Measuring Gender Equality through a Composite Indicator 10 guiding - - PowerPoint PPT Presentation

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
• verall and in selected areas of concern;
• to support the evaluation of the effectiveness
• f 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 WORK

Work 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 MONEY

Money 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 KNOWLEDGE

Knowledge 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 TIME

TIME 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

• ther day (15+ workers)

Workers involved in voluntary or charitable activities, at least once a month (15+ workers)

Variables: domain of POWER

Power 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 HEALTH

Health 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

Women Absolute 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

Equality Inequality 1

Computation of gender gap metric

Gender gap (𝚽) interpretation  0 means gender equality Gender gap (𝚽) 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

Equality Inequality 1 100 𝜟 = 𝟐 + 𝜷 𝒀𝒋𝒖 ∗ 𝟐 − 𝜱 𝒀𝒋𝒖 ∗ 𝟘𝟘

Computation of Final Metric Final Metric

=

Correcting Coefficient

1 +

Gender Gap Metric

* * 99

( )

Aggregation and weighting

Gender Equality Index

Work

Participation Segregation and quality of work

Money

Financial resources Economic resources

Knowledg e

Attainment and segregation Lifelong learning

Time

Care Social

Power

Political Economic

Health

Status Access

Aggregation and weighting

VARIABLE S Equal SUB-DOMAINS DOMAINS GENDER EQUALITY INDEX Weighting Aggregation Equal Experts’ weights Arithmetic Geometric Geometric

Different means

Mean Calculation 10, 20, 50 Arithmetic mean (10+20+50)/ 26.7 Geometric mean

3 10 ∗ 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

Equality Inequality 1 100

Computation of Gender Equality Index

𝐽𝑗

∗ = 𝑒=1 6 𝑡=1 12 𝑤=1 27

𝑥𝑤 𝛥 𝑌𝑗𝑒𝑡𝑤

𝑥𝑡 𝑥𝑒

𝑗 = 1, … , 28 𝑒 = 1, … , 6 𝑡 = 1, … , 12 𝑤 = 1, … , 26 𝑥𝑤, 𝑥𝑡 , 𝑥𝑒 ∈ 0,1 𝑥 = 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 tool Regularly updated Easy to interpret Gender Equality Index