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United Nations Economic Commission for EuropeStatistical DivisionUnited Nations Economic Commission for EuropeStatistical Division
Availability and Quality of Gender Statistics
Angela Me
UNECE Statistics Division
UNECE Statistical Division Slide 2
Issues in looking at the quality of the data
For users: do not leave the issue of quality and availability only to statisticians. There are issues that are not too “statistically technical” that affect the Message and need to be addressed
UNECE Statistical Division Slide 3
Issues in looking at the availability of the data
Do the available data provide evidence for gender analysis?
Under-use of existing data Gender is not properly considered in the
existing sources There are no sources available Miss-use of existing data
UNECE Statistical Division Slide 4
Under-use of existing data: ex. Wages
Activity branch Male Female
Transport/
Communication
23450 15670
Education 20670 18970
Health 27690 22840
Public administration
15460 13250
No total pay gap presented (needed for advocacy)
No pay gap included in the gender publication despite the data are available
UNECE Statistical Division Slide 5
Under-use of existing data: ex. Wages
Do gender statistics publications include data on:
Employment by occupation
Employment by status in employment (self-employers)
Employment by family composition
These data are available if a census and/or a labour force survey was carried out and usually show large gender disparities
UNECE Statistical Division Slide 6
Gender not properly considered
Sex is not included in the data collection Business registers, disease reporting, voting
registers
Sex is not included in the dissemination of the data
Issues that reflect an unequal participation of women an men are not properly collected Quality of work, informal employment, leading
positions
UNECE Statistical Division Slide 7
Data collection not available
Surveys on gender attitudes
Surveys on time-use
Surveys on reproductive health
Surveys on violence against women
UNECE Statistical Division Slide 8
Miss-use of data: Example of Monetary Poverty
Data on income poverty are based on household income or consumption
• Difficult to disaggregate by sex (transfers within households are unknown)
• It is not relevant to use the concept of head of household: it is only a statistical concept that does not reflect the income distribution in households
UNECE Statistical Division Slide 9
Miss-use of data: Example of Monetary Poverty
Data based on income of the head of households (HH) give a biased picture
• Women who declare as HH are usually:• the most educated• The ones that do not live with a partner
• Statistics of men HH are usually based on the largest proportion of households and women HH are a not representing minority
UNECE Statistical Division Slide 10
Miss-use of data: Example of Monetary Poverty
To be measured considering income or consumption by type of household
One-single-person households by sex One-single-parent households by sex One-income-earning-person
households by sex Others… Poverty is more than
monetary measures
UNECE Statistical Division Slide 11
Issues in looking at the availability of the data
How to improve?
Inquiry about all the data available Try to influence the existing sources to make
them more gender-sensitive Advocate for the development of new gender-
sensitive data collection (within the national statistical masterplan)
Avoid the miss-use of data
UNECE Statistical Division Slide 12
Issues in looking at the quality of the data
Why data quality is important?
Wrong data give wrong messages Wrong messages lead to wrong political
interventions or no intervention Advocacy needs to be backed up by solid
data to be credible in the long run
UNECE Statistical Division Slide 13
Issues in looking at the quality of the data
Some of gender related issues
Inadequate definitions and concepts Man-biased data collection (question
wording) Gender-biased responses Gender-biased enumerators
UNECE Statistical Division Slide 14
Inadequate definitions and concepts
Data collection is based on: Households or farm and not on individual The concept of the head of economic
activity Classifications are men-oriented (ex:
occupation -ISCO) Concept definitions (ex: in some countries
employment may include women in long maternity leave)
UNECE Statistical Division Slide 15
Biased question wording
Example: Do you work?
“Work”=interpreted as formal work
People engaged in informal activities are undercounted. Usually women are engaged
more than men in informal sector (particularly agriculture) and therefore are
undercounted
UNECE Statistical Division Slide 16
Biased question wording
More women-sensitive…..
o Are you engaged in any work paid in money or in kind?
o Do you sell products on the street or at the market?
o Are you engaged in agriculture activities to produce goods for the household consumption?
UNECE Statistical Division Slide 17
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
UNECE Statistical Division Slide 18
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
UNECE Statistical Division Slide 19
Gender-biased enumerators
UNECE Statistical Division Slide 20
Issues in looking at the quality of the data
Different sources may provide different data
UNECE Statistical Division Slide 21
Entrepreneurship
An issue of definition and data availability
UNECE Statistical Division Slide 22
Entrepreneurship: definitions
Own-account workers
Employers
Owners
Managers
Self-employed
Members of Ex. Boards
UNECE Statistical Division Slide 23
Entrepreneurship
Labour Force Surveys (LFS)self-employedmanagersemployersown-account workers
Enterprise Surveysemployersmanagersownersmembers of ex. boardsaccess to credit
Registers (Business, taxes, …)SME ownersaccess to credit
UNECE Statistical Division Slide 24
Entrepreneurship
Different sources different perspectives different concepts and definitions
Data from different sources may not be
comparable
UNECE Statistical Division Slide 25
Entrepreneurship: Data Availability