by those faced by women, both in starting a business and in developingit. This distinction is relevant since literature shows that entrepreneurs and managers havedi↵erent behavioral traits: entrepreneurs for instance want to be free to achieve and actualizetheir 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, andattitudes towards risk substantially di↵erent between men and women?

Research has highlighted that women are almost ten times less represented than menin top positions in firms worldwide6: Italian data show that about 26% of workers in themanufacturing 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 than50% 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 islimited and focuses mainly on financial performance indicators.7 Rare exceptions are Matsaand Miller (2012), who looks at operating profits, Smith et al. (2006), with information onvalue added and profits on a panel of Danish firms, and Rose (2007), which looks at TobinQ.

4This 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 ofperformances.

5Note that in our data, and especially in small-sized businesses, these two roles often overlap.6Evidence 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 relatedliterature is concerned with under-representation of women at the top of the wage distribution, see forexample Albrecht et al. (2001). Both phenomena are often referred to as “glass ceiling”.

7For example, Wolfers (2006); Albanesi and Olivetti (2009); and in the strategy literature, Ahern andDittmar (2012); Dezso and Ross (2012); Adams and Ferreira (2007); Farrell and Hersch (2005).

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The e↵ect, though, is still unclear: on the whole, findings show little evidence of a positivee↵ect of female leadership on firm outcomes. However, some studies provide also positiveresults, especially when women cover seats both in the board of directors and in CEOspositions.

A possible reason for these controversial results is that current studies widely di↵er in termof dimension, type and number of firms analyzed, country, definition of female leadershipand indicators of firm performance used. One of the first scholars focusing on this issue isWolfers (2006). He examined di↵erences in returns to holding stocks in female-headed andmale-headed firms using S&P index data over the period 1992-2004. By using a combinationof matching methods and OLS, he found no systematic di↵erences in performance betweenthe two groups. Nevertheless, the author also underlines that his results “(...) reflects theweak statistical power of their test, rather than a strong inference” on the role of financialmarkets in estimating gender gaps in performance (Wolfers, 2006). In contrast to his results,Dezso and Ross (2012), working on the same data and period, found positive results, but onlyto the extent that a firm’s strategy is focused on innovation: they suggest that in innovativecontexts the informational and social benefits of gender diversity and the behaviors associatedwith 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 Danishfirms. He found that the proportion of women in top management jobs tend to be positivelyassociated with firms’ performance, but he also found that the association becomes largelyinsignificant 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 gapsbetween women- and men-led firms is the composition of the workforce: by studying thee↵ect of the gender composition of the first two layers of management on firm and workeroutcomes on a German employer-employee panel dataset, they find that the e↵ect of femaleleadership on performance gaps depends on the share of women in the second layer of theorganization. The interaction between women in various level of the organization has alsobeen 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 workforceincreases 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, Maciset 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 alsothat 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 thecontemporaneous 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 peersin governance positions: by investigating medium and large family-controlled firms in Italybetween 2000 and 2010, he found that the e↵ect of the interaction of a women-dominatedboard of directors with female CEO is positive and significant on firm performance. Thisresult is in line with Blau and Ferber (1990) and Koenig et al. (2011), who found that loneCEO’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 toreduce the risk of communication breakdowns, improve cooperation, and facilitate informa-

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tion exchange, e↵ects that should result in higher-quality board performance and thus inmore e�cient managerial decision-making.

