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Measuring Gender Equality
through a Composite Indicator
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10 guiding principles
•Step 1. Developing a conceptual framework
•Step 2. Selecting indicators
•Step 3. Imputation of missing data
•Step 4. Multivariate analysis
•Step 5. Normalisation of data
•Step 6. Weighting and aggregation
•Step 7. Robustness and sensitivity
•Step 8. Back to the details
•Step 9. Links to other indicators
•Step 10. Presentation and dissemination
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Developing Gender Equality Index: step 1
Purpose of the index
Conceptual Framework
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• to measure gender equality throughout the
Member States and the EU;
• to allow an analysis over time and
geographical areas;
• to focus on the situation of women and men
overall and in selected areas of concern;
• to support the evaluation of the effectiveness
of the measures and policies
Objectives of the Gender Equality
Index
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Developing a solid conceptual framework
based on:
• Key gender equality policies
• Theoretical equality frameworks
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Domains and sub-domains of the
conceptual framework of the Gender
Equality Index
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Developing Gender Equality Index: step 2
Measurement Framework
Selecting the variables
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The conceptual structure has to be
translated into the measurable structure,
and the measurement framework has to
confirm the conceptual framework
Conceptual
framework
Measurement
framework
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Selecting the variables: criteria
• focus on individuals
• Outcome variables
Conceptual
criteria
Quality criteria
• Reliable
• Comparable over time
• Harmonised at EU level
• No more than 10%
missing data points
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Variables: domain of WORKW
ork
Participation
Full-time equivalent employment
(15+population) (LFS)
Duration of working life (years) (LFS)
Segregation
and quality of
work
Employed people in Education, Human Health
and Social Work activities (15-64 employed)
(LFS)
Ability to take an hour or two off during
working hours to take care of personal or
family matters (15+ workers) (EWCS)
Working to tight deadlines (15+ workers)
(EWCS)
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Variables: domain of MONEYM
on
ey
Financial
resources
Mean monthly earnings (PPS; total age group,
working in companies 10 employees or more,
NACE_R2: B-S_X_O - Industry, construction
and services (except public administration,
defense, compulsory social security), 2010
survey)
Mean equivalised net income (PPS, 16+
population)
Economic
resources
Not-at-risk-of-poverty , ≥60% of median
income (16+ population)
S20/S80 income quintile share (16+
population)
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Variables: domain of KNOWLEDGEK
now
ledg
e
Attainment
and
segregation
Graduates of tertiary education (15-74
population, First and second stage of tertiary
education (levels 5 and 6) % from total 15-74
population)
Tertiary students in the fields of Education,
Health and Welfare, Humanities and Art
(tertiary students)
Lifelong
learning
People participating in formal or non-formal
education and training (15-74 population)
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Variables: domain of TIMET
IME
Care
Workers caring for and educating their
children or grandchildren, everyday for one
hour or more (15+ workers)
Workers doing cooking and housework,
everyday for one hour or more (15+ workers)
Social
Workers doing sporting, cultural or leisure
activities outside of their home, at least every
other day (15+ workers)
Workers involved in voluntary or charitable
activities, at least once a month (15+ workers)
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Variables: domain of POWERP
ow
er
Political
Share of Ministers (18+ population)
Share of members of Parliament (18+
population)
Share of members of Regional Assemblies
(18+ population)
Economic
Share of members of boards in largest quoted
companies, supervisory board or board of
directors (18+ population)
Share of members of Central Bank (18+
population)
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Variables: domain of HEALTHH
ealth
Status
Self-perceived health, good or very good (16+
population)
Life expectancy in absolute value at birth
Healthy life years in absolute value at birth
Access
Population without unmet needs for medical
examination (16+ population)
Population without unmet needs for dental
examination (16+ population)
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Additional variables
needed for calculations
Additional
variables used
in calculations
Employment in tertiary sector (15-64, %)
(percentage of persons working in
sectors G-U based on NACE rev.2 out of
total working persons)
Population in age group 18 and older by
sex
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After applying the conceptual and quality
criteria we should have for each variable:
• Availability period and regularity; source of data
• Not available possible proxy variable(s)
• Proxy variable(s) quality criteria
• Reliable
• Accurate
• Comparability with original variable
• Data for selected variables: women/men/total
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Developing Gender Equality Index: steps
3-7
Calculations
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Computation of gender gap
WomenAbsolute
value Average of women and men
Gender
gap= -1
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Computation of gender gap
FTE Women Men Total
EU-28 38.8451 55.6671 46.8028
Average of women and men= (38.8451 + 55.6671)/2 = 94.5122/2 =
47.2561
Women / average of women and men = 38.8451 / 47.2561 = 0.8220
Women / average of women and men – 1 = 0.8220 -1 = - 0.178
Absolute value of - 0.178 = 0.178
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EqualityInequality
0 1
Computation of gender gap metric
Gender gap (𝚼) interpretation 0 means gender equality
Gender gap (𝚼) is reversed by
taking:
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Gender gap metric
W M T Av.
