how do female entrepreneurs perform? evidence from three...

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How do Female Entrepreneurs Perform? Evidence from Three Developing Regions

Shwetlena Sabarwal PREM Gender, World Bank

and Katherine Terrell

Ford School of Public Policy and Ross School of Business University of Michigan

and Elena Bardasi

PREM Gender, World Bank

May 2009

Preliminary, please do not cite. A revised version of this paper is being produced and will be available by July 31, 2009.

Abstract

We estimate performance gaps between male- and female-owned formal enterprises in three developing regions: Eastern Europe and Central Asia (ECA), Latin America (LA), and Sub-Saharan Africa (SSA). We find that in this large part of the developing world female-owned enterprises are significantly smaller than their male-owned counterparts. We also find that gender-based gaps are much less marked in terms of firm efficiency and firm growth, although they remain significant in the LA region. Next we explore possible explanations for the observed differences in firm size by entrepreneurial gender. First, we find that a part of this difference comes from the relatively high concentration of women in low performing sectors like garments, wholesale and retail trade, hotels and restaurants etc. Further, in ECA evidence suggests that female entrepreneurs are significantly less likely than male entrepreneurs to seek formal finance even if they need it; in contrast, female entrepreneurs in LA and SSA are more likely. However, in LAC and SSA there is some evidence that the impact of formal finance on overall sales is less marked for female entrepreneurs than their male counterparts. Another important contribution of this paper is to establish that among formal enterprises, after correcting for selection and controlling for firm characteristics there is no evidence of gender-based discrimination in access to formal finance.

JEL: D24, J16, L25, M21, O16, O54, Keywords: Entrepreneurship, Gender, Finance, Latin America and Caribbean

Acknowledgements: We would like to thank the PREM Gender group at the World Bank for supporting this research and the following individuals for discussions that significantly improved the paper: Andrew Morrison and Jan Svejnar.

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1. Introduction

Are male and female entrepreneurs different species? Strange as the question may

sound, it can be quite central to development research. If entrepreneurship is in fact an

important engine for growth, then poor, developing countries can ill-afford the under-

utilization of its full potential. In other words, these countries need to examine if women

are participating and co-creating at the same rate as men, and if not, then why?

Theoretically, the potential relationship between entrepreneurial gender and

entrepreneurial performance is intriguing partly because of the differing perspectives on

the subject. First, there is the ‘constraint-driven-gap’ perspective, which argues, that there

are substantial gender-specific barriers to entrepreneurship which constrain the

performance of female entrepreneurs. These barriers relate to difficulties that women face

in obtaining credit, in cultivating business networks, in dealing with government and

other officials etc. Many of these barriers might stem from existing cultural norms that

restrict the mobility of women or seclude them in a male-dominated arena. Second, there

is the ‘human capital-driven-gap’ perspective wherein because of existing gender-based

gaps in human capital attainment, female entrepreneurs are not as well equipped as their

male counterparts to manage a business. Finally, there is the ‘preference-driven-gap

perspective’ which argues that there are fundamental differences in the motivations and

approaches that male and female entrepreneurs have towards their businesses.

Based on these perspectives, it is possible to hypothesize different impacts of

female-ownership on firm performance. Existing gaps could translate into female under-

performance in entrepreneurship either because of the constraints or because of their

preferences. On the other hand, it is also possible that in the face of human-capital and

other issues, the selection of women into entrepreneurship might be stronger that that of

men, implying that women who become entrepreneurs might be a more superior sub-

group in terms of innate abilities, motivation, and creativity than men who become

entrepreneurs. In this case female entrepreneurs might over-perform in entrepreneurship

compared to their male counterparts.

Empirically, there has been little rigorous research on the subject, particularly for

developing countries. A large portion of entrepreneurship research in economics has

tended to focus exclusively on male entrepreneurs (Brush 1992) thereby completely

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ignoring the non-negligible phenomenon of female business-ownership. The studies that

have included female entrepreneurs are mostly confined to developed countries and use

small surveys that are usually not representative of the country. In this paper, we address

this significant research gap by providing the first comprehensive analysis of

entrepreneurial performance by gender in three regions of the world, namely Eastern

Europe and Central Asia (ECA), Latin America (LA), and Sub-Saharan Africa (SSA)

using comparable firm-level data from formal enterprises. By analyzing these three

regions separately, we allow for gender differences in firm behavior to vary across these

regions. We contribute to the literature by measuring the size of the gaps using various

measures of performance (sales revenue, efficiency, sales growth, employment growth).

We explore whether these gaps differ in terms of firm size and industrial sector. Then we

test several explanations for these gaps: a) sector concentration, b) demand v. supply

constraints to formal credit; c) gender differences in the returns to formal credit.

The paper proceeds as follows: Section 2 contains a review of the literature; Section

3 describes data, Section 4 provides estimates of numerous measures of performance

gaps. In Section 5 we explore three explanations for these gaps: industrial concentration,

access to credit, and use of credit; Section 6 concludes the paper.

2. Existing Research

Studies asking whether the gender of the entrepreneur affects the performance of

the enterprise yield mixed results. Some studies provide evidence of female under

performance (Brush 1992, Rosa et al 1996), while others do not find gender based

differentials in entrepreneurial performance (Du Rietz and Henrekson 2000, Bardasi

2007).

In general, it is found that women and men owned enterprises differ in terms of

size. Recent evidence from the U.S. suggests that on average men owned businesses are

twice as large as women owned businesses in terms of both sales and assets (Coleman

2007). It has also been shown that on average employer-firms owned by women generate

only 78 percent of the profits generated by comparable male owned businesses (Robb and

Wolken 2002). Also, women have been found to generate less sales turnover relative to

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men, even in same industry comparisons (Loscocco and Robinson 1991, Chagnati and

Parsuraman 1996).

Male and female owned businesses have also been compared in terms of their

survival probabilities. It has been shown that for Dutch businesses, male entrepreneurs

outperform their female counterparts in terms of survival (Bosma et al 2004). Similarly,

in a study from Germany, Lohmann and Luber (2004) show that only 42 percent of self-

employed women remain self-employed after 5 years while the corresponding rate for

male entrepreneurs is 63 percent.

However, the female under-performance hypothesis in entrepreneurship literature in

not universally corroborated. In a study from Australia, Watson (2002) show that women

business owners earn similar rates of return on equity and assets as male business owners,

but have less start-up capital, which explains their lower incomes and profits compared to

men. Using World Bank Enterprise Surveys (2002-2006), Bardasi et al (2007) find that in

Africa, female owned businesses are at least as productive as those of male entrepreneurs

when measured by value added per worker and total factor productivity. Similarly,

Kepler and Shane (2007) show that there are no significant gender differences in terms of

performance outcomes of nascent entrepreneurs. Other studies show that female owned

enterprises do not under-perform in terms of employment creation (Fischer et al 1993,

Chagnati and Parsuraman 1996) or survival rates (Kalleberg and Leicht 1991, Bruderl

and Preisendorfer 1998).

The empirical literature on explanations for gender-based gaps in entrepreneurial

performance can be organized under the three main heads mentioned above, namely,

constraint-driven gaps, human-capital driven gaps, and preference-driven gaps.

Constraint-driven Gaps:

Barriers to female entrepreneurship can arise from existing institutional structures,

both formal and informal. Coate and Tennyson (1992) have noted that it is possible for

labor market discrimination to spillover into markets that are relevant for self-

employment. This discrimination would become further exacerbated if entrepreneurial

ability is perceived to be signaled by earlier investments in human capital (Cressy 1996).

Mayoux (1995) documents some of the most common obstacles faced by women

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entrepreneurs which include obstacles in access to bank credit, problems dealing with

male officials because of norms of female propriety and discrimination and the lack of

information because it is seen to be channeled predominantly through male networks.

