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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Ross School of Business University of Michigan and Elena Bardasi PREM Gender, World Bank May 2009 Preliminary, please do not cite.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 underutilization 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 underperformance 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 subgroup 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 2
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 selfemployed 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 selfemployment. 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
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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: Growth
X t X t 3 , X t X t 3 2
(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
ln Yij K ln K i L ln Li M ln M i Fij I C ij , (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 maleowned 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 womenowned 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 genderbased 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 femaleowned 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: ln(sales) i i Femalei Femalei * Industry C i .
(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?
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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 underperforming 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).
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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)
18
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 selfselected, 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: I
Prob(Loani=1) = ( Femalei j ( sizej * Femalei ) X i ) , j 1
(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.
19
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.
20
~ Multinomial Logit (Need/Apply) = (~ Femalei ~X i ~ Z i ~i )
(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 21
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
22
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 genderbased 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
23
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 maleowned 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
24
(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 genderbased gaps in the impact of formal finance on firm sales.
25
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Table 1: Summary Statistics
Variable Sales* Fixed Assets* Permanent Emp Intermediate Goods* Sales Growth Labor Growth Age Capacity Util
Male 1404.7 887.9 45.6 680.0 0.268 0.122 12.7 81.8
ECA Female 927.2 495.9 32.3 473.1 0.270 0.093 11.7 82.8
*: In '000 US $ **: Two-sample t test with unequal variances
Diff** Sig Sig Sig Sig Not Sig Sig Sig Sig
Male 3411.4 563.9 78.4 1638.2 0.339 0.139 73.9 72.2
LAC Female 2220.4 587.7 53.5 901.2 0.249 0.138 57.4 67.9
Diff** Sig Not Sig Sig Sig Sig Not Sig Sig Sig
Male 842.9 14.6 30.0 444.9 0.370 0.254 10.5 69.3
AFR Female 769.6 13.2 30.4 409.3 0.348 0.238 10.3 69.3
Diff** Not Sig Not Sig Not Sig Not Sig Sig Sig Not Sig Not Sig
Table 2: Performance Gaps
femaleowned Observations R-squared
femaleowned Observations R-squared
ECA LAC AFR Ln (sales) -0.480*** -0.344*** -0.128*** (0.049) (0.039) (0.042) 4903 7393 8233 0.23 0.69 0.28
