Short name	Life satisfaction (1.1)
Long name	Average life satisfaction
Dimension	Subjective well-being
Definition	Life satisfaction is measured through survey questions about individuals’ overall satisfaction with their lives. Respondents typically answer the question: ‘Overall, how satisfied are you with your life as a whole these days?’ using a scale from 0 to 10, where 0 represents ‘not at all satisfied’ and 10 represents ‘completely satisfied’. Averages refer to mean scores of the 0-10 scale, whereas deprivations in life satisfaction typically refer to values of 4 or below. This definition follows the OECD Guidelines on Measuring Subjective Well-being.
Use	This indicator is used to assess overall satisfaction with life.
Type	Subjective
Unit of measurement	Mean values on an 11-point scale, with responses ranging from 0 (not at all satisfied) to 10 (fully satisfied)
Data sources	The recommended data sources are household and well-being surveys. Questions on life satisfaction can be included in ongoing or standalone surveys, leveraging existing national or international frameworks, e.g. of OECD and Eurostat.
Frameworks	OECD How’s Life? Eurostat Quality of Life Framework
More metadata	OECD How’s Life? - Guidelines on Measuring Subjective Well-being (published in 2013, to be updated in October 2025) https://www.oecd.org/en/publications/oecd-guidelines-on-measuring-subjective-well-being_9789264191655-en.html The OECD Well-being Database includes Eurostat data as well as additional life satisfaction estimates for non-European OECD member countries. https://www.oecd.org/wise/oecd-well-being-database-definitions.pdf? (refer to page 26-27). Access indicator data from OECD. Eurostat’s Quality of life methodology within the EU context. Data is collected through the European Union Statistics on Income and Living Conditions (EU-SILC) survey: https://ec.europa.eu/eurostat/cache/metadata/en/ilc_sieusilc.htm Access indicator data from Eurostat

Short name	People who feel their life has meaning and purpose (1.2)
Long name	Extent of feeling things one does in life are worthwhile with the mean average score on an 11-point scale, ranging from 0 to 10.
Dimension	Subjective well-being
Definition	The proportion of individuals who express a sense of meaning and purpose in life (eudaimonic well-being). This is typically measured through survey responses. Respondents typically answer the question: ‘To what extent do you feel the things you do in your life are worthwhile?’ using a scale from 0 to 10, where 0 represents ‘not at all worthwhile’ and 10 represents ‘completely worthwhile’. Averages refer to mean scores of the 0-10 scale, whereas deprivations in eudaimonic well-being can refer to values of 4 or below. This definition follows the OECD Guidelines on Measuring Subjective Well-being.
Use	To evaluate individual perceptions of the overall, to what extent do individuals feel that the things they do in their life are worthwhile.
Type	Subjective
Unit of measurement	Mean values on a 0-10 scale
Data sources	The recommended data sources are household and well-being surveys. Questions on the sense of meaning of life and purpose can be included in ongoing or standalone surveys, leveraging existing frameworks, e.g.: OECD How’s Life? Report 2015 compiles national data ‘Life Satisfaction and Feeling Life is Worthwhile’ based on survey data from 2013, following the OECD Guidelines on Measuring Subjective Well-being. Respondents answer a standardized life purpose question on a scale (0-10). Eurostat EU-SILC (European Union Statistics on Income and Living Conditions)
Frameworks	OECD How’s Life? ideal indicator set (not currently populated)
More metadata	OECD How’s Life? - Guidelines on Measuring Subjective Well-being (published in 2013, to be updated in October 2025) OECD How’s Lie? Measuring Wellbeing, 2015 Report (Refer page 97-98) The 2013 ad hoc module ‘Well-being’ of the EU Statistics on Income and Living Conditions data collection included a measure of eudaimonia that was roughly equivalent to measures used outside of Europe (i.e. feeling that the things you do in life are worthwhile) and was featured in the 2015 edition of How’s Life? (OECD, 2015[3]). However, these data have not been updated since, and no time series is available. Additional readings https://ec.europa.eu/eurostat/cache/metadata/en/ilc_sieusilc.htm

Short name	Negative affect balance (1.3)
Long name	Share of population reporting more negative than positive feelings and states in a typical day.
Dimension	Subjective well-being
Definition	Negative affect balance is assessed using a series of items where respondents answer yes or no to experiencing each emotion or state extensively on the previous day. The indicator represents the percentage of respondents reporting more negative than positive feelings or states on the previous day, and it follows the OECD Guidelines on Measuring Subjective Well-being. The types of positive and negative affect that are included in the OECD Guidelines on Measuring Subjective Well-being as ideal set include feeling pain, happy, worried, calm, sad, angry, joyful, tired and stressed yesterday, collected on a 0-10 scale where zero means not experiencing the feeling at all and 10 means experiencing the feeling all the time.
Use	To monitor and compare the affective component of subjective well-being, referring to feelings and emotional states across nations and population groups and capturing distinct aspects of subjective well-being that are not reflected in evaluative measures such as life satisfaction.
Type	Subjective
Unit of measurement	Percentage of the population
Data sources	The recommended data sources are household and well-being surveys. Questions on negative affect balance can be included in ongoing or standalone surveys, leveraging existing frameworks, e.g. of OECD and the Gallup World Poll. OECD sources data from the Gallup World Poll. Until measures of affect are consistently integrated into official household surveys, data from the Gallup World Poll referring to the emotions of anger, sadness, worry, enjoyment, feeling well-rested and laughing/smiling yesterday, measured on a binary response scale, are used by the OECD for international comparisons. International comparability will depend on the survey questions.
Frameworks	OECD How’s Life?
More metadata	OECD How’s Life? - Guidelines on Measuring Subjective Well-being (published in 2013, to be updated in October 2025) https://www.oecd.org/en/publications/oecd-guidelines-on-measuring-subjective-well-being_9789264191655-en.html Access indicator data from OECD

Short name	Household adjusted disposable income (2.1)
Long name	The 2008 SNA uses the term ‘Household adjusted disposable income’. In the 2025 SNA, it is termed ‘Household disposable income adjusted for social transfers in kind’.
Dimension	Material living conditions
Definition	Household adjusted disposable income is all income received by the household in the form of employment earnings, self-employment, capital income and current transfers in cash and in kind (such as education and health care services by government) less all current transfers paid by the household to other sectors (such as taxes on income and wealth). Household adjusted disposable income is a measure of the maximum value of actual final consumption that a household can afford in the current period without having to reduce its cash, dispose of other assets or increase its liabilities. Like other national accounts aggregates, household adjusted disposable income can be recorded gross or net of depreciation and depletion of capital assets. ‘Household adjusted disposable income’ (2008 SNA) is termed ‘Household disposable income adjusted for social transfers in kind’ in the 2025 SNA. The two are identical. For ease of reference, the shorter 2008 SNA term is used for the indicator.
Use	The SNA emphasizes the importance of indicators like disposable income adjusted for social transfers in kind to better assess material well-being. By incorporating social transfers in kind, this indicator provides a more accurate measure of the economic resources accessible to households, thereby offering deeper insights into living standards and economic welfare
Type	Objective
Unit of measurement	The unit of measurement is the national currency. Depending on the use of the indicator, it can be expressed in per capita values, purchasing power parity (PPP), percentage of GDP, inflation-adjusted constant prices, and equivalized household income.
Data sources	National accounts, household income statistics. National data are harmonized by OECD and Eurostat for cross-country comparability.
Frameworks	OECD How’s Life? Eurostat Quality of Life Framework
More metadata	https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary%3AAdjusted_household_disposable_income https://ec.europa.eu/eurostat/cache/metadata/de/sdg_10_20_esmsip2.htm Access indicator data from OECD Access indicator data from Eurostat

