Gender equality index 2017 : methodological report.
Date: 2017-10-31
Institutions: European Institute for Gender Equality
Subjects: evaluation method, gender equality, report, sexual discrimination, sexual violence, social indicator, statistical method, women's rights

Gender Equality Index 2017

Methodological 
Report

Acknowledgements

The authors of the Gender Equality Index 2017. Methodo-
logical report are:  Davide Barbieri, Dr Marre Karu, Dr Irene 
Riobóo Lestón, Blandine Mollard, Dr Jolanta Reingardė. 

Bonfils,  Dr  Anne  Laure  Humbert,  Dr  Viginta  Ivaškaitė-Ta-
mošiūnė, Dr Anna Rita Manca, Ligia Nobrega, Merle Paats, 
Dr Jolanta Reingardė and Dr Irene Riobóo Lestón.

Important  contributions  were  also  made  by  Hedvika  Jan-
eckova, Dr Paula Franklin, Raffaele Lelleri, Diletta Luminari, 
Ligia  Nobrega,  Dr  Zuzana  Madarova,  Liina  Osila,  Merle 
Paats and Dr Lina Salanauskaitė.  

A special thank you goes to Dr Jane Pillinger for editing of 
the report. 

Many  thanks  to  other  colleagues  at  the  European  Insti-
tute for Gender Equality for their intellectual contributions, 
administrative support and encouragement.

The  report  is  based  on  the  methodological  work  of  the 
1st edition of the Gender Equality Index done by Laura de 

The methodology of the Gender Equality Index has greatly 
benefited  from  expert  advice  received  from:  EIGE’s  Work-
ing  Group  on  the  Gender  Equality  Index;    the  European 
Union Agency for Fundamental Rights (FRA); the European 
Foundation  for  the  Improvement  of  Living  and  Working 
Conditions  (Eurofound)  and  the  European  Commission, 
in  particular  the  Gender  Equality  Unit  at  DG  JUST,  Joint 
Research Centre (JRC) and Eurostat.  

The European Institute for Gender Equality is very grateful 
to  the  many  other  individuals  and  institutions  who  pro-
vided valuable contributions and support to the update of 
the Gender Equality Index.

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Gender Equality is responsible for the use that might be made of the following information.

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Gender Equality Index 2017 
Methodological Report

Abbreviations

AHP

EIGE

EHIS

analytic hierarchy process

European Institute for Gender Equality

European Health Interview Survey

EIGE WMID

EIGE Women and Men in Decision-Making database

EM

EQLS

EU

expectation-maximisation

European Quality of Life Survey

European Union

EU SILC

European Union Statistics on Income and Living Conditions

EU LFS

European Union Labour Force Survey

EWCS

European Working Conditions Survey

FRA

FTE

EHIS

European Union Agency for Fundamental Rights

full-time equivalent

European Health Interview Survey

ISCED

International Standard Classification of Education

PCA

PPS

SES

SD

principal component analysis

purchasing power standard

Structure of Earnings Survey

standard deviation

OECD

Organisation for Economic Cooperation and Development

 
Contents

Introduction    � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �  5

1�  Main steps in bulding and calculating the Gender Equality Index  � � � �  7

2�  Gender Equality Index: the core domains � � � � � � � � � � � � � � � � � � � � � � � � � � �  9

2.1. 

Indicators and measures  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  9

2.1.1.  Selecting indicators  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  9

2.1.2.  Processing indicators  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.1.3.  Imputation of missing values  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .   11

2.2.  Gender gap metric  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  11

2.2.1.  Gender gaps  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  12

2.2.2.  Correcting coefficient  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  13

2.3.  Computing the index  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.3.1.  Weighting   . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  15

2.3.2.  Aggregation  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.3.3.  Normalisation  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.3.4.  Calculating the final metric  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  17

2.4.  Statistical analysis  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3�  The satellite domains   � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �   23

3.1. 

The satellite domain of violence   . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.1.1.  Selecting indicators  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.1.2.  Computing the index for the domain of violence  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.2.  The satellite domain of intersecting inequalities  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Annexes  � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �   28

References   � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �   59

Gender Equality Index 2017 − Methodological Report

3

Introduction 

The  Gender  Equality  Index  is  a  comprehensive  measure 
for  monitoring  progress  in  gender  equality  across  the  EU 
over time. It measures gender gaps and takes into account 
the context and different levels of achievement of Member 
States across a range of relevant policy areas. It shows the 
different outcomes of EU and national policies for women 
and  men  and  contributes  to  the  development  and  imple-
mentation  of  evidence-based  policymaking  in  the  area  of 
gender  equality.  The  Gender  Equality  Index  2017  provides 
scores for 2005, 2010, 2012 and 2015.

This  report  gives  a  specific  focus  to  describing  the  meth-
odology  underpinning  the  Gender  Equality  Index.  It  pre-
sents  the  different  steps  taken  in  the  computation  of  the 
Gender  Equality  Index  2017  as  well  as  the  changes  made 
to the methodological and measurement framework in the 
process  of  updating  the  Index.  Section  1  provides  a  brief 
overview  of  the  main  steps  in  building  the  Gender  Equal-
ity  Index.  Section  2  presents  in  greater  depth  the  steps 
taken  and  the  methodological  considerations  and  choices 
that  were  made  during  the  development  and  calculation 
of  the  core  Gender  Equality  Index.  The  satellite  domains 
and their links to the Gender Equality Index are presented 
in  Section  3.  An  extensive  list  of  annexes 
provides  important  data  and  information 
in  relation  to  the  calculation  of  the  Gen-
der Equality Index based on data of 2005, 
2010, 2012 and 2015.

The  conceptual  framework  of  the  Index 
was  designed  by  considering  different 
theoretical approaches to gender equality 
and EU policy priorities identified through 
an  analysis  of  relevant  strategic  policy 
documents.  A  more  detailed  discussion 
of the conceptual framework is presented 
in  the  1st  edition  of  the  Gender  Equality 
Index  (EIGE,  2013).  It  describes  gender 
equality in several areas of economic and 
social  life  in  the  EU  and  in  the  Member 
States.  These  areas  are  summarised  into 
a  hierarchical  structure  of  domains  (core 
and  satellite)  and  sub-domains.  The  core 
domains  —  of  work,  money,  knowledge, 
time,  power  and  health  —  constitute 
the  core  index.  In  addition,  two  satel-
lite  domains  are  identified:  violence  and 
intersecting  inequalities.  They  are  called 

satellites, and therefore are not included in the core Index, 
because they focus on specific phenomena that apply only 
to a selected group of the population. The domain of vio-
lence  measures  gender-based  violence  against  women, 
while 
inequalities  examines  gender  gaps 
among specific population groups (people with disabilities, 
lone parents, migrants, etc.).

intersecting 

The conceptual framework captures the areas of interest that 
are measured by a composite indicator on gender equality 
at EU level. The main challenge in the development of the 
Index  was  to  translate  the  conceptual  framework,  under-
pinned  by  policy  and  theory,  into  a  measurable  structure, 
which is supported by data and statistical considerations.

The  development  of  a  measurement  framework,  consist-
ent with the identified conceptual framework, was carried 
out applying a solid statistical methodology. The resulting 
framework  allows  full  comparability  of  the  results  across 
the different domains and sub-domains, and consequently 
the identification of the strengths and weaknesses in gen-
der equality of each Member State. Additionally, it permits 
comparability across Member States and over time.

POWER

KNOWLEDGE

MONEY

GENDER
EQUALITY
INDEX

WORK

TIME

VIOLENCE
VIOLENCE

HEALTH

INTERSECTING INEQUALITIES

Gender Equality Index 2017 − Methodological Report

5

1� Main steps in building 

and calculating the Gender 
Equality Index

As with any other composite indicator, the Gender Equality 
Index is a measure obtained by compiling individual indi-
cators  (1)  on  the  basis  of  an  underlying  multidimensional 
concept  of  gender  equality.  In  other  words,  it  is  a  mathe-
matical combination of a set of individual indicators, which 
aims to provide a summary of a complex reality of gender 
equality.

Measuring  gender  equality  through  a  single  index  raised 
several  empirical  challenges  during  the  development 
phase,  principally  related  to  data  availability,  data  quality, 
selecting variables or aggregating them. These challenges 
were  overcome  using  a  solid  methodology  based  on 
sound statistical principles, consisting of much more than 
just  the  combination  of  variables.  In  particular,  by  using 
a  solid  methodology,  the  Gender  Equality  Index  provides 
a more realistic picture of the global level of gender equal-
ity in the EU than the individual variables can do separately.

The  computation  of  the  Gender  Equality  Index  is  based 
on  the  internationally  accepted  10-step  methodology  on 
building composite indicators developed by the European 
Commission’s  Joint  Research  Centre  (JRC)  and  the  Organ-
isation  for  Economic  Co-operation  and  Development 
(OECD) (Nardo et al., 2008):

1.  Developing  a  theoretical  framework  that  defines 
and  structures  what  is  measured  and  provides  the 
basis  for  the  selection  and  combination  of  variables 
into a meaningful index.

2.  Selecting  variables  based  on  the  analytical  sound-
ness,  measurability,  country  coverage,  cross-country 
comparability, and relevance of indicators.

3. 

Imputing missing data in order to obtain a complete 
dataset for all countries.

4.  Conducting  a  multivariate  analysis  to  study  the 
overall structure of the dataset, assess its suitability, and 
guide subsequent methodological choices.

5.  Normalising the data, if needed, to ensure the com-

parability of variables.

6.  Weighting and aggregating indicators according to 
both  the  theoretical  framework  and  the  results  of  the 
multivariate analysis.

7.  Conducting  an  uncertainty  and  sensitivity  analy-
sis  to  assess  the  robustness  of  the  index  in  terms  of 
all  possible  sources  of  uncertainty  in  its  development 
(choice  of  imputation  method,  normalisation  scheme, 
weighting system or aggregation method).

8.  Returning  to  the  data  in  order  to  analyse  what 
domains and sub-domains are driving the index results.
Identifying  possible  association  with  other  varia-
bles and existing known indicators.

9. 

10.  Presenting  and  disseminating  the  index  results  in 

a clear and accurate manner.

The measurement framework of the Gender Equality Index 
includes  the  development  of  both  a  metric  to  calculate 
gender  gaps  and  the  methodology  to  aggregate  gender 
gaps  in  sub-domains,  domains  and  the  Gender  Equality 
Index. It includes the calculation of final scores of the Gen-
der  Equality  Index  for  each  Member  State  and  the  EU-28. 
The  calculation  of  the  different  elements  of  the  Gender 
Equality Index is briefly summarised in Box 1.

(1)  In this document the terms ‘indicator’ and ‘variable’ are considered as 

synonyms and therefore used interchangeably.

Gender Equality Index 2017 − Methodological Report

7

Box 1: Calculating the Gender Equality Index in brief

1.  Selection  and  processing  of  indicators�  The  Gender  Equality  Index  is  composed  of  31  indicators,  divided 
between 14 sub-domains, which make up the six domains (work, money, knowledge, time, power and health).

2.  Calcutating gender gaps� A single measure of gender equality for the indicators is developed. Gender gaps 
are calculated and transformed so that the value of 1 can be interpreted as full achievement of gender equal-
ity, while any value below 1 indicates some degree of gender inequality in a given indicator. The value of 0 
theoretically refers to full inequality.

3.  Calculating  the  correcting  coefficient�  Correcting  coefficients  are  calculated  and  applied  to  each  gender 
gap. This means that Member States with similar gender gaps are treated differently if their levels of achieve-
ment differ. The higher the level of achievement, the lower the correction of the gender gap.

4.  Calculating the gender gap metric� The final metric for each indicator is a combination of the gender gap 
and the correcting coefficient. It is dimensionless (allowing comparability since measurement units of variables 
have been eliminated), and bound between [1; 100].

5.  Calculating the Index (aggregating, weighting, and normalisation)

1.  Aggregation  of  variables  of  each  sub-domain,  creating  indices  at  the  sub domain  level  (value  bound  [1; 

100]), and using arithmetic mean of the metrics of the indicators.

2.  Aggregation of the sub-domains into domains, using geometric means of the scores of sub-domains (value 

bound [1; 100]).

3.  Aggregating the scores of the domains into overall Gender Equality Index, using geometric means of the 
six  scores  of  the  domain,  by  applying  experts’  weights  to  the  domains,  obtained  through  the  analytic 
hierarchy process (AHP). The Gender Equality Index takes a value on a scale of 1 to 100, where value of 100 
stands for complete gender equality, and 1 for full gender inequality.

8

Gender Equality Index 2017 − Methodological Report

2� Gender Equality Index: the core 

domains

2.1.  Indicators and measures

Variables  were  considered  provided  they  were  sex  disag-
gregated and they met the following criteria:

2�1�1�  Selecting indicators

between Member States;

  Harmonised  at  EU 

level  and  thereby  comparable 

A  further  important  consideration  in  the  development 
of  the  Gender  Equality  Index  was  the  choice  of  variables 
to  be  included  in  a  composite  indicator  that  could  ade-
quately  measure  the  conceptual  framework  and  meet 
a number of methodological criteria. An initial selection of 
variables  framed  the  review  of  the  main  official  statistical 
sources  at  European  level,  with  criteria  applied  that  were 
both conceptual and statistical. Conceptually, all the varia-
bles included needed to:

  measure a relevant aspect of gender equality;
 
reflect an equal share of assets and resources;
 
focus  on  individuals,  rather  than  on  institutions  or 
countries  (for  example,  to  include  ‘healthy  life  years’, 
rather than ‘health care expenditure’);
consist of outcome variables, which measure a current 
status  as  opposed  to  process  or  input  variables  (for 
example,  by  selecting  ‘time  spent  on  care  activities’, 
rather than ‘provision of childcare services’).

 

Next,  the  quality  criteria  defined  by  Eurostat  (2011)  in  its 
Code of Practice were used together with other quality cri-
teria  needed  for  the  computation  of  synthetic  indicators. 

  Accessible,  updated  on  a  regular  basis,  punctual  and 

comparable over time;

  Accurate, measuring in a reliable way the phenomenon 

it intends to measure, and sensitive to change;

  Comprehensive  and  easily  interpretable,  intuitive  and 
sufficiently simple to be unambiguously interpreted in 
practice;

  With a clear meaning with respect to gender inequality 

or equality;

  With no more than 10 % of missing data points.