ECA LAC AFR ECA LAC AFR ECA LAC AFR Output per worker Sales growth Employment Growth -0.138*** -0.195*** -0.066** 0.007 -0.067*** 0.007 -0.012 -0.025*** -0.011 (0.022) (0.022) (0.029) (0.005) (0.011) (0.011) (0.013) (0.009) (0.009) 4903 7393 8208 3659 5947 5951 4847 6936 6738 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 -2.630*** -4.619*** 0.041 -0.023** -0.118*** 0.007 (0.451) (0.908) (0.094) (0.009) (0.015) (0.012) 4601 5591 5181 3115 4539 5077 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
femaleowned micro fmicro small fsmall medium fmed Observations R-squared
ECA LAC vastd vastd -4.187*** -1.873* (0.732) (0.957) -30.661*** -43.941*** (0.490) (1.733) 3.232*** -2.458 (0.922) (2.848) -22.752*** -36.141*** (0.538) (1.127) 2.638** -4.899*** (1.093) (1.848) -15.945*** -32.536*** (0.503) (0.982) 3.228*** 1.58 (1.053) (1.544) 4601 5591 0.62 0.59
AFR ECA vastd sgrth 4.188*** -0.004 (0.168) (0.011) -29.343*** 0.013* (0.125) (0.007) -4.258*** 0.007 (0.214) (0.014) -28.316*** 0.004 (0.114) (0.008) -4.070*** 0.017 (0.218) (0.016) -26.032*** 0.005 (0.116) (0.007) -4.016*** 0.018 (0.227) (0.015) 5180 3659 0.97 0.04
Standard errors in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% Robust regressions after controlling for Country and Sector
LAC AFR sgrth sgrth -0.072*** 0.02 (0.016) (0.021) -0.037 0.006 (0.031) (0.019) -0.106** -0.046 (0.046) (0.035) 0.015 0.005 (0.020) (0.015) -0.169*** -0.015 (0.032) (0.027) -0.068*** -0.009 (0.017) (0.016) 0.112*** -0.008 (0.026) (0.029) 5947 5947 0.2 0.09
Table 4: Returns to Scale
lnLL lnKK lnMM Sum
Observations R-squared
ECA Male lnYY 0.310*** (0.043) 0.040*** (0.008) 0.669*** (0.045) 1.019
LAC AFR Female Male Female Male Female lnYY lnYY lnYY lnYY lnYY 0.355*** 1.034*** 0.720*** 0.364*** 0.308*** (0.091) (0.104) (0.081) (0.064) (0.034) 0.023* 0.032 0.031* 0.013*** 0.012* (0.011) (0.026) (0.016) (0.004) (0.007) 0.671*** 0.191*** 0.389*** 0.727*** 0.737*** (0.076) (0.031) (0.038) (0.042) (0.036) 1.049 1.257 1.140 1.104 1.057 Constant or Increasing Returns to Scale Sig Diff Not Diff Not Diff 2283 832 3439 1915 3850 1227 0.96 0.97 0.87 0.92 0.95 0.95
Robust standard errors in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% Standard Errors clustered by Sector
Table 5: Performance Gaps by Industry
F*construction Garments F*garments Food F*food Chemicals F*chemicals Metals F*metals Non-metals F*non-metals Electronics F*electronics Other Mfg F*other mfg Textiles F*textiles Wholesale & Retail F*wholesale & retail Hotels & Rest F*hotels & rest IT F*IT Other Serv F*other serv Other F*Other Observations R-squared
Ln(sales) ECA -0.397*** (0.141) -0.306** (0.126) -0.424** (0.209) 0.231** (0.096) 0.085 (0.209) 0.107 (0.151) 0.495 (0.409) -0.275** (0.107) 0.126 (0.252) -0.664*** (0.153) 0.653** (0.322) -0.444 (0.663) 0.221 (1.627) 0.053 (0.113) 0.138 (0.248) 0.117 (0.234) 0.326 (0.438) -0.407*** (0.077) -0.179 (0.167) -0.861*** (0.126) -0.045 (0.247) -0.944*** (0.296) 0.184 (0.588) -1.062*** (0.101) -0.303 (0.189) 0.569** (0.277) 1.103 (0.797) 4903 0.23
Standard errors in parentheses
LAC -1.833*** (0.362) -0.997*** (0.184) 1.113*** (0.376) -0.850*** (0.176) 1.265*** (0.370) -1.470*** (0.178) 1.266*** (0.373) -1.642*** (0.250) 1.621*** (0.446) -2.198*** (0.282) 2.457*** (0.518)
-1.972*** (0.179) 2.246*** (0.373) -1.415*** (0.187) 1.249*** (0.381) -1.227*** (0.201) 1.714*** (0.395)
-2.600*** (0.305) 2.096*** (0.658) -0.927*** (0.188) 1.807*** (0.397)
7393 0.69