Short name	Relative income poverty (2.2)
Long name	At-risk-of-poverty rate: Share of individuals with household disposable income below the relative income poverty line
Dimension	Material living conditions
Definition	The at-risk-of-poverty rate is the share of people with an equivalised disposable income (after social transfer) below the at-risk-of-poverty threshold, which is set, depending on the source, between 50-60% % of the national median equivalised disposable income after social transfers. This indicator does not measure wealth or poverty, but low income in comparison to other residents in that country, which does not necessarily imply a low standard of living.
Use	To measure the proportion of individuals experiencing income poverty, assess economic inequality, and guide policies aimed at reducing poverty and improving income distribution.
Type	Objective
Unit of measurement	Percentage of individuals
Data sources	Household income statistics based on survey and/or administrative data, and national accounts. International sources include: EU Statistics on Income and Living Conditions (EU-SILC) is a cross-sectional and longitudinal survey conducted annually across EU member states, providing harmonized microdata on income, social inclusion, and living conditions. OECD Income Distribution Database (IDD), based on household surveys and tax records, collected from national statistical offices across OECD member countries. The database follows the Canberra Handbook (2011) and applies, wherever possible, adjustments like setting negative income values to zero. The IDD sets the relative income poverty line at 50% of the national median income.
Frameworks	OECD How’s Life? Eurostat Quality of Life Framework
More metadata	OECD’s data source: OECD Income Distribution Database. Access the indicator data from OECD Eurostat’s data is collected through the European Union Statistics on Income and Living Conditions (EU-SILC) survey: https://ec.europa.eu/eurostat/cache/metadata/en/ilc_sieusilc.htm Access indicator data Eurostat

Short name	Difficulty making ends meet (2.3)
Long name	Share of individuals who declare to have difficulty or great difficulty in making ends meet.
Dimension	Material living conditions
Definition	Difficulty in making ends meet refers to the percentage of people who report having difficulty or great difficulty in making ends meet. The question is asked to the household reference person, and the information is available at household level only.
Use	To assess financial strain within the population and complement objective indicators with perception-based estimates, monitor economic insecurity, and inform policies aimed at reducing financial hardship and improving household economic stability.
Type	Subjective
Unit of measurement	Percentage of individuals
Data sources	The recommended data source is household income statistics based on administrative data and/or surveys. International sources include: European Union Statistics on Income and Living Conditions (EU-SILC) survey
Frameworks	Eurostat Quality of Life Framework OECD How’s Life?
More metadata	Eurostat’s data is collected through the European Union Statistics on Income and Living Conditions (EU-SILC) survey: EU statistics on income and living conditions (EU-SILC) methodology - economic strain Refer article on Ability to make ends meet becoming harder Access indicator data from Eurostat The OECD Well-being Database includes Eurostat data as well as additional data on difficulty making ends meet for non-European OECD member countries. Access indicator data from OECD

Short name	Financial insecurity (2.4)
Long name	Share of individuals with equivalised liquid financial assets below 3 months of the annual national relative income poverty line.
Dimension	Material living conditions
Definition	Financial insecurity, a measure of wealth deprivation, refers to the percentage of people who are not currently income-poor, but who have liquid financial wealth below three months of the annual national relative income poverty line. Liquid financial wealth includes cash, quoted shares, mutual funds and bonds net of liabilities. These people are considered as ‘financially insecure’ as, in the event of a shock, their liquid financial wealth would be insufficient to support them at the level of the income poverty line for more than three months.
Use	To measure financial vulnerability by assessing the population's liquid financial reserves, monitor economic security, and inform policies aimed at enhancing financial stability and reducing economic inequalities.
Type	Objective or subjective based on self-assessments.
Unit of measurement	Percentage of Individuals
Data sources	The recommended data source is household income statistics based on administrative data and/or surveys. International sources include: OECD Guidelines for Micro Statistics on Household Wealth. The income concept used to compute this indicator follows as much as possible that used for reporting income poverty, i.e. household disposable income. However, for most countries, information on household disposable income is not available in the data sources used for the computation of wealth statistics; for this reason, the choice made here has been to rely on the concept of gross income (i.e. the total sum of wages and salaries, self-employment income, property income and current transfers received, all recorded before payment of taxes) when information on disposable income was not available.
Frameworks	OECD How’s Life?
More metadata	OECD’s data source: OECD Wealth Distribution Database. Reference document for further reading - OECD PROJECT ON THE DISTRIBUTION OF HOUSEHOLD WEALTH Access indicator data from OECD

Short name	Labour force participation rate (3.1)
Long name	Share of the working-age population that is either employed or actively seeking employment.
Dimension	Work and leisure
Definition	Labour force participation rate is the ratio of the total labour force divided by the total working-age population. The working age population refers to people aged 15 to 64. This indicator is measured as a percentage of each age group.
Use	The labour force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country’s human resources and in making projections of the future supply of labour. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity – crucial information for the financial planning of social security systems. The indicator is also useful for understanding the labour market behaviour of different segments of the population. The level and pattern of labour force participation depend on employment opportunities and the demand for income, which may differ from one category of persons to another.
Type	Objective
Unit of measurement	Percentage (%)
Data sources	The Labour Force Participation Rate (LFPR) is usually measured through labour force surveys (LFSs) and population censuses, ensuring nationally representative data without geographic limitations. The Labour Force Survey (LFS) is the most widely used method, conducted periodically by national governments to collect data on employment status, unemployment, and economic activity. Additionally, population censuses provide comprehensive workforce estimates, though less frequently. International sources include the ILO’s ILOSTAT database and OECD’s employment indicators.
Frameworks	ILOSTAT Labour Force Statistics framework OECD How’s Life? (the OECD Well-being Database considers the employment rate of the adult population aged 25-64)
More metadata	https://ilostat.ilo.org/methods/concepts-and-definitions/description-labour-force-statistics/ Handbook on measuring quality of employment. A statistical framework Access indicator data from OECD Access indicator data from Eurostat

Short name	Unemployment (3.2)
Long name	Unemployment Rate: Share of the labour force that is jobless and actively seeking employment
Dimension	Work and leisure
Definition	Unemployment refers to the percentage of the labour force that is jobless and actively seeking employment. This includes individuals who are without work, available for work, and actively looking for a job during the reference period. It is a key indicator used to gauge labour market conditions and economic health. The OECD How's Life? framework uses long-term unemployment which is measured as the percentage of the labour force that has been unemployed for one year or more.
Use	To monitor the labour market, identify economic downturns, and guide employment policies.
Type	Objective
Unit of measurement	Percentage of the labour force
Data sources	The primary source is the Labour Force Survey. Most LFSs follow ILO standards, defining unemployed individuals as those without work, actively seeking employment and available for work. OECD’s How’s Life? Framework focuses on long-term unemployment, while Eurostat’s EU-LFS collects harmonized unemployment data across EU member states through household surveys. ILO compiles global data through ILOSTAT, ensuring a standardized approach to tracking labour market trends and employment policy effectiveness.
Frameworks	OECD How’s Life? Eurostat Quality of Life Framework ILOSTAT Labour Force Statistics framework
More metadata	Eurostat’s data is collected through the European Union Labour Force Survey (EU-LFS): https://ec.europa.eu/eurostat/cache/metadata/en/lfsa_esms.htm Handbook on measuring quality of employment. A statistical framework Access indicator data from Eurostat Access indicator data from OECD