Additionally, preference was given to the indicators devel-
oped  in  the  framework  of  the  Beijing  Platform  for  Action 
in  the  EU  and  Europe  2020  indicators.  The  application  of 
those  criteria  provided  a  potential  set  of  variables  to  be 
used in the computation of the Index.

Finally, after applying the mentioned criteria and the statis-
tical analysis presented in section 2.4, 31 indicators were 
used  for  the  calculation  of  the  3rd  edition  of  the  Gender 
Equality  Index  (EIGE,  2017a).  Gender  Equality  Index  2017 
contains  several  updates  regarding  the  selection  of  the 
indicators  (see  Box  2).  The  list  of  indicators  can  be  found 
in Annex 1.

Gender Equality Index 2017 − Methodological Report

9

Box 2: Main updates regarding the selection of the indicators in the Gender 
Equality Index 2017

 

The sub-domain of social power (in the domain of power) and the sub-domain of behaviour (in the domain 
of  health)  are  populated  with  data.  They  had  been  left  empty  in  previous  editions  due  to  lack  of  suitable 
indicators.

  Age  ranges  for  the  reference  populations  are  more  harmonised,  and  the  range  of  age  has  been  extended, 

 

 

 

when possible.
The indicator on ‘Tight deadlines’ (in the domain of work) has been replaced by Eurofound’s ‘Career Prospects 
Index’. The new indicator measures the continuity of employment as assessed through a person’s employment 
status and type of contract, job security, and career prospects (Eurofound, 2016).
The  indicator  ‘Time  spent  on  care  activities’  (in  the  domain  of  time),  previously  from  the  European  Working 
Conditions  Survey  (EWCS),  has  been  replaced  by  a  new  indicator  from  the  European  Quality  of  Life  Survey 
(EQLS). The new indicator covers the whole adult population (aged 18+), regardless of their employment sta-
tus, whereas previously it just covered the working population. In addition the indicator ‘Care for children and 
grandchildren ’ was extended and includes for the first time ‘Caring for elderly and disabled people’. The indi-
cator ‘Cooking and houseworking’ has also been extended to overall population (+ 18).
The indicators in the domain of power have been calculated on the basis of a 3-year average in order to avoid 
sharp fluctuations in the time series where there have been small changes in the actual number of women in 
small decision-making bodies. For example, depending on the country, there are some decision-making bod-
ies with only 5-8 members and therefore having one more or one less woman might lead to a large increase 
or decrease in percentage.

2�1�2�  Processing indicators

In this step the indicators selected were processed in order 
to ensure that they measured gender equality in a homog-
enous  way.  This  is  particularly  the  case  when  it  comes  to 
the  sign  or  the  direction  in  the  interpretation  of  the  var-
iable.  In  this  case,  the  direction  of  all  indicators  included 
in a composite index needed to be homogenous. For the 
Gender  Equality  Index  it  was  decided  that  all  variables 
would  have  a  positive  sign,  i.e.  higher  value  would  indi-
cate  being  closer  to  EU  targets  or  ‘desirable  situation’.  In 
practice,  the  majority  of  the  preselected  variables  already 
had  a  positive  sign,  so  higher  values  could  be  regarded 
positively.  For  example,  variables  measuring  ‘participation 
in  tertiary  education’  or  ‘healthy  life  years’  have  a  positive 
direction,  as  it  is  desirable  to  increase  educational  attain-
ment or to live a long healthy life. On the contrary, the vari-
able measuring ‘being at risk of poverty’ implies a negative 
sign or interpretation, which means that for the Index the 
indicator was reversed to ‘not being at risk of poverty’. Two 
ways of reversion (directional adjustment) of the variables 
was used:

  Calculation of the complementary value to 1 (1-value). 
This is the case of ‘population at risk of poverty’, where 

20 % of people at risk of poverty is equivalent to 80 % 
not at risk of poverty.

  Calculation of its inverse (1/value). This is the case of the 
variable  ‘income  quintile  share’.  The  S80/S20  income 
quintile  share  compares  the  20  %  of  the  population 
with  the  highest  income  with  the  20  %  of  the  popu-
lation with the lowest, while its inverse, S20/S80, keeps 
comparing the same percentages, meaning the higher 
the share the greater the equality.

The next step in the processing of the indicators was related 
to the expression of most variables in relative terms. This was 
done  in  order  to  facilitate  the  comparison  of  populations 
with  different  structures  and  sizes.  Ratios  were  obtained 
by  dividing  the  variable  of  interest  by  its  closest  reference 
population.  Annex  1  shows  the  reference  population  used 
to  transform  each  variable.  For  example,  to  measure  the 
population without ‘unmet needs for medical examination’, 
the  number  of  women  and  men  with  those  unmet  needs 
was divided by the total population aged 16 years or over. 
In a further example, the indicator for the ‘labour force par-
ticipation’ is measured as women and men in employment 
(FTE, Full-time employment) out of the total reference pop-
ulation  (population  of  15  years  old  or  over;  either  women, 
men, or total), as indicated in the Annex 1.

10

Gender Equality Index 2017 − Methodological Report

The variables were converted in relative terms according to 
the following formula:

(1)

  for  group 

,  women  (w),  men  (m), 
where  the  variable 
average (a) or total (T); for the 
 is 
divided by the closest reference population in order to be 
i
).  When  the  variable  does 
expressed  in  relative  terms  (
not  need  to  be  expressed  in  relative  terms  (for  example 
.
mean income), then 

-th country in the period 

X

k

t

X

The transformation to relative terms was not carried out for 
variables where the unit of measurement did not relate to 
persons. For example, ‘duration of working life’ is measured 
in years. In relation to monetary variables analysed, such as 
‘mean  monthly  earnings’  that  is  measured  using  the  pur-
chasing power standard (PPS), which enables comparabil-
ity using an artificial currency that accounts for differences 
in price levels between Member States (2).

In the case of indicators in the domain of power, the refer-
ence population has been chosen as the population over 
18  in  each  country,  and  as  mentioned  above,  in  line  with 
the  calculation  of  the  variables  in  the  domain  of  power, 
the  3-year  average  has  been  used.  For  example,  for  the 
indicators measuring the share of members of the national 
parliaments,  the  indicator  was  calculated  as  the  percent-
age  of  women  in  parliaments  averaged  over  3  years  (i.e. 
for 2015, using the average of 2014, 2015 and 2016) among 
the population in each country aged 18 and older (closest 
reference population) also averaged over the same 3 years. 
Due to the characteristics of data in the domain of power, 
which  is  often  provided  on  a  quarterly  or  biannual  basis, 
a  simple  average  from  all  available  data  points  for  each 
year  has been  computed. For a year with  data  for all four 
quarters  available,  for  example,  the  annualised  data  point 
for series in year t will be:

providers, data sources, method of calculation (whether it 
was EIGE’s calculation with microdata or EUROSTAT calcu-
lation under request), original code of the variable and the 
time reference of data used.

2�1�3�  Imputation of missing values

In  order  to  work  with  a  complete  dataset,  the  imputa-
tion of some missing values needed to be carried out. An 
imputation is a mathematical procedure which allows the 
estimation of a data point when it is not available. The dif-
ferent types of imputation used include the following:

  Variable with only 1 year available for all the countries 
or  it  is  available  for  only  limited  number  of  years.  This 
information  is  used  for  subsequent  years  so  that  it 
avoids  showing  changes  in  time.  For  example,  using 
the  EWCS  survey,  which  is  available  only  for  the  years 
2005,  2010  and  2015,  data  for  2012  has  been  imputed 
using the 2010 data.

  Variable  with  a  missing  value  for  a  certain  country  in 
a  certain  year,  but  with  data  available  for  other  years. 
Missing data is imputed with data from the closest year.
  Variables  with  missing  EU-28  average.  The  average 

(non-weighted) of 28 values is imputed.

  Variable with a missing value for a certain country in all 
years. The missing value is imputed using the expecta-
tion-maximisation (EM) algorithm available in SPSS (3).

2.2. Gender gap metric

Following  the  processing  of  the  indicators  to  improve 
their comparability and their potential aggregation into an 
index, the next step was to define a metric that combines 
the women and men figures in a single measure. The met-
ric selected for this purpose has the following properties:

t

t

t

t

t

 

(2)

The final list of indicators included in the 3rd edition of the 
Gender Equality Index is presented in the table in Annex 1, 
which includes the following: name of the indicator, refer-
ence population, short name, detailed description (includ-
ing brief methodological notes and survey questions), data 

(2)  As Eurostat’s glossary states: ‘Theoretically, one PPS can buy the same 
amount  of  goods  and  services  in  each  country.  However,  price  dif-
ferences across borders mean that different amounts of national cur-
rency units are needed for the same goods and services depending 
on the country. PPS are derived by dividing any economic aggregate 
of a country in national currency by its respective purchasing power 
parities.’ (http://ec.europa.eu/eurostat/statistics-explained/index.php/
Glossary:Purchasing_power_standard_(PPS)).

The  metric  measures  gender  gaps  by  taking  into 
account  the  relative  position  of  women  and  men.  In 
line with the gender perspective of the Gender Equal-
ity Index, all gaps, regardless of whether they are to the 
advantage of women or men, were taken into consid-
eration  and  treated  in  the  same  way.  This  means  that 
gender  equality  is  posited  as  being  relevant  to  both 
women  and  men  in  order  to  emphasise  that  gender 
gaps  are  detrimental  to  both  genders  in  society.  In 
interpreting  the  Gender  Equality  Index,  it  is  impor-
tant  also  to  emphasise  that  it  measures  gender  gaps 
rather  than  the  specific  position  of  women  and  men 

(3)  http://www.psych-it.com.au/Psychlopedia/article.asp?id=267

Gender Equality Index 2017 − Methodological Report

11

 
 
 
 

individually. Therefore, it is not possible to derive infor-
mation about either women or men directly from the 
scores.
It identifies the equality point in order to allow for the 
interpretation  and  comparison  of  the  scores,  ensuring 
that  it  comprises  a  value  that  can  be  associated  with 
gender equality.

The metric that combines the values for women and men 
of each variable, 

, can be expressed as:

indicators  comparable  across  domains  and  sub-domains, 
across  countries  and  over  time.  Figure  1  illustrates  the 
range of the metric applied to each indicator.

As formula 3 shows, the metric is obtained by multiplying 
two elements, the correcting coefficient 
 and the dif-
ference  between  1  and 
  (gender  gap)..  They  are  res-
caled to avoid the presence of zeros, which would impede 
possibilities  to  aggregate  indicators,  sub-domains  and/or 
domains. Those two elements are now explained in more 
detail.

(3)

2�2�1�  Gender gaps

  measures  the  gaps  between  women  and  men  as 

follows:

(4)

From  this  step  onwards,  the  original  variables  disaggre-
gated  by  sex  are  no  longer  in  use,  and  are  replaced  by 
their  transformation  through  a  metric,  which  is  dimen-
sionless  (allowing  comparability  since  measurement  units 
of  variables  have  been  eliminated)  and  bound  between 
[1;  100].  It  satisfies  the  property  of  interpretability  of  each 
variable considered in terms of distance from the equality 
point, set at 100. This maintains comparability across indi-
cators within each country. An added benefit of using this 
metric is that the normalisation step (step number 5 in the 
JRC-OECD  methodology)  is  not  needed,  since  using 
removes  the  presence  of  different  units  of  measurement 
and  the  distorting  effect  of  different  scales,  making  all 

Figure 1� Range of the metric applied to each indicator

where  the  calculation  is  carried  out  for  the  variable  X  for 
the i-th country in the period t in order to obtain the per-
)  represents  over  the  average  of 
centage  that  women  (
)  (4).  For  example, 
the  two  values  of  women  and  men  (
the FTE employment  rate for women in  2015 for  Belgium 
is  37.4  %,  while  the  average  FTE  employment  rate  for  the 
same country in the same year is 44.5 %. On this basis, the 
formula (4) would be:

Inequality

1

Equality

100

(4)  We  are  using  the  average  of  the  two  values  of  women  and  men 
(non-weighted  average)  instead  of  using  the  total  of  the  indica-
tor  (weighted  average)  for  mathematical  reasons,  in  order  to  avoid 
extreme  value  out  of  the  range  0-1  in  case  of  perfect  equality.  For 
example, if the value of FTE employment rate in 2015 of the country 
X of women is 40 % and the same for men, the formula (3) using the 
average of women and men (40 %) takes the value of 0 (40/40-1, that 
reversed will lead to the value of 1, meaning perfect equality). How-
ever, considering different size of female and male population in the 
labour market, the total FTE could differ from 40 % (let’s say 39 %) and 
the formula (3) would not take the value of 0 as expected in the case 
of perfect equality.

12

Gender Equality Index 2017 − Methodological Report

 
 
 
 
 
 
As  previously  mentioned,  the  approach  followed  in  the 
computation of the Gender Equality Index implies that all 
gaps,  regardless  of  whether  they  are  to  the  advantage  of 
women or men, were taken into consideration and treated 
in the same way. However, this means that it is necessary 
for  the  metric  to  ensure  that  there  are  no  ‘compensatory 
effects’. For example, this prevents a situation arising where 
a  Member  State  scores  high  in  gender  equality  because 
women’s  bad  result  in  one  variable  is  compensated  by 
men’s equally bad result in another variable. This is solved 
by  using  absolute  values  of  the  gaps,  thereby  avoiding 
compensation effects between women and men.

The  gender  gap  is  a  relative  indicator  that  can  be  calcu-
lated  for  any  values  for  women  and  men  in  the  interval 
[0;  1],  identifying  gender  equality  point  at  1  and  the  ine-
quality at 0. For reasons of interpretability, this indicator is 
reversed by taking its complementary value: 
. This 
yields values where 1 stands for complete gender equality, 
with any value below that indicating a proportional lack of 
gender  equality  in  a  given  indicator,  with  full  gender  ine-
quality at 0. In the example given above, the formula takes 
the value of (1-0.16) = 0.84.

2�2�2� Correcting coefficient

The correcting coefficient 
 is the other element in the 
metric used to transform the original variables. It makes it 
possible to take into account the country context by com-
paring  the  levels  achieved  in  all  Member  States  for  each 
indicator.  In  this  way,  an  indicator  with  a  good  score  is 
the reflection of both low gender gaps and high levels of 
achievement.

