1. Empirical explanation of Africa’s jobless growth: Drawing lessons for youth employment strategy Samuel G. Asfaha Employment Policy and Analysis Programme, ITC-ILO 19 February 2015

2. Scene setting

3. Rapid and sustained poverty reduction requires ‘inclusive growth’ that allows people to contribute to and benefit from the development process.

4. Questions that may help to check the inclusiveness of economic growth may include: - Are certain social groups being excluded from the growth and development process? - Which dimensions of poverty are leading or lagging? - Has economic growth been transformative, leading to more and better jobs?

5. Causes of employment challenges Demographic changes (population growth, ageing …) Urbanization (rural – urban migration) Globalization (competition, external shocks, …) Technological progress (cost of adjustment to technological shocks … ) Macroeconomic crises … Etc. 5

6. From a development perspective – both the number and quality of jobs do matter! With high rate of working poverty and poor working conditions (and inequality) – it does not make much sense to talk just about unemployment in developing countries! 6

7. Working poverty, 1991, 2000, 2008 7 ILO, 2010

8. Top 10 unequal countries No.Gini indexYear of Information 1Lesotho63.21995 2South Africa63.12005 3Botswana63.01993 4Sierra Leone62.91989 5Central African Republic61.31993 6Namibia59.72010 7Haiti59.22001 8Honduras57.72007 9Colombia55.92010 10Guatemala55.12007 Source: CIA: the World Fact Book

9. Main presentation

10. This presentation is based on an on-going research by Asfaha and Edlira ◦ Research title: Explaining Africa’s jobless growth ◦ Status: Draft (third revision), ◦ Some of the findings may still be preliminary.

11. Africa Impressive growth in Africa since 2000 But, less conversion of growth into jobs – a situation of jobless growth …. With large cross-country variations UNECA report (2010) ◦ “Africa has been unable to create enough jobs to significantly reduce unemployment despite impressive growth before crisis”

13. Mean values of Macroeconomic and Structural Variables for the period 2000-2012, by income and geographic area Real Annual GDP growth Industry as % of GDP FDI as % GDP Trade Openess Labour Market Rigidity Index All4.8429.890.0679.0136.76 BY REGION NORTH AFRICA4.5942.290.0483.1642.93 CENTRAL AFRICA5.7746.010.0892.9751.60 EAST AFRICA5.2218.970.0247.5530.38 WEAST AFRICA4.7420.870.0973.7238.20 SOUTH AFRICA4.2433.770.0498.9728.58 BY INCOME LOW-INCOME ECONOMIES4.5719.790.0764.1637.43 LOWER-MIDDLE INCOME ECONOMIES4.4333.840.0592.0134.98 UPPER-MIDDLE-INCOME ECONOMIES6.0849.620.0497.9137.77 Note: Authors calculation

14. Many empirical studies on employment intensity of growth following Okun’s seminal paper (1962) But, only limited studies on the determinants of the employment elasticity of growth.

15. Key concept: Employment elasticity of growth: A concept used to measure the percentage change in employment associated with a percentage change in economic growth. In its simplest form - a numerical measure of how employment varies with economic output (Kapsas 2005).

16. Calculating the employment elasticity growth and its determinants

17. Crivelli et. al. (2012) An unbalanced panel of 167 countries over the period 1991–2009 to assess the effect of structural and macroeconomic policies. Finding - employment elasticities are positively affected by structural policies related to labor and product market flexibility and reducing government size as well as macroeconomic policies aimed at increasing macroeconomic stability.

18. Regional studies Döpke (2001) or Mourre (2004) on Europe - ◦ both find that labour market flexibility as dominant determinant of the employment elasticities in European economies. ◦ In addition, Mourre (2004) finds that job intensity of growth predominated in the services sector; while Döpke (2001) emphasized the positive role of less exchange rate volatility.

19. Our study Unbalanced panel of 49 African countries over the period 2000–2012 Variables: demographic, macroeconomic policy variables, structural and institutional variables. Data from KILM and Opendataafrica

20. The research questions What factors may explain observable cross-country variations on employment elasticity of growth in Africa? Do demographic, macroeconomic, institutional and structural variables affect employment elasticity of growth for vulnerable groups (women and youth) differently?

