“When you empower women, you empower Africa..fostering women entrepreneurship in Africa is crucial for the development of the continent” Juan Somavia, Director, ILO Mary Hallward-Driemeier, Manju Shah and Reyes Aterido World Bank

Are there gender differences in entrepreneurship and performance? Refining the investigation  PART I  Allow for differences across countries  Group countries by proxy of women’s economic opportunities (e.g. female literacy)  Distinguish micro from larger, more formal enterprises  Women are more often located in smaller firms  PART II  Look at how women’s enterprises are defined: ownership vs. decision making authority  Other characteristics of entrepreneurship may matter too (experience, how and why become an entrepreneur, family background, management techniques used)

Key Issues to be Examined  Characteristics of entrepreneurs  Conditional on participation, do women select themselves into particular sectors?  Within sectors, are female owned enterprises different from those owned by men?  Determinants of productivity differentials: Does gender matter? Do other characteristics of the entrepreneur matter?

Part I: Comparisons across countries and sizes of firms  This paper uses Enterprise Survey data from 22 SSA countries to examine the characteristics of female entrepreneurs and firm performance.  Countries are grouped on female literacy rates  Female literacy gaps give almost same grouping  Will look first at microenterprises, then larger more formal enterprises

Pct of Female Entrepreneurs in Microenterprises increases dramatically with increase in female literacy rates

Sector composition differs across gender- women micro-entrepreneurs are concentrated in garments and food sector

Summary of findings: micro  Controlling for sector and country differences, we find that:  Female owners are likely to have higher education levels than men in low literacy environments. This reverses in higher literacy countries  Female owners are more likely to own land, and receive formal finance than men.  In moderate literacy countries, formal finance for women is correlated with land ownership, this is not true in low literacy environments.  Results imply that the few women who can enter the microenterprise sector in low literacy environments are those that are economically empowered through asset ownership or micro-finance programs, and those that have beaten the odds and received some education.  In high literacy countries, the microenterprise sector is equally represented by men and women. Percentage of women entrepreneurs having higher education is slightly lower than men. Women are also less likely to receive loans, compared to men, but this difference is insignificant.

Probit Results: Characteristics of Female Ownership Variable Model 1Model 2Model 3 Model 4 Model 1Model 2Model 3 Model 4 Model 1Model 2Model 3 Model 4 Intercept -0.65***-0.69***-0.78***-1.31***-0.43***-0.31***-0.47***-0.41* (0.135)(0.133)(0.147) (0.387) (0.128)(0.115)(0.128) (0.240) (0.145)(0.126)(0.145) (0.364) Log(size) (0.096)(0.060)(0.119) Log(age) *-0.04 (0.071)(0.044)(0.080) Register (0.123)(0.080)(0.156) Secdary *** (0.135) (0.145) (0.089)(0.090) (0.094) (0.156) (0.195) Vocat 0.38*0.36*0.35*0.27***0.26***0.21*-0.5*** (0.198) (0.210) (0.110) (0.117) (0.188) (0.218) Univ ** ***-0.41***-0.35* (0.153)(0.154) (0.165) (0.097) (0.104) (0.179)(0.180) (0.210) Ownland 0.32*** 0.34***0.32***0.33***0.38*** (0.128)(0.129) (0.136) (0.086) (0.091) (0.193)(0.195) (0.208) Food **0.4 (0.479)(0.251)(0.497) Textgarm 0.68*0.84***0.57 (0.362)(0.214)(0.391) Furniture *-1.47*** (0.483)(0.258)(0.601) Metal **-- (0.501)(0.293)-- Services 0.75**0.62***0.42 (0.354)(0.195)(0.348) Retail 0.62*0.31*0.06 (0.326)(0.183)(0.320) N LLr

Microenterprise Productivity: Are Female Owned firms less productive?  Median productivity is lower for female owned enterprises.  This productivity difference remains even after controlling for differences in sectors of operation, education, capital, and access to formal finance, in low literacy environments. The difference becomes insignificant in high literacy countries.