Gender gaps 

Level of achievement

The  purpose  of  the  correcting  coefficient  is  to  compare 
the performance of each country with the best performer 
in the EU-28. In a particular variable, the further the score 
of a country diverges from the level of the best performer, 
the more the score will be adjusted.

The correcting coefficient of the 3rd edition of the Gender 
Equality  Index  has  been  modified.  In  previous  editions  of 
the  Index a correcting coefficient was calculated  for each 

year. This was done by taking the quotient of the distance 
for each Member State of its total level (5) in a given indi-
cator (expressed in relative terms and reversed if necessary, 
)  to  a  benchmark.  This  means  that  in  every  year  the 
benchmark  was  the  highest  performing  Member  State  in 
that same indicator in the same year (

).

Despite the conceptual relevance that the correcting coef-
ficient  has  in  the  measurement  of  gender  equality,  there 
were two limitations. First, the denominator was a bench-
mark  that  has  varied  in  every  edition  of  the  index.  It  was 
dependent  on  the  result  of  the  best  performing  country 
of  each  year,  making  it  difficult  to  carry  out  a  trend  anal-
ysis  of  the  Index.  Second,  the  impact  of  the  correcting 
coefficient in the final metric of each indicator was higher 
when  compared  to  the  impact  of  the  actual  gender  gap. 
It is desirable to have the contribution of gender equality 
higher than the contribution of the correcting coefficient, 
overall as well as for every Member State and each variable.

One of the priorities in the development of the 3rd of the 
Gender  Equality  Index  was  to  carry  out  research  and  find 
solutions  aimed  at  overcoming  these  limitations.  In  order 
to solve these issues, two changes have been made. First, 
the  benchmark  was  frozen  and  kept  constant  through-
out  the  years,  and  second,  the  formula  was  modified  to 
increase the impact of the gender gaps.

In relation to the first aspect of benchmarks that changed 
across  the  years,  the  solution  was  to  fix  or  freeze  one 
benchmark for all years under question. It was decided to 
take  the  quotient  of  the  distance  for  each  country  of  its 
total  level  in  a  given  indicator  to  that  of  the  highest  per-
forming Member State for the same indicator in the 2005, 
2010, 2012 and 2015, as follows:

(5)

In this way, the denominator has become a fixed quantity 
in each edition of the index; in other words the benchmark 
will not change over the years. If  in  subsequent  years the 
level achieved in a specific indicator of a specific country is 
larger than its fixed benchmark, the value of its correcting 
coefficient will be trimmed to 1 (i.e. there will be no correc-
tion applied). When these fixed benchmarks are obsolete, 
they will be moved and the time series will be recalculated 
in order to keep time comparability.

(5)  In the 1st Gender Equality Index, this measure considered the average 
between women and men (unweighted average). In the 2nd edition 
of  the  Gender  Equality  Index  (EIGE,  2015)  it  was  modified  by  taking 
the  values  for  the  total  population  (weighted  average).  Totals  were 
available for most indicators, with the exception of the indicators ‘life 
years  at  birth’,  ‘healthy  life  years  at  birth’  and  ‘mean  equivalised  net 
income’, for which the average is used instead of the total.

Gender Equality Index 2017 − Methodological Report

13

 
 
In  relation  to  the  second  limitation,  analysis  of  the  contri-
butions that the correcting coefficient and the gender gap 
have in the metric of each indicator has been done. Several 
options were tested to find a balance between the impact 
of  the  equality  component  and  the  level  of  achievement 
(see  Annex  2  for  a  detailed  explanation  of  the  analysis  of 
the contribution of correcting coefficient).

After  testing  several  options,  the  new  correcting  coeffi-
cient for a given indicator is expressed as:

Additionally,  the  two  indicators  that  refer  to  the  share  of 
people  who  spend  their  time  on  caring  for  dependent 
family  members,  and  cooking  and  housework,  are  also 
uncorrected  because  what  matters  are  gender  inequali-
ties rather than the level of involvement in these activities, 
which may depend on other factors, such as fertility rates 
or  cultural  traditions  of  eating  out  rather  than  cooking 
at  home.  In  this  case,  it  is  difficult  to  argue  that  100  %  of 
women or men should spend time in caring and/or house-
work activities (following the principle that the higher the 
value, the better is the situation).

(6)

Taking the example of one indicator, the FTE employment 
rate, formula (5) takes the following values:

2.3. Computing the index

The  correcting  coefficient  takes  values  between  0  and  1 
and for each indicator penalises countries with low overall 
achievement.  Following  this  example,  the  formula  (6)  for 
the FTE employment rate in Belgium in 2015 will take the 
value of:

For  example,  for  the  indicator  FTE  employment  rate  for 
Belgium  in  2015,  the  application  of  formula  (3)  with  the 
correcting coefficient will lead to a final metric of 71.6.

In the 3rd edition of the Gender Equality Index, correcting 
coefficients  are  applied  to  most  variables.  The  indicators 
under  the  domain  of  power  are  not  corrected  because 
they represent shares. This means, for example, that when 
the  representation  of  women  and  men  add  up  to  100  %, 
perfect  equality  is  reached  only  when  women  and  men 
are  equally  represented.  In  addition,  the  number  of  per-
sons  in  decision-making  positions  is  fairly  fixed  and  it  is 
therefore  not  desirable  to  maximise  the  number  of  these 
positions. For example, what is important is to increase the 
share  of  women  on  company  boards  and  not  to  increase 
the size of boards per se.

The construction of the Gender Equality Index relies on the 
necessity of choosing between various alternatives related 
to the imputation of missing data, as well as the methods 
to  be  used  for  normalization,  weighing  and  aggregation. 
The different choices needed introduce subjectivity in the 
process of computing the Index.

To reduce this subjectivity as much as possible, the 1st edi-
tion  of  the  Gender  Equality  Index  adopted  a  multi-mod-
elling  principle.  This  means  that  a  set  of  potential  indices 
were computed in order to select the most robust for the 
Gender Equality Index. Those sets of potential indices were 
obtained through the combination of different alternatives 
related  to  the  formula,  that  will  be  presented  below  and 
summarised in Table 3. On that basis, 3,636 formulas were 
considered  and  therefore  3,636  indices  were  computed. 
Then,  in  order  to  select  the  most  robust  index  among 
them  all,  a  robustness  analysis  was  carried  out.  This  pro-
cess,  which  is  explained  in  depth  in  the  section  “statisti-
cal  analysis”,  provided  the  formula  of  the  Gender  Equality 
Index, whose characteristics are presented in Table 1.

Considering  that  this  3rd  edition  of  the  Gender  Equal-
ity  Index  has  included  new  indicators  and  new  aspects 
of  the  methodology,  the  robustness  analysis  was  carried 
out again in order to confirm that the formula used in the 
computation  of  the  Index  remains  the  most  robust,  that 
is to say, the one that depends more on the values of the 
variables rather than on the formula used.

14

Gender Equality Index 2017 − Methodological Report

 
 
 
 
 
Table 1: 

Characteristics of the Gender Equality Index

Weighting

Aggregation

Normalisation

Imputation

Variables

Equal

Arithmetic

Sub-domains

Domains

Equal

Geometric

Experts (AHP)

Geometric

Metric by construction acts as a normalisation method

Closest values
Average of MS for EU-28
Expectation-Maximisation (EM)

No imputation

No imputation

2�3�1�  Weighting

The  first  key  decision  taken  concerned  the  weights 
assigned to each variable, sub-domain and domain during 
the  aggregation  process.  These  were  then  tested  in  the 
multi-modelling procedure of the Index. Four methods for 
assigning  weights  were  tested:  equal  weights,  a  modified 
version of equal weights, weights retrieved from statistical 
analysis,  and  finally  weights  derived  from  expert  opinions 
(using the analytic hierarchy process AHP).

In the first method, all domains, sub-domains and variables 
are  assigned  equal  weights  (or  equivalently  no  weights 
are  assigned).  Although  equal  weights  may  appear  to  be 
a simple solution, it is far from a neutral one, and as with 
any  other  weighing  method,  involves  a  normative  judge-
ment. Due to differences in the spread of values, or alterna-
tively high correlations, some elements can have a greater 
degree of influence in the final composite score.

Secondly, a modification to the methods of equal weights 
was  also  tested  with  the  variable  of  Segregation  in  work. 
Although  conceptually  Segregation  was  placed  as  a  sep-
arate  sub-domain,  the  multivariate  analysis  presented 
a  structure  where  Segregation  was  consistently  loaded 
with  another  sub-domain.  This  is  symptomatic  of  a  high 
degree of correlations between related  issues.  As  a  result, 
this  method  attributed  a  weight  representing  half  the 
sub-domain, distributing the other half equally among the 
remaining indicators.

to reify the structure at this level (6). It is important to note 
that  this  weighting  method  only  corrects  for  overlapping 
information,  as  evidenced  by  correlated  variables,  and 
is  not  a  measure  of  theoretical  importance  among  the 
variables.

Finally,  the  fourth  weighting  method  tested  was  based 
on  experts’  opinion.  It  is  a  participatory  method  which 
requires  the  input  of  gender  experts  (Nardo  et  al,  2008). 
This  method  is  particularly  relevant  since  the  Gender 
Equality  Index  is  underpinned  by  EU  policy  and  this 
method  provides  a  basis  to  assess  and  discuss  gender 
policy  action.  It  is  based  on  experts’  opinion  rather  than 
technical  measurements.  Its  strength  is  thus  in  providing 
a  systematic  representation  of  experts’  opinion  and  in 
increasing the transparency and legitimacy of the Gender 
Equality Index as a tool to support gender equality policy 
in the EU. The experts consulted consisted of members of 
EIGE’s Working Group on Gender Equality Index and EIGE‘s 
Expert Forum (7). Experts’ opinion on weights were sought 
at  the  domain  level,  including  only  core  domains,  using 
a  participatory  approach  called  analytic  hierarchy  process 
(AHP). This consultation process was undertaken in the last 
quarter of 2012.

Analytic  hierarchy  process  (AHP)  is  time-consuming  but 
easy to perform, even though assigning weights to a com-
plex phenomenon such as gender equality is not an easy 
task.  It  combines  both  qualitative,  by  asking  to  express 

The  third  method,  as  an  alternative,  retrieves  weights 
endogenously  from  the  data.  The  multivariate  analysis, 
used to confirm the structure of the Index using principal 
component  analysis  (PCA),  provides  correlations  between 
the  indicators  used  and  their  respective  domains.  These 
correlations, called ‘factor loadings’, can be used to deter-
mine weights by rescaling them so they add up to one in 
each domain. This weighting method can only be applied 
at  the  sub-domain  level,  since  the  PCA  can  only  be  used 

(6)  The  data  matrix  the  gender  equality  index  uses  has  not  enough 
degree of freedom. In statistics we refer to degree of freedom (df) as 
the number of values in a final calculation of a statistics that are free 
to vary. If n is the number of observation and k the number of inde-
pendent variables the df is (n-k). In other words the df is the minimal 
number of values which should be specified to determine all the data 
points. In the Index case the data matrix is 28x31, which returns 0 df.
(7)  The  Working  Group  is  a  board  officially  established  by  EIGE’s  Man-
agement board in 2011 to provide the technical support in the con-
struction  of  the  Gender  Equality  Index  and  in  developing  a  strategy 
to  disseminate  it.  The  Expert  Forum  is  the  Institute’s  advisory  body. 
Its  principle  function  is  to  provide  expertise  knowledge  in  the  field 
of gender equality.

Gender Equality Index 2017 − Methodological Report

15

a  preference  between  two  domains,  and  quantitative 
aspects,  by  assigning  a  score  to  the  preference  inten-
sity.  The  AHP  is  based  on  ordinal  pair-wise  comparison 
of  domains.  Experts  were  first  asked  to  make  a  pair-wise 
comparisons of domain, and secondly, to assign a strength 
of preference to the selected domain on the scale from 1 
(equal  importance  of  domains)  to  9  (the  most  important 
domain). The relative weights assigned by each expert can 
then be computed in order to obtain the overall score for 
each domain (8). The procedure also tests whether weights 
are  consistent,  that  is  if  they  are  numerically  coherent 
across pair-wise comparisons. For example, if work is more 
important  than  power,  and  power  more  important  than 
health, then health cannot be more important than work. 
However,  since  incoherence  is  an  integral  part  of  human 
thinking,  an  inconsistency  threshold  is  generally  tolerated 
(Saaty,  1990).  The  AHP,  while  solving  for  inconsistency, 
was able to keep 60 % of experts’ weights. These experts’ 
weights were averaged before being tested. Only average 
experts’ weights were used.

There  is  no  general  consensus  as  to  what  an  appropriate 
weighing measure should be. Furthermore, there exists an 
inherent  bias  in  the  selection  of  a  weighting  method,  as 
they all represent a subjective choice which has a bearing 
on  the  final  scores.  The  selection  of  a  weighing  method 
goes hand-in-hand with choices of aggregation methods. 
The final decision of weighting was made through robust-
ness  analysis  involving  all  relevant  aspects  to  calculate 
a composite indicator.

The robustness analysis confirmed the use of equal weights 
in  the  aggregation  of  the  indicators  at  sub-domain  level 
and  in  aggregating  sub-domain’s  score  at  domain  level. 
Additionally, different weights used to aggregate the score 
of  the  different  domains  to  calculate  the  Gender  Equality 
Index  have  been  confirmed.  Table  2  sets  out  the  weights 
used  for  the  Gender  Equality  Index  in  each  of  the  six 
domains.

2�3�2� Aggregation

This step groups the variables in order to create indices at 
the sub-domain level, at the domain level and at the level 
of the overall Gender Equality Index. Three main aggrega-
tion methods were tested for their applicability in forming 
a  composite  indicator:  arithmetic,  geometric  and  har-
monic means. The arithmetic mean allows full compensa-
bility, offsetting a poor performance in some variables by 
a sufficiently large advantage in other variables, while the 
geometric and harmonic decrease the potential compen-
satory  effect.  The  phase  of  aggregation  and  weighing  is 
highly interconnected. In all cases of arithmetic, geometric 
and  harmonic  aggregation,  the  weights  represent  trade-
offs  between  the  variables.  Moreover,  the  compensability 
between variables is higher in the combination of arithme-
tic aggregation and equal weights.