It has been hypothesized that observed differences in entrepreneurial performance

by gender may be due to discrimination against female entrepreneurs in accessing

finance. Several studies suggest that raising capital is more difficult for women than men

(Brush 1992, Carter and Cannon 1992, Carter 2000). In their study using data from

Business Environment and Enterprise Performance Survey (BEEPS) from Europe,

Muravyev et al (2007) find that female managed firms have a 5.4 percent lower

probability of securing a bank loan than male managed firms. They also evaluate

existence of financial constraints by looking at interest rates and find that female

managed firms on average pay 0.6 percent higher interest rates than their male

counterparts. Both these factors suggest discrimination against female entrepreneurs and

the authors suggest that this discrimination is found to be higher in the least financially

developed countries in the region. This is corroborated by Aidis et al (2007), who using

original survey data from Lithuania and Ukriane, show that access to funds is a more

important barrier for female business owners than their male counterparts.

Cavalluzzo et al (2002) also find evidence of a credit access gap between firms

owned by white males and while females with female denial rates increasing with lender

concentration. In contrast several studies (Cavalluzzo and Cavalluzzo 1998,

Blanchflower et al 2003, Storey 2004 and Cavalluzzo and Wolken 2005) find no

statistically significant effect of gender in access to finance.

Alternatively, significant differences in male and female access to finance may be

accounted for by differences in other characteristics affecting their credit worthiness

including human capital factors, personal wealth etc. For instance, women may have

more difficulties in securing a loan than males because they tend to start smaller

businesses and concentrate in the services sector and are more likely to work part time in

the business (Verheul and Thurik 2001). Aside from overt gender differentials in access

to credit, gender gaps might also exist in terms of other dimensions of business finance.

For instance, there is some evidence to suggest that men re-invest a larger share of profits

generated back into their business (Grasmuck and Espinal 2000).

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Finally, it is contended that female entrepreneurs often face gender related barriers

in terms of social capital. For instance, Rumniska-Zimny (2002) finds that females are

constrained with respect to their access to information, networks and collateral. In the

context of transition economics, Smallbone and Welter (2001) argue that in transition

economies of Eastern Europe women generally have had less access to informal

networks, in part because they have fewer contacts from Soviet times. It has also been

shown that female entrepreneurs tend to be more homophilious than male entrepreneurs,

which might explain gender based differences in entrepreneurial performance. For

instance Brush (2006, p. 620) claims that female entrepreneurs include more women in

their social networks whereas networks of male entrepreneurs are more gender balanced.

Human Capital-Driven Gaps

Cowling and Taylor (2001) found that in Britain self-employed women were better

educated than their wage and unemployed counterparts. A similar education affect was

not seen for male entrepreneurs. On the other hand male entrepreneurs (job creating)

were found to be older than all other groups of male workers, where as this age effect was

absent in the case of female entrepreneurs. Brush (1992) argues that men are more likely

than women to have education and experience which emphasizes technical and

managerial elements which might impact their entrepreneurial performance. Studies have

also shown that men are more likely to have been employed prior to starting a business

than women entrepreneurs and hence have more work experience (Brush 1992, Kepler

and Shane 2007).Other studies also show that on average women entrepreneurs possess

fewer years of work experience than male entrepreneurs and male and female workers in

the wage sector (Aronson 1991, Lee and Rendall 2001). Watkins and Watkins (1984)

claim that women entrepreneurs are more likely to start a business without having a

demonstrable record of achievement, vocational training and experience compared to

their male counterparts.

Degree of risk aversion has been considered an important predictor of

entrepreneurial success (Schumpeter, 1939; Evans and Leighton, 1989; Earle and Sakova,

2001) and some papers show that women tend to have higher risk aversion (Jianakopolos

and Bernasek 1998, Barber and Oden 2001, Dohmen et al 2005). These differences could

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have important implications for business performance if higher risk aversion leads

women to restrict investment in their business ventures. In contrast Masters and Meier

(1988) found that female entrepreneurs are more similar than different to male

entrepreneurs in their risk taking propensity. In related work, the Global Entrepreneurship

Report 2005 found that fear of failure is significantly higher for women in middle income

countries than men (Minniti et al 2005).

Females and males are also seen to differ on some other dimensions which might

affect their probability of entrepreneurial success. In a laboratory experiment Niederle

and Versterlund (2005) find that in tasks where women and men perform equally well,

women shy away from competition and this behavior is not explained by uncertainty in

payment schemes. In contrast, men are drawn to competition. In the experiment these

patterns lead to lower earnings for women, especially the high performing ones.

Similarly, Kepler and Shane (2007) claim that male nascent entrepreneurs examine more

ideas and gather more information while pursuing a new start-up than female nascent

entrepreneurs.

However, there is also a large body of empirical research from the 1980s claiming

that male and female entrepreneurs are more similar than different across a spectrum of

sociological, psychological and demographic traits (Hisrich and Brush 1983, Chagnati

1986, Longstreth et al 1987). In fact Sexton and Bowman-Upton (1990) find that the only

significant gender based difference between male and female business owners is that

women business owners reflect lower risk taking propensity and energy levels.

Preference-Driven Gaps:

It has been argued that reasons for becoming an entrepreneur differ by gender

(Delmar and Davidsson 2000, Boden Jr. 1999, Shane et al 1991). For women the desire

to effectively combine work and family responsibilities often motivates them to start their

own business due to the option of flexible work arrangements. It has been shown that

women, especially women with young children, cite flexibility of work schedule and

other family related reasons to become self-employed while this is not true in the case of

men (Boden Jr. 1999). Using Contingent Work Survey data from USA, Boden Jr. (1999)

show that having young children positively and significantly affects women’s probability

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of being self-employed, while no such effect is seen in the case of males. Data from U.S.

shows that self-employed women either have very long working weeks or very short

working weeks, implying that female self-employed workers show greater dispersion in

working hours than other groups (Devine 1994). Similarly, Lombard (2001) finds a

positive relationship between women’s demand for flexibility (in terms of variability in

work hours) and participation in self-employment, with the relationship being strongest

for women with small children. This would imply that if wage employment provides

flexible work arrangements and family related support mechanisms then female

entrepreneurship levels will decline. Kovalainen et al (2002) find a negative relationship

between the statutory maternity leave in days and the rate at which women start their own

business. Meanwhile, Verheul et al (2004) found that importance of family is linked

positively to entrepreneurship for both and men and women.

Verheul et al (2004) also find that life satisfaction (answer to the question: how

satisfied are you with life) is positively and significantly linked to entrepreneurship only

in the case of women. Using original survey data from Lithuania and Ukraine, Aidis et al

(2007) show that although ‘independence’ is cited as an important motivation for starting

one’s own business in the case of both women and men, women are more likely to cite

necessity and other push factors (such as need to supplement household income etc.) as

important. Men on the other hand are more likely to cite pull factors (availability of

resources, opportunity to increase income etc.) as primary motivations for starting own

business than their female counterparts. In consonance with above, evidence from Italy

shows that men are more likely to enter into self-employment following layoff or for

career advancement while women are more likely to enter from inactivity or

unemployment (Rosti and Chelli 2005).

Preference gaps can also arise in industry-selection. When comparing performance

of male and female entrepreneurs at the macro level, it becomes imperative to take into

account their relative sectoral concentrations. It has been suggested that female

entrepreneurs are disproportionately concentrated in the small scale sector and this might

in part explain existing gender gaps in entrepreneurial performance. Mayoux (1995)

claims that ‘Women are overwhelmingly clustered in a narrow range of low investment,

low profit activities for the local market’.

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Women entrepreneurs are seen to be heavily concentrated in certain industries, most

notably sales and services (Bates 1995). Women are also seen to mainly occupy the

service sector in terms of their overall labor market concentration (Verheul et al. 2004)

and this could affect their entrepreneurial choice. Parker (2008) argues that to the extent

that entrepreneurs identify opportunities to start businesses in similar industries in which

they formerly worked, the relative concentration of female workers in clerical and

administrative jobs could explain the relative concentration of female entrepreneurs in the

services sector. On the other hand industries like construction etc remain heavily

dominated by men (Bates 1995). Also, it has been shown that women are less likely than

men to operate business in high-technology sectors (Loscocco and Robinson 1991, Anna

et al 1999). It has been suggested that the differences in female and male entrepreneurs’

choice of sector and product/service (Fischer et al. 1993, Brush 1992, Chagnati and

Parsuraman 1996) could be linked to gender gaps in opportunities.

