Transcript Economic Indicators for Gender Analysis Some observations Ko Oudhof Statistics Netherlands UNECE Work Session on Gender Statistics 6-8 october 2008 Geneva.

Economic Indicators for Gender
Analysis
Some observations
Ko Oudhof
Statistics Netherlands
UNECE Work Session on Gender Statistics 6-8 october 2008 Geneva
1
Papers in the session
• Gender and Economic Statistics
– Heather Dryburgh, Statistics Canada
• Gender Pay Gap: Data availability and measurement issues
– Elisa Benes, UNECE Statistical Division
• Measuring gender equality in the economy in countries of
Central Asia
– Ewa Zimny and Enrico Bisogno, UNECE
• Who benefits more? Benefit of the government by gender.
•
A Dutch example of gender budget analysis
– Saskia Keuzenkamp, Statistics Netherlands
• Social Accounting Matrix (SAM)
– Maria Isabel Quintela, National Statistical Institute, Portugal
UNECE Work Session on Gender Statistics 6-8 october 2008 Geneva
2
Abstract starting point: social process
FACTOR/
CAUSE
Nongendered
→
Male
ACTORS &
POSITIONS
EFFECT
--- (?)
Nongendered
Male
→
Male
Female
Female
Female
Institutional
Institutional
Institutional
UNECE Work Session on Gender Statistics 6-8 october 2008 Geneva
3
Gendering Economic Indicators (1)
• WITHIN ACTORS/POSITIONS
– male/female paid work participation
– occupational segregation
– within households (roles, decisions)
• EFFECTS
– genderised contribution to GDP (informal work)
– genderised consumption (markets)
• CAUSES
– genderised consequencies of budgets
– equality policy
UNECE Work Session on Gender Statistics 6-8 october 2008 Geneva
4
Gendering Economic Indicators (2)
UNECE:
Measurement/data gender pay gap
Cause ≠ Male/Female ≠ Effect
Netherlands
Gendering tertiary income (government budget)
More general and more monetary than most
GBA’s
Institutional  Male/Female ≠ Effect
UNECE Work Session on Gender Statistics 6-8 october 2008 Geneva
5
Gendering Economic Indicators (3)
Portugal
Social accounting matrix
Institutional (compensation) Male/Female (labour
input)  Institutional
Canada
Available data on labour market, time use,
income etc. and possible gaps
Available: Cause ≠ Male/Female ≠ Effect
Gap 1: Institutional  male/female ≠ Effect
Gap 2: Cause ≠ Male/female  Institutional
UNECE Work Session on Gender Statistics 6-8 october 2008 Geneva
6
IF YES? - QUESTIONS
(≈ reflections Canada)
Papers raise questions for other countries:
• Would it be conceivable to genderise the elements?
– Yes, it has been done, look at papers
• Would it have any use to do it?
– Idem, but what considering the national context?
• Would it be feasible?
– Considering policy as well as data needs
• Would it deserve any priority?
• Would it be possible to bring about any priority?
• When should it start?
UNECE Work Session on Gender Statistics 6-8 october 2008 Geneva
7
AND BEYOND THAT….
• Why not the ultimate model:
– Male/Female Cause  Male/Female  Male/Female
Effect
• Gender pay gap
– Entepreneurs etc  pay gap
– Pay gap  economic contribution
• Tertairy income
– Decision makers  income
– tertiary income  social participation
• SAM
– Entepreneurs etc  paid labour
– Paid labour Entrepreneurs etc.
UNECE Work Session on Gender Statistics 6-8 october 2008 Geneva
8
IF YES? - QUESTIONS
Questions for all (?) countries:
• Would it be conceivable to genderise the
elements?
• Would it have any use to do it?
• Would it be feasible?
• Would it deserve any priority?
• Would it be possible to bring about any
priority?
• When should it start?
UNECE Work Session on Gender Statistics 6-8 october 2008 Geneva
9