European Institute for Gender Equality (EIGE)
EIGE
European Institute for Gender Equality, Gedimino pr. 16, LT-01103 Vilnius, Lithuania
+370 5 215 7444
August 2023
The data is based on EIGE’s ‘Survey of gender gaps in unpaid care, individual and social activities’. The survey was carried out in all 27 Member States of the European Union with a total sample size of more than 60 000 respondents aged 16-74. Fieldwork was conducted between August and November 2022.
In most countries, data were collected via Computer Assisted Web Interviews (CAWI) using established online access panels. In Malta and Luxembourg, however, respondents were interviewed via Computer Assisted Telephone Interview (CATI) due to a lack of robust online access panels. Further information (including a technical report) about the survey can be found here [add when published].
The survey collected data on the use of time spent on unpaid activities by people aged 16-74 in the EU. Indicators are grouped in the following 10 themes, reflecting the key areas covered within the survey:
Level of education. Responses are based on the ISCED 2011 classification of eight education levels.
Informal care refers to unpaid care activities for family members, relatives, neighbours, or friends (including children) who have needed support as a result of mental, physical frailty, disability, or old age for at least 3 months. Informal care concerns daily living activities (e.g. dressing, showering, eating, moving around, using the toilet) and key activities of daily living (e.g. grocery shopping, preparing meals, managing money, and managing housework).
Childcare refers to unpaid care of children aged less than 25, including parental childcare, grandparenting, and any other forms of childcare outside of family care. Childcare includes personal care, assistance with school tasks, managing children’s activities, leisure, supervision, and emotional support. It does not cover long-term care (for children) related to long-standing health problems and/or disabilities.
Housework refers to activities that people do without being paid, such as grocery shopping, housework chores (cooking, cleaning, doing laundry, etc.), financial and administrative matters (paying bills, etc.), management and planning (preparing shopping lists, planning meals, etc.), house and general maintenance tasks (gardening, etc.).
Leisure refers to time spent outside of paid and unpaid work. Leisure activities are sport, religious, cultural activities, relaxing, meeting family and friends, sightseeing, holidays, watching TV, listening to the radio, and hobbies. Leisure excludes volunteering and life sustaining activities (e.g. personal care, eating, sleeping, visiting doctor).
Volunteering refers to unpaid activities where someone gives their time to help a not-for-profit organisation or an individual they are not related to. Volunteering includes being engaged in cultural, educational, sporting, charitable activities, distributing food, teaching, medical support, animal care, art and music, environmental work, support fundraising, donations, etc.
Political activities refer to running or helping a political campaign, distributing campaign material, signing a petition, protesting, contacting officials, etc.
The statistical unit is the individual.
The survey targeted respondents from 16 to 74 years of age across all Member States, with exception of Romania, Malta, and Luxembourg.
27 EU Member States
2022
2022
All indicators are expressed as percentages (of all relevant respondents)
2022
Data collected through this survey are used in EIGE’s Gender Equality Index, specifically for the calculation of the domain of Time. In addition, the indicators will help monitor achievements against the strategic objectives of the Beijing Platform for Action (BPfA) in the EU, particularly under Area F, ‘Women and the economy’, Area C, ‘Women and Health’, and Area K. ‘Women and the environment’.
This new dataset will also substantially contribute to the monitoring of the EU Gender Equality Strategy 2020-2025 and the EU Care Strategy
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No direct identification of a person is possible from the indicators based on the survey.
Data processing, validation, and coding have been carried out based on the GDPR/EU Regulation 2016/679. In order to guarantee respondent anonymity, personal identification variables were excluded from microdata delivered to EIGE.
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EIGE publishes the results in its dedicated Gender Statistics Database:
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No regular news releases.
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No micro-data are disseminated.
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Available upon request
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Steps were taken during the collection of data to support data quality, including:
No quality concerns have been identified.
This data is relevant to decision-makers and others in relation to on-going policy and legal changes related to gender equality and unpaid care. For instance, this new dataset will substantially contribute to the monitoring of the EU Gender Equality Strategy 2020-2025 and the EU Care Strategy.
No user opinions have been collected.
The sample size is considered reliable for all 27 Member States selected for the survey.
The data are considered to be of high accuracy.
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The same methodology was applied across countries, and as such the data can be considered comparable. The use of population size weights also supports cross-country comparison of results. In addition, the questionnaire ensured the correct translation of country-specific terminology.
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Not relevant.
Each survey indicator included in the Gender Statistics Database has full internal coherence, as it is based on the same data source.
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There is no planned revision.
EIGE’s ‘Survey of Gender Gaps in Unpaid Care, Individual and Social Activities’, conducted in 2022.
The collection of the data for this survey was a one-off activity.
Data were collected via Computer Assisted Web Interviews (CAWI) using established online access panels. In Malta and Luxembourg, however, respondents were interviewed via Computer Assisted Telephone Interview (CATI) due to a lack of robust online access panels.
The data validation started with the preparation and cleaning of the data. First, only complete interviews were included in the sample and system variables were removed. Then, national variables were harmonised and all countries merged into one. The CATI datafiles were integrated and additional variables necessary for a multi-country datafile (country and languages) were created. Quality checks unveiled the need for data cleaning in several variables. In particular, new variables limiting the range of the answers were incorporated to correct inconsistencies in open questions.
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CAWI weighting (25 Member States). The sample was weighted per country, adjusting the unweighted sample to the official data using national population statistics from Eurostat. The variables gender, age, region, and level of education were used to calculate the weighting factors. The iterative “Rim weighting” (also known as IPF, iterative proportional fitting) procedure was used.
CATI weighting (Malta and Luxembourg). The CATI dual frame weighting was a multi-stage process, which included:
Population size weights. The responses obtained in each country should have a weight proportionate to the population size of the country and the country sample size. For this purpose, the PSW was computed as the proportion between the population ratio (between the Member State’s population aged 16–74 and the total EU-27 population aged 16–74), and the sample ratio (between the Member State sample size and the total sample size). PSW have been used in the calculation of the indicators when data from two or more countries was combined, in combination with calibration weights (calibration weight × PSW).
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