WHOSE BUSINESS IS IT ANYWAY?GENDER AND ENTREPRENEURSHIP IN SUB-SAHARAN AFRICA

“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 AteridoWorld 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

Mali

Guine

a

Gambi

a

Guine

a-Biss

au

Sene

gal

Mozam

biqu

e

Maurit

ania

Ghana

Burun

di

Angol

aDRC

Ugand

a

Niger

ia

Rwanda

Zambi

a

Tanz

ania

Kenya

Swaz

iland

Botsw

ana

Sout

h Afri

ca

Namib

ia0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Adult Female Literacy Rate (% of Females)% of Microenterprises with Female OwnershipPolynomial (% of Microenterprises with Female Ownership)

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

Textile and Gar-

ments; 25.6

Food; 9.6

Other Manufac-turing; 21.9

Furniture; 24.8

Metals and

Metal Pdts; 17.3

Chemicals; 0.6 Plastics; 0.2

Male owned Microen-terprises

Textile and Gar-

ments; 55.4

Food; 17.7

Other Manufac-

turing; 14.0

Furniture; 7.0

Metals and Metal Pdts; 4.3 Chemicals; 1.6

Female Owned Mi-croenterprises

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 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4Intercept -0.65*** -0.69*** -0.78*** -1.31*** -0.43*** -0.31*** -0.47*** -0.41* 0.19 0.04 0.18 0.15

(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) -0.01 -0.03 0.15

(0.096) (0.060) (0.119)Log(age) -0.07 -0.08* -0.04

(0.071) (0.044) (0.080)Register 0.1 -0.08 -0.12

(0.123) (0.080) (0.156)Secdary 0.18 0.18 0.08 0.21*** 0.21*** 0.14 -0.14 -0.14 -0.16

(0.135) (0.135) (0.145) (0.089) (0.090) (0.094) (0.156) (0.156) (0.195)Vocat 0.38* 0.36* 0.35* 0.27*** 0.26*** 0.21* -0.5*** -0.5*** -0.27

(0.198) (0.198) (0.210) (0.110) (0.110) (0.117) (0.188) (0.188) (0.218)Univ 0.2 0.21 0.11 0.21** 0.21** 0.15 -0.42*** -0.41*** -0.35*

(0.153) (0.154) (0.165) (0.097) (0.097) (0.104) (0.179) (0.180) (0.210)Ownland 0.32*** 0.32*** 0.34*** 0.32*** 0.33*** 0.38*** 0.2 0.18 0.26

(0.128) (0.129) (0.136) (0.086) (0.086) (0.091) (0.193) (0.195) (0.208)Food 0.52 0.56** 0.4

(0.479) (0.251) (0.497)Textgarm 0.68* 0.84*** 0.57

(0.362) (0.214) (0.391)Furniture -0.25 -0.5* -1.47***

(0.483) (0.258) (0.601)Metal -0.26 -0.58** --

(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 857 857 857 765 1503 1503 1503 1420 443 443 443 394

LLr -386.38 -385.93 -383.34 -347.12 -949.71 -946.70 -942.55 -855.05 -286.08 -290.40 -285.67 -243.44

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 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4 Gp 1 Gp1 Gp 1 Gp 1 Gp 2 Gp 2 Gp 2 Gp 2 Gp 3 Gp 3 Gp 3 Gp 3Intercept 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*** -0.03 -0.03 -0.03 -0.03 -0.28** -0.3*** -0.16 -0.13 (0.106) (0.103) (0.108) (0.109) (0.066) (0.066) (0.061) (0.061) (0.126) (0.128) (0.127) (0.121)Log(Cap/Labor) – – 0.25*** 0.25*** – – 0.3*** 0.29*** – – 0.3*** 0.23*** – – (0.026) (0.026) – – (0.019) (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 – -0.21 -0.27 -0.24 – 0.24 0.19 0.16 – -0.52 -0.34 -0.27 – (0.316) (0.317) (0.32) – (0.195) (0.180) (0.180) – (0.447) (0.415) (0.390)Furniture – 0.05 0.27 0.68 – 0.01 0 0.11 – -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 – -0.18 -0.13 -0.12 – -0.05 0.11 -0.01 – -0.4 -0.25 -0.24 – (0.192) (0.192) (0.19) – (0.155) (0.152) (0.140) – (0.336) (0.322) (0.310)Services – 0.04 0.02 0.01 – 0.29** 0.32*** 0.31*** – -0.33 -0.15 -0.15 – (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*** 0.65*** – -0.16 -0.1 0.14 – (0.151) (0.154) (0.150) – (0.116) (0.107) (0.110) – (0.268) (0.261) (0.240)N 760 760 611 611 1437 1437 1334 1334 391 391 339 339Adj. Rsq 0.18 0.2 0.26 0.33 0.04 0.1 0.2 0.25 0.09 0.15 0.25 0.27

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

Food Processing30%

Textile and Garments

51%

Metal

Products4%

Furni-

ture5%

Other Manufacturing9%

Food Process-ing 28%

Textile and Garments

21%Furniture

17%

Metal Products

15%

Other Manufac-

turing20%

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 SMLEsGroup 1 Group 2 Group 3

Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4Intercept -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 -0.02 -0.14*** -0.05

(0.058) (0.033) (0.061)lage -0.07 -0.04 -0.15***

(0.048) (0.029) (0.061)secdary 0.31*** 0.3*** 0.2* -0.14* -0.13* -0.18*** -0.11 -0.12 -0.1

(0.096) (0.096) (0.103) (0.070) (0.070) (0.075) (0.150) (0.151) (0.161)vocat 0.37*** 0.37*** 0.33*** -0.01 -0.01 -0.04 -0.38** -0.38** -0.38**

(0.115) (0.115) (0.123) (0.074) (0.074) (0.079) (0.174) (0.174) (0.192)univgt 0.28*** 0.27*** 0.2* -0.12* -0.11* -0.03 -0.44*** -0.43*** -0.27

(0.095) (0.095) (0.112) (0.066) (0.066) (0.073) (0.152) (0.152) (0.172)ownland 0.11 0.09 0.13 -0.08* -0.07 0.01 -0.21* -0.17 -0.21

(0.076) (0.076) (0.089) (0.047) (0.047) (0.057) (0.114) (0.116) (0.186)formfin 0.19 0.04 -0.2

(0.144) (0.081) (0.131)Ownland*Formfin -0.01

0.120.44*

(0.214) (0.123) (0.252)email -0.04 -0.12** -0.03

(0.094) (0.059) (0.123)Inspector Visit 0.21** -0.04 -0.003

(0.097) (0.060) (0.116)Bribes 0.24*** 0.08 -0.05

(0.088) (0.048) (0.133)food 0.36*** 0.55*** -0.15

(0.154) (0.110) (0.322)textgarm 0.57*** 1.05*** 0.92***

(0.155) (0.112) (0.222)woodfurn -0.03 -0.3** 0.04

(0.296) (0.146) (0.261)metal -0.26 -0.3* 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 1740 1740 1740 1673 4124 4124 4124 3998 879 879 879 819

LLr -821.34 -829.587 -820.696-755.70

-2255.81 -2258.12 -2254.73-2033.77

-422.931 -427.421 -421.86-366.60

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 Literacy Moderate Literacy High LiteracyVariable Model 1 Model 2 Model 1 Model 2 Model 1 Model 2Intercept 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) (0.02) (0.01) (0.01) (0.03) (0.03)Log(Labor) 0.92*** 0.85*** 0.83*** 0.82*** 0.73*** 0.77***  (0.05) (0.05) (0.03) (0.03) (0.06) (0.06)caput 0.00** 0.003* 0*** 0.004*** 0.01*** 0.01*  (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)female 0.14 0.08 -0.08 -0.02 -0.28** -0.12 (0.10) (0.10) (0.05) (0.05) (0.14) (0.14)food 0.31*** -0.07 -0.06  (0.10) (0.06) (0.17)textgarm 0.04 -0.23*** -0.47***  (0.11) (0.07) (0.15)woodfurn -0.02 -0.11 0.08  (0.18) (0.07) (0.16)metal 0.12 -0.03 0.09  (0.12) (0.08) (0.17)chemical 1.93*** 0.19 0.23  (0.31) (0.11) (0.19)Adj Rsq 0.5 0.53 0.7 0.72 0.73 0.74N 909 909 2016 2016 380 380Note: 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

(1) (2)Female participation in ownership -0.069

(0.099)Female prime decision maker -0.164*

(0.097)Micro 2-4 -0.077 -0.075

(0.258) (0.257)Small 5-10 0.223 0.220

(0.250) (0.249)Medium 11-30 0.556** 0.550**

(0.269) (0.267)Large >30 0.539* 0.544*

(0.293) (0.291)Age medium 6-15 0.095 0.095

(0.099) (0.099)Age mature >15 0.133 0.136

(0.130) (0.131)Legal status: private limited 0.679** 0.676**

(0.294) (0.267)Legal status: single-owner 0.346 0.385

(0.299) (0.274)Legal status: partnership 0.404 0.348

(0.338) (0.317)Exporter 0.349** 0.329**

(0.156) (0.156)Capital city 0.356*** 0.357***

(0.098) (0.097)Sector dummies Yes YesCountry dummies Yes YesConstant 6.689*** 6.716***

(0.436) (0.418)Observations 634 634R-squared 0.20 0.20

Control matters

Other characteristics of entrepreneurs1. 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* -0.159 -0.172* -0.167* -0.007 -0.164* -0.141

(0.096) (0.097) (0.098) (0.097) (0.146) (0.097) (0.099)

Education 0.087***

(0.021)

Age of entrepeneur -0.053

(0.080)

Years run enterprise 0.007

(0.009)

Father had been entrepreneur 0.117 0.254*

(0.088) (0.133)

Female dec. mker*father entrep. -0.259

(0.182)

Mother had been entrepreneur -0.207 -0.017

(0.175) (0.277)

Female dec. mker*mother entrep. -0.381

(0.357)

