measuring gender equality through a composite indicator · gender gap metric corrected with...

Measuring Gender Equality

through a Composite Indicator

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

Developing Gender Equality Index: step 1

Purpose of the index

Conceptual Framework

• 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

Developing a solid conceptual framework

based on:

• Key gender equality policies

• Theoretical equality frameworks

Domains and sub-domains of the

conceptual framework of the Gender

Equality Index

Developing Gender Equality Index: step 2

Measurement Framework

Selecting the variables

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

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

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)

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)

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)

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)

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)

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)

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

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

Developing Gender Equality Index: steps

3-7

Calculations

Computation of gender gap

WomenAbsolute

value Average of women and men

Gender

gap= -1

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

EqualityInequality

0 1

Computation of gender gap metric

Gender gap (𝚼) interpretation 0 means gender equality

Gender gap (𝚼) is reversed by

taking:

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)

Computation of correcting

coefficient

Correcting

coefficient

Total (at country level)=

Maximum total value across all

countries

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

Computation of Final Metric

Including gender gaps

and level of achievement

Rescaled from scale 0 to1

to the scale 1 to 100

and

EqualityInequality

1 100

𝜞 = 𝟏 + 𝜶 𝑿𝒊𝒕 ∗ 𝟏 − 𝜰 𝑿𝒊𝒕 ∗ 𝟗𝟗

Computation of Final Metric

Final

Metric=

Correcting

Coefficient 1 +

Gender

Gap Metric* * 99( )

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

Aggregation and weighting

VARIABLE

S Equal

SUB-DOMAINS

DOMAINS

GENDER EQUALITY

INDEX

WeightingAggregation

Equal

Experts’ weights

Arithmetic

Geometric

Geometric

Different means

Mean Calculation 10, 20, 50

Arithmetic

mean(10+20+50)/ 26.7

Geometric

mean310 ∗ 20 ∗ 50 21.5

Mean experts’ weights

WORK 0.19

MONEY 0.15

KNOWLEDGE 0.22

TIME 0.15

POWER 0.19

HEALTH 0.10

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

Developing Gender Equality Index: steps

8-10

Analysing the results and presenting

• Methodology

– Conceptual framework

– Measurement framework

• Analysing the results

– Unpacking the index

– At variable level

– Contextualising

Gender equality index: report

Color code and images

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.

• Concept

• Selecting the variables

• Calculations

• Analysing and presenting

Conclusions

Measurement

toolRegularly updated

Easy to interpret

Gender Equality Index