Mainstreaming Gender in the Production of Labour Statistics

1. Mainstreaming Gender in the Production of Labour Statistics Workshop on Household Surveys and Measurement of Labour Force with Focus on Informal Economy Maseru, Lesotho, 14-18 April 2008

2. Overview • Need for labour statistics • Quality of labour statistics • Gender mainstreaming to improve quality of data • What is gender mainstreaming in statistical production • How to mainstream gender into statistical production • Concluding remarks

3. The need for official labour statistics Official labour statistics are essential to: • Assess current situation of the labour market and the situation of those in the labour force including: working conditions, rights at work, participation in decision-making, industrial relations, etc • Identify and quantify issues in the labour market so that policies and action plans can be designed and formulated to meet set targets and goals • Monitor progress towards set targets and goals

4. Quality of labour statistics Quality of statistical data depends largely on • Relevance to user’s needs • Accuracy • Timeliness and punctuality • Accessibility and clarity • Comparability • Coherence

5. Gender mainstreaming to improve quality Goal of gender mainstreaming in statistical production • To ensure that statistics adequately capture and reflect existing differences and inequalities in the situation of women and men in all areas of life Goal of gender mainstreaming in labour statistics • To ensure that labour statistics adequately capture and reflect women’s and men’s access to and participation in the labour force as well as the outputs and returns from their participation Overarching goal • To improve the quality of the statistics produced in terms of: relevance, accuracy, clarity.

6. What gender mainstreaming implies Roles, norms, expectations, aspirations associated with being female or male Gender mainstreaming implies • Taking into account gender-based factors at all stages in the statistical production Gender mainstreaming DOES NOT imply • A focus on women only. It implies a focus on the relative situation of both women and men in society • It does not mean to disaggregate statistics by sex. It goes beyond sex disaggregation

7. Why the focus on gender Distinction between Sex and Gender • Sex is not the same as gender • Sex refers to relatively fixed biological differences between women and men • Gender refers to socially constructed differences between sexes, that is, roles and responsibilities assigned by groups to women and men on the basis of their sex • Gender differences may be changed • Sex differences are fixed and unchangeable

8. Why the focus on gender Gender-based factors shape work patterns

9. Why the focus on gender Gender-based factors lead to various forms of labour market segregation • Entry to/exit from the labour market • Labour force, Employment, Unemployment, Labour turnover • Types of economic activities carried out • Occupations, industries, status in employment, institutional sector, size of establishment, place of work, occupational injuries, diseases and fatalities • Labour inputs • Hours worked, work schedules, absenteeism • Returns to labour • Wages, overtime payments, fringe benefits, social security benefits, regular and irregular payments Sex is a proxy to capture the impact of gender-based factors

10. Why the focus on gender Gender-based factors also impact the production of statistics • Issues identified as priorities requiring data • Methods developed for data collection and processing • Tabulations produced • Analysis conducted • Dissemination formats Sex is an appropriate proxy for gender to the extent that • Issues address gender concerns in population • Methods explicitly take into account possible gender biases • Analysis examines underlying causes of gender differences • Dissemination targets relevant groups

11. Why the focus on gender Gender-based factors also impact the production of statistics

12. Consider gender-based factors at all stages of production How to mainstream gender into statistics Identify key issues or concerns Determine the statistics needed Assess quality of existing data and sources Identify data gaps Identify new sources Specify methodological improvements Collect/compile the statistics needed Tabulate Analyze Disseminate

13. Statistical production Mainstreaming gender How to mainstream gender into statistics Identify key issues or concerns Determine the statistics needed Assess quality of existing data and sources Identify data gaps Identify new sources Specify methodological improvements Collect/compile the statistics needed Tabulate Analyze Disseminate Consider gender concerns, policy goals and causes of gender differences Consider social and cultural factors that can produce gender-biases in data collection Highlight gender issues, Shed light on underlying causes

14. Statistical production Mainstreaming gender Stage 1: Issue identification Consider gender concerns, policy goals and causes of gender differences Identify key issues or concerns

15. Example: Issue identification Consider gender equality goals and policy priorities 1997 SADC Declaration on Gender and Development 2007 SADC Draft protocol on Gender and Development • Article 7: Productive resources and employment • Multiple roles for women • Access to property and resources • Equal access to employment • Article 17: Monitoring and evaluation Member States shall, by 2015, develop, monitor and evaluate systems and plans setting out targets, indicators and time frames based on this Protocol. Each SADC country shall collect and analyse baseline data against which progress in achieving targets will be monitored. Basis for National Gender Policies & Gender Action Plans

16. Example: Issue identification Consider gender equality goals and policy priorities 2007 SADC Draft protocol on Gender and Development Article 7: Productive resources and employment Equal access to employment (a) equal pay for equal work and equal remuneration for jobs of equal value for women and men; (b) the eradication of occupational segregation and all forms of employment discrimination; (c) the recognition of the economic value of, and protection of, women engaged in domestic work; and (d) the appropriate minimum remuneration of women formally engaged in domestic work.