Hundley (2001) claims that women’s choices with respect to industrial sector can

be important in explaining gender differences in entrepreneurial performance. In this

paper he shows that industrial choice explains about 9 to 14 percent of the gender based

self-employment earning differential. This was largely due to the concentration of women

in personal services sector and their under-representation in the more lucrative

professional services and construction industries.

Systematic gender differences can exist in terms of other firm attributes as well. In a

study from Africa, Bardasi et al (2007) found that for manufacturing and services sector,

women entrepreneurs are more likely than their male counterparts to be engaged in

‘family enterprises’. Others have emphasized relative concentration of female

entrepreneurs in the informal sectors. However, in a study of micro and small enterprises

in Bolivia (McKenzie and Sakho 2007) it was seen that there is no significant effect of

gender on the entrepreneur’s decision to make the enterprise ‘formal’.

3. Data

In this paper we use data from three developing regions, namely, Eastern Europe

and Central Asia (26 countries), Latin America (13 countries), and Sub-Saharan Africa

(22 countries). Data for Eastern Europe and Central Asia (ECA) come from the 2005

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Business Environment and Enterprise Performance Survey (BEEPS) data, produced by

the World Bank and the European Bank for Reconstruction and Development (EBRD).

Data for Latin America (LA) and Sub-Saharan Africa (SSA) come from the 2006 and

2007 World Bank Enterprise Surveys. By adhering to similar sampling techniques and

questionnaires both these data sources yield comparable enterprise-level data. One

difference is that for ECA only firms that are at least three years old are interviewed.

Since, this is not the case for LAR and SSA, some selection issues arise for the ECA

sample.

The samples are constructed by stratified random sampling from a national registry

of firms; implying that only registered firms (i.e., not informal firms) are included in the

sample. Further, the sampling methodology for the survey generates samples that are

representative for the whole economy. The sample of firms in each country is stratified

by size, sector and location, using simple random sampling or random stratified

sampling. For large economies firms are stratified at the two digit industry level. For

small economies there may not be enough firms to stratify at the two digit level, in that

case, a sample of firms is randomly selected from the manufacturing, retail, and rest of

the economy sectors. In each country, the sectoral composition of the sample in terms of

manufacturing versus services was determined by their relative contribution to GDP.

Firms that operate in sectors subject to government price regulation and prudential

supervision, such as banking, electric power, rail transport, and water and waste water,

were excluded from the sample.

The data enable us to identify the gender of the principal owner of privately held

shareholding companies, partnerships and sole proprietorships. Hence in this paper we

define male v. female entrepreneurs as “male v. female sole or principle owner of

privately held shareholding companies, partnerships and sole proprietorships.” Other

strengths of these data from our perspective include the fact that the same survey

instrument was administered in a number of developing countries from different regions.

In addition, that there are a host of performance variables for each firm; and there are a

set of questions dealing with institutional factors, especially in the area of finance, which

may affect the relative performance of male and female owned business. The weaknesses

of the data include, a) the small number of firms sampled in each country; b) inability to

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identify the gender of the other owners of the firm when there is more than one; c) lack of

demographic information on the entrepreneurs; and d) the numerous missing answers to

some variables of interest (e.g., capital).

In the empirical analysis we pool country level data to construct region specific

datasets. We do not pool data for different regions. We begin with an analytical sample of

4,903 firms for ECA, 7,393 firms for LA, and 8,235 firms for SSA1. These analytical

samples are created as follows: first, from the base data only firms that are privately held

companies, partnerships and sole proprietorships are retained. Next, firms that have

missing information on the sex of the principal owner (or owners), on sales, or on the

number of permanent employees are dropped. Finally, to control for outliers, firms in the

top 0.1% of the sample in terms of firm sales are dropped2. Some basic firm

characteristics in the analytical sample have been summarized by region and

entrepreneurial gender in Table 1; the distributions of firms in terms of sector and size are

shown in Figures 1 and 2, respectively.

4. Performance Gaps

In this section we first measure performance gaps between male- and female-owned

firms in a number of ways: in terms of firm size (total sales), sales per worker, value

added, sales growth, employment growth, and efficiency (value added and total factor

productivity). Then, in Section 4.2, we ask whether the scale of operation of male and

female entrepreneurs is suboptimal.

4.1 Differences in Firm Size, Efficiency, and Growth

In general, female entrepreneurs fare worse than their male counterparts (see Table

2). Controlling for country and sector, sales revenue of the average female entrepreneur is

significantly smaller than sales revenue of her male counterparts. These differences are

1 There is a question as to whether the sample should be weighted to be representative of the relative sizes of these economies or population. Since the number of firms in the countries in the analytical sample is roughly similar to their relative size within the region in terms of country potential, we do not re-weight our dataset. 2 In the case of Sub-Saharan Africa, two observations with extremely high values for Fixed Assets are also dropped.

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most marked for ECA, and least for SSA. Similar gaps are also observed for sales per

worker, although in this case, gaps are highest for LA.

We also examine gender based gaps in firm growth over a three year period, both in

terms of employment and sales. To reduce standard errors, we normalize growth by using

the formula (see for e.g. Davis and Haltwinger 1992)3:

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3

tt

tt

XXXX

Growth , (1)

where X=number of permanent employees for measuring employment growth and

X=sales revenue for measuring sales growth. Using this formula growth is bounded

between -2 and +2. Also, we lose observations where firms are less than three years old.

We find that female-owned firms do significantly worse than male-owned firms in terms

of sales and employment growth only in the case of LA (see Table 2). Lack of noticeable

gender-based gaps on growth measures could be a reflection of female-owned enterprises

starting from a lower base. With this consideration in mind, female underperformance in

firm growth in LA is troubling.

With respect to productive efficiency, we ask -- are female entrepreneurs less

productive in terms of the revenue that they generate from given inputs than males? This

is done with three firm level measures: 1) output (sales revenues) per work; 2) Value

Added (Sales-intermediate goods); and 3) total factor productivity (TFP). TFP is obtained

from estimating a Cobb-Douglas production function with pooled firm-level data from all

countries available for a given region:4

ijijiM

iL

iK

ij CIFMLKY lnlnlnln ,

(2)

where lnY is the log of sales revenues, i and j index firm and industries, respectively. The

inputs include: K, capital stock (at replacement value); L, labor (number of permanent

employees) and M, intermediate material input. F is a dummy variable equal to one for a

3 The measure is monotonically related to the conventional growth rate measure, and the two measures are approximately equal for small growth rates. If G is the conventional growth rate measure, then the two growth rate measures are linked by the identity G= 2g/(2 - g). 4 Equation (2) can also be interpreted as a first order approximation for more complicated revenue (production) functions.

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female entrepreneur; I is a set of industry fixed effects and C is a set of country fixed

effects.

The estimated coefficient on F in equation (1) as well as the other two measures

of efficiency presented in Table 2, indicates that conditioning on country and sector, the

average female-owned firm is significantly lower in efficiency in ECA and LA but not in

SSA. The gaps in efficiency are greatest for female entrepreneurs in LA and least in

SSA, where gaps in value added per worker and TFP are not significantly different and

average gap in output per worker is one-half of that in ECA.On the whole, evidence of

female-underperformance in these three areas – size, growth and efficiency -- is found

most consistently for LA. In contrast, female-owned firms in SSA are smaller but no less

efficient or growth-oriented. Evidence from ECA suggests large gaps in firm size, but

very small (though statistically significant) gaps in firm efficiency and no gaps in firm

growth rates.

Next, we evaluate whether gender gaps in firm productivity and growth vary

systematically with firm size. The rationale for this line of inquiry is that it is possible

that women entrepreneurs tend to be in a certain size category which may be less efficient

and this would affect the results. For this we create firm size dummies by dividing the

data on quintiles based on number of permanent employees. We name the four size

categories, micro firms (0-5 employees), small (6-10 employees), medium (11-25

employees), and large (more than 25 employees). These size dummies are interacted with

the dummy for female ownership. The results are shown in Table 3.