Short name	Perceived job security (3.3)
Long name	Share of employed persons who state that they might lose their job in the following 6 months.
Dimension	Work and leisure
Definition	The proportion of employed individuals who report expecting a possible loss of their job within the next six months, based on self-reported survey responses.
Use	The indicator intends to capture the job security as perceived by the respondent. The information should be used to complement other indicators on security of employment, like job tenure or the percentage of employed persons with fixed-term contracts. Job security as perceived by the workers may not be closely related to the formal stability of their jobs. Only a combination of both types of indicators will provide a comprehensive picture regarding employment security
Type	Subjective
Unit of measurement	Percentage
Data sources	Labour force survey is the recommended data source. Data on employed persons who indicate they might lose their job in the next six months shall be collected in household or population surveys. Since the respondent’s own perception should be captured, establishment surveys or administrative data are not suitable for collecting the information required for the indicator. In Europe, the European Working Conditions Survey 2010 (EWCS) provides data on self-perceived job security. The EWCS provides harmonised data for 34 European Countries. It should be noted that the sample size limits the possibilities for detailed analyses at the national level.
Frameworks	UNECE Measuring Quality of Employment
More metadata	Data on the source, reference period, population coverage and geographic coverage, the definition and operational definitions (item of questionnaire) of perceived job security. Handbook on measuring quality of employment. A statistical framework (refer page 200-202) Access indicator data from Eurostat

Short name	Job satisfaction (3.4)
Long name	Average satisfaction with the job on a scale from 0 (not at all satisfied) to 10 (completely satisfied)
Dimension	Work and leisure
Definition	Average job satisfaction refers to the mean score of individuals’ self-reported satisfaction with their job, measured on a scale from 0 to 10, where 0 indicates ‘not at all satisfied’ and 10 indicates ‘completely satisfied’. This indicator reflects the overall contentment of workers with their job, encompassing various factors such as work environment, job responsibilities, relationships with colleagues, and compensation.
Use	To evaluate job satisfaction levels among workers, monitor workforce well-being, and inform policies aimed at improving workplace conditions and employee engagement.
Type	Subjective
Unit of measurement	Average rating on a scale of 0-10
Data sources	Labour force survey or working condition surveys are recommended. International sources include: Eurostat’s European Union Statistics on Income and Living Conditions (EU-SILC) survey, which collects self-reported job satisfaction with different aspects of their jobs such as work content, working conditions, work-life balance, pay and benefits, job security and relationships with colleagues and superiors across EU member states. Eurostat Quality of Life Framework and aligns with the Handbook on Measuring Quality of Employment to ensure statistical accuracy and comparability. Data is disaggregated by demographic and socio-economic factors to provide insights into workforce well-being.
Frameworks	Eurostat Quality of Life Framework OECD How’s Life?
More metadata	Eurostat’s data is collected through the European Union Statistics on Income and Living Conditions (EU-SILC) survey: https://ec.europa.eu/eurostat/cache/metadata/en/ilc_sieusilc.htm Handbook on measuring quality of employment. A statistical framework Access indicator data from Eurostat

Short name	Long working hours (3.5)
Long name	Share of employed persons usually working 49 hours or more per week
Dimension	Work and leisure
Definition	Percentage of employees aged 15+ usually working 49 hours or more per week
Use	The indicator on long working hours provides information about the share of persons in employment whose hours usually worked exceed 48 hours per week. It is an indicator of exposure to overwork, i.e., persons having a working time that exceeds the threshold beyond which can have negative effects on workers, not only on workers’ health, but also on their safety (for instance, increasing injury hazard rates) and on work-life balance. It is recommended to use at least a 48-hour threshold to construct the indicator to enhance international data comparability. The principle of the 8-hour day or the 48-hour week threshold was first adopted in ILO Convention No. 1 (Hours of Work (Industry) Convention, 1919) and later in Convention No. 30 (Hours of Work (Commerce and Offices) Convention, 1930). This threshold was referenced in 2008 in the Resolution concerning the measurement of working time. National thresholds used to define long working hours might complement the information provided for the indicator.
Type	Objective
Unit of measurement	Percentage
Data sources	The LFS is the recommended data source, as it provides estimates of employed persons and allows disaggregation by, e.g., economic activity, age, and gender. The indicator follows the 48-hour workweek threshold, first established in ILO Conventions No. 1 (1919) and No. 30 (1930), and reaffirmed in the 2008 Resolution on measuring working time, to ensure international comparability. If an LFS is unavailable, other household surveys with employment modules may be used. The data aligns with the OECD How’s Life? framework and is further detailed in the Handbook on Measuring Quality of Employment (page 144). Comprehensive metadata includes information on data periodicity, series breaks, coverage, and thresholds.
Frameworks	UNECE Measuring Quality of Employment OECD How’s Life? (OECD Well-being Database considers a threshold of 50 hours a week)
More metadata	Handbook on measuring quality of employment. A statistical framework (Refer to pages 144-146) As a minimum, data on the source (periodicity, breaks in series, etc.), reference period, population coverage, job coverage (main job or all jobs), definition of hours threshold and geographic coverage should be provided. Access indicator data from OECD Access indicator data from Eurostat

Short name	Work injuries (3.6)
Long name	Share of employed population covered in the event of work injury.
Dimension	Work and leisure
Definition	The percentage of employed individuals who are covered by work injury insurance or equivalent social protection schemes that provide financial or medical support in the event of a workplace injury. Coverage typically includes compensation for lost income, medical expenses, and rehabilitation.
Use	To measure the extent of social protection coverage for workplace injuries, assess gender disparities in access to work injury benefits, and inform policies aimed at improving occupational safety and social security systems for workers.
Type	Objective
Unit of measurement	Percentage
Data sources	The recommended sources are the LFS and administrative records. Social security agencies and workers' compensation institutions report official data on coverage, while some national labour force surveys capture self-reported work injury insurance status. Some countries include work injury coverage questions in household or labour force surveys to supplement administrative data. The ILO Social Protection Database and the UN SDG Database (Indicator 1.3.1). provides data and classification, and definitions of fatal and non-fatal occupational injuries to facilitate international comparability.
Frameworks	Sustainable Development Goals
More metadata	Link to more metadata in SDG Database: https://unstats.un.org/sdgs/dataportal/SDMXMetadataPage?1.3.1-SI_COV_WKINJRY Handbook on measuring quality of employment. A statistical framework refers to ’fatal and nonfatal occupational injuries’ (page 59- 67) Access indicator data from SDG Database – SDG 1.3.1 UNSDG