Aggregation  methods  have  remained  the  same  in  the 
3rd  version  of  the  Gender  Equality  Index,  notably  result-
ing in arithmetic means to aggregate the indicators in the 
sub-domains, geometric means to aggregate sub-domains 
in the domains, and to aggregate domains to calculate the 
Gender Equality Index.

2�3�3� Normalisation

Despite  the  multi-modelling  approach  used  in  the  previ-
ous steps, the Gender Equality Index uses the metric intro-
duced  in  formula  (3)  as  a  unique  normalisation  method. 
This metric is considered as a normalisation method since 
it adjusts for the measurement unit and it corrects for the 
range of variation of each variable by bounding it between 
[0; 1]. Additionally, it satisfies the property of interpretability 
of each variable considered in terms of distance from the 
equality  point,  set  at  1,  and  keeps  comparability  among 
variables within each country. As a result, no new normal-
isation method has been introduced in the 3rd edition of 
the Gender Equality Index.

Table 2:  Mean experts’ weights used for the Gender Equality Index (rounded) (9)

Work

0.19

Money

Knowledge

0.15

0.22

Time

0.15

Power

Health

0.19

0.10

(8)  The  overall  score  for  each  domain  for  each  expert  are  calculated 
using the Saaty’s eigenvector method (EM) (Saaty, T. L. (1990), Eigen-
vector and logarithmic least squares, European Journal of Operational 
Research 48, 156-160).

16

Gender Equality Index 2017 − Methodological Report

(9)  Weights  with  15  digits,  used 
Index,  are 
0.154066793988684) 

Equality 
(Money, 
(Time,  0.14589100376959) 
0.0991180446771528).

the 

following: 

in  the  calculation  of  Gender 
(Work,  0.193293420026752) 
0.216676323111808) 
(Health, 

(Knowledge, 

(Power,  0.190954414426013) 

2�3�4� Calculating the final metric

2.4. Statistical analysis

Having  followed  these  steps,  the  calculation  of  the  Gen-
der  Equality  Index  becomes  possible.  It  follows  aggrega-
tion  steps  provided  by  the  measurement  framework,  as 
confirmed  by  statistical  assessment,  multivariate  analysis 
and robustness analysis. First, all variables within each sub-
domain are aggregated, creating indices at the sub domain 
level.  Second,  these  are  aggregated  at  the  domain  level. 
Finally, all the domain indices are aggregated, creating the 
overall Gender Equality Index.

The final metric of Gender Equality Index is the following:

t

t

(7)

1, ..., 31

1, ..., 31

t

where 
  identifies  the  best  Gender  Equality  Index  for 
,  Γ (Xivt )  is  the  metric 
the  i-th  country  during  the  time 
described  in  formula  3,  used  at  variable  level  (v),  while 
  is  the  expert  weights  used  at  domain  level  and 
retrieved from the analytic hierarchical process (AHP) with 
the network of EIGE’s experts in the 1st edition of the Gen-
der Equality Index (see Table 2).
1, ..., 31

t

Using the example, the indicator of FTE employment rate 
has  been  aggregated  at  sub-domain  level  together  with 
the  indicator  of  duration  of  working  life  (DWL),  using 
the  average  of  the  metric  of  the  two  indicators.  For  FTE 
employment  rate  the  metric  is  71.6,  and  for  DWL  it  is 
83.4.  The  average  of  the  two  is  77.5  which  is  the  score  of 
the  sub-domain  of  participation.  Next,  the  scores  of  the 
sub-domain  of  participation  (77.5)  and  the  sub-domain 
of  quality  of  work  and  segregation  (70.2)  are  aggregated 
using  the  geometric  mean  with  equal  weights,  in  order 
to  obtain  the  score  for  the  domain  of  work  (73.8).  Finally, 
the  scores  of  the  six  domains  are  aggregated  using  the 
geometric  mean  with  different  weights  for  the  domains, 
using  the  experts’  weights  defined  with  the  analytic  hier-
archical process (AHP).

The final score of Belgium is thus calculated as:

A  number  of  statistical  steps  were  followed  to  inform 
and verify the final selection of the indicators in the Gen-
der  Equality  Index.  The  pre-selected  variables,  that  fulfil 
the  conceptual  and  statistical  criteria,  were  processed  to 
ensure  that  they  all  measured  gender  equality  aspects 
in  a  homogeneous  way.  Once  the  preselected  variables 
had been transformed in relation to the direction of their 
interpretation,  their  expression  in  relative  terms  and  their 
aggregation into gender gaps adjusted by level of achieve-
ments, they then went through an in-depth statistical anal-
ysis in order to decide on their inclusion in the final list of 
indicators used in building the Gender Equality Index. The 
statistical  analysis  of  the  preselected  variables  provides 
information  about  their  individual  quality  and  also  about 
their global internal consistency. These following statistical 
steps formed the basis of the final selection of the indica-
tors in the Gender Equality Index.

, and 
The main descriptive statistics of each metric 
the  gender  gap  adjusted  by  level  of  achievement,  are 
presented  in  Annex  3.  The  third  column  is  the  standard 
deviation,  showing  that  those  with  greater  variability  will 
have  a  greater  impact  in  the  computation  of  the  Index. 
For  example,  the  indicator  ‘Share  of  members  of  public 
research  funding’  has  a  higher  standard  deviation,  and, 
therefore, a higher impact in the Index, than the indicator 
‘Life expectancy’. The two last columns make is possible to 
identify the presence of outliers (10). As there are no cases 
with  simultaneous  ‘anomalous’  values  of  Skewness  and 
Kurtosis, no outlier correction was needed.

Next, a multivariate analysis was carried out to examine 
the structure of the data by measuring the extent to which 
the conceptual framework has statistical support from the 
preselected  variables.  Two  methods,  which  are  described 
below, have been applied to 
: cross-correlations anal-
ysis  and  principal  component  analysis  (PCA).  The  results 
obtained  informed  the  decision  of  the  final  list  of  indica-
tors to include in the computation of the Gender Equality 
Index (see Annex 1).

The  first  of  these  two  methods,  cross-correlations anal-
ysis,  measured  the  association  between  variables.  This 
tool had a double aim: to understand the inter-relationship 

(10)  Outliers defined as cases with |skewness|>2 and |kurtosis|>3.5. Skew-
ness  is  a  measure  of  the  asymmetry  of  a  distribution.  It  is  equal  to 
0  if  the  distribution  is  symmetric  (same  tails  on  both  sides  and  dis-
tribution  balanced  around  the  mean).  It  is  negative  if  the  left  tail  is 
longer and the mass of the distribution is concentrated on the right 
side,  and  it  is  positive  in  the  opposite  case.  Kurtosis  is  a  measure  of 
the shape of a distribution. It measures how tall and sharp the central 
peak is.

Gender Equality Index 2017 − Methodological Report

17

t

1, ..., 31

 
 
between  the  variables,  and  to  further  refine  the  dataset, 
keeping only variables with meaningful and coherent cor-
relations.  The  correlation  matrix  of  the  dataset,  calculated 
with  the  Pearson’s  correlation  coefficient,  is  presented  in 
Annex  4  (11).  Although  the  data  originates  from  different 
sources and variables are measured mainly at macro level, 
significant  levels  of  association  between  variables  can  be 
observed.  Additionally,  attention  was  given  to  selecting 
variables  for  each  domain  that  did  not  present  significant 
negative  correlations  among  them.  Also  it  was  important 
to  avoid  double  counting  of  the  selected  variables.  This 
means  that  there  is  no  pair  of  variables  with  a  Pearson’s 
correlation coefficient greater than 0.92 (12). Finally, all varia-
bles present significant positive correlations with their own 
sub-domain and the majority of them have a positive and 
significant  correlation  with  the  Index.  These  associations 
are the pillars of the statistical structure of the data.

The  correlation  analysis  was  followed  by  a  statistical  pro-
cedure  called  principal  component  analysis  (PCA)  that 
attempted  to  find  natural  groupings  (factors  or  compo-
nents)  based  on  the  internal  relationship  of  the  variables. 
The main difficulty resided in finding a suitable set of var-
iables  that  together  form  statistically  coherent  groupings 
that  can  be  related  to  the  conceptual  framework.  Taken 
together,  these  factors  provide  the  measurement  frame-
work used for the Gender Equality Index. Initially, variables 
were grouped according to their meaning within domains 
and  sub-domains,  which  were  established  using  con-
ceptual  mapping.  Next,  the  PCA  was  applied  for  2010  at 
domain level, allowing for the development of factors that 
could map sub-domains. This was applied to each domain 
separately  because  of  the  high  number  of  variables  used 
for the analysis relative to the number of observations (28 
countries) which prevented the use of a PCA technique to 
confirm  the  overall  theoretical  structure.  Data  referred  to 
2010, and was used to not only determine the structure of 
the  Index  as  originally  defined  in  the  1st  edition,  but  also 
the  formula  (aggregation  and  weighting)  as  is  presented 
below (robustness analysis).

The set of variables that work well together, verifying sta-
tistically the conceptual framework, represent the final set 
selected  for  computing  the  Gender  Equality  Index.  The 
results  of  this  exploratory  analysis  obtained  for  the  final 
list of variables is shown in Annex 5. The PCA provides the 

(11)  The Pearson’s correlation coefficient (r) is a measure of the linear cor-
relation between two variables. It takes values between + 1 and − 1, 
where 1 is total positive linear correlation (direct proportionality), 0 is 
no linear correlation, and − 1 is total negative linear correlation (direct 
inverse proportionality).

(12)  Earnings  and  Income  variables  present  very  high  correlations.  How-
ever,  it  is  conceptually  relevant  to  keep  both  in  the  Index  because 
they take into consideration different aspects, including pensions.

18

Gender Equality Index 2017 − Methodological Report

measurement  framework  for  the  Gender  Equality  Index 
which consists of the six domains of gender equality iden-
tified for the Index at theoretical level, 14 sub-domains and 
31  variables.  Two  sub-domains  were  split  in  the  statistical 
structure.  Within  the  domain  of  work,  the  sub-domain 
of  segregation  was  merged  with  quality  of  work.  In  the 
domain  of  health,  the  sub-domain  of  behaviour  was  split 
in  two  in  the  measurement  framework,  which  was  finally 
solved  by  aggregating  them  with  a  half  weight  each  in 
a single sub-domain.

Subjectivity is introduced in composite indicators through 
the choices made to compute them. To remove subjectiv-
ity, the Gender Equality Index adopts the principle of mul-
ti-modelling. This means than instead of relying on a single 
model, all possible indices are computed in order to select 
the one that best measures gender equality. The four main 
grounds of subjectivity relate to the choices that are made 
in  terms  of  operational  choices  such  as  normalisation, 
weighting  and  aggregation  at  the  level  of  sub-domains 
and  then  domains.  These  different  options  considered  to 
compute  the  Gender  Equality  Index  are  described  in  the 
1st edition of the Gender Equality Index (EIGE, 2013).

Robustness analysis was carried out next to test several 
alternative decisions regarding imputation of missing data, 
as  well  as  the  methods  used  for  weighing  and  aggrega-
tion.  This  3rd  edition  of  the  Gender  Equality  Index  made 
a number of adjustments to the formula, including adding 
a  number  of  new  variables,  and  running  the  robustness 
analysis  again  was  needed.  The  procedure  is  explained 
in-depth  in  the  1st  edition  of  the  Gender  Equality  Index 
(EIGE,  2013,  pp.  4952).  Since  the  quality  of  a  model  also 
depends  on  the  soundness  of  its  assumptions,  good 
modelling  practice  requires  that  the  modeller  provide 
an  evaluation  of  the  confidence  in  the  model,  assessing 
the  uncertainties  associated  with  the  modelling  process 
and  the  subjective  choices  taken  (Nardo  et  al.,  2008).  The 
choice of one method against another has implications for 
the  final  result  of  the  country  score  as  well  as  its  ranking. 
However, it is possible to evaluate how and by how much 
the  results  change  when  different  methodological  deci-
sions are made in the computation of the Gender Equality 
Index.  The  robustness  analysis  follows  a  multi-modelling 
principle:  this  means  that  since  it  is  not  desirable  to  trust 
one  model,  the  approach  is  to  test  a  multitude  of  possi-
ble models based on various combinations of the normal-
isation, weighing, missing data and aggregation decisions 
that can be made. In other words, since there is no unique 
recipe in constructing composite indicators, the approach 
is to compute them all, before making a final selection. The 
robustness  analysis  of  the  Gender  Equality  Index  is  based 
on the combination of the factors presented in Table 3.

Table 3: 

Source of uncertainty and alternatives tested

Type of uncertainty

Alternatives

Weights

Aggregation

Missing data

Equal weights

Modified equal weights

PCA weights

Expert’s weights (AHP)

Arithmetic mean

Geometric mean

Harmonic mean

100 simulations for imputed data

The  possible  alternatives  tested  within  the  computation 
of  the  Index  have  involved  different  combinations  based 
on  the  four  alternatives  weights  (Equal  weights,  Modified 
equal  weights,  PCA  weights,  Expert’s  weights  (AHP))  and 
the  three  aggregation  functions  (arithmetic,  geometric 
and  harmonic  mean).  Compensatory  methods  relating  to 
aggregation  have  been  gradually  adopted,  which  means 
that the compensation allowed is higher within the aggre-
gation  at  the  level  of  the  variables,  where  the  arithmetic 
mean  is  always  considered.  However,  it  is  gradually  less 
compensatory  within  the  sub-domain  and  domain  level, 
where only geometric or harmonic means (13) are allowed. 
Weights  also  present  a  large  source  of  uncertainty,  which 
needs  to  be  accounted  for.  The  four  methods  outlined 
above are used in the robustness analysis. The last source 
of  uncertainty  deals  with  the  issue  of  the  estimation  of 
missing  data.  Estimations  for  missing  data  were  sampled 
from  their  probability  distribution  through  Monte  Carlo 
simulations (100 runs).

