1. Thierry Kangoye and Zuzana Brixiova African Development Bank Group Gender Gap in Employment and Entrepreneurship in Swaziland CSAE Conference, 17-19th March 2013 1

2. Motivation Methodology Findings Conclusions 2 Outline Motivation and contribution of the study Related literature Data and empirical strategy Main results Conclusions

3. 3 Motivation Significant labor market challenges in Swaziland Bleak employment prospects for youth ‘‘Female’’ face of labor market challenges Entrepreneurship as a career/livelihood option for marginalized groups (youth, women) Access to credit is key for SME and job creation and growth Credit constraints are an issue, with economy-wide effects: Reduce efficiency of capital allocation, lead to high labor intensity of production; Exacerbate income inequality Motivation Methodology Findings Conclusions

4. 4 Contribution of the study Evidence on the gender gap in employment and entrepreneurship in Swaziland; Analysis of wage gap, using O. decomposition; First analysis of this topic utilizing two integrated labor force surveys (2007 & 2010) Analysis of female entrepreneurs’ access to both formal and informal finance Motivation Methodology Findings Conclusions

5. 5 Research interest on female entrepreneurship: Who are the women entrepreneurs in Swaziland? What are the key factors driving the gender gap in employment, wages and entrepreneurship in Swaziland? Motivation Methodology Findings Conclusions

6. 6 Literature 1/2 Gender gap in entrepreneurship: Women less likely to get formal financing or pay higher interest rates (Murayev et al 2007) No gender gap in LAC in terms of access to credit by entrepreneurs (Bruhn, 2000) Women entrepreneurs in SSA are more likely to use internal and informal finance (Howard and Finnegan, 2004) Gender gap determined by : financial literacy (Buvinic and Berger (1990), Lusardi and Tufano (2009)), legal and institutional factors (Hallward-Driemeier, 2011), individual preferences (Beck et al., 2011) Motivation Methodology Findings Conclusions

7. 7 Literature 2/2 Gender gap in access to credit: Significantly fewer women than men own and manage businesses worldwide (Devine, 1994; Georgellis and Wall, 2005; Kim, 2007) Socioeconomic characteristics (education, wealth, family, experience, preferences) importantly determine gender gap (Cowling and Taylor, 2001; Blanchflower, 2004; Minniti et al, 2005 ; Luke and Munshi, 2010) Mixed evidence on women’s firms growth potential compared with men’s in developed countries. Motivation Methodology Findings Conclusions

8. 8 The data: 2007 & 2010 Labor Force Surveys LFS 2007: first comprehesive LFS in Swaziland Large and representative sample: over 3400 households and 13,000 individuals sampled (10-12 households in each EA) Two-state sample design: 8 domains (4 regions, rural and urban) In-person interviews in each sampled household Motivation Methodology Findings Conclusions

9. Widening Gender Gap in Unemployment Unemployment rates, by gender, 1995-2010 (% of labor force ) Motivation Methodology Findings Conclusions

10. Female Labor Market Disadvantage as Lack of Jobs By gender MaleFemale Total Unemployment Rate (% of LFS)24.030.3 Youth Unemployment Rate (15 - 24)50.355.2 Teenager Unemployment Rate (15 - 19)49.354.9 Young Adult Unemployment Rate (20 - 24)50.155.3 Adult Unemployment Rate (25+)17.422.1 Ratio of Youth to Adult Unemployment Rates2.92.5 Youth Unemployment Ratio (% of pop)16.317.2 Youth Employment Ratio (% of pop)16.314.0 Youth LF Participation Rate (% of pop)32.631.2 Share of Youth in Total Unemployment (%)41.745.0 Share of Young Adults in Unemployment (%)32.233.8 Motivation Methodology Findings Conclusions

11. Unemployment gap highest for adult women Unemployment, by age categories (% of relevant LFS) Motivation Methodology Findings Conclusions

12. Female Labor Market Disadvantage as Exclusion Labor force participation (% of population) Motivation Methodology Findings Conclusions

