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
Overview
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•
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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
2
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 decisionmaking, 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
3
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
4
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.
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What gender mainstreaming implies
Gender mainstreaming implies
Roles, norms, expectations, aspirations
associated with being female or male
• 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
-Work outside home
-Work long hours
-Work in physically demanding jobs
-Work in hazardous occupations
-Provider role
-Physically mobile
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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
No
Many women carry
out a number of
unpaid productive
activities
Yes
Questionnaire does Questionnaire explicitly probes
not probe for the
for unpaid economic activities
measurement of
such as threshing, food
unpaid work
processing, poultry rearing, etc
Women tend to be
Coverage excludes Coverage does not omit
concentrated in small enterprises below a enterprises below a size limit
enterprises
certain size limit
Women tend to
predominate in
seasonal work
A short reference
period is set that
misses women’s
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
Identify key issues or concerns
Determine the statistics needed
Assess quality
of existing data
and sources
Mainstreaming gender
Consider gender concerns,
policy goals and causes of
gender differences
Identify data gaps
Identify new sources
Consider social and cultural
factors that can produce
gender-biases in data collection
Specify methodological improvements
Collect/compile the statistics needed
Tabulate
Analyze
Disseminate
Highlight gender issues,
Shed light on underlying causes
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Stage 1: Issue identification
Statistical production
Mainstreaming gender
Identify key issues or concerns
Consider gender concerns,
policy goals and causes of
gender differences
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 MDG’s, 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
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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
Women’s reproductive
role
Occupational
segregation
Employers’ prejudices
Individual choices,
preferences
Different security of
employment
Different career
opportunities
Different roles in
decision making
Limited role models
for future generations
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Stage 2: Determine needed statistics
Statistical production
Determine the statistics needed
Mainstreaming gender
Consider gender concerns,
policy goals and causes of
gender differences
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
Occupational
segregation
•Marital status
•Number of children and age
•Family members requiring care
Women’s reproductive role
Different security of
employment
•Status in employment
•Type of contract
Different roles in
decision making
•Marital status
•Number of children and age
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Stage 3: Assemble the statistics needed
Statistical production
Assess quality
of existing data
and sources
Identify data gaps
Identify new sources
Mainstreaming gender
Consider social and cultural
factors that can produce
biases in data collection
Specify methodological improvements
Collect/compile the statistics needed
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
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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
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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?
Current
• Misses secondary activities for women who
primarily kept house
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…
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Example: Pakistan –Labour Force Survey
2005-06
Lists activities that count as work including:
• Home based activities:
– Agriculture
– Fetching water
– Milling & food processing
– Collecting firework
– Handicrafts
– Other personal or
community work
– Construction & major repairs
activities
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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)
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•
•
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Publicity campaign
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Concepts where biases predominate: definition of work
Enumerator hiring and training
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Gender balance in hiring
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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
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Example: India –Census 2001
Problem
• Criticism that Census did not capture women’s 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 women’s economic activity,
particularly marginal work.
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Example: India –Census 2001
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Stage 4: Analyse and disseminate statistics
Statistical production
Mainstreaming gender
Tabulate
Analyze
Highlight gender issues,
Shed light on underlying causes
Disseminate
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
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Example: Data analysis and presentation
Employment rates of women and men,
UK 2005
100
80
79
60
71
Women
Men
40
20
0
Source: Labour Force Survey, Spring 2005, Office for National Statistics, UK
31
Example: Data analysis and presentation
Employment rates of women and men
by parental status, UK 2005
100
90
80
73
73
60
68
Women
Men
40
20
0
No dependent children
All dependent children
Source: Labour Force Survey, Spring 2005, Office for National Statistics, UK
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Example: Data analysis and presentation
Employment rates of women and men
by age of youngest child and parental status
UK 2005
100
90
91
80
73 73
71
90
77
89
79
Women
60
56
Men
40
20
0
No
dependent
children
Under 5
5 to 10
11 to 15
16 to 18
Source: Labour Force Survey, Spring 2005, Office for National Statistics, UK
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Concluding remarks
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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 women’s and men’s
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!
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)
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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)
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