Short name	Leisure time (3.7)
Long name	Time allocated to leisure and personal care, hours per day, people in full-time employment.
Dimension	Work and leisure
Definition	Time off is measured in hours per day for people in full-time employment. It includes personal care time (e.g., sleeping, eating, drinking, other personal care activities, and associated travel) and leisure time (e.g., sports, socializing, attending events, watching TV, listening to music, other leisure activities, and associated travel). Only main activities are recorded, which may underestimate leisure time, as it often overlaps with other tasks (e.g., talking on the phone while cooking). Data is collected through Time Use Surveys (TUS), where participants record their activities and durations over a 24-hour period.
Use	To assess work-life balance and the allocation of time for leisure and personal care among individuals in full-time employment, monitor disparities in time use, and inform policies aimed at promoting well-being and reducing work-related stress.
Type	Objective
Unit of measurement	Hours per day
Data sources	The time allocated to leisure and personal care for people in full-time employment is measured using Time Use Surveys (TUS), which track how individuals allocate their daily hours. The main data sources include national statistical offices, OECD’s How’s Life? report, and CES (Conference of European Statisticians) recommendations on time-use data collection.
Frameworks	OECD How’s Life? CES Recommendations (Guideline for Harmonizing Time-Use Surveys)
More metadata	OECD calculations based on public-use time use survey microdata when available; Eurostat’s Harmonised European Time Use Surveys (database), https://ec.europa.eu/eurostat/web/time-use-surveys; and estimates provided by NSOs. Access indicator data from OECD Handbook on measuring quality of employment. A statistical framework refer indicator ‘Weekend work’ (3b3) – page 164-165) Eurostat’s data is collected through the European Union Statistics on Income and Living Conditions (EU-SILC) survey: https://ec.europa.eu/eurostat/cache/metadata/en/ilc_sieusilc.htm Access indicator data from Eurostat

Short name	Satisfaction with leisure time (3.8)
Long name	Mean average satisfaction with time use on a 0-10 scale, with responses ranging from 0 (not at all satisfied) to 10 (completely satisfied)
Dimension	Work and leisure
Definition	The percentage of individuals reporting satisfaction with their time use refers to the proportion of people who express their satisfaction with how they allocate and manage their time. This is typically measured through a survey question where individuals rate their satisfaction on a scale such as ‘very satisfied’, ‘satisfied’, and ‘dissatisfied’. The indicator reflects how well individuals believe they manage their time across various activities, including work, leisure, and personal commitments.
Use	To evaluate perceptions of time use, particularly leisure time, monitor well-being and work-life balance, and guide policies aimed at enhancing time management and access to leisure activities.
Type	Subjective
Unit of measurement	Country average rating on a scale of 0-10
Data sources	Recommended sources are household and social well-being surveys and Time Use Surveys. Data can be collected through structured interviews (face-to-face, telephone, or online). Respondents rate their satisfaction with time use on a Likert-scale (0-10) or categories such as very satisfied, satisfied, dissatisfied. International sources include OECD’s How’s Life? report, Eurostat’s EU-SILC survey, and global well-being studies such as the Gallup World Poll.
Frameworks	OECD How’s Life? Eurostat Quality of Life Framework
More metadata	Eurostat’s data is collected through the European Union Statistics on Income and Living Conditions (EU-SILC) survey: The OECD Well-being Database includes Eurostat data as well as additional time use satisfaction estimates for non-European OECD member countries. Access indicator data from OECD Access indicator data from Eurostat

Short name	Informal care and household work (3.9)
Long name	Share of time spent on unpaid domestic chores and care work.
Dimension	Work and leisure
Definition	This indicator measures the proportion of time spent in a day on unpaid domestic and care work by men and women. Unpaid domestic and care work encompasses activities related to providing services for the personal use of household members or family members residing in other households. These activities are categorized under the major divisions ‘3. Unpaid domestic services for household and family members’ and ‘4. Unpaid caregiving services for household and family members’ in the International Classification of Activities for Time-Use Statistics 2016 (ICATUS 2016).
Use	To measure gender disparities in unpaid domestic and care work, monitor progress toward gender equality, and inform policies aimed at reducing the burden of unpaid work and promoting shared responsibility within households.
Type	Objective
Unit of measurement	Percentage of time in a day
Data sources	Recommended sources are household surveys, LFS, social well-being surveys and Time Use Surveys. Data can be collected using time-use diaries and recall surveys, where respondents report daily activities based on ICATUS 2016 classifications for unpaid domestic and caregiving work. International sources include the OECD time use database, Eurostat Quality of Life Framework, and the SDG Database (Indicator 5.4.1).
Frameworks	OECD Time Use Database Eurostat Quality of Life Framework
More metadata	Link to more metadata in SDG Database: https://unstats.un.org/sdgs/dataportal/SDMXMetadataPage?5.4.1-SL_DOM_TSPD The indicator used in the Eurostat QoL Framework contains data on ‘Time spent in total work (paid and unpaid work as main or secondary activity) by sex and by form of work’, not distinguishing the type of activity. Access indicator data: Query for SDG 5.4.1. SDG Database Access indicator data from OECD

Short name	Satisfaction with housing (4.1)
Long name	Share of the population reporting satisfaction with their dwelling, categorized by levels of satisfaction.
Dimension	Housing
Definition	The percentage of the population reporting satisfaction with their dwelling, categorized by levels of satisfaction. It measures individuals' self-reported assessment of their housing conditions, considering factors such as space, comfort, amenities, and overall living environment. Responses are typically categorized into various levels of satisfaction, ranging from very dissatisfied to very satisfied.
Use	To assess public satisfaction with housing quality, monitor living standards, and inform policies aimed at improving housing conditions and accessibility.
Type	Subjective
Unit of measurement	Percentage of population (16 years and over)
Data sources	The recommended data sources are household surveys or targeted well-being surveys by including questions on respondents’ satisfaction with their current dwelling. International sources include: The European Union Statistics on Income and Living Conditions (EU-SILC) survey assesses the percentage of the population reporting satisfaction with their dwelling across EU Member States and associated countries.
Frameworks	Eurostat Quality of Life Framework
More metadata	Eurostat’s data is collected through the European Union Statistics on Income and Living Conditions (EU-SILC) survey: https://ec.europa.eu/eurostat/cache/metadata/en/ilc_sieusilc.htm Access indicator data from Eurostat

Short name	Population living in a dwelling with major deficiencies (4.2)
Long name	Share of the total population living in dwellings with major deficiencies
Dimension	Housing
Definition	This indicator refers to the percentage of the population living in dwellings that have major deficiencies, such as a leaking roof, damp walls, floors or foundation, or rot in window frames or floors. These conditions reflect significant issues related to the quality and habitability of housing, potentially affecting residents' health and well-being. Data is typically collected through household surveys or housing quality assessments.
Use	To evaluate housing quality, identify populations living in substandard conditions, and inform policies aimed at improving housing standards and addressing deficiencies.
Type	Objective
Unit of measurement	Percentage of population
Data sources	The recommended data sources are household surveys and administrative registers. International sources include: The European Union Statistics on Income and Living Conditions (EU-SILC) survey measures the percentage of the total population living in dwellings with major deficiencies.
Frameworks	Eurostat Quality of Life Framework CES Recommendations for the Census of Population and Housing
More metadata	Eurostat’s data is collected through the European Union Statistics on Income and Living Conditions (EU-SILC) survey: https://ec.europa.eu/eurostat/cache/metadata/en/ilc_sieusilc.htm Access indicator data from Eurostat

