1. 1 Roundtable on Managing for Development Results Hanoï, 5-7th February 2007 Using Household Surveys to Measure Employment and the Informal Sector in Africa Jean-Pierre CLING, François ROUBAUD IRD, DIAL http://www.dial.prd.fr

2. 2 Introduction A striking paradox:  Labour is the main source of income in Africa, especially among the poor…  …But employment issues (diagnosis, link to poverty, policies, etc.) are hardly addressed in PRSPs (UNECA). One reason: lack of information (data & analysis).

3. 3 Lack of data: a consequence Does Africa come last or second for youth (15-24) unemployment rate ?

4. 4 Questionable academic wisdom High & increasing unemployment rates (esp. Youth) High rigidities of labor markets Increasing weight of the informal sector

5. 5 Introduction I. The state of labour statistics in African countries II. DIAL’s 1-2-3 Surveys III. Some innovative results on African labour markets IV. Using household surveys for monitoring & evaluation Conclusion

6. 6 I. The state of labour statistics in African countries  Labour market indicators are available in very few Sub- saharan African (SSA) countries. (ILO/LABORSTAT presents unemployment rates for only 10 countries).  When they exist, differences in definitions, coverage, time- period and data sources make labor indicators hardly comparable across countries and across time periods.  Worst, developing countries specificities, particularly in SSA, are not taken into account.

7. 7 The need for specific surveys on employment & the informal sector:  Traditional labour market indicators need to be enlarged and adapted to less developed countries (esp. Africa).  A regional approach is much needed.

8. 8 II. DIAL’s 1-2-3 Surveys  Identical survey methodology.  Internationallly accepted labour market concepts (ILO) and harmonized nomenclature  comparable indicators The experience in Africa so far:  Surveys carried out simultaneouly in seven main West Sub-saharan African cities (AFRISTAT/NSOs).  Continous 1-2-3 survey in Madagascar since 1995.  National surveys carried out in RD Congo (2004) and Cameroon (2005)  Survey conducted in Bujumbura, Burundi (2006)

9. 9 The 1-2-3 Survey

10. 10 1-2-3 Survey characteristics in Africa Sources : 1-2-3 Surveys, Phase 1, 2001/2003, National Statistical Institutes, AFRISTAT, DIAL, authors’ calculations.

11. 11 Capacity building: West Africa (2001-2004) - 44 support missions - 7 regional seminars (AFRISTAT, Bamako) Cameroon (2005-2006) - 7 support missions - training seminar (DIAL, Paris) RD Congo (2005-2007) - 8 support missions - training seminar (AFRISTAT, Bamako) - participation to the ADP

12. 12 Unemployment  Average WAEMU unemployment rates (ILO definition) = 11,4 %. 12,5% (Douala) 14,7% (Yaoundé) 15% (Kinshasa)  Average unemployment rates according to enlarged definition (including high percentage of discouraged workers) = 16 % 16% (Douala) 18% (Yaoundé) 24% (Kinshasa)  LDCs’ specificity: unlike developed countries, unemployment rates increase with education level On average for 7 WAEMU big cities, less than 8% for those who never went to school vs. 17% for those with higher education. III. Some innovative results about African labour markets

13. 13 Sources : 1-2-3 Surveys, Phase 1, 2001/2003, National Statistical Institutes, AFRISTAT, DIAL. DIAL’s calculations. Figure 1: Youth and adult urban unemployment rates

14. 14 Underemployment and employment quality  Working hours : 47,5 hours per week (higher in informal sector) 46h in Douala, 48h in Yaounde and 46h in Kinshasa  Underemployment rate (67% on average in WAEMU); 3 components : - visible underemployment: working less than 35h against their will (14%) (14% Douala; 10% Yaounde; 25% Kinshasa) -invisible underemployment : more than half of occupied active population (55%) earn less than minimum wage (37%-38% Douala and Yaounde; 44% Kinshasa) -unemployed

15. 15 Informal sector weight In WAEMU: 70-80% of employment; In Yaounde: around 60% Figure 2 : Informal sector & employment* in Yaoundé (% of total employment) *Informal employment = workers without labour contract or wage slip Sources : Enquêtes 1-2-3, phase 1, sept agglomérations UEMOA, PARSTAT(2001-2002), Cameroun (2005), RDC Kinshasa (2004)

16. 16 IV. Using surveys for monitoring & evaluation Monitoring indicators (PRSPs) Use available data to analyse policy impact Integrate policy concerns into the survey design.

17. 17 Conclusion: Mainstreaming employment in PRSPs Putting in place household surveys to measure employment and the informal sector Integrating employment surveys in NSDS Investment in analysis & research.

18. 18 Merci!