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 users 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 womens and mens 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.

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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

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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

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Why the focus on gender
Gender-based factors shape work patterns
Sex Gender-based norms and expectations Possible implications for labour force participation
Female -Caring role -Limited physical mobility -Does not seek work -Work at home or for family business -Performs unpaid work -Work part-time or seasonally -Work as nurse, teacher -Drop out of work during childbearing or childrearing years
Male -Provider role -Physically mobile -Work outside home -Work long hours -Work in physically demanding jobs -Work in hazardous occupations
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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
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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

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Why the focus on gender
Gender-based factors also impact the production
of statistics
Gender issue Statistical production considers gender issues Statistical production considers gender issues
Gender issue No Yes
Many women carry out a number of unpaid productive activities Questionnaire does not probe for the measurement of unpaid work Questionnaire explicitly probes for unpaid economic activities such as threshing, food processing, poultry rearing, etc
Women tend to be concentrated in small enterprises Coverage excludes enterprises below a certain size limit Coverage does not omit enterprises below a size limit
Women tend to predominate in seasonal work A short reference period is set that misses womens economic contributions Seasonality of work is taken into account through the selection of an adequate reference period or by spreading the survey at various points in the year
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How to mainstream gender into statistics

• Consider gender-based factors at all stages of
  production

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
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How to mainstream gender into statistics

• Statistical production

• Mainstreaming gender

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
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Stage 1 Issue identification

• Statistical production

• Mainstreaming gender

Consider gender concerns, policy goals and
causes of gender differences
Identify key issues or concerns
Steps
Identify gender issues in labour force through user-producer dialogue
Take into account gender equality goals and policy priorities National plans for equal opportunities, gender policy National plans for development, employment Monitoring requirements for MDGs, PRSP, etcetera
Consider factors underlying gender issues in labour force and possible consequences
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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

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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.

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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
Womens reproductive role
Different career opportunities
Employers prejudices
Different roles in decision making
Individual choices, preferences
Limited role models for future generations
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Stage 2 Determine needed statistics

• Statistical production

• Mainstreaming gender

Consider gender concerns, policy goals and
causes of gender differences
Determine the statistics needed
Steps
Define the statistics and indicators needed to address the identified issues and priorities
Define also the statistics and indicators related to the factors underlying the identified issues Define key tabulations needed to address identified gender issues and priorities. Consider that the tabulations may require inclusion of stratifying variables underlying gender differentials

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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

Womens reproductive role
Different roles in decision making

• Marital status
• Number of children and age

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Stage 3 Assemble the statistics needed

• Statistical production

• Mainstreaming gender

Consider social and cultural factors that can
produce biases in data collection
Steps
Assess the extent to which concepts and methods used in data collection take into account gender issues or introduce gender-biases Concepts Definitions and classifications Methods Study design, questionnaire, data collection procedures Specify methodological improvements
Collect/compile the statistics needed
Raise awareness among public. Consider that the publicity campaign may not reach all population equally
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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
  womens work

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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

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Example USA Labour Force Survey
Prior 1994 What were you doing most of last week -working, keeping house, or something else?
Misses secondary activities for women who primarily kept house
Current Q1. Does anyone in this household have a business or a farm? Q2. Last week, did you do any work for pay or profit? Q3. LAST WEEK, did you do any unpaid work in the family business or farm?
Increase in number of workers who usually worked less than ten hours (women primarily)
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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
Lists activities that count as work including Lists activities that count as work including
Home based activities Home based activities
Agriculture Milling food processing Handicrafts Construction major repairs Fetching water Collecting firework Other personal or community work activities
26
Example Review of coding and classification
systems and terminologies
Nepal 2001 Census
Set up of an Occupation and Industry Classification Committee to review gender bias in classifications Result Review and creation of more detailed 4-digit classifications that include detailed breakdowns for common female activities
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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
Problem Criticism that Census did not capture womens economic activity properly
Strategies Expanded definition of work to capture unpaid work Manual and training of enumerators to probe for specific paid and unpaid economic activities Sensitization campaign to improve public recognition of economic activities Targeting of districts with particularly high underreporting of female economic activity
Outcome Improvements in netting womens economic activity, particularly marginal work.
29
Example India Census 2001
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Stage 4 Analyse and disseminate statistics

• Statistical production

• Mainstreaming gender

Highlight gender issues, Shed light on
underlying causes
Steps
Produce defined tabulations highlighting gender differentials
Include sex, age and other relevant characteristics
Emphasize key gender issue in data presentation with a simple, clear message
Identify and disseminate results to user groups
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
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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

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Food for thought

• Have we reviewed our data collection procedures
  to assess the extent to which we are accurately
  capturing womens and mens employment
  situations?
• What have we reviewed?
• What do we need to review?
• How can we improve our current practices?

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Thank you!
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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)