These regressions show that gender based gaps in firm productivity are mediated

through firm size, but these effects differ substantially by region. Examining results for

value added, in ECA, female under-performance is evident only in the case of large

firms. In fact, for all other firm categories female-owned firms perform better than male-

owned firms. For LA, female underperformance is seen among large and small firms

(and not among micro and medium firms). However, in contrast to ECA, female

entrepreneurs do not perform better than male entrepreneurs for any size category.

Results in Africa are remarkably different. Female entrepreneurs perform significantly

better in the case of large firms, but significantly worse in all other size categories. This is

in direct contrast to the results for ECA.

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Why are female entrepreneurs disadvantaged in terms of productivity only among

large firms in ECA, and among all but large firms in SSA? This is an interesting question

which merits further investigation; however, it seems to suggest that the nature of female

underperformance, and by implication, gender-specific constraints in entrepreneurship

are different in different regions. In terms of sales growth, no gender-specific differences

are found among firms in ECA and SSA. For LA, female-owned enterprises grow more

slowly in the case of large, micro, and small firms. They grow faster in the case of

medium firms.

The central question that arises from the preceding analysis is – why are women-

owned enterprises consistently smaller than those of men in this large part of the

developing world? Further, the consistent difference is sales-per worker also needs to be

examined. Finally, it appears that in ECA and SSA economically significant gender-

based gaps are found only in the context of firm size and not in terms of firm-efficiency

and firm-growth. In contrast, female entrepreneurs in LA consistently under-perform on a

number of dimensions. It is useful to ask therefore what explains the differing patterns of

relative performance of female entrepreneurs across the three regions. We attempt to

answer both these questions within the constraints imposed by the data.

4.2 Are Women’s firms operating at a suboptimal scale?

In order to determine the extent to which the scale of operation of male and female-

owned firms are different and suboptimal, we test for returns to scale in the framework of

the production function for manufacturing sector firms. We estimate equation (1)

separately for men and women using a robust variance method and clustering the

standard errors by industry. We perform two-tailed Wald tests to learn if men’s returns to

scale are constant (i.e., Ho: k + l + m = 1) and, similarly, if women’s returns to scale

are constant (Ho: k + l + m = 1). We then test for decreasing returns to scale, using a

one-tailed Wald test (see Table 4). For all the regions we cannot reject the hypothesis of

constant or increasing returns to scale (Ho: k + l + m ≥ 1). This result suggests that in

developing countries firms are often sub-optimally small. In other words, by increasing

the scale of production firms would be able to increase returns disproportionately more

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than the costs. Why firms are not able to expand their scale of operation to a more

efficient level might be related to existence of financial, infrastructural or other

constraints.

It is interesting to note however, that scale of operation is sub-optimal for both

male- and female-owned firms. Returns to scale are significantly higher (i.e. degree of

sub-optimality is significantly higher) for female-owned firms only in the case of ECA,

and even here the differences are small.

5. What explains the smaller size of female-owned firms?

Through our preliminary analysis, we have found that in a large part of developing

world women are operating smaller businesses than men. Further, we find that they are

less efficient and less growth-oriented than men in the LA region. We now consider the

various possible explanations for the observed gender based gaps in firm performance. In

this paper we examine the evidence on gender based gaps in industry concentration

(Section 5.1), in access to credit (Section 5.2) and use of credit (Section 5.3). The first

relates to the ‘preference-driven gap’ perspective while the second and third concern the

‘constraint-driven gap’ perspective. Since our data does not contain information on the

background characteristics of entrepreneurs we cannot test the ‘human capital-driven

gap’ perspective in this paper.

5.1 Are Female Entrepreneurs disproportionately concentrated in poor

performing industries?

The literature has hypothesized that the poorer performance of female-owned

businesses can be attributed to the fact that they are “crowded” in “poor performing”

industries. It is important to note at the outset that the available sectoral disaggregation in

LA is slightly different than what can be constructed for ECA and SSA. Despite these

differences, we find some consistent patterns in the relative sectoral concentration of

female entrepreneurs across these three regions (see Figure 1). In ECA and SSA, the most

important sector, where 28% of the female entrepreneurs are found, is wholesale and

retail trade re whereas in LA, food processing is the most important (almost 25% of all

female entrepreneurs). Garments and leather is among the top four most important

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sectors for women in all three regions; Food processing is among the top four in two

regions, but there end the similarities. It is interesting to note that construction and

transportation, considered a male dominated sector, is among the top four in ECA. In all

three regions very few women are operating in IT or metals and non-metals.

Can the relatively poor performance of female-owned firms be attributed to the

sectors these firms are concentrated in or is it the gender gaps within sectors that drive

overall differences? To test this hypothesis more rigorously, we examined the relative

performance of male- and female-owned businesses within sectors and across sectors by

using the following specification:

iiiii CIndustryFemaleFemalesales *)ln( . (3)

The coefficient β gives a measure of the overall performance of female-owned businesses

when controlling for female relative performance within industry. The coefficient of the

interaction term between female ownership and industry gives a measure of the female

relative performance within industry.

The results presented in Table 5 do provide some evidence of relative female

concentration in low performing industries. In all the regions, overall firm performance in

terms of sales is significantly lower in garments sector, wholesale and retail sector, hotels

and restaurants (we do not have this information for LA), and miscellaneous services

sector when compared to the construction sector. A large proportion of female

entrepreneurs are in fact concentrated in these low-performing sectors, while construction

is typically a highly male-dominated sector. This evidence of consistent female

concentration in low-performing sectors across regions is powerful. It indicates that an

important part of the puzzle of female under-performance lies with the choice of sector.

Also, that across regions women entrepreneurs seem to be entering similar sectors. In

terms of policy, the interesting question is whether women are ‘pulled’ or ‘pushed’ into

these sectors. If this is a question of choice then what are the features of these sectors that

make them attractive to women? Also, is there any policy rationale for trying to change

female entrepreneurs’ preferences in terms of sectoral choice?

17

With respect to their relative performance within an industry, in SSA female

entrepreneurs do not perform worse than their male counterparts. In fact female

entrepreneurs do significantly better in both metals and non-metals manufacturing. In

ECA, the results are slightly more mixed, in that, female entrepreneurs do relatively

better in non-metals manufacturing but relatively worse in construction and garments.

The largest gender-based gaps within industry are found in LA, with female

entrepreneurs outperforming male entrepreneurs within many sectors, but under-

performing in others.

The critical question that emerges from these results is – why do women in all these

developing regions overwhelmingly choose to enter relatively low-performing sectors

like garment manufacturing, retail and wholesale trade, and hotels and restaurants? This

hypothesis has not been rigorously tested but some qualitative evidence seems to suggest

that it is because of a number of factors. Women entrepreneurs appear to choose sectors

where it is easier to combine work with household responsibilities, where they can utilize

skills they have mastered as a part of their socialization process, which require a

minimum of initial investment, where they can easily get credit from suppliers, and

sectors for which there is a readily tested, and large market (UDEC 2002).

Based on these arguments it is possible to contend that female entrepreneurs exhibit

their somewhat specific preferences (some of which are outlined above) not only in the

sectors they choose but also in their market behavior. For instance, as argued in section 2,

women entrepreneurs might prefer to remain small because smaller businesses make it

easier for them to combine work with household responsibilities. Due to lack of

information on entrepreneurial preferences and motivations in our data, we cannot test

this hypothesis directly. However, indirect evidence seems to suggest that in our sample

of formal entrepreneurs, female entrepreneurs are fairly growth-oriented. This is

suggested by the fact that in all the three regions the average rates of sales growth over

three years among female-owned businesses is quite close to that of male-owned

businesses. (in two regions they are not significantly different). The same can be said for

employment growth (not show shown here).

18

5.2 Are Female Entrepreneurs more constrained in terms of access to credit?

Not surprisingly, access to formal credit is considered to be one of the most

important predictors of entrepreneurial success and survival. In recent years, some

empirical research has shown that female-owned businesses tend to have less access to

formal credit (Carter and Rosa 1998, Coleman 2007) than male-owned businesses. If this

is true, then, gender gaps in access to formal credit could go a long way in explaining

gender based gaps in entrepreneurial performance.

