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

Objectives of the Gender Equality Index • 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

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

Conceptual Measurement framework framework The conceptual structure has to be translated into the measurable structure, and the measurement framework has to confirm the conceptual framework

Selecting the variables: criteria Conceptual criteria • focus on individuals • Outcome variables Quality criteria • Reliable • Comparable over time • Harmonised at EU level • No more than 10% missing data points

Variables: domain of WORK Full-time equivalent employment (15+population) (LFS) Participation Duration of working life (years) (LFS) Employed people in Education, Human Health and Social Work activities (15-64 employed) Work (LFS) Ability to take an hour or two off during Segregation working hours to take care of personal or and quality of family matters (15+ workers) (EWCS) work Working to tight deadlines (15+ workers) (EWCS)

Variables: domain of MONEY Mean monthly earnings (PPS; total age group, working in companies 10 employees or more, NACE_R2: B-S_X_O - Industry, construction Financial and services (except public administration, defense, compulsory social security), 2010 resources survey) Money Mean equivalised net income (PPS, 16+ population) Not-at-risk-of- poverty , ≥60% of median income (16+ population) S20/S80 income quintile share (16+ Economic population) resources

Variables: domain of KNOWLEDGE Graduates of tertiary education (15-74 population, First and second stage of tertiary education (levels 5 and 6) % from total 15-74 Attainment population) and Knowledge Tertiary students in the fields of Education, segregation Health and Welfare, Humanities and Art (tertiary students) People participating in formal or non-formal Lifelong education and training (15-74 population) learning

Variables: domain of TIME Workers caring for and educating their children or grandchildren, everyday for one hour or more (15+ workers) Care Workers doing cooking and housework, everyday for one hour or more (15+ workers) TIME Workers doing sporting, cultural or leisure activities outside of their home, at least every other day (15+ workers) Social Workers involved in voluntary or charitable activities, at least once a month (15+ workers)

Variables: domain of POWER Share of Ministers (18+ population) Share of members of Parliament (18+ Political population) Share of members of Regional Assemblies Power (18+ population) Share of members of boards in largest quoted companies, supervisory board or board of directors (18+ population) Economic Share of members of Central Bank (18+ population)

Variables: domain of HEALTH Self-perceived health, good or very good (16+ population) Life expectancy in absolute value at birth Status Healthy life years in absolute value at birth Health Population without unmet needs for medical examination (16+ population) Access Population without unmet needs for dental examination (16+ population)

Additional variables needed for 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) Additional variables used Population in age group 18 and older by in calculations 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 Gender Women Absolute -1 = value Average of women and men gap

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

Computation of gender gap metric Gender gap ( 𝚽 ) interpretation  0 means gender equality Gender gap ( 𝚽 ) is reversed by taking: Equality Inequality 0 1

Gender gap metric Examples (EU-28, 2012) Gender Gender W M T Av. W/Av W/Av gap gap (w,m) -1 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

Computation of correcting coefficient Correcting Total (at country level) = coefficient Maximum total value across all countries

Gender gap metric corrected with Correcting Coefficient Examples (FTE, 2012) Women Men Differ. Total Gender Correct. Correct betwee gap coeffic. . Metric n metric women and men 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 and Rescaled from scale 0 to1 to the scale 1 to 100

Computation of Final Metric 𝜟 = 𝟐 + 𝜷 𝒀 𝒋𝒖 ∗ 𝟐 − 𝜱 𝒀 𝒋𝒖 ∗ 𝟘𝟘 Final ( ) Correcting Gender 1 + = 99 * * Coefficient Gap Metric Metric Equality Inequality 1 100

Aggregation and weighting Participation Work Segregation and quality of work Financial resources Money Economic resources Knowledg Attainment and segregation e Gender Lifelong learning Equality Care Time Index Social Political Power Economic Status Health Access

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

Different means Mean Calculation 10, 20, 50 Arithmetic (10+20+50)/ 26.7 mean Geometric 3 10 ∗ 20 ∗ 50 21.5 mean

Mean experts’ weights WORK 0.19 MONEY 0.15 KNOWLEDGE 0.22 TIME 0.15 POWER 0.19 HEALTH 0.10

Computation of Gender Equality Index 𝑗 = 1, … , 28 𝑒 = 1, … , 6 𝑥 𝑒 𝑥 𝑡 𝑡 = 1, … , 12 6 12 27 𝑤 = 1, … , 26 ∗ = 𝐽 𝑗 𝑥 𝑤 𝛥 𝑌 𝑗𝑒𝑡𝑤 𝑥 𝑤 , 𝑥 𝑡 , 𝑥 𝑒 ∈ 0,1 𝑒=1 𝑡=1 𝑤=1 𝑥 = 1 Equality Inequality 1 100

Developing Gender Equality Index: steps 8-10 Analysing the results and presenting

Gender equality index: report • Methodology – Conceptual framework – Measurement framework • Analysing the results – Unpacking the index – At variable level – Contextualising

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.

Conclusions • Concept • Selecting the variables • Calculations • Analysing and presenting

Gender Equality Index Measurement tool Regularly updated Easy to interpret