Value Added AFR ECA LAC -0.241 -1.961 -3.85 (0.252) (1.309) (8.299) -1.354*** -2.952** 13.554 (0.134) (1.147) (23.431) 0.31 -2.264 1.109 (0.275) (1.915) (8.563) 0.226* 1.276 7.808 (0.130) (0.884) (23.410) -0.179 0.375 -8.638 (0.278) (1.926) (8.466) 1.708*** 0.793 -15.627 (0.189) (1.390) (23.425) -0.481 1.894 4.601 (0.385) (3.664) (8.508) -0.570*** -3.065*** 15.555 (0.142) (0.984) (24.011) 1.209*** 2.294 -6.43 (0.368) (2.293) (10.526) -0.863*** -3.636*** -0.199 (0.135) (1.405) (22.651) 1.130*** 4.751 0.001 (0.305) (3.011) (0.001) 0.915** -2.829 (0.451) (6.579) 0.851 3.301 (1.271) (14.733) 0.14 -0.268 2.125 (0.144) (1.029) (23.399) 0.206 1.607 12.952 (0.314) (2.254) (8.524) 0.736*** -0.901 -0.13 (0.261) (2.129) (23.435) -0.628 9.463** -14.046 (0.478) (3.997) (8.663) -1.109*** -4.873*** (0.123) (0.716) 0.043 -0.701 (0.264) (1.541) -1.170*** -4.111*** (0.135) (1.141) -0.243 -2.609 (0.274) (2.264) -1.352*** -1.798 (0.165) (2.738) 0.416 -3.519 (0.369) (5.776) -0.897*** -4.945*** (0.243) (0.935) 0.349 -2.73 (0.435) (1.736) -0.934*** 3.999 (0.141) (2.458) 0.32 141.623*** (0.296) (7.073) 8233 4601 5591 0.29 0.23 0.36
AFR 0.663 (1.999) 0.466 (1.230) -0.355 (2.008) 2.729** (1.230) -1.27 (2.010) 55.688*** (1.256) -18.623*** (2.071) 1.038 (1.234) -0.136 (2.054) 0.908 (1.232) 0.065 (2.024) 3.362** (1.501) 70.839*** (3.502) 1.638 (1.236) -0.51 (2.030) 28.973*** (1.295) -28.858*** (2.138) 0.11 (1.229) -0.689 (2.006) -0.711 (1.403) -0.637 (2.185) 0.359 (1.262) -0.325 (2.183) 0.203 (1.366) -0.082 (2.300) -1.174 (1.323) -0.139 (2.207) 5181 0.93
Table 6: Access to Credit: Descriptive Statistics Variable Collateral as a % of loan Rate of interest Duration of loan (mths) Size of loan* % any financing from bank % with Bank A/C % Loan in last 3 Yrs Last loan needed collateral Did not need a loan Needed but did not apply Needed and Applied Got a loan after applying *: In '000 US $ Why did the firm not apply for credit? Did not need a loan Burdensome Appl Proced Strict Collateral Req High Interest Rates Informal Payments Reqd Did not think it wd be app Insuff maturity term Other Total
Male 159.9 14.3 29.7 na 52.5 87.2 13.9 86.1 28.7 22.2 49.2 94.7
ECA Female 165.8 14.4 32.0 na 50.4 86.0 13.2 84.4 28.5 26.6 44.9 92.6
Male 118.3 na 31.5 524.3 74.0 88.1 9.2 62.2 36.6 20.3 43.1 88.9
LA Female 151.5 na 26.6 276.8 75.6 91.6 11.9 74.8 31.9 25.0 43.1 91.8
Male 160.3 14.9 35.3 673.8 66.5 78.6 4.3 87.4 25.0 57.5 17.5 53.5
SSA Female 147.2 14.7 36.4 287.0 69.8 82.9 5.9 90.3 25.3 53.7 20.9 57.0
ECA Male 56.9 5.4 5.7 25.1 2.3 3.6 na 0.9 100.0
Female 51.9 4.9 7.6 27.4 2.4 3.8 na 1.9 100.0
LAC Male 64.4 8.1 5.1 10.1 na 0.8 0.1 11.5 100.0
Female 56.0 8.5 4.6 16.1 na 2.1 0.1 12.5 100.0
AFR Male 30.5 21.2 14.3 18.2 na 8.3 2.1 5.4 100.0
Female 32.2 18.2 13.2 20.2 na 8.9 2.0 5.2 100.0
Table 7 : Difference in Access to Credit by Gender (Table contd on next page) Step 1: Multinomial Logit Marginal Effects Don't Need ECA LAC % of sales paid for before 0.000 0.002*** delivery (0.000) (0.000) % of wkg capital financed by 0.003*** 0.006*** retained earnings (0.000) (0.000) Femaleowned -0.030* -0.045*** (0.017) (0.017) Need But Don’t Apply ECA LAC % of sales paid for before 0.000 -0.003*** delivery (0.000) (0.000) % of wkg capital financed by 0.002*** 0.001* retained earnings (0.000) (0.000) Femaleowned 0.059*** -0.015 (0.018) (0.014) Need and Apply ECA LAC % of sales paid for before 0.000 0.001*** delivery (0.000) (0.000) % of wkg capital financed by -0.005*** -0.006*** retained earnings (0.000) (0.000) Femaleowned -0.029 0.060*** (0.021) (0.018) (Need and apply is the base outcome) Selectivity correction based on multinomial logit Bootstrapped standard errors (100 replications)
AFR -0.002*** (0.000) -0.001*** (0.000) 0.017 (0.022) AFR 0.003*** (0.000) 0.004*** (0.000) -0.058*** (0.027) AFR -0.001*** (0.000) -0.003*** (0.000) 0.040* (0.023) .