According to these findings, companies with a substantial female presence, either inthe workforce or in their boards, are likely to benefit from assigning women to leadershippositions. In a slightly di↵erent vein, Parrotta and Smith (2013) document the existenceof a negative association between female CEO and the variability of firm outcomes. Theirfindings are in line with the experimental evidence that women typically exhibit higher riskaversion than men (Croson and Gneezy, 2009; Eckel and Grossman, 2008) and that womengenerally exhibit less willingness than men to engage in competitive activities and worseperformance 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 relationshipbetween female entrepreneurs and firms’ performance. Empirical evidence regarding thisrelationship provides mixed results. Part of the reason for mixed results lies in the fact thatthese studies di↵er in the types of firms under analysis, in the definition of female enterprisesand in the main outcomes of interest: Depalo and Lotti (2013) employ the definition offemale entrepreneur given by Italian law n.215/92 and use a panel sample of medium andlarge family firms collected between 2005 and 2010. This restricts the analysis only tocompanies where women owned at least two thirds of total assets and covered at least twothirds of corporate board seats. Working on this sample, they find no significant gaps interms 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, Gagliarducciand Paserman (2014) also found that once controlled for establishment-level fixed e↵ects andspecific time trends, e↵ects on sales per worker, total employment and investment per workerdisappeared. Their definition of female-owned firm, though, was based on the fraction ofwomen among proprietors. Their results reveal a substantial sorting of female entrepreneursacross establishments: small and less productive establishments that invest less, pay theiremployees 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 runby Bardasi et al. (2011), who found significant gender gaps between male- and female-ownedcompanies 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 performancegap lies in the fact that women run smaller firms and that they tend to concentrate in sectorsin 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, buttheir sample includes firms up to only 20 employees. Du Rietz and Henrekson (2000) alsoused an extensive multivariate regression with a large number of firm-level controls (amongthem, firm size, sector, full capacity utilization). In doing so, he also found that femaleunderperformance disappears for three out of four performance variables once firm-level

8In literature, this e↵ect is definedstereotype-threat.

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controls are added.9Overall, these results show that the underperformance hypothesis offemale-owned firms is rejected once firm-level features such as firm age, size and capacityutilization 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 individualrunning the company: they are barriers related to additional di�culties that women mightface in obtaining credit, in cultivating business networks, in dealing with government andother o�cials and to existing cultural norms that restrict the mobility of women or excludethem from a male-dominated arena. Proxies for these kind of obstacles are country-specificindicators on female political participation, fertility rates, female literacy rates, etc.(Aidiset al., 2007) and will be examined further.

The second explanation refers to the existence of individual characteristics, motivationand preferences of women as entrepreneurs: according to this hypothesis, women are morerisk adverse than men so their performance is lower (Masters and Meier, 1988), or they opt forsmaller business because of a desire to better accommodate their family needs (Jianakoplosand 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 theentry into entrepreneurship, especially in accessing credit. Low access to credit, then, mightindirectly a↵ect firm performance: di�culties in obtaining a loan have been identified asthe 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 wherewomen are major shareholders and managers at the same time - are less likely to obtain aloan than their male counterparts and, conditional on obtaining it, they face higher interestrates and have to pledge higher collateral than men. Both studies are based on a sub-sampleof BEEPS entrepreneurial ventures for year 2005.

On the reasons behind low access to credit, Bardasi et al. (2011) emphasize the roleof unobservable individual characteristics, such as creditworthiness, ability and motivation,human capital, experience and education; Verheul et al. (2012), instead, claim that the mainreason for lower access to credit among women is endogenous to their preferences: they tendto concentrate in some sector, such as services, which need less capital and have fewer marketgrowth opportunities while banks typically lend on the basis of hard assets, such as plantand 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 workersand 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’ profitabilityand their work did not find any gender di↵erential; Bosma et al. (2004) considers survival

9But it is important to notice that their performance variables are all dummies based on survey questions,not size-related performance indicators.

10This literature does not distinguish between women in entrepreneurship and women in top executivepositions so it is strongly related to personal characteristics of the manager discussed in the above paragraph.