Short name	Overcrowding (4.3)
Long name	Share of households living in overcrowded conditions
Dimension	Housing
Definition	The overcrowding rate (the percentage of households living in overcrowded conditions) follows the EU-agreed definition, which considers the space requirements based on the age and gender composition of the household. A household is considered overcrowded if there is less than one room available per person, according to the following criteria: one room for each couple, each single person aged 18 or older, each pair of people of the same gender aged 12 to 17, each single person aged 12 to 17 not included in the previous category, and each pair of children under age 12.
Use	To assess housing adequacy, monitor living conditions, and inform policies aimed at improving housing conditions.
Type	Objective
Unit of measurement	Percentage of households
Data sources	The recommended data source is household survey and administrative registers, leveraging international frameworks as outlined below: OECD Affordable Housing Database: provides data on housing conditions, including overcrowding rates. This database offers information from national household surveys and censuses across OECD member countries. The data are harmonized to ensure comparability and are used to monitor access to quality housing and support policy evaluations. European Union Statistics on Income and Living Conditions (EU-SILC): data on overcrowding are collected through the EU-SILC survey. The survey employs a standardized methodology to ensure data comparability between countries. Multiple Indicator Cluster Surveys (MICS): Implemented by UNICEF and NSO, MICS collects data on various health and population metrics, including housing conditions. These surveys often gather information on the number of household members and the number of rooms used for sleeping, allowing for the calculation of overcrowding indicators.
Frameworks	OECD How’s Life? Eurostat Quality of Life Framework
More metadata	Access indicator data from OECD Eurostat’s data is collected through the European Union Statistics on Income and Living Conditions (EU-SILC) survey: https://ec.europa.eu/eurostat/cache/metadata/en/ilc_sieusilc.htm Access indicator data from the links below: Eurostat

Short name	Housing affordability (4.4)
Long name	Share of housing cost in household adjusted disposable income.
Dimension	Housing
Definition	Housing affordability refers to the percentage of household adjusted disposable income required to cover housing costs. Housing costs include rent (including imputed rentals for housing held by owner-occupiers) and maintenance (expenditure on the repair of the dwelling, including miscellaneous services, water supply, electricity, gas and other fuels, as well as expenditure on furniture, furnishings, household equipment and goods and services for routine home maintenance).
Use	To evaluate housing affordability and understand the financial well-being of households
Type	Objective
Unit of measurement	Percentage
Data sources	Household income statistics, household surveys and national accounts. Data on housing affordability are available from the OECD National Accounts database.
Frameworks	OECD How’s Life? CES Recommendations for the 2020 Censuses of Population and Housing
More metadata	Access indicator data from OECD and Eurostat

Short name	Housing cost overburden (4.5)
Long name	Share of households in the bottom 40% of the income distribution spending more than 40% of their disposable income on housing costs
Dimension	Housing
Definition	Housing cost overburden refers to the percentage of households in the bottom 40% of the income distribution that spend more than 40% of their disposable income on housing costs. This threshold is based on the methodology used by Eurostat for EU member countries. Housing costs include actual rents and mortgage payments (both principal repayment and interest), but exclude imputed rents for owner-occupied homes, unlike the housing affordability measure sourced from National Accounts.
Use	To measure housing cost overburden among low-income households, monitor affordability challenges, and guide policies aimed at improving housing accessibility and reducing financial strain on vulnerable populations.
Type	Objective
Unit of measurement	Percentage of households in the bottom 40% of the income distribution.
Data sources	Recommended data sources are household surveys, administrative registers and national accounts. International sources include: OECD Affordable Housing Database (AHD): The AHD compiles cross-national information to assist countries in monitoring access to quality affordable housing and to strengthen the knowledge base for policy evaluation. It aggregates data from various national household surveys and administrative records, providing insights into housing affordability, conditions, and public policies across OECD member countries. European Union Statistics on Income and Living Conditions (EU-SILC): The survey serves as a primary source for assessing housing cost overburden among low-income households in Europe.
Frameworks	OECD How’s Life? EU Quality of Life Framework
More metadata	Access indicator data from OECD Eurostat’s data is collected through the European Union Statistics on Income and Living Conditions (EU-SILC) survey: https://ec.europa.eu/eurostat/cache/metadata/en/ilc_sieusilc.htm Access indicator data from Eurostat

Short name	Satisfaction with own neighbourhood (4.6)
Long name	Share of the population reporting satisfaction with their neighbourhood, categorized by levels of satisfaction
Dimension	Housing
Definition	The percentage of the population reporting satisfaction with their neighbourhood, categorized by levels of satisfaction. This indicator reflects individuals' subjective assessment of their living environment, including factors such as safety, access to amenities, social cohesion, and the overall quality of life in their neighbourhood. Responses are typically categorized into various satisfaction levels, ranging from very dissatisfied to very satisfied.
Use	To assess public perceptions of their local environment, monitor quality of life and urban planning outcomes, and inform policies aimed at improving neighbourhood conditions and community well-being.
Type	Subjective
Unit of measurement	Percentage of population (16 years and over)
Data sources	The recommended data source is household surveys or similar. International sources include the Eurostat Urban Audit Perception Survey. This survey gathers subjective assessments from residents across European cities.
Frameworks	N/A
More metadata	Link to more metadata from Eurostat: https://ec.europa.eu/eurostat/cache/metadata/en/urb_esms.htm Access indicator data from Eurostat

Short name	Energy poverty (4.7)
Long name	Share of households reporting they cannot afford to keep their dwelling adequately warm.
Dimension	Housing
Definition	Energy poverty refers to the percentage of households reporting an inability to afford keeping their home adequately warm. The question is typically asked to the household reference person, and the data is available at the household level only.
Use	To measure the prevalence of energy poverty, monitor social and economic inequalities, and inform policies aimed at improving energy affordability and access for vulnerable populations.
Type	Subjective
Unit of measurement	Percentage of households
Data sources	Recommended data sources are household surveys. International sources include the European Union Statistics on Income and Living Conditions (EU-SILC) survey.
Frameworks	OECD How’s Life?
More metadata	Link to more metadata from Eurostat: https://ec.europa.eu/eurostat/cache/metadata/en/sdg_07_60_esmsip2.htm Access indicator data from Eurostat

Short name	Life expectancy (5.1)
Long name	Average number of years a person is expected to live from birth, assuming current age-specific mortality rates
Dimension	Health
Definition	Life expectancy at birth is a summary measure of mortality rates, representing the number of years a newborn is expected to live based on the current age-specific death rates. It provides an estimate of the average lifespan for a given cohort, recognizing that the actual age-specific death rates for that cohort cannot be determined in advance.
Use	To measure overall population health and longevity, monitor trends in mortality, and inform policies aimed at improving healthcare systems and addressing health inequalities.
Type	Objective
Unit of measurement	Years (calculated as a mean average of the country)
Data sources	The recommended data source is household survey and administrative records, leveraging international frameworks as outlined below: EU-SILC survey OECD Life expectancy database.
Frameworks	OECD How’s Life? Eurostat Quality of Life Framework
More metadata	Link to more metadata from Eurostat: https://ec.europa.eu/eurostat/cache/metadata/en/demo_mor_esms.htm Access indicator data from Eurostat Access indicator data from OECD