The  robustness  analysis  involved  combining  all  possi-
ble  sources  of  variations  (simulations  of  imputed  data; 
all  weight  and  aggregation  alternatives).  Altogether,  this 
resulted in the computation of 3 636 sets of scores. These 
correspond to the overall Index distribution of all the pos-
sible  scenarios  generated  within  these  assumptions.  The 
selection process for the best Gender Equality Index, first, 
relied  on  the  identification  of  the  median  Index  within 
these  3  636  scenarios.  Second,  the  differences  between 
each scenario and the median index were computed. The 
best  index,  according  to  the  robustness  analysis,  consists 

(13)  In  the  geometric  mean,  each  variable  is  multiplied  as  opposed  to 
the  arithmetic  mean  where  they  are  summed  up.  Variables  weights 
are  computed  as  exponents  in  the  multiplication.  To  avoid  close  to 
zero values biasing the geometric mean, the variables were rescaled 
linearly on the interval [1, 100].

of  the  one  that  lies  closest  to  the  median  set  of  scores. 
In  particular  the  best  Index  was  chosen  considering  the 
Euclidean distance di,j as sum of squared root of the differ-
ence between each index It
i,j and the overall median index 
It
me (see formula 8).

(8)

The combination closest to the median of the distribution 
of  possible  scores,  was  adopted  to  compute  the  Gen-
der  Equality  Index.  Its  characteristics  rely  on  the  use  of 
the  arithmetic  mean  and  equal  weights  at  variable  level; 
geometric  mean  and  equal  weights  at  sub-domain  level; 
and geometric mean and expert’s weights at domain level.

The Gender Equality Index, so defined, is the most robust 
combination of assumptions among all possible scenarios. 
It  provides  lower  levels  of  compensability  at  sub-domain 
and  domain  levels  since  it  relies  on  geometric  means.  In 
addition,  since  it  uses  equal  weights  and  linear  aggre-
gation  at  the  bottom,  it  allows  higher  compensability 
is  not  problematic 
between  variables.  This  approach 
because the correlation matrices do not include high neg-
ative correlations.

Conducting  a  robustness  analysis,  as  described  above, 
allows the level of confidence associated with the selected 
final Index score to be quantified. Figure 2 illustrates how 
the main results of the Index can change along with varia-
tions in assumptions. It shows the distribution of the differ-
ence between all possible ranks obtained out of the 3 636 
scenarios considered and the rank of the Gender Equality 
Index  selected.  It  provides  an  overview  of  the  robustness 
of  the  Index  with  respect  to  the  sources  of  uncertainty 

Gender Equality Index 2017 − Methodological Report

19

 
 
considered  and  shows  a  clear  peak  around  zero,  which 
represents  no  differences  in  rankings.  This  is  a  sign  of 
robustness in itself.

A closer look of the distribution in Table 4 shows that 32 % 
of cases have not shifted positions and have kept the exact 
same  ranking,  while  in  59  %  the  shift  in  rank  is  at  most 
of  one  position,  and  in  73  %  of  cases  it  has  changed  at 
most by two positions. Overall, this analysis demonstrates 
that  the  Gender  Equality  Index  is  robust  and  stable  with 
respect of the selected sources of uncertainties.

Additionally, in order to assess the structure of the selected 
index,  the  correlation  matrix  between  the  overall  Index, 
domains  and  sub-domains  was  examined  (see  Annex  6). 
The  Pearson’s  correlation  matrix  shows  very  strong  corre-
lations with the domains of both power and time (r = 0.90 
and r = 0.89), and the weakest correlation is between the 
Index  and  the  domain  of  health  (r  =  0.65)  in  2015.  These 
results  confirm  the  structure  of  the  domains  as  mean-
ingful  in  explaining  the  overall  Index.  Overall  this  means 
that the domains of gender equality selected, individually 
or  together,  successfully  describe  overall  levels  of  gender 
equality.

Figure 2: Histogram of all possible rank differences (28*3 636 values)

35000 

30000 

25000 

20000 

15000 

10000 

5000 

0 

y
c
n
e
u
q
e
r
F

7
2
-

4
2
-

1
2
-

8
1
-

5
1
-

2
1
-

9
-

6
-

3
-

0

3

6

9

2
1

5
1

8
1

1
2

4
2

7
2

 Index_i rank - Gender Equality Index rank  

Table 4: 

Percentage of cases in the shifted rank

Rank difference Interval

Percentage of cases

[– 27, – 13]

[– 12, – 8]

[– 7, – 5]

[– 4, – 2]

[– 1, 1]

[0]

[– 2, 2]

[2, 4]

[5, 7]

[8, 12]

[13, 27]

20

Gender Equality Index 2017 − Methodological Report

0.1

1.5

5.3

14.7

59.0

32�2

73�1

13.6

4.1

1.2

0.6

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
The structure of the Gender Equality Index is also confirmed 
at sub-domain level. There are not significant negative cor-
relations among the sub-domains and all of them present 
significant  positive  correlations  within  their  own  domain. 
Consistently in almost every case, sub-domains contribute 
most  to  their  respective  domains.  For  example,  Financial 
resources  registers  the  highest  correlation  (r  =  0.97)  to  its 

own domain money. A further sign of good fit of the Index 
resides  in  the  fact  that  all  the  domains  and  sub-domains 
are  significantly  correlated  with  the  Index  at  a  5  %  level 
of significance. Overall, this analysis demonstrates that the 
Gender Index Equality is a robust measure with an internal 
structure  that  is  both  statistically  coherent  and  consistent 
with the conceptual framework of the Index.

Gender Equality Index 2017 − Methodological Report

21

3� The satellite domains

3.1.  The satellite domain of 

violence

Violence is a satellite domain of the Gender Equality Index. 
This  status  stems  from  both  conceptual  and  statistical 
considerations. Conceptually, this domain looks at gender 
based violence against women, since it recognises that vio-
lence  is  an  expression  of  power  linked  to  the  domination 
of  some  forms  of  masculinity,  mostly  over  women  (EIGE, 
2013). It is rooted in the unequal status of men and women 
in society which implies that violence against women is the 
corollary of structural inequalities experienced by women 
in the field of work, health, money, power, education and 
time use. From this point of view, violence against women 
must be incorporated alongside the other domains of the 
Gender  Equality  Index.  From  a  statistical  perspective,  the 
domain of violence cannot be treated in the same way as 
the other domains of the Gender Equality Index, because it 
does not measure gaps between women and men. Rather, 
it  measures  a  phenomenon  that  applies  to  women  only. 
The overall objective is not to reduce the gaps of violence 
between  women  and  men,  but  to  eradicate  violence 
altogether  (EIGE,  2013,  p.  31).  This  fundamental  difference 
between  the  domains  of  the  Gender  Equality  Index  and 
the domain of violence justifies the fact that this domain is 
treated as a satellite.

When  the  Gender  Equality  Index  was  first  developed  in 
2013,  the  satellite  domain  of  violence  was  left  empty  due 
to lack of comparable data across all EU Member States. It 
was  qualified  by  the  authors  as  the  ‘largest  statistical  gap 
in measuring the progress on gender equality at EU level’ 
(EIGE,  2013,  p.  139).  The  completion  of  an  EU-wide  survey 
on  violence  against  women  by  the  European  Agency  for 

Fundamental  Rights  (FRA)  in  2012  constituted  an  unprec-
edented advance in assessing the magnitude of the issue 
in the EU.

Building  on  the  FRA  survey  findings,  the  2nd  edition  of 
the  Gender  Equality  Index  (EIGE,  2015)  presented  a  first 
attempt  at  populating  the  satellite  domain  of  violence 
by  constructing  a  composite  indicator  of  direct  violence 
against  women.  The  report  did  not  provide  scores  for 
individual Member States but instead clustered them into 
three  broad  groups  according  to  their  levels  of  disclosed 
violence in relation to the EU average (EIGE, 2015, p. 131).

For the 3rd edition of the Gender Equality Index, the satel-
lite domain of violence sought to achieve the three follow-
ing objectives (14):

 

 

 

To provide a user-friendly statistical tool to monitor the 
extent of the most common forms of violence against 
women in the EU in a comparable manner.
To identify additional forms of violence against women 
in need of regular monitoring and for which data col-
lection requires more coordination and efforts.
To define a set of contextual factors likely to affect the 
extent of violence against women. The analysis of those 
factors is expected to provide insights into which mac-
ro-level aspects of society are likely to alleviate the risk 
of women being subjected to violence and to support 
the analysis of what constitutes a successful integrated 
approach  to  prevent  and  address  the  phenomenon. 
The structure is presented in Figure 3.

The following sections describes the measurement frame-
work of the satellite domain of violence for the 3rd edition 
of the Gender Equality Index.

(14)  To  support  this  development,  the  University  of  Erlangen-Nurnburg, 
Germany, provided EIGE with some analysis. Professor Monika Schro-
ettle was the project director.

Gender Equality Index 2017 − Methodological Report

23

Figure 3:  Measurement structure of the domain of violence

CONTEXTUAL FACTORS

ADDITIONAL INDICATORS

COMPOSITE MEASURE

One single score

3�1�1�  Selecting indicators

In line with the objectives stated previously, a three-tiered 
structure  of  measurement  was  defined  as  the  most  likely 
to  provide  a  comprehensive  measurement  of  the  phe-
nomenon  of  violence  against  women  in  the  EU,  includ-
ing: 1. a set of indicators on the extent of violence against 
women  that  will  form  the  composite  measure,  2.  a  set  of 
additional indicators covering a broader range of forms of 
violence, and 3. a set of contextual factors for the advanced 
interpretation of the first and second sets of indicators.

In  this  report  only  the  set  of  indicators  aggregated  into 
a  composite  measure  are  described.  The  full  measure-
ment  framework  of  violence  against  women  is  presented 
in ‘Gender Equality Index 2017. Measurement framework of 
violence against women’ (EIGE, 2017b).

When selecting indicators to form the composite measure, 
the  specific  criteria  imposed  by  the  nature  of  the  Gender 
Equality  Index  (individual  level,  outcome  based,  no  more 
than 10 % of missing values) were applied. Additional crite-
ria were also taken into account.

3. whose inclusion does not decrease the meaningfulness 
of the composite measure, 4. that are widely criminalised, 
and  5.  for  which  comparison  of  data  between  Member 
States  is  possible.  Finally,  to  ensure  the  highest  statistical 
robustness of the composite measure, the number of vari-
ables was limited to the minimum.

The composite measure for the extent of violence against 
women  seeks  to  provide  a  comprehensive  image  of 
the  extent  of  violence  against  women,  its  severity  and 
its  under-reported  nature.  To  this  end,  it  includes  three 
sub-domains described in Table 5.

Table 5: 

Structure of the composite measure

Sub-domain

Concept measured

Prevalence

Extent of violence against women

Severity

Heath consequences of violence against women and 
multiple victimisation

Disclosure

Reporting of violence against women to institutions 
or disclosing to anyone else

In particular, EIGE included only the main forms of violence: 
1. for which comparable and valid data are available, 2. that 
potentially  concern  all  women  in  the  general  population, 

Table  6  presents  the  details  of  each  variable  selected  for 
the composite measure.

24

Gender Equality Index 2017 − Methodological Report

Table 6: 

Indicators included in the composite measure

Sub-domain

Variable

Data source

Denominator

Percentage of women having experienced physical and/or sexual 
violence since age 15

FRA, 2012

All respondents (18-74)

Prevalence

Percentage of women having experienced physical and/or sexual 
violence in the past 12 months

FRA, 2012

All respondents (18-74)

Percentage of women victims of intentional homicide by a current or 
former partner or family member, per 100 000 inhabitants

Eurostat crim_hom_vrel 100 000 inhabitants

Percentage of women who have experienced physical and/or sexual 
violence from several types of perpetrators (current partner, former 
partner and/or non-partner)

FRA, 2012

Severity

Percentage of women who experienced health consequences of 
physical and/or sexual violence since age 15

FRA, 2012

Percentage of women who experienced health consequences of 
physical and/or sexual violence in the past 12 months

FRA, 2012

Disclosure

Percentage of women having experienced physical and/or sexual 
violence in the past 12 months and have not told anyone

FRA, 2012

Respondents having experienced 
physical and/or sexual violence 
(18-74)

Respondents having experienced 
physical and/or sexual violence 
since age 15 (18-74)

Respondents having experienced 
physical and/or sexual violence in 
the past 12 months (18-74)

Respondents having experienced 
physical and/or sexual violence 
(18-74)

Note:  The indicator on femicide is part of the concept of the composite measure, but it is not included in the measurement due to lack of EU wide 

official comparable data.

The  variable  of  femicide  (measured  by  the  indicator  ‘Per-
centage  of  women  victims  of  intentional  homicide  by 
a current or former partner or family member, per 100 000 
inhabitants’)  was  excluded  from  calculation  due  to  the 
data being available only for a limited number of Member 
States.

For  the  measurement  of  multiple  victimisation,  several 
variables were tested and one was discarded. It sought to 
capture  the  likelihood  of  women  experiencing  violence 
multiple  times  in  their  social,  professional  or  emotional 
lives.  The  variable  that  was  excluded  was  the  ‘Percentage 
of women having experienced physical and/or sexual vio-
lence more than once’.

However,  the  FRA  questionnaire,  is  built  on  the  basis  of 
acts  of  violence  (slap,  kick),  which  makes  it  difficult  to 
distinguish  separate  episodes  of  violence  and  to  evalu-
ate  to  what  extent  women  have  gone  through  several 
episodes  of  violence.  To  compute  multiple  victimisation, 
the FRA study included women who experienced at least 
one violent act several times since the age of 15 or in the 
12 months prior to the interview. Therefore, the only differ-
ence  between  multiple  victimisation  and  prevalence  was 
that multiple victimisation excluded the few women who 
responded  ‘only  once’  for  all  acts  of  violence.  As  a  result, 

that  variable  proved  to  be  too  highly  correlated  to  varia-
bles in the Prevalence sub-domain.

To  overcome  this  constraint,  another  approach  was  taken 
to the measurement of multiple victimisation. It consisted 
of  measuring  whether  women  had  been  victimised  by 
several  different  types  of  perpetrators  such  as  a  current 
partner,  a  former  partner,  a  colleague,  a  family  member, 
or  someone  unknown.  This  variable  captures  the  aver-
age  number  of  different  types  of  perpetrators  involved 
in  women’s  experiences  of  violence.  For  this  variable,  the 
denominator is ‘all respondents having experienced phys-
ical and/or sexual violence’. On average, in the EU, women 
who have experienced violence were victimised by 1.6 dif-
ferent  types  of  perpetrators.  The  values  ranged  from  1.2 
types in Greece to 1.8 types in Slovakia. This made visible 
the  important  finding  that  violence  is  likely  to  arise  from 
several  dimensions  in  women’s  personal,  professional, 
social, family and other lives.