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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 64Angola 171 43 Greece 55 10 Namibia 74 26Argentina 466 230 Guatemala 564 145 Nicaragua 478 227Armenia 173 17 Guinea 105 30 Niger 14 1Bangladesh 756 12 Guyana 80 53 Oman 38 1Belarus 21 8 Honduras 474 131 Panama 135 101Benin 117 10 Hungary 134 77 Paraguay 191 158Bolivia 193 144 India 3,239 288 Peru 233 108Bosnia and Herze 21 6 Indonesia 27 5 Philippines 212 123Botswana 48 64 Ireland 86 46 Poland 247 106Brazil 1,253 236 Jamaica 33 13 Portugal 32 22Bulgaria 23 5 Jordan 287 50 Romania 174 66Burkina Faso 26 9 Kazakhstan 127 58 Russian Federati 54 15Burundi 75 27 Kenya 113 6 Rwanda 35 22Cambodia 15 2 Korea, Rep. 93 12 Senegal 91 5Cameroon 39 26 Kyrgyz Republic 25 9 Slovak Republic 18 1Cape Verde 16 9 Lao PDR 46 118 Slovenia 19 4Chile 879 334 Latvia 16 5 South Africa 284 27Colombia 299 315 Lebanon 65 22 Spain 66 28Costa Rica 91 163 Lesotho 19 4 Swaziland 55 14Croatia 24 6 Lithuania 82 48 Syrian Arab Repu 146 4Czech Republic 49 11 Madagascar 143 49 Tajikistan 37 5Dominican Republ 99 11 Malawi 0 25 Tanzania 309 59Ecuador 502 154 Malaysia 460 34 Thailand 570 79Egypt, Arab Rep. 672 209 Mali 59 3 Turkey 745 123El Salvador 549 273 Mauritania 68 11 Uganda 302 80Eritrea 17 2 Mauritius 101 13 Ukraine 76 28Estonia 14 7 Mexico 770 250 Uruguay 179 128Ethiopia 209 0 Moldova 70 25 Uzbekistan 19 5Gambia, The 28 5 Mongolia 73 55 Vietnam 420 110Georgia 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 theentrepreneur: (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 formale 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; Bruderl and Preisendorfer, 1998).

Our paper strongly relates to the work of Bardasi et al. (2011) and it extends it consideringfirms 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-

11Standardized data is country data that has been matched to a standard set of questions. This formatallows cross-country comparisons and analysis but sacrifices those country-specific survey questions which

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0 1,000 2,000 3,000

Other transport equipmentAuto and auto components

Other manufacturingPaper

Non−metallic and plastic materiaWood and furniture

Chemicals and pharmaceuticsElectronics

Metals and machineryBeverages

FoodGarments

LeatherTextiles

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 elaborationon BEEPS standardized data 2005.

ropean Bank for Reconstruction and Development (EBRD) for examining the quality of thebusiness environment in di↵erent regions. Interviews cover topics ranging from firm financingto labour, corruption and infrastructure. Only registered firms are included in the sample,which is based on national registry collected firms, representative of the manufacturing andservice sectors. The sectoral contribution to “manufacturing” versus “services” is determinedby their relative contribution to GDP. In each country, the sample is stratified by size, sectorand geographic region, using simple random sampling. All survey variables refer to the fiscalyear before the interview took place.

One of the main strengths of these data is that they are collected homogeneously acrosscountries, allowing for cross-country comparison of results. However, weaknesses include thepresence of a very small sample in some countries and the numerous missing answers to somevariables of interest (e.g. intermediate goods) which considerably limited the constructionof our the dependent variables.

For our analysis, we restrict our attention to the 2005 cross-section since it is the newestwave containing a representative sample for our variable of interest, defined as “Gender ofthe principal owner of the firm”.12 The BEEPS standardized dataset originally contained71,789 firms ranging across all economic activities from 94 countries for the year 2005. Oncewe 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 tomatch some of the variables. One of the compromise has been to consider interviews occurring in di↵erentyears as belonging to the same questionnaire. This is the reason why we controlled for country-year fixede↵ect although it is a cross-sectional dataset.

12A peculiarity of this wave is that its questionnaire also reports whether the manager/director coincideswith the owner or not, although this information has not yet been used in our analysis.