Table 7 : Difference in Access to Credit by Gender (Contd) Step 2 Logit Dep. Var Applied for loan and got it ECA LAC AFR Femaleowned 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 1965 Sigma 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
femaleowned Sales 3 yrs ago Any financing F*any
ECA LAC lnYY lnYY 0.004 0.017 (0.008) (0.023) 0.989*** 0.980*** (0.002) (0.003) 0.006 -0.018 (0.005) (0.016) -0.002 -0.102*** (0.010) (0.026)
Have a Loan (any year) F*have a loan
AFR ECA lnYY lnYY 0.023 0.001 (0.018) (0.007) 0.944*** 0.988*** (0.003) (0.002) 0.003 (0.011) -0.025 (0.022) 0.012** (0.006) 0.003 (0.010)
LAC AFR ECA LAC AFR ECA lnYY lnYY lnYY lnYY lnYY lnYY 0.046*** 0.017 0.001 -0.063*** 0.002 0.004 (0.017) (0.013) (0.006) (0.012) (0.011) (0.006) 0.974*** 0.937*** 0.989*** 0.980*** 0.943*** 0.989*** (0.003) (0.003) (0.002) (0.003) (0.003) (0.002)
0.149*** (0.015) -0.206*** (0.023)
0.103*** (0.018) -0.064** (0.029)
Loan last 3 yrs
0.019** (0.008) 0.012 (0.015)
F*loan last 3 yrs
0.038* (0.023) 0.004 (0.035)
0.038 (0.025) 0.061 (0.042)
% of wkg capital fin from Bank F*% wkg cap Observations R-squared
3659 0.99
5906 0.98
5947 0.97
Standard errors in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% Standard Errors clustered by Sector
3659 0.99
5904 0.98
LAC AFR lnYY lnYY -0.030** 0.006 (0.014) (0.012) 0.977*** 0.937*** (0.003) (0.003)
4866 0.97
3659 0.99
5942 0.98
5947 0.97
0.001 0.001*** 0.002*** (0.001) (0.001) (0.001) 0.001 -0.002*** -0.002** (0.001) (0.001) (0.001) 3609 5942 5690 0.99 0.98 0.97
Figure 1: Relative Sectoral Concentration of Female Entrperneurs
Wholesale & Retail
Wholesale & Retail
Other services
Other services
Garments & Leather Garments & Leather
Const & Transp Food
Const & Transp
Hotels&restaurants
Food
Other Mfg Other Mfg Metals Non-Metals
Metals
Textiles
Non-Metals
Chemicals
Textiles
IT Chemicals
Other
IT
Electronics 0
5
10
15 ECA
20 SSA
25
30
35
0
5
10
15
20 LA
25
30
35
Fig 2: Enterprises by Gender and Size
LA
ECA
SSA
60 M
50
F
40 30
F M
F
M
20
F M
M M F
M F
F
10
M F M F
M F
F M
F M
0 0--5
6--10
11--25
> 25
0--5
6--10
11--25
> 25
0--5
6--10
11--25
> 25
Figure 3: Differences in Patterns of Financing by Entrepreneurial Gender LA
ECA
SSA
85
M
80 75 70
F F M
F M
65
F
60
M
M F
F
M
55 50 45 % of Wkg K from Retained Earnings
% of New Inv. from Retained Earnings
% of Wkg K from Retained Earnings
% of Wkg K from Retained Earnings
LA
ECA
30
% of New Inv. from Retained Earnings
% of New Inv. from Retained Earnings
SSA
25 M F
20 F M
15 10 5
M F
M
F
F
M F M
0 % of Wkg K from Borrowing from Banks
% of New Inv. from Borrowing from Banks
% of Wkg K from Borrowing from Banks
% of New Inv. from Borrowing from Banks
% of Wkg K from Borrowing from Banks
% of New Inv. from Borrowing from Banks
