European Institute for Gender Equality (EIGE)
European Institute for Gender Equality, Gedimino pr. 16, LT-01103 Vilnius, Lithuania
+370 5 215 7444
17/07/2024
The European Institute for Gender Equality (EIGE) Gender Statistics Database collects data on the numbers of women and men in key decision-making positions across a number of different life domains in order to provide reliable statistics that can be used to monitor the current situation and trends through time.
The domains covered include politics, public administration, judiciary, business and finance, social partners and NGOs, environment, media, science and research, sports, transport, COVID-19, and EU funding. The decision-making positions covered are specific to each area of decision-making and are described in detail in the section on statistical concepts and definitions.
Data on central banks cover the head (governor), deputy head (Deputy/Vice-governor) and members of the decision-making bodies of the entity responsible for overseeing the monetary system in 38 countries (where relevant).
The women and men in decision-making (WMID) data are organised into life domains and then by types of organisation and the different decision-making positions within the hierarchy of each organisational type. The domains covered are:
Details of the organisations and positions covered are provided in the section on statistical concepts and definitions.
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General definitions:
A decision-making position is a position from which it is possible to take or influence a decision:
Central banks
Organisations covered:
Positions covered:
Mapping tables:
Notes:
The statistical unit in WMID data is the organisational unit as defined by each specific topic (e.g. a house of parliament or a large listed company). Data are then collected on the numbers of men and women occupying decision-making positions within that unit.
Data cover all persons occupying specified positions in the organisational units covered (see statistical concepts and definitions).
The WMID data cover the 27 EU Member States, United Kingdom, six EU candidate countries (including Bosnia and Herzegovina, Montenegro, North Macedonia, Albania, Serbia and Türkiye), one potential candidate (Kosovo(*)) and the remaining three EEA countries (Iceland, Liechtenstein and Norway).
The Swiss National Bank serves as the national bank of Liechtenstein. Therefore, data on central banks are collected for 37 countries covered by the women and men in decision-making data.
(*) This designation is without prejudice to positions on status, and is in line with UNSCR 1244 and the ICJ Opinion on the Kosovo Declaration of Independence.
Data on the governors and members of the central banks were first collected in the 3rd quarter of 2003 (for 25 countries) while data for deputy/vice-governors are available from 2007. The geographic coverage of the database has expanded through time so for the countries listed below the time series starts in the period indicated:
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Number of persons and percentages.
Data on central banks are collected on an annual basis. Typically, data are collected between May and September.
No legal acts are applicable. The Council of the European Union has committed to ongoing monitoring of the implementation of the Beijing Platform for Action and as part of this commitment the European Commission has been collecting data on decision-making since 2003, a task that has been taken over by EIGE since 2017.
The WMID database was managed by the European Commission until end 2016 and then transferred to EIGE.
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Generally, data are disseminated within one month of the data collection (see section on reference period). Data are published on the EIGE Gender Statistics database
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Data are disseminated to all types of users simultaneously via the EIGE Gender Statistics database.
Annually.
No regular news release.
From 2017, EIGE will publish regular bulletins on gender statistics, which may cover data on decision-making. The European Commission's Annual Report on Equality usually includes a section on this topic.
Micro-data are not made available.
WMID data are the primary source of information for indicators to monitor the implementation of Area G of the Beijing Platform for Action.
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WMID data are collected either directly from the organisations covered or from umbrella organisations providing data on behalf of member organisations. The majority of data are collected from the websites or other publications produced by the organisations concerned but some are also collected from direct contacts. There are three main risks in the data collection: ensuring the right decision-making bodies and positions are identified; ensuring that the gender of the people in these positions is correctly recorded; and ensuring that the information is up-to-date.
The data are collected by a team of experienced researchers and are subject to routine validation that includes:
WMID data need to be viewed bearing in mind the inherent diversity of institutional and organisational structures and in the scope of decision-making responsibilities for nominally similar positions. In this sense, there will always be some limitations to the extent to which data can be considered as fully comparable between countries. That being said, the data are considered to be of good quality, collected from reliable sources and with careful application of a common methodology. The data are comprehensive (cover all relevant organisations) and complete (data are available for all relevant positions in each organisation covered) in the vast majority of cases.
WMID data are the primary source of information for indicators to monitor the implementation of Area G (Power and decision-making) of the Beijing Platform for Action. The data are therefore widely used by the European Commission (DG JUST) and the European Institute for Gender Equality for analysis in this area and for reporting to the Council of the European Union.
The data are also widely used by researchers in this area.
No user satisfaction surveys are carried out.