Due to statistical reasons, the variable on disclosure of life-
time physical and sexual violence had to be excluded from 
the calculations. It showed negative correlations with other 
variables,  the  most  significant  ones  being  with  the  varia-
bles of health consequences.

Gender Equality Index 2017 − Methodological Report

25

3�1�2�  Computing the index for the 

domain of violence

3.2. The satellite domain of 
intersecting inequalities

Variables within each sub-domain were aggregated using 
an  arithmetic  mean.  Similarly,  sub-domains  values  were 
then  aggregated  using  an  arithmetic  mean.  No  weights 
were applied.

The current metric is the following:

For indicators:

For the composite measure:

(9)

(10)

1, ..., 7

1, ..., 7

1, ..., 7

The metric of the satellite domain departs from the other 
domains of the Gender Equality Index in several ways. The 
most  visible  difference  is  the  interpretation  of  the  values. 
While  for  the  Gender  Equality  Index,  1  means  the  high-
est  level  of  inequality  and  100  means  the  most  gender 
equal  society,  for  the  satellite  domain  of  violence  against 
women,  the  scale  was  reversed  in  order  to  align  with 
the  common  interpretation  of  data  on  violence  against 
women. As a result, the higher the value of the composite 
measure,  the  more  prevalent,  severe  and  under-reported 
the phenomenon of violence. A value of 1 for the compos-
ite  measure  would  describe  a  situation  where  no  women 
experience  violence  while  a  value  of  100  would  refer  to 
a  situation  where  violence  against  women  is  extremely 
common, highly severe and not disclosed.

Finally,  regarding  the  linking  of  this  satellite  domain  with 
the core Gender Equality Index, EIGE convened a consulta-
tion  meeting  to  gather  the  opinion  of  various  experts  on 
the development of the measurement of violence against 
women in the EU in Vilnius, in November 2016. This meet-
ing  was  followed,  in  February  2017,  by  a  meeting  of  the 
Gender Equality Index Working Group. In light of data lim-
itations, it was decided that linking the satellite domain of 
violence with the Gender Equality Index might negatively 
affect the robustness and reliability of the Gender Equality 
Index. It was therefore decided that the composite meas-
ure  of  violence  against  women  would  be  calculated  and 
analysed, but that it will not affect countries’ scores under 
the Gender Equality Index.

26

Gender Equality Index 2017 − Methodological Report

Although  the  Index  focuses  on  gender  inequality  as  the 
most pervasive and entrenched form of inequality world-
wide, it also acknowledges diversities within societies and 
among women and men. The population consists of peo-
ple  with  very  different  characteristics  that  intersect  and 
can consequently create and influence the life experiences 
of and levels of inequality experienced by different groups 
of women and men. For this purpose, the Gender Equality 
Index  incorporates  a  satellite  domain  called  ‘Intersecting 
inequalities’ that highlights the complexity of gender ine-
qualities by pointing out that some women and men face 
group-based  inequalities  rooted  in  other  social  and  cul-
tural  power  differentials  such  as  age,  ethnicity,  race,  class, 
nationality, sexuality, or religion (Kabeer, 2010).

A full understanding of gender inequalities asks that gen-
der  gaps  are  addressed  alongside  other  power  asym-
metries  in  society.  EIGE’s  Gender  Equality  Index  measures 
gender  gaps  in  areas  relevant  to  EU  policy;  however,  for 
better  policymaking  and  a  thorough  understanding  of 
gender inequalities, the diversity among men and women 
needs to be taken into account. In order to produce effec-
tive  and  non-exclusive  policy  measures  and  social  inter-
ventions,  systemic  social  inequalities,  their  causes  and 
consequences  also  need  to  be  examined  and  taken  into 
account.

Sitting alongside the six core domains, intersectionality has 
been  labelled  as  a  satellite  domain  as  it  is  not  a  seventh 
domain in which gender gaps can be found or combined 
into a composite indicator. Instead, it adds a cross-cutting 
perspective that unmasks some of the differences among 
women  and  among  men.  By  doing  so,  an  intersectional 
analysis approach is applied to all domains of the Index, to 
shed more light on multiple inequalities.

The satellite domain of Intersecting inequalities is very dif-
ferent  from  the  satellite  domain  of  Violence.  Rather  than 
being  a  separate  domain,  it  is  an  analytical  tool,  applied 
to the whole Index. This analysis is carried out at the varia-
ble level, while for the Index all variables are disaggregated 
by  sex.  For  intersectional  analysis  variables  are  disaggre-
gated  additionally  by  sex  and  one  more  intersection  at 
a  time  (e.g.  age  and  gender;  age  and  education,  etc.).  As 
a result, it is possible to see the levels/situations of different 
sub-groups  separately  as  well  as  to  look  at  gender  gaps 
within  sub-populations.  If  the  gender  gaps  vary  across 
the  sub-populations,  it  means  that  gender  interacts  with 
other  characteristics  to  create  additional  inequalities  (for 

 
 
 
 
instance, if among the low educated the gender difference 
is  larger/smaller  than  among  the  high  educated).  A  situa-
tion where the gender gap varies across the groups, such 
as  educational  groups, this would be a clear  indication of 
intersectionality. From a policy perspective, it enables EIGE 
to  identify  which  groups  of  men  and  women  are  least/
most disadvantaged and to indicate possible areas where 
more targeted policy measures are needed.

The first stage involved a theoretical overview of intersec-
tionality  (EIGE,  2017c)  in  order  to  identify  possible  inter-
sections  (social-demographic  characteristics)  that  would 
be  relevant  from  the  point  of  view  of  the  Index.  Based 
on  theoretical  considerations,  previous  research  and  data 
availability,  five  intersections  were  selected  for  further 
investigation:  family  type,  age,  country  of  birth,  disability 
and education.

 

 

 

Intersection of gender and family type� Four family 
types were analysed: 1. Single, 2. Lone parent, 3. Couple 
without  children,  and  4.  Couple  with  children.  These 
family  types  are  based  on  the  relationships  between 
the members of the households, i.e. couple is defined 
as two adults living in the same household and declar-
ing  to  be  in  a  relationship  (married  and  not).  Children 
are  only  those  economically  dependent  household 
members (i.e. aged below 18 or up until 24 years, if in 
education)  who  are  declared  to  be  children  or  step-
children  of  the  couple  or  one  parent  (in  case  of  lone 
parent  household).  These  family  types  differ  from  the 
usual  types  of  households  which  are  based  on  the 
composition  of  the  household,  i.e.  counting  of  adult 
and dependent household members, and not depend-
ent  on  their  actual  relationship.  Not  all  possible  types 
of  family  are  considered  for  the  analysis  —  families 
with  different  mixed  compositions  are  left  out  for  the 
clarity of interpretation. The source used for the Health 
behaviour  (EHIS)  did  not  allow  any  disaggregation  in 
reference to this intersection.
Intersection  of  gender  and  age�  Where  possible 
four  age  groups  are  analysed:  15/16-24,  25-49,  50-64, 
65+,  but  occasionally,  depending  on  the  need,  more 
detailed  analysis  was  carried  out  or  other  age  groups 
analysed.
Intersection  of  gender  and  country  of  birth�  As 
a  proxy  for  migrant  status  the  variable  of  country  of 
birth is used. Three categories are distinguished, wher-
ever  possible:  1.  National  born:  born  in  the  reporting 
country,  2.  EU  born:  born  in  EU-28  countries,  except 
reporting  country,  and  3.  Non-EU  born:  born  in  any 
other  country  outside  of  EU-28.  Where  further  disag-
gregation  was  not  possible,  only  two  intersections 

were  analysed  (national-born  and  non-national  born) 
without  differentiating  between  EU  born  and  non-EU 
born non-nationals. For this intersection, the data avail-
ability is limited and instead of EU-28, the EU average is 
calculated based on smaller number of countries when 
a three-category intersection is looked at (national; EU 
born and non-EU born). For instance, in the data of EU 
SILC,  this  disaggregation  is  available  only  for  23  coun-
tries  (data  for  Germany,  Estonia,  Latvia,  Malta  and  Slo-
venia are not available). Also in EU LFS there are missing 
data.
Intersection of gender and disability� As a proxy for 
the disability status, a question ‘limitations in everyday 
life’ is used. This intersection is not available in EU LFS, 
EWCS  and  SES.  Where  possible  EU  SILC  and  EQLS  are 
used  as  data  sources.  The  questions  differ  slightly  in 
these surveys, but it is anticipated that they will be still 
relatively comparable (15).
Intersection of gender and education� Three stand-
ard  levels  of  education  are  used:  1.  Low  educated 
(ISCED  0-2),  2.  Medium  educated  (ISCED  3-4),  and  3. 
High educated (ISCED 5-8).

 

 

The  level  of  detail  and  the  intersections  analysed  vary 
across  the  domains,  depending  on  the  data  availability. 
Since  the  purpose  is  to  shed  more  light  into  each  of  the 
domains  and  variables,  the  comparability  of  the  domains 
is not needed, and the analysis is flexible and varies across 
the domains and variables. The following variables are not 
included to the intersectionality analysis:

  Duration of working life (domain of work);
 

Tertiary students in the fields of education, health and 
welfare, humanities and art (tertiary students) (domain 
of knowledge);
Life  expectancy  in  absolute  value  at  birth  (domain  of 
health);

 

  Healthy life years in absolute value at birth (domain of 

health);

  Domain  of  power:  due  to  the  lack  of  data  on  the 
social-demographic  characteristics  (other  than  gen-
der)  of  the  decision-makers,  the  domain  of  power  is 
excluded from the intersectionality analysis.

For this 3rd edition of the Gender Equality Index, the inter-
sectionality analysis is done at EU-28 level, covering either 
2014 or 2015, depending on the data available and micro-
data  analysis  (using  EU  SILC  and  EU  LFS  2014,  EQLS  2015; 
EWCS 2016).

(15)  EU SILC: HS.3 For at least the past 6 months, to what extent have you 
been limited because of a health problem in activities people usually 
do?  EQLS:  Q50  (Q44)  Are  you  limited  in  your  daily  activities  by  this 
physical or mental health problem, illness or disability?

Gender Equality Index 2017 − Methodological Report

27

Annexes
Annex 1.  List of indicators of Gender Equality Index

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Segregation and quality of work

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Sub-domain

Behaviour

Access

Domain

Health

Additional variable

Gender Equality Index 2017 − Methodological Report

35

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 2.  Analysis of the contribution of the correcting coefficient

The approach of calculation of the contribution of correcting coefficient is based on the decomposition of the corrected 
gender gap used in the Gender Equality Index (see, for instance, Permanyer, 2015). For example, the metric of each indi-
cator (formula 3) could be simplified and written as:

 is the achievement component (component related to level of achievement of the indicator, i.e. the part of the 
where 
metric derived from the application of the correcting coefficient) and e the equality component (component related to 
the gender gaps, i.e. the part of the metric derived from the gender gap), applying to the formula (2) the natural loga-
rithm, then the corresponding contributions can be approximated as:

where

. 

+

 = 100

So, if we take into consideration a single indicator for a single country in 1 year, we could calculate the contribution of 
the correcting coefficient in the final metric. For example, for the indicator FTE employment rate we have seen that the 
application of formula 3 for Belgium in 2015 has returned a value of 71.6. Following this approach, the contribution of the 
gender equality component is 51.6 %, and the remaining 48.4 % is related to the contribution of the level of achievement 
of Belgium. We can calculate the contribution of the gender equality component and the achievement component for 
each indicator for each country. The contribution of both components add up to 100. For example, for the Duration of 
working  live  indicator  the  contribution  of  gender  equality  component  for  Belgium  in  2015  is  36.3  %  (the  achievement 
component  is  63.7  %),  and  so  on  for  all  the  31  indicators.  For  indicators  of  power  and  caring  and  housework  the  con-
tribution of gender equality component is 100 % since they have not been corrected. We can calculate the average of 
the gender equality component contribution for the 31 indicators for each country (for example, for Belgium in 2015 the 
average is 55 %), and we will have an estimation of the impact of gender equality component on the final score of Index 
for this country.

The new formula of the correcting coefficient increases the contribution of the gender equality component, being the 
average contribution higher for that component than for the achievement component (light purple highlighted cells in 
Table  7)  in  the  majority  of  the  countries.  For  example,  the  former  coefficient  implies  an  average  impact  of  the  gender 
equality component around 43.2 % in 2015 in the EU-28, while with the new one that value increases to 51.9 %. Improve-
ments are registered for all the countries.