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26,201 firms. Table 1 describes the composition of our sample across countries in terms ofgender: women-run firms are almost 22% of the total sample and they concentrate mainly inArgentina, Brazil, Chile, Colombia, Egypt, El Salvador, Nicaragua, India and Mexico. Theseare also the most populous countries in terms of firms interviewed. Only a few countries ofthe sample belong to the EU area and they show very few observations. In all countries, thenumber of male-owned firms prevails over women-owned, with Ethiopia showing observationsonly for male-owned firms.

Figure 1 shows instead the sample distribution by industry sector: male-owned firmspredominate in all sectors, while women-owned are concentrated mainly in Garments andFood. The most male-dominated sector in relative terms is Electronics and overall thepresence of women is very limited with respect to that of men. This suggest that almostall sector are male-dominated, although this distribution does not take into account thedimension of firms observed.

Aggregate summary statistics on that are shown in Table 2 which summarizes averagevalues, median and number of observations for three variables of interest, considering thewhole sample in 2005: labour productivity, annual sales and total employment, which is ourproxy for firm size (all variables are in log form). Labour productivity has been built as a ratiobetween annual sales and total employment. Table 2 shows that the number of observationsfor labour productivity is less than those in sales and employment. This is because for somefirms either data on sales or on total employment were missing. Nevertheless, it is worthnoticing that although both aggregate means for sales and for employment are lower forfemale than for men, average labour productivity for women-owned firms is slightly higherthat that of men: it seems that women-lead firms, although less numerous, are relativelymore productive (in terms of sales per permanent worker). This descriptive evidence mightbe probably driven by firms belonging to the Latin and Caribbean regions, since in this areafemale labour productivity di↵erentials are positive and the number of firms interviewed wasvery high, as Table3 shows.

In Table 3, the disaggregation of firms in five macro-regions based on their geographicallocation13 shows that in African and Middle Eastern countries there are positive di↵erentialsfor gender on all productivity indicators but this result is observed on a relative low numberof firms. European and Central Asian countries, instead, show negative di↵erentials on allvariables of interest. Labour productivity di↵erentials for women are negative only in theECA 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 firmsseem to dominate for all our variables. Indeed, male-owned Epanechnikov kernel densitiesare shifted to the right with respect of those of female-owned.14.

13AFR = Africa Region countries; EAP= East Asian and Pacific countries; ECA= European and CentralAsian countries; LCR= Latin and Caribbean Region; MNA= Middle East and Northern African countries

14Note that Stata calculates and uses by default the optimal width

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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 firmperformance (in log form). (1) Labour Productivity is defined as sales per employee; (2) Sales refers to fiscalyear 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 womanamong 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 productivitygap 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 firmperformance expressed in log form (either labour productivity or sales) and the main regressoris a dummy representing the gender of the main owner. This specification enables us toinvestigate how di↵erences in performance are related to gender. Note that Year fixed e↵ectare inserted although we are using a cross-section because BEEPS standardized dataset 2005contains interviews collected in previous years. Our baseline regression model is the following

lnYf,c

= c+ ↵D

femown

f,c

+ d

c,t

+ d

s

+ "

f,c

(1)

where Y

f

, c, s is a proxy for a firm’s performance, either labour productivity, measured bytotal sales per employee or annual sales. D

femown

f,c

is a dummy which equals 1 if the owneris female and 0 otherwise. Therefore, coe�cient ↵ measures how women-owned di↵er withrespect to the baseline (men-owned firms). To account for heterogeneity across countries, weintroduce country-year fixed e↵ect (d

c,t

). Industry fixed-e↵ects (ds

) are also included to allowfor peculiar features of each sector. Standard errors are clustered at the firm-level, althoughfor robustness we can also cluster them by country, industry and country-industry-year levels.

As a robustness check, we run a second specification

lnYf,c

= c+↵D

femown

f,c

+�1Sizef,c+�2Agef,c+�3fof,c+�4Techf,c

+�5Qual

f,c

+d

c,t

+d

s

+"

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

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Table 3: Descriptive statistics by region

AFR

Gender Lab. Prod. Sales Empl.