Short name	Perceived health (5.2)
Long name	Share of the population 16 years or over reporting ‘good’ or ‘very good’’ health
Dimension	Health
Definition	Perceived health reflects individuals' self-assessed overall health status. It is commonly expressed as the percentage of adults reporting ‘good’ or ‘very good’ health. The data are collected through general household surveys or detailed health interviews, typically using questions like: ‘How is your health in general?’ with responses often categorized as ‘very good’, ‘good’, ‘fair’, or ‘poor’. Some countries outside Europe (e.g., Australia, Canada, Chile, Israel, New Zealand, the United States) use different response scales, which may cause upward bias in estimates. In the OECD Health Status database, responses are standardized into three categories: ‘good/very good’ (positive responses), ‘fair’ (neutral) and ‘bad/very bad’ (negative). Respondents are generally aged 16 or older, though the specific age range varies between countries.
Use	To measure overall population health and longevity, monitor trends in mortality, and inform policies aimed at improving healthcare systems and addressing health inequalities.
Type	Subjective
Unit of measurement	Percentage of the population (16 years and over)
Data sources	The recommended data sources are household surveys or targeted health surveys. In some countries, the Ministry of Health may be able to provide data. International sources include the EU-SILC survey.
Frameworks	OECD How’s Life? Eurostat Quality of Life Framework
More metadata	Eurostat’s data is collected through the European Union Statistics on Income and Living Conditions (EU-SILC) survey: https://ec.europa.eu/eurostat/cache/metadata/en/hlth_silc_01_esms.htm Access indicator data from Eurostat The OECD Well-being Database includes Eurostat data and additional data on perceived health for non-European OECD member countries. Access indicator data from OECD

Short name	Overweight population (5.3)
Long name	Share of the population classified as overweight (BMI equal to or greater than 25)
Dimension	Health
Definition	The percentage of the population classified as overweight, based on a Body Mass Index (BMI) of 25 or greater, which includes both pre-obese and obese categories. This indicator reflects the proportion of individuals with a BMI that exceeds the threshold for a healthy weight, and it is commonly used to assess the prevalence of overweight and obesity in a population. Data is typically collected through health surveys, ‘self-reported’ or in some countries, data is also collected through measurements.
Use	To monitor the prevalence of overweight individuals, evaluate public health risks associated with excess weight, and inform policies aimed at promoting healthy lifestyles and reducing obesity-related health issues.
Type	Objective - Standardized measurement (though data is self-reported)
Unit of measurement	Percentage of the population
Data sources	The recommended data sources are household surveys or targeted health surveys. In some countries, the Ministry of Health may be able to provide data. The European Health Interview Survey (EHIS) measures the population aged 15 years and over. The last survey (wave) conducted was in 2019. In the European Health Interview Survey (page 81), participants are asked to self-report their height and weight using the following questions: 1. Height Without Shoes: ‘How tall are you without shoes?’ 2. Weight Without Clothes and Shoes: ‘How much do you weigh without clothes and shoes?’ These self-reported measurements are used to calculate the Body Mass Index (BMI) for each participant.
Frameworks	OECD How’s Life? (OECD Well-being Database considers obesity prevalence, which is defined as a BMI of 30 and above) Eurostat Quality of Life Framework
More metadata	Eurostat’s data is collected through the European Health Interview Survey (EHIS): https://ec.europa.eu/eurostat/cache/metadata/en/hlth_det_esms.htm Access indicator data from Eurostat European Health Interview Survey (page 81)

Short name	Population with severe long-standing limitations due to health problems (5.4)
Long name	Share of the population reporting severe long-standing limitations in usual activities due to health problems
Dimension	Health
Definition	The percentage of the population reporting severe long-standing limitations in their usual daily activities due to health problems. This indicator reflects self-reported limitations that are both significant in intensity and persist over an extended period, providing insight into the prevalence of severe functional impairments and long-term health challenges within the population.
Use	To measure the prevalence of severe health-related limitations, monitor trends in disability, and inform policies to improve healthcare access and social inclusion.
Type	Subjective
Unit of measurement	Percentage of population
Data sources	The recommended data sources are household surveys or targeted health surveys. In some countries, the Ministry of Health may be able to provide data. International sources include the European Health Interview Survey and the EU SILC.
Frameworks	Eurostat Quality of Life Framework
More metadata	Eurostat’s data is collected through the European Health Interview Survey (EHIS): Access indicator data from Eurostat

Short name	Deaths from suicide, alcohol abuse and drug overdose (5.5)
Long name	Combined deaths from suicide, alcohol abuse and drug overdose per 100,000 population (age-standardised based on the 2015 OECD population structure)
Dimension	Health
Definition	Suicide, alcohol- and drug-related deaths measure severe mental illness and addiction, reported as combined deaths from suicides, alcohol, and drug abuse per 100,000 population (standardized to a given year). Data are derived from official death registries via the WHO Mortality Database and OECD population statistics. The indicator is calculated using ICD-10 codes covering intentional self-harm, substance abuse disorders, and alcohol-related diseases (for a list of the ICD-10 codes, see https://www.oecd.org/content/dam/oecd/en/topics/policy-sub-issues/measuring-well-being-and-progress/oecd-well-being-database-definitions.pdf)
Use	To monitor mortality rates associated with preventable and socially driven causes, assess the effectiveness of mental health and substance abuse interventions, and inform policies aimed at reducing deaths from suicide, alcohol abuse, and drug overdose.
Type	Objective
Unit of measurement	Rate per 100,000 population
Data sources	The recommended data sources are household surveys or targeted health surveys. In some countries, the Ministry of Health may be able to provide data. International sources include the OECD How’s Life? Well-being Database
Frameworks	OECD How’s Life?
More metadata	Access indicator data from OECD

Short name	People who experience symptoms of anxiety or depression (5.6)
Long name	Share of the population aged 15 years and over reporting depressive symptoms or symptoms of generalized anxiety in the past two weeks.
Dimension	Health
Definition	The percentage of the population aged 15 years and over who self-report experiencing depressive symptoms or symptoms of generalized anxiety, two of the most common mental health disorders. In line with the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV), depressive symptoms relate to little interest or pleasure in doing things; feeling down, depressed or hopeless; trouble falling or staying asleep, or sleeping too much; feeling tired or having little energy; poor appetite or overeating; feeling bad about yourself or that you are a failure or have let yourself or your family down; trouble concentrating on things, such as reading the newspaper or watching television; moving or speaking so slowly that other people could have noticed, or being restless; and symptoms of generalised anxiety related to excessive worry, restlessness and irritability over the past two weeks.. These indicators are typically derived from self-reported data collected through EHIS - reflects the prevalence of mental health conditions within the population (EHIS_2020 - Page 52.)). The recommended validated instruments are the Patient Health Questionnaire (PHQ-4 or 8) and the Generalised Anxiety Disorder (G2 or G7) instrument.
Use	Helps monitor mental health trends, inform public health strategies, and guide resource allocation for mental health services and interventions.
Type	Subjective
Unit of measurement	Percentage of population (15 years and over)
Data sources	The recommended data sources are household surveys or targeted health surveys. In some countries, the Ministry of Health may be able to provide data. International sources include: European Health Interview Survey (EHIS) (database) National Statistical Offices.
Frameworks	OECD How’s Life? Eurostat Quality of Life Framework
More metadata	OECD Measuring Population Mental Health Eurostat’s data for depressive symptoms is collected through the European Health Interview Survey (EHIS): https://ec.europa.eu/eurostat/cache/metadata/en/hlth_det_esms.htm Access indicator data (depressive symptoms available now, symptoms of generalised anxiety will be added to the next EHIS wave) from Eurostat European health_interview_survey_2020 (Page 52)?