In our data we find evidence of gender-based gaps in sales per worker even after

controlling for sector, the most obvious explanations for which seem to be lower

capitalization among female-owned firms within industry. This phenomenon appears

starkly in the case of ECA region where the average value of fixed assets is much lower

for female owned firms ($496) than for their male-owned counterparts ($888). However,

these average gaps are insignificant for LA (male - $564, female- $588) and SSA (male-

$14.6, female-$13.2). Despite this, significant gaps in sales per worker by entrepreneurial

gender could signal gender based gaps in access to capital which is known to be highly

correlated with access to formal finance.

Enterprise Survey data include some detailed questions on firms’ access to finance

and credit. We disaggregate these data by the gender of the entrepreneur (see Table 6 and

Figure 3). Theoretically, the sex of the entrepreneur could affect both the demand for

credit and also the supply of credit. In addition, it is conceivable that once obtained,

efficiency in the use of formal credit differs by gender of the entrepreneur. In this paper,

we address the following three questions:

Demand for formal credit: Are female entrepreneurs less likely to apply

for bank loans than male entrepreneurs? (Section 5.2)

Supply of formal credit: Are female entrepreneurs less likely to obtain

bank loans than male entrepreneurs? (Section 5.2)

Use of formal credit: Is the impact of formal finance similar on firm size

for male and female entrepreneurs? (Section 5.3)

19

We carry out a comprehensive analysis of access to credit, including both demand

and supply dimensions by simultaneously examining different states of access to credit,

specified in the model as: a) not needing a loan; b) needing a loan but not applying; c)

needing a loan and applying. Those who apply for a loan (category c) can then have two

different outcomes: c1) having loan application approved, c2) having loan application

rejected.

In dealing with the question of access to credit, issues of selection bias become

immediately apparent. We are interested in the studying the probability of obtaining

formal credit and its relationship to entrepreneurial gender, however, there is a population

that does not apply for formal credit because it does not need external financing. Further,

data reveals that there is also a population that needs a loan but does not apply for a

number of reasons5. For these two populations we do not observe the probability of

obtaining a loan. So, clearly the observed sample that applies for formal loans is a self-

selected, non-random sub-sample of the total population, and for obtaining the true

relationship between entrepreneurial gender and probability of obtaining formal credit we

need to correct for this selection.

However, unlike the traditional selection models, in this case the selection does not occur

over a binary choice, but instead over three exclusive choices a), b) and c), described

above. To correct for this selection, we specify a maximum-likelihood logit model with

multinomial sample selection in the first stage, as outlined in Dubin and McFadden

(1984), and extended in Bourguignon (2007). This model is an extension of the standard

Heckman two-stage selection model (1979) to enable it to handle selection correction

over a multinomial model. The main equation is as follows:

Prob(Loani=1) = ))*((1

iijj

I

ji XFemalesizeFemale

,

(4)

where, Loan equals 1 if the firm obtained a loan in the last fiscal year and 0 otherwise.

Once again, Female is a dummy variable indicating if one of the principal owners is

female; and X is a vector of firm specific characteristics. The size variable represents

5 Possible reasons for not applying include complexity of application procedure, unfavorable interest rates, unattainable collateral requirements, insufficient size of loan or maturity and other reasons.

20

dummies indicating four size categories based on quintiles of number of employees, viz.

micro firms (less than 5 employees), small (6-10 employees), medium (11-25

employees), large (more than 25 employees). Vector X includes firm specific

characteristics that could affect its likelihood of obtaining formal credit.

For predicting the likelihood of obtaining formal credit, vector X includes the

variables that characterize the creditworthiness of the firm from the point of view of the

bank. These would include measures of firm performance and risks associated with the

firm. We use a number of measures for firm performance – current performance is

measured through -value added in the last fiscal year, and capacity utilization in the last

fiscal year. Longer term performance is measured using sales growth over the last three

years (standardized). The degree of risk experienced by the firm is proxied by using

different variables in different regions depending upon the data availability. For ECA we

use categorical variables measuring the degree of competition faced by the firm in its

local market (no competitors, less than five competitors, and more than five competitors).

For LA we use these variables for competition and also include measures for the extent of

diversification of the firm i.e. the share of firm’s sales that come from the main area of

business activity. For SSA, we include the measure for diversification and also a measure

for managerial experience (years of experience in current industry). In all regions, we

also control whether the firm has another relationship with a bank using a dummy for

whether the firm has an existing bank account. In addition, we control for whether the

firms accounts are checked by an external auditor. Due to the high variation in

informality among firms in LA, we also control for whether the firm was registered when

in started for the LA region. Finally, we control for other relevant firm characteristics

which include firm age and age square, and industry and country fixed effects.

The selection equation distinguishes between firms that: a) did not need a loan in

and therefore did not apply; b) need a loan but did not apply; c) need a loan and applied

for it6.

6 It can be argued that the decisions regarding a) whether a loan is needed or not, b) whether to apply for a loan or not, are sequential and hence more appropriately modeled through a nested model. However, in practice it is hard to distinguish between factors that would influence the choice between needing and not needing a loan and the choice between applying and not applying for a loan. The issue is further complicated by psychological factors such as ex-post rationalization of behavior etc.

21

Multinomial Logit (Need/Apply) = )~~~~~( iiii ZXFemale (5)

where X is a vector of variables that identify the selection equation (instruments).

The model comprising equations (4) and (5) assumes that ~ N (0,1), ~ ~ N (0,1), and

corr ( ,~ )= . For the selection instruments (Vector Z) we include two identifying

variables, both of which are likely to be correlated with need for formal credit but not

with probability of obtaining formal credit. The first variable is the percentage of sales

paid for before delivery; this is likely to be negatively related to firm’s probability of

seeking formal credit. The second is the percentage of working capital financed through

retained earnings (proxy for retained earnings and firm preferences for financing). Also

included is the dummy for female ownership that helps indicate whether female

entrepreneurs are different in their propensity to seek formal credit.

The results for the model are shown in Table 7. We find that conditional on firm

performance, firm risk and other firm characteristics and after correcting for selection;

female-owned firms are as likely as their male-owned counterparts to obtain a loan in all

the three regions. This is a strong result and appears to indicate that among formal

enterprises there is no gender-based discrimination in access to credit in a large part of

the developing world. In fact, among small firms in LA, female-owned firms are more

likely than their male counterparts to obtain a loan. In terms of other firm characteristics,

micro and medium sized firms are less likely than large firms to obtain a loan in ECA. In

LA also medium sized firms are less likely to obtain a loan. Among firm performance

characteristics, in LA degree of capacity utilization is positively related to the probability

of getting a loan, in SSA it is the value added. Somewhat surprisingly, in ECA firms with

medium competition are less likely than firms with high competition to obtain a loan.

The multinomial logit for selection correction reveals some interesting findings.

Most importantly we find that in ECA, female-owned firms are significantly more likely

than their male-owned counterparts to need a loan but not apply for it. In other words, the

hypothesis of demand constraints for formal financing among female entrepreneurs finds

support in ECA region. In contrast, in LA and SSA, female entrepreneurs are

significantly less likely to need a loan but not apply for it. This is one of the most

interesting results of this paper. For both LA and SSA this finding seems to contradict

22

some existing beliefs that suggest female entrepreneurs as being less likely to access

formal financing because they are more risk averse, or less financially literate.

In LA, female owned firms are less likely than male-owned firms to claim that they

did not need a loan in the last fiscal. In all the regions, percentage of working capital

financed through retained earnings is negatively related to the probability of ‘needing and

applying for a loan’.

The most important empirical question that emerges from this analysis is – why are

female-owned firms constrained in terms of seeking formal finance in ECA, and not in

other regions. This result is particularly surprising in light of the relatively higher human

capital attainment among women in ECA compared to their counterparts in LA and SSA.