The completeness of WMID data depends on the extent to which the organisations covered openly publish, or are willing to share, information about their key decision-making personnel. In general, there is increasing pressure on organisations of all types to be completely transparent about their organisational structure and operational practices so that completeness of the data has improved through time.
In the case of central banks, data are complete.
Data for the six EU candidate countries and one potential candidate (i.e., IPA beneficiaries) were not collected in 2024.
In principle, the WMID data accurately describe the situation for the area of decision-making concerned, though in some areas the coverage of organisations is restricted to limit the cost and burden of the data collection, and this could potentially impact on overall accuracy.
In the case of central banks, data can be considered fully accurate.
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Data are released within one month of collection.
Punctuality is 100%.
WMID data are considered comparable between countries but it is necessary to bear in mind the different institutional settings that exist and the fact that decision-making structures vary between organisations within countries as well as between countries. There are, therefore, inherent differences between countries in the way that decision-making is organised but the WMID methodology aims to allow for such variation so that the organisations and positions covered in each country are as comparable as possible.
In the case of central banks, the organisational structure varies from one country to another and there are often several decision-making bodies, with distinct functions and differences in the decision-making powers. In some cases, for example, the board of directors may be the main decision-making body of the central bank in terms of general policy, administration and management, but the board of governors has sole responsibility for monetary policy. In other cases, a supervisory board technically holds the supreme power within the bank structure but may in fact have little impact in terms of decision-making, serving mainly to supervise the activities of the body/bodies responsible for policy and general management.
In general, WMID data are comparable through time in each area of decision-making. Nevertheless, it is necessary to bear in mind that the organisations covered and the decision-making structures within these may vary through time (e.g. government ministries may be reorganised following a change of government, and the constituents of the blue-chip indices used to define the sample of large listed companies are updated by the responsible stock exchange on a regular basis).
In the period 2003-2007, only one body (the “highest” decision-making body) was covered. Following a methodological review in 2008, data were collected for multiple bodies in order to include all positions with decision-making power and obtain a more uniform coverage. Data can be considered fully comparable over time from 2008.
Occasionally, there are changes in the governance systems of the bank, affecting the highest decision-making bodies covered over time:
In general, there are few other sources of data on decision-making against which to assess the coherence of WMID data. Some national data exist for selected areas of decision-making but often the methodology is not the same (i.e. the coverage of organisations and/or positions within these varies) so that direct comparison is not possible.
The United Nations Economic Commission for Europe (UNECE) compiles data on the numbers of men and women members of boards in central banks in Europe from official national sources. Data are published in the UNECE statistical database, but there is no breakdown by president. There may be differences in the decision-making body covered. A full review of the coherence between EIGE and UNECE datasets is due in 2023.
Internal coherence of the data (e.g. through time or across countries) is ensured through careful application of the WMID methodology, and routine validation of data.
Data are collected by a research team contracted by the European Institute for Gender Equality. There is no burden on Member States.
Revisions to data are infrequent. Occasionally, for example, a response to a request to verify the information collected for a particular organisation is received after the data collection has closed and the results disseminated. If the information leads to a change in the data, then the update is made at the next available opportunity and at the latest within one month.
There is no fixed revision schedule. Any necessary revisions are made on an ad hoc basis.
The following revisions were applied to data on central banks:
The WMID data are a form of administrative data, being derived from the records that organisations keep regarding the personnel occupying key positions within the organisation. In all areas of decision-making, the data cover all organisations within the scope defined by the methodology. In some areas (e.g. national level politics) all relevant organisations/bodies are covered (e.g. parliaments and governments) whilst in others the methodology restricts coverage so that the data effectively represent a sample of all organisations within the broad type of organisation (e.g. data on decision-making in large companies are restricted to the nationally registered constituents of the main blue-chip index for the country).
Annually.
Direct collection of data from official websites.
See section on quality assurance.
Data collected from individual organisations are aggregated to the national level by position.
EU-27 figures are based on an aggregate of data at national level, with the percentages of men and women calculated from these aggregates. The shares of men and women observed at the EU level are therefore weighted averages rather than an average of the shares at country level.
Figures for IPA beneficiaries are based on an aggregate of data at national level for the six candidate countries (Bosnia and Herzegovina, Montenegro, North Macedonia, Albania, Serbia, and Türkiye) and one potential candidate (Kosovo).
Different aggregates can also be computed using the pre-defined table. For example, an “EU candidate countries” aggregate can be created by adding data for Bosnia and Herzegovina, Montenegro, North Macedonia, Albania, Serbia, and Türkiye for the relevant position.
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