Male - mean 7.73 10.78 3.05p50 (7.72) (10.49) (2.77)N 2,529 2,535 2,546Female - mean 7.66 10.83 3.18p50 (7.83) (10.74) ( 2.89)N 574 575 573

EAP

Gender Lab. Prod. Sales Empl.

Male - mean 5.84 9.51 3.65p50 (5.85) (9.16) (3.40)N 3,195 3,205 3,204Female - mean 6.06 9.48 3.38p50 (5.89) (9.31) (3.14)N 656 654 657

ECA

Gender Lab. Prod. Sales Empl.

Male - mean 3.34 6.60 3.23p50 (3.46) (6.42) (3.16)N 2,548 2,557 2,564Female - mean 3.17 6.13 2.92p50 (3.25) (5.99) (2.83)N 771 770 783

LCR

Gender Lab. Prod. Sales Empl.

Male - mean 5.62 9.05 3.40p50 (4.79) (8.29) (3.22)N 7,349 7,354 7,371Female - mean 5.94 9.12 3.16p50 (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 3.59p50 (4.09) (7.88) (3.40)N 1,928 1,934 1,947

Female - mean 4.55 8.38 3.83p50 (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 offirm performance (in log form) observed in 94 selected countries grouped by region: AFR = Africa Regioncountries; 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 onBEEPS Standardized data 2005.

10

0.0

5.1

.15

.2

0 5 10 15Ln(Labour Productivity)

Woman−lead Man−lead

Gender refers to main firm’s owner

Our elaboration on BEEPS 2005

0.0

5.1

.15

0 5 10 15 20Ln(Sales)

Woman−lead Man−lead

0.1

.2.3

.4

1 2 3 4 5 6Ln(Employment)

Woman−lead Man−lead

Figure 2: Figures report density distribution of three main indicators of firm performance by gender (inlog form): (1) Labour Productivity is defined as sales per employee; (2) Sales refers to fiscal year prior tothe survey and is measured in thousands of LCUs. (3) Employment is defined as average n. workers in theyear 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 proprietorshipowned 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 theintroduction of new technology in the last three years. Controlling for these factors allowus to partially account for potential omitted variables that might influence productivityvariables. The results of both specifications are shown in Table 4. All coe�cients arehighly significant and show evidence of a negative performance of female-owned firms withrespect 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 expectedvalue of labour productivity for female by 12,5% with respect to men, Column (1). Thepercentage gap is approximately 22% for gender di↵erentials on performance gaps in annualsales (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 magnitudeof ↵ is reduced in both specifications. Robustness check (See Section 7) regressions showsimilar results. It is worth noticing, though, that in robustness check regressions while our

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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 YesSector FE Yes Yes Yes YesN.Obs. 25,779 20,221 25,823 20,275Adj. 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 andmale owners. Specifications include: without (1) and with (2) firm-level controls. Baseline category is maleowner. See the Section3 for further explanation on country-year FE. Size is defined by the average n. ofworkers in the year prior to the survey. FO is a dummy for foreign ownership; similarly, Tech is a dummyfor technology advancement and Qual is a dummy for ISO certification. Robust standard errors clustered atfirm-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 lastthree years, the dummy variable for female owner refers to last fiscal year only because datadid not allow us to check whether proprietorship changed over the three years consideredfor robustness. Overall, results are in line with evidences from previous literature: limitedevidence of underperformance of female enterprises exists, on both productivity variablesconsidered, but its magnitude is lower when controlling for firm-level factors. In the followingof this research, we would like to test to which extent these productivity gaps between femaleand male enterprises are reduced once the level of gender inequality in the country wherethe firm is located is taken into account.