One explanation could be the high collateral rates for women in ECA. The average value

of collateral (as a percentage of loan) for women in ECA is 166% and is the highest rate

in the regions, the comparable numbers being 152% in LA and 147% in SSA. Although

the average collateral requirements are also high for men (160%) in ECA, a significantly

larger proportion of women (7.6%) claim that they did not apply for a loan due to strict

collateral requirements than men in ECA (5.7%). More women in ECA also cite high

interest rates as the primary reason for not applying compared to men (see Table 6), even

though average interest rate charged to men and women in ECA appears to be the same.

Men in ECA on the other hand are much more likely than women to not apply for a loan

because they did not need it and not because of perceived high costs. These factors seem

to suggest, albeit somewhat indirectly, that women in ECA might perceive cost of

applying for loans to be high. Such a gap might explain why female entrepreneurs are

significantly more likely than their male counterparts to need a loan but not apply for it.

It is also worth noting that although the relative percentage of entrepreneurs that

needed a loan and applied for it is the highest in ECA (between 20 and 25%) compared to

the other regions. However, ECA lags behind other regions in terms of other (non-loan

financing from banks. Only about 50% of the firms in ECA obtain some financing from

Banks either for financing their working capital or their new investment. In contrast, this

number is about 75% for LA and between 65 and 70% in SSA. In this respect also

gender-gaps are the widest in ECA. Male entrepreneurs finance about 8.4% of their

working capital through bank financing on average, while the comparable numbers for

23

the female entrepreneurs is 6%, the gap is significant at the 1% level. In contrast, for LA

and SSA the percentage is a little higher for women than men.

However, it also needs to be noted that actual gender-based gaps in cost of

financing seem to be high in LA also. First, cost of collateral for women in LA at 151.5%

is much higher than that for men at 118%. Secondly, although we are missing direct data

on rates of interest in LA, about 16% of female entrepreneurs that did not apply for a loan

claim high interest rates to be the main reason compared to only 10% of men. Despite

these differences, female entrepreneurs are as likely as their male counterparts to apply

for the loan if they need it as seen in Table 7.

5.2 Are Female Entrepreneurs more constrained in terms of use of formal credit?

Next, are female and male-owned firms equally efficient in the use of bank finance.

For this we examine the whether there are any systematic differences in the impact of

formal finance on firm size by entrepreneurial gender. We regress log of sales revenue on

different measures of access to formal credit (sequentially), including, a binary variable

identifying firms that have any part of their working capital and/or new investment

financed through banks, a binary variable indicating if the firm has a loan, a binary

variable indicating if the firm has a loan that was approved/received in the three years

before the last fiscal year, and the average share of working capital financed through

borrowing from commercial banks. For each of these variables an interaction term with

the dummy for female ownership is included in the regression to capture gender based

differences in the impact of using formal credit. To avoid the problem of reverse

causality, given that firms with larger sales might be more likely to obtain financing from

banks, we control for lagged sales (sales three years ago) in these regressions. As in other

regressions, we control for industry and country fixed effects. The results are shown in

Table 8.

Once more we find that results vary by firm region. For ECA, there are no gender-

based gaps in the impact of formal bank financing on overall firm sales. Firms with a

loan and also firms that obtained a loan in the last three fiscal years have higher firm

sales. In contrast, in LA and SSA there is some evidence of gender-based gaps in the

impact of formal finance on firm sales. These gaps are evident in the case of firms who

24

have a loan, and in the case of LA, among firms that have any financing from the banks.

Further, the effect of financing working capital from banks is much stronger for male-

owned firms than their female-owned counterparts for both LA and SSA. Since, there are

little or no gender based gaps in human capital attainment in ECA, but significant gaps in

LA and SSA, these differences might indicate female disadvantage in efficiently using

formal financing due to gender-based gaps in education and training.

6 Conclusions

It has been argued that women face gender-specific barriers as entrepreneurs and

these barriers lead to gender-based gaps in the performance of enterprises. On the other

hand, it has also been contended that female entrepreneurs have different motivations and

preferences than their male counterparts and it is these differences that drive observed

gaps in entrepreneurial performance. So far, not much empirical evidence on the subject

exists, despite the established importance of entrepreneurship in mainstream development

literature. This paper attempts to fill this research gap by first testing the hypothesis of

female underperformance in entrepreneurship and then exploring possible explanations

for observed results. For this analysis, we use firm-level data from formal enterprises in

61 countries across three developing regions, namely ECA, LA, and SSA. We compare

male and female entrepreneurs on a number of performance indicators and then test two

possible explanations for observed results.

Our first finding is that on average female-owned enterprises are significantly

smaller (in terms of overall sales and sales per worker) than their male-owned

counterparts in each region, even after controlling for country and sector. Further, we find

that gender-based gaps in firm productivity (in terms of value added and TFP) are

observed in both ECA and LA, although they are much less pronounced for ECA.

Finally, gender-based gaps in firm growth (in terms of sales and employment) are found

only in the LA region. For both firm productivity and firm sales growth, we test whether

gender-based gaps vary systematically by firm size category (micro, small, medium, and

large). For firm productivity, we find that in ECA, gender-based gaps are seen only for

large firms, while in SSA gender-based gaps are seen in all but the large firms. In LA

results are more mixed; in that gender-based gaps are seem for both large and small firms

25

(but not micro and medium firms). No evidence of female under-performance is seen

within size categories in ECA and SSA, however, in LA female under-performance is

seen in all but medium firms. In terms of overall firm scale, we find that in all regions,

both male- and female-owned firms are operating in the regions of constant or increasing

results to scale and hence are sub-optimally small.

We find limited evidence that female entrepreneurs are disproportionately

concentrated in low performing sectors. Women in all these developing regions are more

likely to be in relatively sectors like garment manufacturing, retail and wholesale trade,

and hotels and restaurants. Also, there is some evidence of female under-performance in

certain sectors in ECA and LA. It should also be noted however that in these regions

female entrepreneurs also perform better than male entrepreneurs in certain sectors.

We test whether there are gender-based gaps in access to bank financing and find

that conditional on firm performance, firm risk and other firm characteristics and after

correcting for selection; female-owned firms are as likely as their male-owned

counterparts to obtain a loan in all the three regions. In ECA, female-owned firms are

significantly more likely than their male-owned counterparts to need a loan but not apply

for it. In other words, the hypothesis of demand constraints for formal financing among

female entrepreneurs finds support in ECA region. In contrast, in LA and SSA, female

entrepreneurs are significantly less likely to need a loan but not apply for it. For ECA,

there are no gender-based gaps in the impact of formal bank financing on overall firm

sales. Firms with a loan and also firms that obtained a loan in the last three fiscal years

have higher firm sales. In contrast, in LA and SSA there is some evidence of gender-

based gaps in the impact of formal finance on firm sales.

26

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Table 1: Summary Statistics

ECA LAC AFR

Variable Male Female Diff** Male Female Diff** Male Female Diff**

Sales* 1404.7 927.2 Sig 3411.4 2220.4 Sig 842.9 769.6 Not Sig

Fixed Assets* 887.9 495.9 Sig 563.9 587.7 Not Sig 14.6 13.2 Not Sig

Permanent Emp 45.6 32.3 Sig 78.4 53.5 Sig 30.0 30.4 Not Sig

Intermediate Goods* 680.0 473.1 Sig 1638.2 901.2 Sig 444.9 409.3 Not Sig

Sales Growth 0.268 0.270 Not Sig 0.339 0.249 Sig 0.370 0.348 Sig

Labor Growth 0.122 0.093 Sig 0.139 0.138 Not Sig 0.254 0.238 Sig

Age 12.7 11.7 Sig 73.9 57.4 Sig 10.5 10.3 Not SigCapacity Util 81.8 82.8 Sig 72.2 67.9 Sig 69.3 69.3 Not Sig

*: In '000 US $

**: Two-sample t test with unequal variances

Table 2: Performance Gaps

ECA LAC AFR ECA LAC AFR ECA LAC AFR ECA LAC AFR

Ln (sales) Output per worker Sales growth Employment Growth

femaleowned -0.480*** -0.344*** -0.128*** -0.138*** -0.195*** -0.066** 0.007 -0.067*** 0.007 -0.012 -0.025*** -0.011