21. Estimation Results Table 1. LONG-TERM ELASTICITIES, BY REGION, INCOME LEVEL, GENDER AND AGE TOTALMALEFEMALEYOUTH All 0.110.080.210.10 BY REGION NORTH AFRICA0.220.170.88-0.01 CENTRAL AFRICA0.040.000.100.02 EAST AFRICA0.110.060.090.05 WEST AFRICA0.050.040.090.10 SOUTH AFRICA0.160.150.160.22 BY INCOME LOW-INCOME ECONOMIES0.080.050.070.09 LOWER-MIDDLE INCOME ECONOMIES 0.130.140.230.12 UPPER-MIDDLE-INCOME ECONOMIES 0.150.070.520.07

23. The effect of Demographic variables on female employment elasticities of growth specificat ion 1 specificat ion 2 specificat ion 3 specificat ion 4 specificat ion 5 specificat ion 6 specificat ion 7 coef/t Population size0.116***0.106***0.101***0.104***0.180***0.096***0.036*** (8.752)(7.734)(8.097)(8.644)(13.089)(5.177)(3.713) Share of Urban Population0.009***0.010***0.005***-0.002** (8.561)(8.189)(2.734)(-2.435) Population density-0.064***-0.061***-0.035*-0.011 (-4.257)(-4.341)(-1.956)(-1.004) Gender Gap in Labour Participation 0.012***0.011***0.003***0.002** (12.677)(10.936)(2.678)(2.173) Female Employment-to- population Ratio -0.012*** (-14.701) Exports of oil-0.014*** (-6.390) Reported cases of Malaria-0.004*** (-3.030) _cons-1.127***-0.451***-0.864***-0.027-1.577***-0.786***-0.003 (-8.906)(-3.536)(-7.967)(-0.242)(-11.058)(-3.081)(-0.030) Number of observations624 572312 Adjusted R20.1750.1040.2670.3160.4160.1660.067 note: *** p<0.01, ** p<0.05, * p<0.1

24. The effect of Demographic variables on youth employment elasticities of growth specificat ion 1 specificat ion 2 specificat ion 3 specificat ion 4 specificat ion 5 specificat ion 6 specificat ion 7 coef/t Population size0.045***0.043***0.042***0.045***0.091***0.053***0.024** (5.764)(5.675)(5.558)(5.963)(11.423)(3.327)(2.481) Share of Urban Population0.002***0.003***0.003*-0.003*** (3.471)(3.284)(1.757)(-3.100) Population density-0.035***-0.061***-0.058***0.007 (-4.263)(-7.425)(-3.600)(0.651) Youth Employment-to- population Ratio -0.002***-0.001*0.001**-0.003*** (-5.707)(-1.775)(2.011)(-5.616) Gender Gap in Youth Labour Participation 0.004*** (5.469) Exports of oil-0.008*** (-6.941) Reported cases of Malaria-0.003*** (-3.764) _cons-0.330***-0.106-0.069-0.295***-0.401***-0.365*0.332*** (-4.479)(-1.498)(-0.985)(-4.410)(-4.484)(-1.757)(3.059) Number of observations624 572312 Adjusted R20.0590.0680.0890.0850.2440.1300.137 note: *** p<0.01, ** p<0.05, * p<0.1

28. Conclusion & policy insight

29. Selected references Crivelli E., D. Furcerri and J.Toujas – Bernate (2012), “Can policies affect employment intensity of Growth? A Cross Country Analysis”, IMF Working paper, August. Kapsos, S. (2005). “The Employment Intensity of Growth: Trends and Macroeconomic Determinants,” ILO Employment Strategy Papers 12 (Geneva: International Labour Organization). Mourre, G. 2004. “Did the Pattern of Aggregate Employment Growth Change in the Euro Area in the Late 1990s?” European Central Bank Working Paper Series, 2004, No. 358 (Brussels: European Central Bank). Döpke, J. (2001). “The ‘Employment Intensity’ of Growth in Europe.” Kiel Working Paper No. 1021 (Kiel: Kiel Institute for the World Economy).