Productivity Regressions - Microenterprises Model 1Model 2Model 3Model 4Model 1Model 2Model 3Model 4Model 1Model 2Model 3Model 4 Gp 1 Gp 2 Gp 3 Intercept 8.92***8.45***6.64***6.52***8.00***7.74***5.65***5.61***8.18***8.52***6.28***6.46*** (0.109)(0.173)(0.257) (0.260) (0.117)(0.145)(0.186) (0.180) (0.145)(0.279)(0.381) (0.360) female -0.25***-0.28***-0.36***-0.37*** **-0.3*** (0.106)(0.103)(0.108) (0.109) (0.066) (0.061) (0.126)(0.128)(0.127) (0.121) Log(Cap/Labor) ––0.25*** ––0.3***0.29***––0.3***0.23*** –– (0.026) –– (0.019) –– (0.033) (0.034) Secondary –––0.29***–––0.1–––0.21 –––(0.112)–––(0.075)–––(0.157) Vocational –––0.1–––0.17*–––0.47*** –––(0.173)–––(0.094)–––(0.178) University –––0.18–––0.16*–––0.6*** –––(0.129)–––(0.083)–––(0.175) Formal Finance –––0.01–––-0.02–––0.77*** –––(0.153)–––(0.092)–––(0.154) Food – – – – (0.316)(0.317) (0.32)– (0.195)(0.180) – (0.447)(0.415) (0.390) Furniture – – –-1.13***-1.21***-1.00*** – (0.251)(0.257) (0.15)– (0.166)(0.143) (0.150)– (0.424)(0.407) (0.390) Garments – – – – (0.192) (0.19)– (0.155)(0.152) (0.140)– (0.336)(0.322) (0.310) Services – –0.29**0.32***0.31***– – (0.186)(0.187) (0.180)– (0.135)(0.124) (0.120)– (0.300)(0.295) (0.280) Retail –0.7***0.69***0.68–0.67***0.65*** – – (0.151)(0.154) (0.150)– (0.116)(0.107) (0.110)– (0.268)(0.261) (0.240) N Adj. Rsq

Female Owned SMLEs are concentrated in food processing and garments sectors Sector Distribution of Female Owned Formal Firms Sector Distribution of Male Owned Formal Firms

Characteristics of Female Entrepreneurs in SMLEs  Education: In low literacy environments, women entrepreneurs have higher education than men. This reverses in moderate and high literacy environments where women own smaller, younger enterprises.  Women entrepreneurs in low literacy environments face greater bureaucratic burden-they are more likely to require bribe payments, and subject to inspections

Characteristics of Female Entrepreneurship in SMLEs Group 1Group 2Group 3 Model 1Model 2Model 3Model 4Model 1Model 2Model 3Model 4Model 1Model 2Model 3Model 4 Intercept -1.07***-0.93***-1.13***-1.38***-0.43***-0.5***-0.43*** -0.55*** -0.47***-0.72***-0.42***-0.12 (0.098)(0.099)(0.111)(0.242)(0.100)(0.082)(0.100)(0.181)(0.165)(0.107)(0.167)(0.295) Lwork *** (0.058)(0.033)(0.061) lage *** (0.048)(0.029)(0.061) secdary 0.31***0.3***0.2*-0.14*-0.13* -0.18*** (0.096) (0.103)(0.070) (0.075)(0.150)(0.151)(0.161) vocat 0.37*** 0.33*** ** (0.115) (0.123)(0.074) (0.079)(0.174) (0.192) univgt 0.28***0.27***0.2*-0.12*-0.11* ***-0.43***-0.27 (0.095) (0.112)(0.066) (0.073)(0.152) (0.172) ownland * * (0.076) (0.089)(0.047) (0.057)(0.114)(0.116)(0.186) formfin (0.144)(0.081)(0.131) Ownland* Formfin * (0.214)(0.123)(0.252) ** (0.094)(0.059)(0.123) Inspector Visit 0.21** (0.097)(0.060)(0.116) Bribes 0.24*** (0.088)(0.048)(0.133) food 0.36*** 0.55*** (0.154)(0.110)(0.322) textgarm 0.57*** 1.05*** 0.92*** (0.155)(0.112)(0.222) woodfurn ** 0.04 (0.296)(0.146)(0.261) metal * 0.13 (0.210)(0.169)(0.279) residd 0.84*** 0.75*** 0.37* (0.148)(0.104)(0.195) retaild 0.44*** 0.49*** 0.11 (0.145)(0.105)(0.200) N LLr

Productivity in SMLES  Female owned enterprises have lower productivity than male owned firms, after controlling for capital and labor use.  This difference can be explained to some degree by sector of operation. Once sector dummies are included, the difference is no longer significant.