(0.049) (0.039) (0.042) (0.022) (0.022) (0.029) (0.005) (0.011) (0.011) (0.013) (0.009) (0.009)

Observations 4903 7393 8233 4903 7393 8208 3659 5947 5951 4847 6936 6738

R-squared 0.23 0.69 0.28 0.61 0.89 0.24 0.04 0.2 0.09 0.04 0.12 0.03

ECA LAC AFR ECA LAC AFR

Value Added TFP

femaleowned -2.630*** -4.619*** 0.041 -0.023** -0.118*** 0.007

(0.451) (0.908) (0.094) (0.009) (0.015) (0.012)

Observations 4601 5591 5181 3115 4539 5077

R-squared 0.16 0.35 0.89 0.98 0.97 0.97

Standard errors in parentheses

* significant at 10%; ** significant at 5%; *** significant at 1%

Robust regressions after controlling for Country and Sector

Table 3: Performance Gaps by Size


vastd vastd vastd sgrth sgrth sgrth

femaleowned -4.187*** -1.873* 4.188*** -0.004 -0.072*** 0.02

(0.732) (0.957) (0.168) (0.011) (0.016) (0.021)

micro -30.661*** -43.941*** -29.343*** 0.013* -0.037 0.006

(0.490) (1.733) (0.125) (0.007) (0.031) (0.019)

fmicro 3.232*** -2.458 -4.258*** 0.007 -0.106** -0.046

(0.922) (2.848) (0.214) (0.014) (0.046) (0.035)

small -22.752*** -36.141*** -28.316*** 0.004 0.015 0.005

(0.538) (1.127) (0.114) (0.008) (0.020) (0.015)

fsmall 2.638** -4.899*** -4.070*** 0.017 -0.169*** -0.015

(1.093) (1.848) (0.218) (0.016) (0.032) (0.027)

medium -15.945*** -32.536*** -26.032*** 0.005 -0.068*** -0.009

(0.503) (0.982) (0.116) (0.007) (0.017) (0.016)

fmed 3.228*** 1.58 -4.016*** 0.018 0.112*** -0.008

(1.053) (1.544) (0.227) (0.015) (0.026) (0.029)

Observations 4601 5591 5180 3659 5947 5947

R-squared 0.62 0.59 0.97 0.04 0.2 0.09



Robust regressions after controlling for Country and Sector

Table 4: Returns to Scale

ECA LAC AFR

Male Female Male Female Male Female

lnYY lnYY lnYY lnYY lnYY lnYY

lnLL 0.310*** 0.355*** 1.034*** 0.720*** 0.364*** 0.308***

(0.043) (0.091) (0.104) (0.081) (0.064) (0.034)

lnKK 0.040*** 0.023* 0.032 0.031* 0.013*** 0.012*

(0.008) (0.011) (0.026) (0.016) (0.004) (0.007)

lnMM 0.669*** 0.671*** 0.191*** 0.389*** 0.727*** 0.737***

(0.045) (0.076) (0.031) (0.038) (0.042) (0.036)

Sum 1.019 1.049 1.257 1.140 1.104 1.057

Observations 2283 832 3439 1915 3850 1227

R-squared 0.96 0.97 0.87 0.92 0.95 0.95

Robust standard errors in parentheses


Constant or Increasing Returns to Scale

Standard Errors clustered by Sector

Sig Diff Not Diff Not Diff

Table 5: Performance Gaps by Industry

Ln(sales) Value Added


F*construction -0.397*** -1.833*** -0.241 -1.961 -3.85 0.663

(0.141) (0.362) (0.252) (1.309) (8.299) (1.999)

Garments -0.306** -0.997*** -1.354*** -2.952** 13.554 0.466

(0.126) (0.184) (0.134) (1.147) (23.431) (1.230)

F*garments -0.424** 1.113*** 0.31 -2.264 1.109 -0.355

(0.209) (0.376) (0.275) (1.915) (8.563) (2.008)

Food 0.231** -0.850*** 0.226* 1.276 7.808 2.729**

(0.096) (0.176) (0.130) (0.884) (23.410) (1.230)

F*food 0.085 1.265*** -0.179 0.375 -8.638 -1.27

(0.209) (0.370) (0.278) (1.926) (8.466) (2.010)

Chemicals 0.107 -1.470*** 1.708*** 0.793 -15.627 55.688***

(0.151) (0.178) (0.189) (1.390) (23.425) (1.256)

F*chemicals 0.495 1.266*** -0.481 1.894 4.601 -18.623***

(0.409) (0.373) (0.385) (3.664) (8.508) (2.071)

Metals -0.275** -1.642*** -0.570*** -3.065*** 15.555 1.038

(0.107) (0.250) (0.142) (0.984) (24.011) (1.234)

F*metals 0.126 1.621*** 1.209*** 2.294 -6.43 -0.136

(0.252) (0.446) (0.368) (2.293) (10.526) (2.054)

Non-metals -0.664*** -2.198*** -0.863*** -3.636*** -0.199 0.908

(0.153) (0.282) (0.135) (1.405) (22.651) (1.232)

F*non-metals 0.653** 2.457*** 1.130*** 4.751 0.001 0.065

(0.322) (0.518) (0.305) (3.011) (0.001) (2.024)

Electronics -0.444 0.915** -2.829 3.362**

(0.663) (0.451) (6.579) (1.501)

F*electronics 0.221 0.851 3.301 70.839***

(1.627) (1.271) (14.733) (3.502)

Other Mfg 0.053 -1.972*** 0.14 -0.268 2.125 1.638

(0.113) (0.179) (0.144) (1.029) (23.399) (1.236)

F*other mfg 0.138 2.246*** 0.206 1.607 12.952 -0.51

(0.248) (0.373) (0.314) (2.254) (8.524) (2.030)

Textiles 0.117 -1.415*** 0.736*** -0.901 -0.13 28.973***

(0.234) (0.187) (0.261) (2.129) (23.435) (1.295)

F*textiles 0.326 1.249*** -0.628 9.463** -14.046 -28.858***

(0.438) (0.381) (0.478) (3.997) (8.663) (2.138)

Wholesale & Retail -0.407*** -1.227*** -1.109*** -4.873*** 0.11

(0.077) (0.201) (0.123) (0.716) (1.229)

F*wholesale & retail -0.179 1.714*** 0.043 -0.701 -0.689

(0.167) (0.395) (0.264) (1.541) (2.006)

Hotels & Rest -0.861*** -1.170*** -4.111*** -0.711

(0.126) (0.135) (1.141) (1.403)

F*hotels & rest -0.045 -0.243 -2.609 -0.637

(0.247) (0.274) (2.264) (2.185)

IT -0.944*** -2.600*** -1.352*** -1.798 0.359

(0.296) (0.305) (0.165) (2.738) (1.262)

F*IT 0.184 2.096*** 0.416 -3.519 -0.325

(0.588) (0.658) (0.369) (5.776) (2.183)

Other Serv -1.062*** -0.927*** -0.897*** -4.945*** 0.203

(0.101) (0.188) (0.243) (0.935) (1.366)

F*other serv -0.303 1.807*** 0.349 -2.73 -0.082

(0.189) (0.397) (0.435) (1.736) (2.300)

Other 0.569** -0.934*** 3.999 -1.174

(0.277) (0.141) (2.458) (1.323)

F*Other 1.103 0.32 141.623*** -0.139

(0.797) (0.296) (7.073) (2.207)

Observations 4903 7393 8233 4601 5591 5181R-squared 0.23 0.69 0.29 0.23 0.36 0.93