Short name	Avoidable (or premature) mortality (5.7)
Long name	Potential years of life lost (PYLL) due to medical conditions and fatal accidents, per 100,000 population (age-standardized).
Dimension	Health
Definition	Premature mortality, measured as years of potential life lost (PYLL) due to various medical conditions and fatal accidents per 100,000 population, refers to deaths occurring before the age of 75. The indicator is calculated by subtracting 75 (the selected age threshold for premature mortality in OECD calculations) from the actual age of death for each individual, multiplying this by the number of deaths at each age, and summing these values across all age groups. This approach assigns greater weight to deaths at younger ages; for example, an infant dying at age 1 contributes 74 PYLL (75 – 1), while a person dying at age 74 contributes 1 PYLL (75 – 74). The indicator is age-standardized to account for differences in population age structures across OECD countries, ensuring that countries with the same age-specific death rates but a younger population structure are not unfairly reported with higher scores.
Use	To measure premature mortality by quantifying the years of potential life lost due to specific causes of death, evaluate public health outcomes, and inform policies aimed at reducing preventable deaths and improving life expectancy.
Type	Objective
Unit of measurement	Country average (years per 100,000 persons)
Data sources	The recommended data source is administrative records, leveraging existing frameworks, e.g.,: OECD Health Statistics. This repository provides detailed information on health status, including mortality rates and Potential Years of Life Lost (PYLL). The data is collected from national statistical agencies and health ministries of OECD member countries.
Frameworks	OECD How’s Life?
More metadata	Access the indicator data from OECD.

Short name	Secondary educational attainment (6.1)
Long name	Share of the population aged 25-34 who have completed at least upper secondary education.
Dimension	Knowledge and skills
Definition	Educational attainment among young adults is defined as the percentage of individuals aged 25 to 34 who have completed at least upper secondary education. Upper secondary education is classified according to the International Standard Classification of Education (ISCED) as level 3 or higher. This encompasses both general programs designed to prepare students for higher education and vocational education and training (VET) programs.
Use	To assess educational attainment levels within the population, monitor progress in formal education, and guide policies aimed at improving access to and completion of secondary education.
Type	Objective
Unit of measurement	Percentage of individuals in the target age group (25-34 years)
Data sources	The recommended data sources are household surveys and administrative records, leveraging the existing framework as outlined below: OECD Educational Attainment and Labour Force Database: Data are gathered from national statistical agencies and other relevant authorities within each country. The database is part of the OECD's Indicators of Education Systems (INES) program. European Union Labour Force Survey (EU-LFS): The survey collects information through interviews with individuals in private households, covering topics such as employment status, occupation, working hours, and educational attainment. The EU-LFS follows standardized definitions and classifications to ensure comparability across countries and over time.
Frameworks	OECD How’s Life?) Eurostat Quality of Life Framework
More metadata	Access indicator data from OECD. Eurostat’s data is collected through the European Union Labour Force Survey (EU-LFS): https://ec.europa.eu/eurostat/cache/metadata/en/trng_lfs_4w_esms.htm Access indicator data from Eurostat

Short name	Youth not in employment, education or training (6.2)
Long name	Share of youth (aged 15-24) not in employment, education or training (NEET).
Dimension	Knowledge and skills
Definition	Youth not in employment, education or training (NEET) refers to the number of youth (i.e. people aged 1524) who are not in employment, education or training, as a percentage of the population of the same age. This indicator identifies young people who are disengaged from both the labour market and educational opportunities, reflecting potential challenges in transitioning to employment or further education. It is typically calculated as the share of the total youth population within this age group.
Use	To measure the proportion of young people disengaged from both the labour market and education systems, identify vulnerable groups, and inform policies aimed at promoting youth employment, education, and skills development.
Type	Objective
Unit of measurement	Percentage of youth (15-24 years)
Data sources	The recommended data sources are household surveys and administrative registers. International sources include the Database compiled by OECD Labour Market and Social Outcomes of Learning Network through an annual questionnaire that draws on National LFSs.
Frameworks	OECD How’s Life? Eurostat Quality of Life framework
More metadata	Link to ILO’s Labour Force Statistics descriptions: https://ilostat.ilo.org/methods/concepts-and-definitions/description-labour-force-statistics/ Eurostat’s data is collected through the European Union Labour Force Survey (EU-LFS): https://ec.europa.eu/eurostat/cache/metadata/en/trng_lfs_4w_esms.htm Access indicator data from OECD Access indicator data from ILOSTAT Access indicator data from Eurostat

Short name	Lifelong learning (6.3)
Long name	Share of individuals participating in formal or non-formal education and training within the last 4 weeks
Dimension	Knowledge and skills
Definition	The proportion of individuals who have participated in formal or non-formal education and training activities within the last four weeks. Formal education refers to structured learning programs provided by schools, universities, or other accredited institutions, leading to recognized qualifications. Non-formal education includes organized learning activities outside the formal education system, such as workshops, courses, or vocational training, which may not lead to formal certification. This indicator is typically measured through self-reported survey data.
Use	To monitor participation in lifelong learning, evaluate access to education and training opportunities, and inform policies aimed at improving skills development and workforce adaptability.
Type	Objective
Unit of measurement	Percentage of individuals in the target population (18-64 years)
Data sources	The recommended data sources are household surveys and administrative registers, leveraging the existing framework as outlined below: EU Labour Force Survey (EU-LFS), which is used to measure participation in formal and non-formal education and training. The EU-LFS collects data on individuals aged 25 to 64 who have engaged in such educational activities within the last 4 weeks. CES recommendations - adhere to the Classifications of Learning Activities (CLA) and ISCED frameworks. CES aims to standardize the measurement of educational participation, facilitating the analysis and comparison of lifelong learning trends across Europe.
Frameworks	Eurostat Quality of Life Framework CES Recommendations (Classification of learning Activities)
More metadata	Eurostat’s data is collected through the European Union Labour Force Survey (EU-LFS): https://ec.europa.eu/eurostat/cache/metadata/en/trng_lfs_4w_esms.htm Access indicator data from Eurostat