Low LiteracyModerate LiteracyHigh Literacy VariableModel 1Model 2Model 1Model 2Model 1Model 2 Intercept 4.91***5.14***5.5***5.78***5.58***5.78*** (0.24)(0.26)(0.17)(0.19)(0.42)(0.44) Log(Cap) 0.25***0.24***0.3***0.28***0.35***0.33*** (0.02) (0.01) (0.03) Log(Labor) 0.92***0.85***0.83***0.82***0.73***0.77*** (0.05) (0.03) (0.06) caput 0.00**0.003*0***0.004***0.01***0.01* (0.00) female **-0.12 (0.10) (0.05) (0.14) food 0.31*** (0.10)(0.06)(0.17) textgarm ***-0.47*** (0.11)(0.07)(0.15) woodfurn (0.18)(0.07)(0.16) metal (0.12)(0.08)(0.17) chemical 1.93*** (0.31)(0.11)(0.19) Adj Rsq N Note: Excluded sector is Other Manufacturing Country Dummies included but not reported Gender Differences in Productivity: Regression Results-SMLEs

Part I: Conclusions  Broader indicators of women’s opportunities can be important in determining who succeeds in becoming an enterpreneur, as well as whether there is any gender gap in performance  Micro-enterprises: more educated women participate in low-literacy countries, yet gap persists  SMLEs: men are relatively more educated in high-literacy countries, and sector gap is larger  Where upside potential is higher in higher technology sectors, women’s concentration in traditional sectors results in lower relative productivity

Part II: Characteristics of successful enterpreneurs  This section draws on supplemental module run in 5 countries that had completed Enterprise Survey: Ghana, Mali, Mozambique, Senegal, Zambia  Refine definition of women’s enterprises  Motivation for becoming an enterpreneur  How enterprise was acquired  Prior experience  Management techniques used

(Paritial) ownership does not matter – control does  Bardasi, Blackman and Guzman (2007) and Bardasi and Sabaral (2009), find few significant differences by gender using ‘female participation in ownership’  Even restricting to sole proprietorships and partnership may not be sufficient as the majority of partnerships, the female owner is not a major decision maker

Control matters

Other characteristics of entrepreneurs 1. Experience – less important than family background  A. Former workers who start their own business are believed to bring important skills with them  Yet find little effects. Prior experience in the informal sector is associated with lower current performance  B. Djankov et al. and Bertrand and Schoar find entrepreneurs are more likely to have a family background of entrepreneurism, although family enterprises can be less productive  Having a father who ran an enterprise is significantly associated with better current performance  Role model; network and contacts  Impact is signficant – but not for women entrepreneurs

(1)(2)(3)(4)(5)(6)(7) Female decision maker-0.169* *-0.167* * (0.096)(0.097)(0.098)(0.097)(0.146)(0.097)(0.099) Education0.087*** (0.021) Age of entrepeneur (0.080) Years run enterprise0.007 (0.009) Father had been entrepreneur * (0.088)(0.133) Female dec. mker*father entrep (0.182) Mother had been entrepreneur (0.175)(0.277) Female dec. mker*mother entrep (0.357) Firm characteristicsYes Sector dummiesYes Countries dummiesYes Constant6.803***7.203***7.115***7.048***6.971***7.132***7.100*** (0.330)(0.334)(0.320)(0.326) (0.322)(0.321) Observations634 R-squared Education and family background are important

… But prior work experience is not significant

2. How and why became entrepreneur  Literature on ‘opportunity entrepreneurs’ vs. ‘necessity entrepreneurs’ predict former are more likely to be successful  Yet find that not to be the case. Those who report they would otherwise be unemployed have higher relative productivity  Starting a business rather than joining a business may demonstrate greater entrepreneurial talents  Yet ‘joining a family business’ is the only one associated with better performance

How and Why you become an entrepreneur

3. Management Practices  We look at four dimensions (Bloom & Van Reenen 2007):  A) Tracking formal objectives  B) Monitoring employee performance  C) Have processes for improvements  D) Participation in decision making  All are positively associated with better performance  Women score highest on D and lower on A-C, but no gender differences in impact

Management quality is associated with higher productivity

Gender differences by family background – not by education or management qualities

Part II: Conclusions  Important to look at control and not just ownership to understand gender differences  Women benefit as much as men from education and management skills  Implies training would help women enterpreneurs  Other characteristics were not significant – except for family background  And that helps account for the gender gap