Table 6: Access to Credit: Descriptive Statistics

VariableMale Female Male Female Male Female

Collateral as a % of loan 159.9 165.8 118.3 151.5 160.3 147.2Rate of interest 14.3 14.4 na na 14.9 14.7Duration of loan (mths) 29.7 32.0 31.5 26.6 35.3 36.4Size of loan* na na 524.3 276.8 673.8 287.0% any financing from bank 52.5 50.4 74.0 75.6 66.5 69.8% with Bank A/C 87.2 86.0 88.1 91.6 78.6 82.9% Loan in last 3 Yrs 13.9 13.2 9.2 11.9 4.3 5.9Last loan needed collateral 86.1 84.4 62.2 74.8 87.4 90.3Did not need a loan 28.7 28.5 36.6 31.9 25.0 25.3Needed but did not apply 22.2 26.6 20.3 25.0 57.5 53.7Needed and Applied 49.2 44.9 43.1 43.1 17.5 20.9Got a loan after applying 94.7 92.6 88.9 91.8 53.5 57.0*: In '000 US $

Why did the firm not apply ECA LAC AFRfor credit? Male Female Male Female Male Female

Did not need a loan 56.9 51.9 64.4 56.0 30.5 32.2Burdensome Appl Proced 5.4 4.9 8.1 8.5 21.2 18.2Strict Collateral Req 5.7 7.6 5.1 4.6 14.3 13.2High Interest Rates 25.1 27.4 10.1 16.1 18.2 20.2Informal Payments Reqd 2.3 2.4 na na na naDid not think it wd be app 3.6 3.8 0.8 2.1 8.3 8.9Insuff maturity term na na 0.1 0.1 2.1 2.0Other 0.9 1.9 11.5 12.5 5.4 5.2Total 100.0 100.0 100.0 100.0 100.0 100.0

ECA LA SSA

Table 7 : Difference in Access to Credit by Gender (Table contd on next page)

Step 1: Multinomial Logit Marginal EffectsDon't Need ECA LAC AFR

0.000 0.002*** -0.002***(0.000) (0.000) (0.000)0.003*** 0.006*** -0.001***(0.000) (0.000) (0.000)

Femaleowned -0.030* -0.045*** 0.017(0.017) (0.017) (0.022)

Need But Don’t Apply ECA LAC AFR0.000 -0.003*** 0.003***(0.000) (0.000) (0.000)0.002*** 0.001* 0.004***(0.000) (0.000) (0.000)

Femaleowned 0.059*** -0.015 -0.058***(0.018) (0.014) (0.027)

Need and Apply ECA LAC AFR0.000 0.001*** -0.001***(0.000) (0.000) (0.000)

-0.005*** -0.006*** -0.003***(0.000) (0.000) (0.000)

Femaleowned -0.029 0.060*** 0.040*(0.021) (0.018) (0.023)

(Need and apply is the base outcome) .Selectivity correction based on multinomial logitBootstrapped standard errors (100 replications)

% of sales paid for before delivery% of wkg capital financed by retained earnings



Table 7 : Difference in Access to Credit by Gender (Contd)Step 2 LogitDep. Var Applied for loan and got it

ECA LAC AFRFemaleowned 0.125 -0.091 0.036

(0.099) (0.057) (0.070)Micro -0.123** -0.288 -0.033

(0.049) (0.358) (0.101)F*micro 0.07 -0.151 -0.148

(0.063) (0.583) (0.140)Small -0.059 -0.18 -0.115

(0.039) (0.121) (0.076)F*small 0.069 0.248* -0.081

(0.065) (0.145) (0.143)Medium -0.044* -0.118* 0.058

(0.026) (0.070) (0.061)F*medium 0.023 0.125 -0.142

(0.076) (0.099) (0.109)Value added 0.001 0.001 0.001*

(0.001) (0.001) (0.001)Sales Growth -0.017 -0.066 -0.025

(0.039) (0.044) (0.046)Capacity utilization 0.001 0.001* 0.001

(0.001) (0.001) (0.001)Bank a/c -0.042 -0.358 -0.036

(0.046) (0.408) (0.069)External Auditor 0.021 -0.043 -0.004

(0.023) (0.046) (0.050)Registered when started 0.09

(0.089)No comptt 0.003 0.009

(0.037) (0.107)Medium comptt -0.044** 0.054(less than five competitors) (0.022) (0.072)Markup 0.001

(0.001)% Sales from main product 0.001 -0.001

(0.001) (0.002)Manager's experience -0.003

(0.001)Age -0.001 0.007 -0.006

(0.002) (0.005) (0.005)Age Square 0.001 0.001 0.001*

(0.001) (0.001) (0.001)Observations 3090 4518 1965Sigma Sq 0.516 0.572*** 0.423

(1.651) (0.285) (1.272)rho1 -4.31*** -1.480 1.031

(1.771) (2.148) (0.763)rho2 1.752 0.423 0.084

(1.558) (2.098) (1.076)rho3 -1.55* -0.773 -0.667*

(0.797) (2.161) (0.398)Standard errors in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%Regreessions control for Country and Sector

Table 8: Use of Finance

ECA LAC AFR ECA LAC AFR ECA LAC AFR ECA LAC AFR

lnYY lnYY lnYY lnYY lnYY lnYY lnYY lnYY lnYY lnYY lnYY lnYY

femaleowned 0.004 0.017 0.023 0.001 0.046*** 0.017 0.001 -0.063*** 0.002 0.004 -0.030** 0.006

(0.008) (0.023) (0.018) (0.007) (0.017) (0.013) (0.006) (0.012) (0.011) (0.006) (0.014) (0.012)

Sales 3 yrs ago 0.989*** 0.980*** 0.944*** 0.988*** 0.974*** 0.937*** 0.989*** 0.980*** 0.943*** 0.989*** 0.977*** 0.937***

(0.002) (0.003) (0.003) (0.002) (0.003) (0.003) (0.002) (0.003) (0.003) (0.002) (0.003) (0.003)

Any financing 0.006 -0.018 0.003

(0.005) (0.016) (0.011)

F*any -0.002 -0.102*** -0.025

(0.010) (0.026) (0.022)

Have a Loan (any year) 0.012** 0.149*** 0.103***

(0.006) (0.015) (0.018)

F*have a loan 0.003 -0.206*** -0.064**

(0.010) (0.023) (0.029)

Loan last 3 yrs 0.019** 0.038* 0.038

(0.008) (0.023) (0.025)

F*loan last 3 yrs 0.012 0.004 0.061

(0.015) (0.035) (0.042)

0.001 0.001*** 0.002***

(0.001) (0.001) (0.001)

F*% wkg cap 0.001 -0.002*** -0.002**

(0.001) (0.001) (0.001)

Observations 3659 5906 5947 3659 5904 4866 3659 5942 5947 3609 5942 5690R-squared 0.99 0.98 0.97 0.99 0.98 0.97 0.99 0.98 0.97 0.99 0.98 0.97



% of wkg capital fin from Bank

Standard Errors clustered by Sector

Figure 1: Relative Sectoral Concentration of Female Entrperneurs

0 5 10 15 20 25 30 35

Electronics

Other

IT

Chemicals

Textiles

Non-Metals

Metals

Other Mfg

Hotels&restaurants

Food

Const & Transp

Garments & Leather

Other services

Wholesale & Retail

ECA SSA

0 5 10 15 20 25 30 35

IT

Chemicals

Textiles

Non-Metals

Metals

Other Mfg

Food

Const & Transp

Garments & Leather

Other services

Wholesale & Retail

LA

Fig 2: Enterprises by Gender and Size

ECA

M

M

M

M

F

F

FF

0

10

20

30

40

50

60

0--5 6--10 11--25 > 25

LA

M

M

M

M

F

F

F

F

0--5 6--10 11--25 > 25

SSA

M

M

MFM

F

FF

0--5 6--10 11--25 > 25

Figure 3: Differences in Patterns of Financing by Entrepreneurial Gender

ECA

MM

FF

45

50

55

60

65

70

75

80

85

% of Wkg K from RetainedEarnings

% of New Inv. fromRetained Earnings

SSA

M

M

F

F

% of Wkg K fromBorrowing from Banks

% of New Inv. fromBorrowing from Banks

LA

M

M

FF

% of Wkg K fromRetained Earnings


SSA

M

M

F

F

% of Wkg K fromRetained Earnings


ECA

M

M

FF

0

5

10

15

20

25

30



LA

M

MF

F



how do female entrepreneurs perform? evidence from three...

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