36

Gender Equality Index 2017 − Methodological Report

 
 
Table 7:  Mean contribution (%) of the gender equality component of all indicators, by country

MS

EU-28

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

HR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

UK

With old correcting coefficient

With new correcting coefficient

2005

2010

2012

2015

2005

2010

2012

2015

43.6

45.6

43.9

47.2

59.3

46.4

44.5

51.0

44.4

45.4

44.8

40.8

46.2

47.6

43.5

42.3

55.9

41.1

47.6

58.2

46.7

41.4

46.3

42.0

52.3

43.7

51.4

59.7

52.0

43.5

46.6

42.5

48.1

59.5

46.1

43.9

53.0

44.1

46.2

44.8

41.3

46.3

49.5

41.7

45.0

54.9

44.7

48.4

58.2

45.9

43.0

46.6

40.9

50.0

44.0

51.7

57.8

51.6

43.4

45.9

41.9

49.1

57.8

45.6

45.4

55.1

43.2

45.5

45.1

40.6

46.8

48.6

43.0

44.3

54.3

41.8

49.0

59.5

45.9

42.5

45.0

41.1

51.5

44.5

49.8

58.0

51.2

43.2

47.0

42.3

47.3

57.5

44.8

44.5

51.3

41.8

45.3

45.9

40.5

45.3

46.7

43.6

43.8

55.1

41.2

46.9

58.0

44.4

43.7

45.0

42.2

47.5

46.7

51.2

63.2

49.2

51.9

53.9

51.7

54.8

65.2

54.0

51.1

58.1

51.9

53.1

52.7

48.8

53.2

55.2

52.4

50.8

58.5

49.4

53.6

62.1

54.0

49.6

54.2

50.4

56.9

52.7

58.5

62.0

57.6

51.2

53.3

51.5

56.8

65.5

53.6

51.7

58.0

51.5

53.3

53.2

48.3

53.9

56.0

49.1

50.9

60.3

53.2

53.2

65.6

54.3

49.9

54.6

49.7

57.9

53.5

59.7

64.4

56.3

51.3

53.7

50.9

55.1

65.6

53.8

53.1

58.0

50.4

52.5

53.2

48.4

53.9

56.0

50.0

51.6

59.4

51.3

53.3

66.4

55.2

50.5

52.7

49.6

55.5

53.6

59.8

66.3

55.8

51.9

55.0

51.9

55.5

66.4

55.6

54.1

58.1

49.6

53.1

54.0

49.2

53.6

53.5

52.2

52.1

64.7

51.2

54.3

64.3

53.3

51.5

53.7

50.3

56.0

55.4

60.1

70.5

56.6

The following figure presents the mean percentage contribution of the gender equality and achievement components 
in  each  indicator  by  year.  In  the  interpretation  of  those  graphs  it  is  important  to  consider  the  indicators  of  power  and 
time that have not been corrected, that is the value of the final metric of these indicators for each country and EU-28 is 
completely determined by the gender gap. The mean contribution of the gender equality component varies across the 
countries slightly, but remains between 49.2 % and 70.5 % in 2015. Between two and four countries have a contribution 
slightly below 50 %, depending on the year.

Gender Equality Index 2017 − Methodological Report

37

Figure 4:  Mean percentage contribution of the equality and achievements components in each indicator by year

Ce 

C

2005

100 
90 
80 
70 
60 
50 
40 
30 
20 
10 
0 

100 
90 
80 
70 
60 
50 
40 
30 
20 
10 
0 

100 
90 
80 
70 
60 
50 
40 
30 
20 
10 
0 

100 
90 
80 
70 
60 
50 
40 
30 
20 
10 
0 

e
t
F

l

w
D

W
_
g
e
S

y
t
i
l
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b
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x
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F

l

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p
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P

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n
r
a
E

e
m
o
c
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I

y
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r
e
v
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P

0
8
S
/
0
2
S

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a
r
G

t
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P

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a
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_
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S

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2010 

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0
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0
2
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L

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V

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M

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B

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s
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B

l

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M

l

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t
n
e
D

2012 

e
t
F

l

w
D

W
_
g
e
S

y
t
i
l
i

b
i
x
e
F

l

s
t
c
e
p
s
o
r
P

i

s
g
n
n
r
a
E

e
m
o
c
n

I

y
t
r
e
v
o
P

0
8
S
/
0
2
S

d
a
r
G

t
r
a
P

e
r
a
C

E
_
g
e
S

i

g
n
k
o
o
C

e
r
u
s
a
e
L

y
r
a
t
n
u
o
V

l

i

n
M

l
r
a
P

g
e
R

s
d
r
a
o
B

s
k
n
a
B

i

g
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n
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F

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a
d
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M

t
r
o
p
S

x
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e
f
i
L

c
r
e
P
f
l
e
S

Y
L
H

k
s
i
R

v
a
h
e
B

l

a
c
i
d
e
M

l

a
t
n
e
D

2015

e
t
F

l

w
D

W
_
g
e
S

y
t
i
l
i

b
i
x
e
F

l

s
t
c
e
p
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r
P

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s
g
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r
a
E

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m
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c
n

I

y
t
r
e
v
o
P

0
8
S
/
0
2
S

d
a
r
G

t
r
a
P

e
r
a
C

E
_
g
e
S

i

g
n
k
o
o
C

e
r
u
s
a
e
L

y
r
a
t
n
u
o
V

l

i

n
M

l
r
a
P

g
e
R

s
d
r
a
o
B

s
k
n
a
B

i

g
n
d
n
u
F

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a
d
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M

t
r
o
p
S

x
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f
i
L

c
r
e
P
f
l
e
S

Y
L
H

k
s
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R

v
a
h
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B

l

a
c
i
d
e
M

l

a
t
n
e
D

38

Gender Equality Index 2017 − Methodological Report

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
We can also calculate the percentage of Member States for 
which the contribution of the gender equality component 
is higher than 50 %. For example, for FTE employment rate, 
for  20  Member  States  the  contribution  of  gender  equal-
ity  component  is  higher  than  50  %  (that  corresponds  to 
71.2  %  of  Member  States).  The  average  of  the  gender 
equality  component  for  FTE  between  Member  States  is 
60.7  %  for  FTE.  Additionally,  it  is  possible  to  calculate  the 
overall  percentage  of  cases  (Member  States  for  each  of 
the 31 indicators) for which the contribution of the gender 
equality component is higher than 50 %.

As  Table  8  illustrates,  after  improvements  in  the  3rd  edi-
tion  of  the  Gender  Equality  Index,  the  mean  gender  gap 
contribution  increased  to  55.6  %  in  2015.  The  percentage 
contribution  of  the  equality  component  to  the  metric  of 

each  indicator  is  above  the  threshold  of  50  %  for  all  time 
points,  meaning  that  it  is  higher  than  the  contribution  of 
the  achievement  component  in  at  least  half  of  the  cases 
(country-indicators cases). In 52.5 % of the cases (i.e. Mem-
ber States for each of the 31 indicators) the gender equality 
component became higher than 50 % in 2015, compared 
to 43.5 % of cases under the previous methodology.

The  change  in  the  formula  and  increasing  the  contribu-
tion of the gender equality component has the additional 
implication of increasing the overall levels of the scores of 
the Index by around 9 points for the EU-28. Detailed infor-
mation  by  country  can  be  found  in  Figure  5,  which  com-
pares  the  calculation  of  the  Gender  Equality  Index  using 
the new and old correcting coefficient.

Table 8:  Mean gender gap contribution and percentage of cases with a contribution of the equality 

component equal or higher than the contribution of the achievement component

With old correcting coefficient

With new correcting coefficient

2005

2010

2012

2015

2005

2010

2012

2015

Mean gender gap contribution (%)

47.5

47.7

47.6

47.2

54.5

55.0

54.8

55.6

Percentage of cases (%) with gender equality 
component equal or higher than the 
achievement component

44.4

44.2

45.2

43.5

51.4

52.2

52.1

52.5

Gender Equality Index 2017 − Methodological Report

39

Figure 5: Comparison of the Gender Equality Index calculated with the new and old correcting coefficients