17. Example: Issue identification Underlying causes Consequences Sex segregation in education Different returns in wages/salaries Unequal sharing of family responsibilities Gender issue Different security of employment Occupational segregation Women’s reproductive role Different career opportunities Employers’ prejudices Different roles in decision making Individual choices, preferences Limited role models for future generations

18. Statistical production Mainstreaming gender Stage 2: Determine needed statistics Consider gender concerns, policy goals and causes of gender differences Determine the statistics needed

19. Example: Determine needed statistics Consequences Underlying causes Sex segregation in education Different returns in Earnings, benefits • Educational attainment • Tertiary education by field of study • Earnings • Benefits (social security, pension) • Employed population by sex & detailed occupation groups Unequal sharing of family responsibilities Different security of employment Occupational segregation • Marital status • Number of children and age • Family members requiring care • Status in employment • Type of contract Women’s reproductive role Different roles in decision making • Marital status • Number of children and age

20. Statistical production Mainstreaming gender Stage 3: Assemble the statistics needed Assess quality of existing data and sources Identify data gaps Identify new sources Specify methodological improvements Collect/compile the statistics needed Consider social and cultural factors that can produce biases in data collection

21. Stage 3: Assemble the statistics needed Review concepts and methods used in data collection • Coverage and enumeration frame: Consider relevant enumeration units where women may be overrepresented • Small enterprises, mobile units • Sample design: Consider that gender differentials in specific variables may require over-sampling in one or more strata • Gender differentials among ethnic minorities • Concepts, definitions and classifications: Review adequacy • Coverage of definitions, capture secondary & tertiary activities • Classification detail • Reference period • Consider timing of seasonal activities • Questionnaire and language: Consider choice of words, skip patterns • Give examples of activities to better capture women’s work

22. Example: Nigeria –Census 2006 Question wording and skip pattern miss secondary economic activities • 17: if Homemaker, skip: “end interview”. • Alternative: • 17b: list secondary activities • If response is “no” on 17a and 17b, then end interview; otherwise record answers for 17b, 18 and 19

23. Example: USA –Labour Force Survey

24. Example: Pakistan –Labour Force Survey 2005-06 Captures both primary and secondary activity, including production of goods for own consumption…

25. Example: Pakistan –Labour Force Survey 2005-06

26. Example: Review of coding and classification systems and terminologies

27. Stage 3: Assemble the statistics needed Review concepts and methods used in data collection (cont) • Publicity campaign • Concepts where biases predominate: definition of work • Enumerator hiring and training • Gender balance in hiring • Focus training on meaning and use of concepts relevant to gender issues • Raise awareness among enumerators of sex-based stereotypes • Respondent selection • Consider impact of male/female respondent • Consider presence of other persons during interview • Checking & imputation • Avoid imputations based on gender stereotypes, ie: coding of occupational groups

28. Example: India –Census 2001

29. Example: India –Census 2001

30. Statistical production Mainstreaming gender Stage 4: Analyse and disseminate statistics Tabulate Analyze Disseminate Highlight gender issues, Shed light on underlying causes

31. Example: Data analysis and presentation Source: Labour Force Survey, Spring 2005, Office for National Statistics, UK

32. Example: Data analysis and presentation Source: Labour Force Survey, Spring 2005, Office for National Statistics, UK

33. Example: Data analysis and presentation Source: Labour Force Survey, Spring 2005, Office for National Statistics, UK

34. Concluding remarks • Gender mainstreaming in labour statistics is about making more accurate and relevant statistics • Gender mainstreaming requires consideration of gender-based factors at all stages in the production of labour statistics • From planning and design • Through methods, field operations and data processing • To data tabulation, analysis and presentation

35. Food for thought • Have we reviewed our data collection procedures to assess the extent to which we are accurately capturing women’s and men’s employment situations? • What have we reviewed? • What do we need to review? • How can we improve our current practices?

36. Thank you!

37. References • Engendering Population Census in South and West Asia: Collected Papers (UNFPA, 2004) • Engendering Statistics: A Tool for Change (Statistics Sweden,1996) • Gender and Statistics Briefing Note: Introduction (UNSD and OSAGI, 2001) • Gender and Statistics Briefing Note: Production of Statistics (UNSD and OSAGI, 2001) • Incorporating gender issues in labour statistics (ILO, STAT Working papers) • Regional Training of Trainers Workshop on Gender Sensitization of NSS (UNECE/WBI, 2007)