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Key Gender Issues in the Labour Force: African Experiences

Key Gender Issues in the Labour Force: African Experiences. Workshop on Household Surveys and Measurement of Labour Force 14-18 April 2008, Maseru, Lesotho. Dimitri Sanga, Ph.D. Senior Statistician. Outline. Labour force surveys The need for engendering labour force surveys

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Key Gender Issues in the Labour Force: African Experiences

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  1. Key Gender Issues in the Labour Force: African Experiences Workshop on Household Surveys and Measurement of Labour Force 14-18 April 2008, Maseru, Lesotho Dimitri Sanga, Ph.D. Senior Statistician

  2. Outline • Labour force surveys • The need for engendering labour force surveys • Key gender issues emerging from selected African labour force surveys • Mainstreaming gender into labour force surveys • Conclusions

  3. Labour force surveys 3

  4. Objectives Overall objective: Collect information on the characteristics of employment and the distribution of the population based on their position in the labour market 4

  5. Main categories Population of a given age group Active Inactive Employed Unemployed 5

  6. Information Keeps track of the labour market and the economy in general Provides regular information on indicators: Activity rate Average work schedule Proportion of temporary/permanent jobs Proportion of part/full time jobs Unemployment rate 6

  7. Information(Cont’d) May provide information on: Sector: public sector and private enterprises Socio-professional category: employee, family helper, head of an enterprise… Others: employment contract, access to social security, the right to paid leave, sick leave, etc Employment characteristics can be used to: Identify those who are in the informal economy Those who belong to an informal production unit Those holding an informal job in a formal enterprise or in a household 7

  8. Labour force surveys in Africa LFS in developed and medium-income countries: on an infra-annual basis In Africa: Some LFS Equivalent tools: employment surveys or employment segment of household surveys Frequency may be non-defined (because the operation may depend on external financing) In some countries: frequency is at best annual 8

  9. The need for engendering labour force surveys 9

  10. The case for engendered LFS Engendered labour force surveys like other statistical operations help: Conduct unbiased evidence-based policy formulation and decision making Address issues of inequalities and empowerment of women Raise consciousness, persuade policy makers and other stakeholders to take into account the gender dimension in policy formulation and decision making processes 10

  11. Key gender issues emerging from selected African LFS 11

  12. Ethiopia 12

  13. Activity rate by age and sex Activity Rate Whatever the age group: women are less active than men 35.13 65+ 73.38 58.65 60 - 64 92.82 73.76 55 - 59 96.07 77.6 50 - 54 95.28 85.33 45 - 49 97.67 86.59 40 - 44 98.1 84.46 35 - 39 97.87 85.77 30 - 34 97.76 85.62 25 - 29 95.91 81.9 20 - 24 88.7 69.07 15 - 19 73.16 54.47 10-14 66.57 73.46 All ages 84.75 Male Female Source: Ethiopian LFS, 10+ 13

  14. Employment to population ratio Source: Ethiopian LFS, Men are more employed than women in all age groups 14

  15. Employment rates by age group and sex Source: Ethiopia LFS 2005 (10+) 15

  16. Employment by industry and sex Some industries are female dominated while others are male dominated Source: Ethiopian LFS, 2005 (10+) 16

  17. Employment in formal/informal sector Men are mainly employed in the formal sector while female dominate the informal sector Source: Ethiopian LFS, 2005 (10+) 17

  18. Unemployment vs. literacy Source: Ethiopian LFS, 2005 (10+) 18

  19. Unemployment vs. Education level Source: Ethiopian LFS, 2005 (10+) 19

  20. Current female employment by marital status Source: Ethiopian LFS, 2005 (10+) 20

  21. Zambia 21

  22. Proportion of currently unemployed population by sex and age group Source: Zambia LFS 2005 (15+) 22

  23. Unemployment by education level Source: Zambia LFS 2005 (15+) 23

  24. Distribution of employment by occupation and sex Source: Zambia LFS 2005 (15+) 24

  25. Distribution of employment by status Source: Zambia LFS 2005 (15+) 25

  26. Informal/formal sector • The proportion of informal sector employees is higher among females than males • More jobs in the informal sector than in the formal sector Source: Zambia LFS 2005 (15+) 26

  27. Employment in the informal sector by industry and sex Source: Zambia LFS 2005 (15+) 27

  28. Employment status in the informal sector by sex Source: Zambia LFS 2005 (15+) 28

  29. Employment status in the informal sector Source: Zambia LFS 2005 (15+) Women are mainly unpaid family workers in the informal sector 29

  30. Economically active population Source: Zambia LFS 2005 (15+) Primary education: no imbalance Above primary education: more are more likely to be active 30

  31. Mainstreaming gender into LFS 31

  32. Different stages of the survey Gender issues should be taken into account at different stages of the survey: Planning and design Methodology Data collection and processing Data analysis and dissemination 32

  33. Planning and design Study the societal needs in data on the labour force Selection of topics: Should reflect ways in which men and women view, perform, control, benefit from their work activities Coverage: As many topics and types of productive activities Work in widest sense, working time Job-seeking behaviour Moonlighting or combined activities Casual work, subsistence Informal employment 33

  34. Methodology Concepts and definitions: Employment: does it include or not women in long maternity leave? Head of he household: is it always a man? Questionnaire design: Adequate detail: on pots occupied by the respondent Formulation: avoid biased questions: Do you work? (interpreted as formal sector) Are you engaged in any work paid in money or in kind? Do you sell products on the street or at the market? Type and place of work Reflect income components (salaries, benefits, overtime…) 34

  35. Data collection and processing Training of statistician and field staff: an good understanding of gender sensitive statistics Respondent’s choice (proxy or not) Unit: household or individual? Treatment of the collected information: Imputation methods should take into account the gender perspective Missing values 35

  36. Data analysis and dissemination Use effectively gender blind data through appropriate data analysis Have a tabulation policy in support of gender sensitive analysis Relevant dissagregation: By sex (a minimum) Marital status Family/personal characteristics Job characteristics Family context Personal circumstances Gender statistics database 36

  37. Conclusions There is a need to engender statistical processes including LFS Mainstreaming gender into LFS should be done at every step of the survey process There is a need for sensitisation of survey statisticians through training 37

  38. Thank you! African Centre for Statistics Visit us at http:www.uneca.org/statistics/

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