Old

New

2005

100 

80 

60 

40 

20 

0 

100 

80 

60 

40 

20 

0 

100 

80 

60 

40 

20 

0 

100 

80 

60 

40 

20 

0 

BE  BG  CZ  DK  DE  EE 

IE  EL  ES  FR  HR 

IT  CY  LV  LT  LU  HU  MT  NL  AT  PL  PT  RO  SI  SK  FI  SE  UK 

2010

BE  BG  CZ  DK  DE  EE 

IE  EL  ES  FR  HR 

IT  CY  LV  LT  LU  HU  MT  NL  AT  PL  PT  RO  SI  SK  FI  SE  UK 

2012

BE  BG  CZ  DK  DE  EE 

IE  EL  ES  FR  HR 

IT  CY  LV  LT  LU  HU  MT  NL  AT  PL  PT  RO  SI  SK  FI  SE  UK 

2015

BE  BG  CZ  DK  DE  EE 

IE  EL  ES  FR  HR 

IT  CY  LV  LT  LU  HU  MT  NL  AT  PL  PT  RO  SI  SK  FI  SE  UK 

40

Gender Equality Index 2017 − Methodological Report

Annex 3.  Descriptive statistics of the gender gaps adjusted by levels 

of achievement (metric), 2005-2015

2005

Mean

Standard 
Deviation

Min

1st 
quartile

2nd 
quartile

3rd 
quartile

Max

Skewness

Kurtosis

Fte

Dwl

Seg_W

Flexibility

Prospects

Earnings

Income

Poverty

S20/80

Grad

Part

Seg_E

Care

Cooking

Leisure

Voluntary

Min

Parl

Reg

Boards

Banks

Res

Media

Sport

SelfPerc

Life ex

HLY

Risk

Behav

Medical

Dental

73.2

81.5

31.7

63.7

92.5

60.8

59.0

94.9

82.7

63.0

62.7

51.9

79.9

61.5

62.3

54.3

41.9

42.2

50.5

20.0

28.2

60.5

52.6

26.7

82.7

92.4

89.1

77.1

70.6

94.7

94.7

8.4

8.1

6.0

12.2

4.0

14.4

15.7

2.5

10.2

12.5

9.7

11.0

8.1

16.4

19.7

17.7

23.8

19.8

20.1

9.8

20.7

24.9

26.6

15.3

10.4

3.2

4.3

7.5

16.5

4.6

3.8

49.0

53.8

24.5

42.6

83.4

33.8

32.4

91.0

65.1

43.3

48.3

28.7

63.9

31.3

31.6

24.9

4.7

17.0

22.0

6.1

1.0

1.0

1.0

5.7

56.2

85.1

79.4

57.5

27.5

80.0

84.3

66.2

77.8

25.8

54.6

90.7

45.6

42.7

92.5

72.2

51.2

54.2

42.3

74.6

47.2

42.7

40.5

24.3

26.6

35.9

12.6

10.0

49.3

33.1

17.2

76.0

89.7

86.0

71.6

63.1

90.9

93.3

74.2

81.3

30.6

60.4

93.6

64.1

62.6

94.6

83.0

61.2

61.3

52.8

80.5

63.4

63.7

55.5

35.5

36.9

43.0

19.2

24.9

63.7

54.7

23.5

85.4

94.1

89.5

78.3

69.3

96.2

95.7

80.7

87.2

37.3

72.8

95.6

72.9

70.5

97.2

92.2

75.1

69.9

58.5

86.2

75.4

79.1

67.3

55.4

59.9

65.4

25.8

44.1

81.1

77.4

33.6

91.7

95.0

92.5

83.7

85.6

97.9

97.2

84.6

94.1

43.2

92.6

98.3

86.6

95.7

99.3

97.3

84.9

86.4

77.0

93.5

86.4

98.0

92.5

94.4

91.8

92.9

45.3

72.2

98.8

95.0

83.3

98.9

96.0

96.2

89.6

93.4

99.9

99.9

-0.8

-1.3

0.3

0.5

-1.0

-0.2

-0.1

0.2

-0.3

0.0

0.7

0.0

-0.4

-0.2

0.0

0.3

0.8

0.8

0.7

0.7

0.4

-0.9

-0.1

1.9

-0.8

-1.0

-0.4

-0.6

-0.7

-1.5

-1.4

0.7 

3.5

-1.5

-0.3

0.4

-1.2

-0.5

-1.4

-1.4

-1.3

-0.1

-0.1

-0.6

-1.2

-1.2

-0.5

-0.3

-0.2

-0.5

0.1

-0.6

0.4

-0.7

5.4

0.0

-0.2

-0.6

0.1

0.8

2.4

1.9

Gender Equality Index 2017 − Methodological Report

41

2010

Mean

Standard 
Deviation

Min

1st 
quartile

2nd 
quartile

3rd 
quartile

Max

Skewness

Kurtosis

Fte

Dwl

Seg_W

Flexibility

Prospects

Earnings

Income

Poverty

S20/80

Grad

Part

Seg_E

Care

Cooking

Leisure

Voluntary

Min

Parl

Reg

Boards

Banks

Res

Media

Sport

SelfPerc

Life ex

HLY

Risk

Behav

Medical

Dental

75.2

84.1

32.4

63.7

92.5

66.3

64.9

95.1

83.6

68.0

63.1

53.3

79.2

49.5

62.3

53.9

45.8

45.0

50.6

23.7

31.0

60.6

52.7

26.8

85.4

93.5

89.8

77.1

70.6

96.1

96.2

7.7

7.2

7.2

12.2

4.0

12.9

14.0

2.1

9.3

11.4

9.0

9.0

10.3

17.4

19.7

16.7

22.8

19.8

20.2

11.3

18.0

25.0

26.5

15.4

8.1

2.9

3.5

7.5

16.5

3.0

2.5

55.3

61.8

21.5

42.6

83.4

40.8

35.9

91.4

61.9

47.7

48.8

39.5

47.0

21.3

31.6

23.9

8.1

17.9

22.1

6.2

1.0

1.0

1.0

5.7

68.9

86.9

83.6

57.5

27.5

87.7

88.9

70.4

80.5

25.2

54.6

90.7

54.2

50.8

93.2

76.9

59.3

57.3

44.6

72.3

31.9

42.7

41.0

29.2

32.8

36.0

13.4

18.3

49.2

32.9

17.1

79.2

90.8

86.8

71.6

63.1

95.1

94.9

75.6

84.3

33.4

60.4

93.6

69.7

69.1

95.4

82.9

65.6

62.0

53.3

81.2

47.3

63.7

51.7

40.0

38.3

43.6

23.5

29.7

63.8

54.7

23.6

87.2

95.0

90.4

78.3

69.3

96.8

97.0

82.1

89.9

38.1

72.8

95.6

76.3

75.8

96.7

91.9

78.2

70.5

58.7

84.7

64.3

79.1

63.8

63.3

59.4

65.6

30.4

46.3

80.7

77.6

33.8

91.7

95.9

92.7

83.7

85.6

98.2

98.1

89.2

94.6

48.0

92.6

98.3

88.6

93.8

98.7

98.8

90.1

84.8

72.3

94.3

77.5

98.0

94.9

97.4

91.7

93.5

51.5

65.9

98.5

95.6

83.8

99.3

96.8

97.0

89.6

93.4

100.0

99.7

-0.6

-1.1

0.1

0.5

-1.0

-0.3

-0.3

0.1

-0.2

0.2

0.5

0.4

-1.0

0.0

0.0

0.4

0.6

0.7

0.7

0.6

0.1

-0.9

-0.1

1.9

-0.5

-0.9

-0.1

-0.6

-0.7

-1.2

-1.2

0.4

1.9

-0.9

-0.3

0.4

-0.9

-0.9

-1.0

-0.4

-1.1

-0.2

-0.8

1.8

-1.4

-1.2

-0.1

-0.4

-0.3

-0.5

0.3

-0.7

0.4

-0.7

5.4

-0.5

-0.5

-0.8

0.1

0.8

1.0

1.1

42

Gender Equality Index 2017 − Methodological Report

2012

Mean

Standard 
Deviation

Min

1st 
quartile

2nd 
quartile

3rd 
quartile

Max

Skewness

Kurtosis

Fte

Dwl

Seg_W

Flexibility

Prospects

Earnings

Income

Poverty

S20/80

Grad

Part

Seg_E

Care

Cooking

Leisure

Voluntary

Min

Parl

Reg

Boards

Banks

Res

Media

Sport

SelfPerc

Life ex

HLY

Risk

Behav

Medical

Dental

75.3

85.0

33.5

63.7

92.5

66.3

66.0

95.1

83.4

70.6

63.2

53.8

78.7

62.6

62.3

53.9

45.8

47.5

51.2

27.5

31.2

60.5

52.7

26.8

85.4

93.9

90.2

77.1

70.6

96.3

96.3

7.3

6.6

7.1

12.2

4.0

12.9

14.4

2.0

8.8

11.0

9.0

8.9

6.5

15.1

19.7

16.7

23.0

19.0

20.0

12.4

20.1

24.9

26.5

15.4

8.7

2.8

3.4

7.5

16.5

2.5

2.3

59.8

66.5

22.2

42.6

83.4

40.8

36.4

91.2

69.1

53.8

48.1

38.3

64.1

36.6

31.6

23.9

12.2

16.8

18.6

6.2

1.0

1.0

1.0

5.7

65.8

87.4

83.3

57.5

27.5

90.1

89.4

71.0

80.8

26.0

54.6

90.7

54.2

52.1

93.8

75.7

62.0

56.6

46.7

74.8

51.0

42.7

41.0

28.8

36.7

36.9

17.2

12.5

49.0

32.8

17.0

79.4

91.1

87.8

71.6

63.1

94.6

95.3

75.5

84.8

33.1

60.4

93.6

69.7

69.8

95.4

82.7

68.7

61.9

51.9

80.8

65.1

63.7

51.7

41.4

42.9

44.4

26.0

32.1

63.8

54.7

23.6

87.3

95.3

90.6

78.3

69.3

97.0

96.8

81.7

90.2

40.0

72.8

95.6

76.3

78.2

96.6

91.1

80.5

68.9

60.3

84.4

76.2

79.1

63.8

56.9

61.3

65.1

34.4

46.4

80.7

77.5

33.9

91.6

96.1

93.3

83.7

85.6

98.2

98.3

91.4

96.2

48.5

92.6

98.3

88.6

94.5

98.8

97.8

93.9

84.7

73.0

87.3

85.9

98.0

94.9

98.3

87.7

93.0

53.9

78.5

98.0

95.7

84.0

99.7

97.0

98.1

89.6

93.4

99.9

99.6

-0.3

-0.6

0.1

0.5

-1.0

-0.3

-0.2

-0.1

0.1

0.3

0.5

0.3

-0.7

-0.2

0.0

0.4

0.6

0.6

0.7

0.5

0.4

-0.9

-0.1

1.9

-0.8

-0.9

-0.1

-0.6

-0.7

-0.9

-1.3

0.1

0.7

-1.0

-0.3

0.4

-0.9

-0.9

-0.6

-1.3

-1.0

-0.2

-0.7

-0.5

-1.0

-1.2

-0.1

-0.3

-0.5

-0.4

-0.5

-0.4

0.4

-0.7

5.5

-0.1

-0.5

-0.3

0.1

0.8

0.1

1.8

Gender Equality Index 2017 − Methodological Report

43

2015

Mean

Standard
Deviation

Min

1st 
quartile

2nd 
quartile

3rd 
quartile

Max

Skewness

Kurtosis

Fte

Dwl

Seg_W

Flexibility

Prospects

Earnings

Income

Poverty

S20/80

Grad

Part

Seg_E

Care

Cooking

Leisure

Voluntary

Min

Parl

Reg

Boards

Banks

Res

Media

Sport

SelfPerc

Life ex

HLY

Risk

Behav

Medical

Dental

76.5

86.3

33.4

63.7

92.5

69.3

68.3

94.5

81.6

74.4

62.6

53.9

81.7

58.2

62.3

51.3

49.8

50.7

53.7

35.8

33.4

60.5

55.5

26.9

85.4

94.5

89.9

77.1

70.6

96.7

96.7

7.1

5.9

7.4

12.2

4.0

12.5

13.7

2.6

9.7

10.5

9.9

8.6

8.0

16.5

19.7

14.0

23.2

18.6

20.1

15.5

22.2

24.9

22.7

15.5

8.6

2.7

4.0

7.5

16.5

2.6

2.5

60.5

72.5

21.1

42.6

83.4

47.4

39.3

88.5

63.3

56.0

45.8

40.5

69.6

31.0

31.6

25.5

3.7

18.1

21.1

8.5

1.0

1.0

11.8

5.7

63.3

88.2

83.0

57.5

27.5

90.6

90.2

73.1

82.7

26.8

54.6

90.7

56.9

55.5

92.7

71.3

64.9

55.2

46.3

74.5

40.7

42.7

39.5

34.2

38.4

38.7

22.0

17.0

49.1

34.9

16.8

81.0

91.7

86.3

71.6

63.1

95.5

96.1

77.1

86.6

33.6

60.4

93.6

71.3

70.9

94.8

81.0

73.9

60.8

50.9

81.6

58.2

63.7

53.0

47.2

46.6

46.0

34.2

33.3

63.7

56.2

23.7

87.0

95.7

89.4

78.3

69.3

97.2

97.1

80.9

91.1

40.0

72.8

95.6

78.1

79.7

96.6

89.8

84.1

67.8

60.8

87.7

72.8

79.1

58.4

62.6

68.3

63.7

50.3

49.3

80.5

74.2

34.1

91.3

96.7

92.9

83.7

85.6

98.8

98.5

93.2

97.5

48.2

92.6

98.3

96.0

98.0

98.5

97.9

96.6

85.0

69.5

97.6

86.7

98.0

85.8

98.4

87.5

95.9

65.8

78.6

97.8

97.7

84.3

99.0

97.4

99.9

89.6

93.4

99.9

99.9

-0.3

-0.5

0.0

0.5

-1.0

0.0

-0.1

-0.6

-0.1

0.1

0.6

0.3

0.3

-0.1

0.0

0.4

0.3

0.3

0.6

0.2

0.3

-0.9

0.0

1.9

-1.0

-0.9

0.5

-0.6

-0.7

-0.9

-1.1

0.5

0.2

-1.1

-0.3

0.4

-0.9

-0.7

-0.3

-1.2

-0.9

-0.3

-1.0

-0.9

-1.2

-1.2

0.2

-0.3

-0.8

-0.6

-1.1

-0.5

0.4

-1.1

5.5

0.8

-0.5

0.1

0.1

0.8

0.1

0.8

44

Gender Equality Index 2017 − Methodological Report

5
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45

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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47

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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48

Gender Equality Index 2017 − Methodological Report

.
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Annex 5.  Principal component analysis (PCA) for domains 

(2010 data) – factor loadings

Work

Participation

Segregation and quality 
of work

Full-time equivalent employment rate
Duration of working life
Employed people in education, human health and social work activities
Ability to take an hour or two off during working hours to take care of personal or 
family matters
Career Prospects Index
Rotated Component Matrix
Extraction Method: Principal Component Analysis
Rotation Method: Varimax with Kaiser Normalisation

% of variance explained
Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO)

Scree plot: Work

0.942
0.922
– 0.055

– 0.136

0.330

71.3
0.48

– 0.178
0.147
0.830

0.784

0.584

2.0 

1.5 

1.0 

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0.0 

1 

2 

3 

4 

5 

Money

Financial resources

Economic situation

Mean monthly earnings (PPS, working population)
Mean equivalised net income
Not at-risk-of-poverty, ≥ 60 % of median income
Income distribution S20/S80 (%)
Rotated Component Matrix
Extraction Method: Principal Component Analysis
Rotation Method: Varimax with Kaiser NormaliSation

% of variance explained
Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO)

Scree plot: Money

0.193
0.183
0.946
0.920

0.961
0.963
0.133
0.239

93.5
0.57

3.0 

2.5 

2.0 

1.5 

1.0 

0.5 

0.0 

1 

2 

3 

4 

Gender Equality Index 2017 − Methodological Report

49

Knowledge

Attainment and 
participation

Segregation

Graduates of tertiary education
People participating in formal or non-formal education and training
Tertiary students in the fields of education, health and welfare, humanities and art 
(tertiary students)
Rotated Component Matrix
Extraction Method: Principal Component Analysis
Rotation Method: Varimax with Kaiser Normalisation

% of variance explained
Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO)

Scree plot: Knowledge

0.863
0.852

0.191

84.2
0.63

0.153
0.181

0.981

2.0 

1.5 

1.0 

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1 

2 

3 

Time

Care activities

Social activities

People caring for and educating their children or grandchildren, elderly or people 
with disabilities, every day
People doing cooking and/or housework, every day
Workers doing sporting, cultural or leisure activities outside of their home, at least 
daily or several times a week
Workers involved in voluntary or charitable activities, at least once a month
Rotated Component Matrix
Extraction Method: Principal Component Analysis
Rotation Method: Varimax with Kaiser Normalisation

% of variance explained
Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO)

Scree plot: Time

0.932

0.793

0.309

0.237

87.7
0.71

0.179

0.443

0.889

0.917

3.0 

2.5 

2.0 

1.5 

1.0 

0.5 

0.0 

1 

2 

3 

4 

50

Gender Equality Index 2017 − Methodological Report

Power

Political

Economic

Social

Share of ministers
Share of members of parliament
Share of members of regional assemblies
Share of members of boards in largest quoted companies, supervisory board or board of directors
Share of members of central banks
Share of members of research funding organisations
Share of board members in publically owned broadcasting organisations
Share of members of highest decision-making body of the national Olympic sport organisations
Rotated Component Matrix
Extraction Method: Principal Component Analysis
Rotation Method: Varimax with Kaiser Normalisation

% of variance explained
Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO)

Scree plot: Power

0.765
0.821
0.850
0.341
0.205
0.446
0.124
0.460

82.5
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0.285
0.315
0.317
0.782
0.893
– 0.054
0.168
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0.425
0.243
0.186
0.130
0.046
0.763
0.932
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5 

Health

Status

Behaviour

Access

Self-perceived health, good or very good
Life expectancy in absolute value at birth
Healthy life years in absolute value at birth
People who don’t smoke and are not involved in harmful drinking
Population doing physical activities and/or consuming fruits and vegetables
Population without unmet needs for medical examination
Population without unmet needs for dental examination
Rotated Component Matrix
Extraction Method: Principal Component Analysis
Rotation Method: Varimax with Kaiser Normalisation

% of variance explained
Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO)

Scree plot: Health

– 0.052
0.470
0.215
0.967
0.093
0.169
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0.188
0.331
0.005
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0.915
0.169
0.225

0.255
0.233
0.014
0.101
0.332
0.902
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0.913
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0.929
0.160
0.185
0.123
0.162

92.7
0.57

4.0 

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1 

2 

3 

4 

5 

Gender Equality Index 2017 − Methodological Report

51

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i

Gender Equality Index 2017 − Methodological Report

55

 
 
 
 
 
 
 
 
 
 
 
References

European Institute for Gender Equality (EIGE) (2013), Gender Equality Index: Report, Publications Office of the European 

Union, Luxembourg (http://eige.europa.eu/rdc/eige-publications/gender-equality-index-report).

European Institute for Gender Equality (EIGE) (2015), Gender Equality Index 2015: Measuring gender equality in the European 
Union 2005-2012: Report, Publications Office of the European Union, Luxembourg (http://eige.europa.eu/rdc/
eige-publications/gender-equality-index-2015-measuring-gender-equality-european-union-2005-2012-report).

European Institute for Gender Equality (EIGE) (2017a), Gender Equality Index 2017: Measuring gender equality in the European 

Union 2005-2015, Publications Office of the European Union, Luxembourg.

European Institute for Gender Equality (EIGE) (2017b), Gender Equality Index 2017: measurement framework of violence 

against women, Publications Office of the European Union, Luxembourg.

European Institute for Gender Equality (EIGE)(2017c), Gender equality Index 2017: intersecting inequalities, Publications Office 

of the European Union, Luxembourg.

European Foundation for the Improvement of Living and Working Conditions (Eurofound) (2016), Sixth European Working 

Conditions Survey – Overview report (https://www.eurofound.europa.eu/sites/default/files/ef_publication/field_ef_
document/ef1634en.pdf).

Eurostat (2005), European Statistics Code of Practice for the National and Community Statistical Authorities (http://epp.

eurostat.ec.europa.eu/cache/ITY_OFFPUB/KS 77 07 026/EN/KS 77 07 026 EN.PDF).

Fundamental Rights Agency (FRA) (2012), Violence against women: an EU-wide survey, Main results report (http://fra.

europa.eu/en/publication/2014/violence-against-women-eu-wide-survey-main-results-report).

Kabeer, N. (2010), Can the MDGs provide a pathway to social justice? The challenge of intersecting inequalities, 
Brighton: Institute of Development Studies, MDG Achievement Fund (https://www.ids.ac.uk/files/dmfile/
MDGreportwebsiteu2WC.pdf).

Nardo, M., Saisana, M., Saltelli, A., Tarantola, S., Hoffman, A. and Giovannini, E. (2008), Handbook on constructing composite 

indicators: Methodology and user guide, OECD, European Commission, Joint Research Centre (http://www.oecd.
org/std/42495745.pdf).

Permanyer, I. (2015), Why call it ‘equality’ when it should be ‘achievement’? A proposal to un-correct the ‘corrected 

gender gaps’ in the EU Gender Equality Index, Journal of European Social Policy 25:4, 414-430.

Saaty, T. L. (1990), Eigenvector and logarithmic least squares, European Journal of Operational 

Research 48 (1990), 

pp. 156-160.

56

Gender Equality Index 2017 − Methodological Report

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