United Nations Economic Commission for Europe Statistical Division Engendering Labour Statistics UNECE Statistical Division

- UNECE Statistical Division Slide 2 Working age Population Labour Force Employed By occupation By industry By status Unemployed Outside labour force (not active) HouseworkStudyPensionOther Segregation

- UNECE Statistical Division Slide 3 Working age Population Labour Force EmployedUnemployed Outside labour force (not active) HouseworkStudyPensionOther Activity rate Unemployment Rate

- UNECE Statistical Division Slide 4 Working age Population Labour Force EmployedUnemployed Outside labour force (not active) HouseworkStudyPensionOther Employment Rate

- UNECE Statistical Division Slide 5 Working age Population Labour Force EmployedUnemployed Outside labour force (not active) HouseworkStudyPensionOther Labour Force Surveys, Census, Surveys LFS, Census, Registers Enterprise surveys, LFS, Census

- UNECE Statistical Division Slide 6 How can we make the process of collecting, processing and disseminating employment statistics more gender sensitive? Engendering Labour Statistics

- UNECE Statistical Division Slide 7 Where is the gender bias?  Man-biased data collection (question wording)  Inadequate definitions and concepts  Gender-biased responses  Gender-biased enumerators  Gender-blind content of the data collection Engendering Labour Statistics

- UNECE Statistical Division Slide 8 Question wording Formally there is a clear distinction between employed and non employed population ILO definition: a person is currently employed if he/she has worked at least one hour the week previous the survey Work: for income (cash or kind) or unpaid production of goods Engendering Labour Statistics

- UNECE Statistical Division Slide 9 Question wording Prior 1994, US Labour Force Survey (LFS): “What were you doing most of last week—working, keeping house, or something else?” For women who primarily kept house but also did some paid work, this question appears to have led to some underreporting of work Now, US LFS: “Last week, did you do any work for pay or profit?” Following the redesign, the survey found an increase in the number of workers, primarily women, who usually worked fewer than 10 hours per week Engendering Labour Statistics

- UNECE Statistical Division Slide 10 Question wording Can the LFS questions be improved to include all women and men who do work according to the ILO definition? Do the current questions capture persons who have “atypical jobs”? Engendering Labour Statistics

- UNECE Statistical Division Slide 11 Question wording Concepts should be operationalized in a way that respondents can understand it. What does work mean? What does child care mean? Cognitive testing, focus groups help to make sure that the concepts used are interpreted correctly Engendering Labour Statistics

- UNECE Statistical Division Slide 12 Concepts and definitions Women’s work tend to be more heterogeneous than men’s work, but standard classifications are more one- dimensional Some unit of analysis hide the individual (and therefore the gender) dimension household, farm, economic unit Engendering Labour Statistics

- UNECE Statistical Division Slide 13 Gender-biased responses Male respondents may fail to report women Respondents may not understand the content of the questionnaire Respondent give wrong answers to meet social norms Engendering Labour Statistics

- UNECE Statistical Division Slide 14 Meeting social norms: US Survey Example The following questions and results were obtained in an American survey % 'Yes' 53 Have you ever heard of the Taft-Pepper Bill concerning veteran's housing (no such bill!) Have you ever heard of the famous writer, John Woodson? (no such writer!) Have you ever heard of the Midwestern Life Magazine? (no such magazine!) Do you recall that, as a good citizen you voted last December in the special election for your state representative? (no election!) 8 Have you ever heard the word AFROHELIA? (no such word!) Sometimes this type of bias is called prestige error Engendering Labour Statistics

- UNECE Statistical Division Slide 15 Gender-biased enumerators Enumerators may introduce his/her personal view (norm) in the interview Poor training Social pressure Lack of interest Enumerators may establish poor relationship Not gender-correct language Body language Engendering Labour Statistics

- UNECE Statistical Division Slide 16 Enumerator-bias: Example Australian Survey Average number of sex partners reported By women who were watched as they filled in their survey answers: 2.6; By women who knew they were completely anonymous: 3.4; By women who thought they were attached to a lie detector: 4.4 Sydney Morning Herald, August 31, 2003 Engendering Labour Statistics

- UNECE Statistical Division Slide 17 Gender-blind content What are the gender issues in employment? ……….Next activity…………. Engendering Labour Statistics

- UNECE Statistical Division Slide 18 Thank you !