Firm characteristics Yes Yes Yes Yes Yes Yes Yes

Sector dummies Yes Yes Yes Yes Yes Yes Yes

Countries dummies Yes Yes Yes Yes Yes Yes Yes

Constant 6.803*** 7.203*** 7.115*** 7.048*** 6.971*** 7.132*** 7.100***

(0.330) (0.334) (0.320) (0.326) (0.326) (0.322) (0.321)

Observations 634 634 634 634 634 634 634

R-squared 0.22 0.20 0.20 0.20 0.20 0.20 0.20

Education and family background are important

(8) (9) (10) (11) (12) (13)

Female decision maker -0.154 -0.160* -0.166* -0.160* -0.165* -0.232*(0.096) (0.096) (0.097) (0.097) (0.097) (0.140)

Business registered at start 0 .365***(0.109)

Affiliated with other enterprises 0.222*(0.115)

Years of experience -0.005(0.006)

Prior years in formal sector -0.054(0.042)

Prior years in informal sector -0.035(0.022)

Prior years in family business -0.016(0.029)

Firm characteristics Yes Yes Yes Yes Yes YesSector dummies Yes Yes Yes Yes Yes YesCountries dummies Yes Yes Yes Yes Yes YesConstant 6.938*** 7.326*** 7.155*** 7.204*** 7.271*** 7.843***

(0.323) (0.334) (0.325) (0.324) (0.331) (0.421)Observations 634 634 634 634 634 335R-squared 0.22 0.21 0.20 0.20 0.21 0.26

… 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

(15) (16) (17) (18) (19) (20) (21) (22)Female decision maker -0.166* -0.163* -0.161* -0.171* -0.168* -0.165* -0.165* -0.154

(0.097) (0.097) (0.097) (0.098) (0.098) (0.097) (0.097) (0.097)Buy own business -0.078

(0.100)Start own business -0.054

(0.102)Join family business 0.257*

(0.137)Pursue market opportunity 0.070

(0.086)Desire flexibility -0.023

(0.087)Secondary source of income -0.020

(0.085)Primary source of income -0.052

(0.087)Otherwise be unemployed 0.180**

(0.086)Firm characteristics Yes Yes Yes Yes Yes Yes Yes YesSector dummies Yes Yes Yes Yes Yes Yes Yes YesCountries dummies Yes Yes Yes Yes Yes Yes Yes YesConstant 7.147*** 7.160*** 7.115*** 7.098*** 7.130*** 7.135*** 7.137*** 7.054***

(0.324) (0.337) (0.319) (0.329) (0.323) (0.324) (0.323) (0.322)

Observations 634 634 634 634 634 634 634 634

R-squared 0.20 0.20 0.21 0.20 0.20 0.20 0.20 0.21

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

(23) (24) (25) (26) (27)Female decision maker -0.167* -0.147 -0.123 -0.275*** -0.177*

(0.094) (0.096) (0.095) (0.095) (0.092)Strategic Objectives 0.404***

(0.060)Performance Indicators 0.273***

(0.067)Process Improvements 0.378***

(0.069)Participatory Style 0.843***

(0.117)PCA-managment quality 0.244***

(0.027)Firm characteristics Yes Yes Yes Yes YesSector dummies Yes Yes Yes Yes YesCountries dummies Yes Yes Yes Yes YesConstant 7.314*** 7.092*** 7.004*** 7.123*** 7.550***

(0.325) (0.318) (0.316) (0.326) (0.325)Observations 634 634 634 634 634R-squared 0.25 0.22 0.24 0.26 0.28

Management quality is associated with higher productivity

Gender (1) (2) (3) (4)Female decision maker -0.124 -0.176 -0.105 0.055

(0.204) (0.180) (0.201) (0.215)General

Education of entrepren 0.046 0.052* 0.052* 0.048*(0.029) (0.029) (0.029) (0.029)

FemaleDecMk*Educ 0.023 0.016 0.015 0.019(0.034) (0.033) (0.034) (0.033)

FemaleOwner*EducRegistered at start-up 0.265** 0.251** 0.262** 0.262**

(0.104) (0.106) (0.106) (0.105)Affiliated w/ enterp. 0.162 0.181 0.165 0.169

Experience(0.111) (0.110) (0.110) (0.110)

Father was entrepreneur 0.292** 0.268**(0.124) (0.122)

FemaleDecMk*Father -0.254 -0.225(0.171) (0.169)

How AcquireJoin family business 0.570*** 0.553***

(0.206) (0.201)FemaleDecMk*JoinFamBus -0.680*** -0.671***

(0.260) (0.258)Motivation

Otherwise be unemployed 0.285** 0.245**(0.126) (0.124)

FemaleDecMk*Unemployed -0.230 -0.179(0.166) (0.166)

ManagementMngt quality (pca) 0.244*** 0.229*** 0.230*** 0.229***

(0.042) (0.041) (0.042) (0.041)FemaleDecMk*MngtQuality -0.020 -0.009 -0.008 -0.005

(0.053) (0.053) (0.053) (0.053)Constant 6.733*** 7.097*** 7.084*** 6.480***

(0.337) (0.356) (0.361) (0.338)Observations 634 634 634 634R-squared 0.31 0.31 0.31 0.32

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