5. Conclusion

The result obtained is in line with evidences from previous literature: a limited evidenceof 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

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last three year confirms the result. Nevertheless, the cross-sectional nature of the analysisdoes not make it possible to establish causality. Moreover, panel data would have allowed usto overcome the problem of firm-level heterogeneity but data availability limited us to use across-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 tothe 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 thanthe gender of the main owner? And is the e↵ect of female owner significant and relevant wheninteracted with a country-level variable? Inequality-adjusted human development indexesmight turn out to be relevant in this regard, especially the Gender Inequality Index (GII). Inparticular, some of their components, (e.g. share of seats in parliament, maternal mortalityratio, percentage of female labour force participation) might a↵ect the relationship betweengender 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 traditionalfamily 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 overallbusiness 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 initialassumptions including into the regression a large set of country-level indicators, includingthe 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 betweengender and firm performance. In particular, we expect the GII to be significant per se onfirm 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 containingboth variables). Indeed, findings from previous literature suggest evidences of a positivee↵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 mainowner better than simple OLS regression;ii) standard errors clustered at the country- rather than firm-level;

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iii) additional measures of productivity, e.g. labour productivity defined as total sales overnumber 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 wouldhave allowed us to build more precise measures of performance such as TFP (Total FactorProductivity). We had to limit our analysis to labour productivity because data did notcontain 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 isspent in dealing with requirements imposed by government regulations; percent of domesticsales; percentage of working capital from local banks are hidden factors that might a↵ectperformance 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 limitedavailability of panel data;vi) demographic variables other than gender contained in the dataset and referred to firmowner (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 andthe average percentage of women entrepreneurs in the whole country” can be useful to builda 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 mostrecent data. Panel data (BEEPS 2002-05-09) also contain our variables of interest, but onlyon a small sub-sample of firms. Though, conditional on data availability, it is still possible toconduct cross-sectional analysis on di↵erent waves (2002, 2005, 2009 and 2013) and comparethe results.

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Appendix A: definition of variables

Table A1: Variables description

Variable Wording of survey questions and answers’ codes

Female ownerQUESTION: Is the principal owner male? Yes=1 No= 2Dummy variable (reversed) Female Owner: Yes=1 No=0

SalesQUESTION: Total sales one year ago in thousands of LCUs. SizeQUESTION: Average n. of permanent workers one year ago.

Ln. Empl. meanQUESTION: 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.

AgeQUESTION: In what year did your firm begin operations in this country?

Foreign OwnershipQUESTION: Which of the following best describes the largest shareholder or owner in your firm?1)Individual2)Family3)Domestic company4)Foreign company5)Bank6)Investment fund7)Managers of the firm8)Employees of the firm9)Government or government agency10) Other (Specify)

QualityQUESTION: Has your firm received ISO (e.g. 9000, 9002 or 14,000) certification?Yes=1 ; No=2

InnovationQUESTION: Has your company undertaken any of the following initiatives in the last three years?1) Developed a major new product line: Yes=1 ; No=22) Upgraded an existing product line: Yes=1 ; No=23) Introduced new technology that has substantially changedthe way that the main product is produced: Yes=1 ; No=2

Fem emplQUESTION: Average percentage of permanent female workers one year ago.

Fem empl: variable (reversed)QUESTION: What percent of the senior management is male?

Perc timeQUESTION: What percentage of senior management’s time is spent in dealing with requirementsimposed by government regulations?

Perc dom salesQUESTION: 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 ourvariables of interest. Moreover, it reports useful variables for extending the analysis further as explained inlatest section.

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Appendix B: Robustness check

Table B1: Robustness check: regression on two main performance indicators averaged overthe 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 YesSector FE Yes Yes Yes YesN.Obs. 26,182 20,731 26,201 20,731Adj. 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 over3 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. Dummiesare assumed to be time-invariant (see Appendix A for further details). Size is defined by the average n. ofworkers in the three years prior to the survey. FO is a dummy for foreign ownership; similarly, Tech is adummy for technology advancement and Qual is a dummy for ISO certification. Robust standard errorsclustered 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.

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