13. Labor Market Disadvantage of Young Women Gender Total MaleFemale Relaxed' unemployment rate (% of LF) Total (15 + )36.630.842.7 Youth (15 - 24)63.259.266.8 Teenagers (15 - 19)67.464.070.1 Young adults (20 - 24)61.757.565.5 Adults (25 + )27.922.733.7 Youth (15 - 24)25.923.628.0 Teenagers (15 - 19)14.412.616.2 Young adults (20 - 24)38.736.840.4 Discouraged workers (% of population) Youth (15 - 24)9.17.310.8 Teenagers (15 - 19)6.85.77.8 Young adults (20 - 24)11.8 9.214.0 Motivation Methodology Findings Conclusions

14. 14 Gender gap in business revenue in favor of men (density of log of net income from business and Oaxaca Blinder decomposition), with women being more concentrated in small businesses Evidence 1/5 Motivation Methodology Findings Conclusions

15. 15 Evidence 2/5 Gender gap in entrepreneurship in favor of women... Motivation Methodology Findings Conclusions

16. 16 Evidence 3/5 20072010Pooled Natural characteristics Sex (female=1).14(.09).45 (a) (.10).29 (a) (.07) Age (in years)-.0008(.02).03(.02).01(.01) Household-related characteristics Marital status (married=1).55 (a) (.10).41 (a) (.11).48 (a) (.07) Mobility and location Urban location (urban=1)-.11(.11)-.07(.11)-.08(.08) Lenght of stay (since birth=1).16(.10).22 (b) (.11).2 (a) (.07) Education Primary.2(.16)-.05(.15).07(.11) Secondary.27 (c) (.15)-.22(.14).03(.10) Tertiary-.12(.23)-.67 (a) (.23)-.39 (a) (.16) Intercept-1.57 (a) (.38)-2.12 (a) (.43)-1.84 (a) (.28) Obs120811302339 Pseudo R 2 0.040.070.05 Gender gap in entrepreneurship in favor of women... Probit regressions; dependant variable is Entrepreneur Motivation Methodology Findings Conclusions

17. 17 Evidence 4/5 20072010Pooled Natural characteristics Sex (female=1)1.4 (b) (.68)7.78(5.66)2.58 (c) (1.46) Age (in years).12 (a) (.05).53(.38).21 (b) (.10) Household-related characteristics Marital status (married=1)2.51(2.38)7.82(5.77)4.48(2.79) Mobility and location Lenght of stay (since birth=1).84(.98)4.18(3.68)1.94(1.37) Hhohho-.36(.45)-.09(.61).29(.48) Manzini.09(.42)-.21(.55).39(.47) Shiselweni--.5(.48) Education Primary.26(.1)-.58(1.04).63(.52) Secondary1.69(1.16)-3.34(2.39).69(.43) Tertiary-.01(.71)--2.74(1.81) Mills inv. ratio6.45(4.93)23.64(17.98)11.21 (c) (6.89) Intercept-17.67(10.87)-59.79(43.16)-29.27 (c) (15.89) Obs142118308 Pseudo R 2 0.160.120.06 Probit regressions; dependant variable is Entrepreneur Gender gap in access to finance in favor of women... Motivation Methodology Findings Conclusions

18. 18 Access to informal finance is in fact predominant among women... Evidence 5/5 Source of business finance, women 15-29 years Motivation Methodology Findings Conclusions

19. Does Gender Gap in Employment and Entrepreneurship Matter? (Employment and poverty correlated) Motivation Methodology Findings Conclusions

20. Does Gender Gap Matter? (cont.) – Vulnerability to Aggregate Shocks Coping with the Impact of Fiscal Crisis of 2011 Relied on less expensive foods Borrowe d food Purchase d food on credit Gathered wild food Sent HH members to beg Harveste d immature food Rural Male head52.0%33.2%18.8%27.2%7.4%10.1% Female head 59.0%45.9%20.1%39.9%12.3%10.1% Total rural54.8%38.2%19.3%32.3%9.4%10.1% Urban Male head35.1%12.5%7.1%5.1%1.7%1.4% Female head 48.0%20.1%11.8%8.8%3.4% Total urban40.4%15.6%9.0%6.6%2.4%2.2% Motivation Methodology Findings Conclusions

21. 21 Areas of policy actions... Enhancement of (technical) education outcomes alongside of soft skills (business networking) Employment generation with focus on women; encourage women to enter high-paying sectors (finance, ICT) Support to women’s businesses to be provided through training (financial literacy), and change in regulations and actual practices to improve access to credit. Improving land ownership rights of women to improve their access to credit (through collateral Motivation Methodology Findings Conclusions

22. Thank you.