(w,m)
W/Av W/Av
-1
Gender
gap
Gender
gap
metric
FTE 38.8451 55.6671 46.8028 47.2561 0.822 -0.178 0.178 0.822
Educ 24.1 22.8 23.4 23.45 1.0277 0.0277 0.0277 0.9723
Care 44.5692 27.4417 35.2571 36.0055 1.2378 0.2378 0.2378 0.7622
Med 93.2 94.0 93.6 93.6 0.9957 -0.0043 0.0043 0.9957
Examples (EU-28, 2012)
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Computation of correcting
coefficient
Correcting
coefficient
Total (at country level)=
Maximum total value across all
countries
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Gender gap metric corrected with
Correcting Coefficient
Examples (FTE, 2012)
Women Men Differ.
betwee
n
women
and
men
Total Gender
gap
metric
Correct.
coeffic.
Correct
. Metric
BG 42.133 50.321 8.188 46.074 0.911 0.770 0.702
FI 47.748 55.932 8.184 51.597 0.921 0.870 0.801
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Computation of Final Metric
Including gender gaps
and level of achievement
Rescaled from scale 0 to1
to the scale 1 to 100
and
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EqualityInequality
1 100
𝜞 = 𝟏 + 𝜶 𝑿𝒊𝒕 ∗ 𝟏 − 𝜰 𝑿𝒊𝒕 ∗ 𝟗𝟗
Computation of Final Metric
Final
Metric=
Correcting
Coefficient 1 +
Gender
Gap Metric* * 99( )
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Aggregation and weighting
Gender Equality
Index
WorkParticipation
Segregation and quality of work
MoneyFinancial resources
Economic resources
Knowledge
Attainment and segregation
Lifelong learning
TimeCare
Social
PowerPolitical
Economic
HealthStatus
Access
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Aggregation and weighting
VARIABLE
S Equal
SUB-DOMAINS
DOMAINS
GENDER EQUALITY
INDEX
WeightingAggregation
Equal
Experts’ weights
Arithmetic
Geometric
Geometric
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Different means
Mean Calculation 10, 20, 50
Arithmetic
mean(10+20+50)/ 26.7
Geometric
mean310 ∗ 20 ∗ 50 21.5
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Mean experts’ weights
WORK 0.19
MONEY 0.15
KNOWLEDGE 0.22
TIME 0.15
POWER 0.19
HEALTH 0.10
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EqualityInequality
1 100
Computation of Gender Equality
Index
𝐼𝑖∗ =
𝑑=1
6
𝑠=1
12
𝑣=1
27
𝑤𝑣 𝛤 𝑋𝑖𝑑𝑠𝑣
𝑤𝑠𝑤𝑑
𝑖 = 1,… , 28𝑑 = 1, … , 6𝑠 = 1,… , 12𝑣 = 1, … , 26𝑤𝑣, 𝑤𝑠 , 𝑤𝑑 ∈ 0,1
𝑤 = 1
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Developing Gender Equality Index: steps
8-10
Analysing the results and presenting
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• Methodology
– Conceptual framework
– Measurement framework
• Analysing the results
– Unpacking the index
– At variable level
– Contextualising
Gender equality index: report
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Color code and images
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Scale for the scores
The gender equality index measures gender gaps
adjusted for levels of achievements. This produces a
score that ranges from 1 to 100, where 100 stands for full
gender equality.
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• Concept
• Selecting the variables
• Calculations
• Analysing and presenting
Conclusions
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Measurement
toolRegularly updated
Easy to interpret
Gender Equality Index