Short name	Skills in reading (6.4)
Long name	Mean performance on the PISA reading scale for 15-year-old students
Dimension	Knowledge and skills
Definition	The mean performance on the PISA (Programme for International Student Assessment) reading scale for 15-year-old students measures their ability to comprehend, interpret, and use written texts to achieve goals, develop knowledge, and participate in society. PISA assessments are conducted every three years and focus on one of three subjects—reading, mathematics, or science—on a rotating basis. The scores are standardized, with an initial OECD average of 500 points and a standard deviation of 100 points, though the OECD average may vary in subsequent years. Since PISA assessments are school-based, they reflect the cognitive abilities of 15-year-olds currently enrolled in school, excluding those who have dropped out or are home-schooled.
Use	To assess students' reading comprehension and literacy skills, evaluate the quality of education systems, and inform policies aimed at enhancing language proficiency and critical reading abilities.
Type	Objective
Unit of measurement	Mean score (country)
Data sources	The recommended data source is household/facility surveys, administrative registers, as well as national and international competency tests, leveraging the existing framework as outlined below: International comparability: Programme for International Student Assessment (PISA), conducted by OECD every three years, with the focal subject cycling between mathematics, reading and science. National focus: National Standardized Assessments: Many countries conduct their own large-scale standardized tests to assess student learning at different education levels. These assessments vary in structure, frequency, and subjects covered, but are crucial for evaluating national education policies and student achievement. Examples include the National Assessment of Educational Progress (NAEP) of United States, Key Stage Assessments (SATs).
Frameworks	OECD How’s Life?
More metadata	Access indicator data from OECD

Short name	Skills in mathematics (6.5)
Long name	Mean performance on the PISA mathematics scale for 15-year-old students
Dimension	Knowledge and skills
Definition	The mean performance on the PISA (Programme for International Student Assessment) mathematics scale for 15-year-old students measures their ability to understand and apply mathematical concepts, skills, and reasoning to solve real-world problems. PISA assessments are conducted every three years and focus on one of three subjects—mathematics, science, or reading—on a rotating basis. The scores are standardized, with an initial OECD average of 500 points and a standard deviation of 100 points, though the OECD average may vary in subsequent years. Since PISA assessments are school-based, they reflect the cognitive abilities of 15-year-olds currently enrolled in school, excluding those who have dropped out or are home-schooled.
Use	To assess students' mathematical literacy and problem-solving abilities, evaluate the quality of education systems, and guide policies to improve mathematics education and quantitative reasoning skills.
Type	Objective
Unit of measurement	Mean score (country)
Data sources	Recommended data sources are household/facility surveys, administrative records, as well as national and international competency tests, leveraging the existing framework as outlined below: International comparability: Programme for International Student Assessment (PISA), conducted by OECD every three years, with the focal subject cycling between mathematics, reading and science. National focus: National Standardized Assessments: Many countries conduct their own large-scale standardized tests to assess student learning at different education levels. These assessments vary in structure, frequency, and subjects covered, but are crucial for evaluating national education policies and student achievement. Examples include the National Assessment of Educational Progress (NAEP) of United States, Key Stage Assessments (SATs).
Frameworks	OECD How’s Life?
More metadata	,Access indicator data from OECD

Short name	Skills in science (6.6)
Long name	Mean performance on the PISA science scale for 15-year-old students
Dimension	Knowledge and skills
Definition	The mean performance on the PISA (Programme for International Student Assessment) science scale for 15-year-old students measures their ability to apply scientific knowledge and skills to solve real-world problems and interpret scientific phenomena. PISA assessments are conducted every three years and focus on one of three subjects—science, mathematics, or reading—on a rotating basis. The scores are standardized, with an initial OECD average of 500 points and a standard deviation of 100 points, though the OECD average may vary in subsequent years. Since PISA assessments are school-based, they capture the cognitive abilities of 15-year-olds currently enrolled in school, excluding those who have dropped out or are home-schooled.
Use	To assess students' scientific literacy and problem-solving abilities, evaluate the quality of education systems, and inform policies to enhance science education and foster innovation skills.
Type	Objective
Unit of measurement	Mean score (country)
Data sources	The recommended data source is household/facility surveys, administrative registers, as well as national and international competency tests, leveraging the existing framework as outlined below: International comparability: Programme for International Student Assessment (PISA), conducted by OECD every three years, with the focal subject cycling between mathematics, reading and science. National focus: National Standardized Assessments: Many countries conduct their own large-scale standardized tests to assess student learning at different education levels. These assessments vary in structure, frequency, and subjects covered, but are crucial for evaluating national education policies and student achievement. Examples include the National Assessment of Educational Progress (NAEP) of United States, Key Stage Assessments (SATs).
Frameworks	OECD How’s Life?
More metadata	Access indicator data from OECD

Short name	Digital skills (6.7)
Long name	Share of young and adults with information and communication technology skills, including digital competency for performing specific tasks
Dimension	Knowledge and skills
Definition	As per the SDG definition, the proportion of youth and adults with Information and Communications Technology (ICT) skills, categorized by type of skill, is defined as the percentage of individuals who have engaged in specific ICT-related activities within the past three months. From 2023, the percentage of individuals with basic or above-basic ICT skills, broken down by skill area, can also be measured. Eurostat breaks down digital skills according to competency levels, rather than focusing solely on specific ICT-related activities, offering detailed insights into overall digital competency.
Use	To assess the population's digital competencies, monitor progress in digital inclusion and guide ICT policies.
Type	Objective
Unit of measurement	Percentage of individuals, categorized by level of digital skills
Data sources	Recommended data sources are household surveys and administrative registers, leveraging the existing framework as outlined below: The EU Survey on use of ICT in households and by individuals evaluates digital competencies, including information management, communication, problem-solving, and content creation. Respondents are grouped into different levels of digital skills based on their proficiency in these areas. SDG database: The UNESCO Institute for Statistics (UIS) and the International Telecommunication Union (ITU) are responsible for compiling data for SDG Indicator 4.4.1, which aligns with this digital skills indicator.
Frameworks	Eurostat Quality of Life Framework Sustainable Development Goals
More metadata	Link to more metadata in the SDG Database: https://unstats.un.org/sdgs/dataportal/SDMXMetadataPage?4.4.1-SE_ADT_ACTS Link to metadata from Eurostat: https://ec.europa.eu/eurostat/cache/metadata/en/isoc_sk_dskl_i21_esmsip2.htm ITU’s ICT indicators framework tracks how ICT contributes to the SDGs. Focus areas include ICT access, use, affordability, skills and gender equality. Data is collected annually from member states to inform progress on SDGs and guide policy. Additional information from the International Telecommunication Union (ITU). Access the indicator from these links: Eurostat and SDG Database (SDG 4.4.1)

Short name	Crime rate (7.1)
Long name	Number of police-recorded offences by offence category per 100,000 inhabitants
Dimension	Physical Safety
Definition	The number of criminal offences recorded by the police, categorized by type of offence (e.g., theft, assault, drug-related offences), calculated as a rate per 100,000 inhabitants, within a specific geographic area and time period, typically a calendar year.
Use	To monitor trends in criminal activity, evaluate law enforcement effectiveness, and inform policy development and resource allocation for crime prevention and public safety.
Type	Objective
Unit of measurement	Rate per 100,000 inhabitants
Data sources	Recommended sources are administrative registers. Eurostat and the United Nations Office on Drugs and Crime (UNODC) compile crime statistics using the International Classification of Crime for Statistical Purposes (ICCS) from national administrative sources across EU Member States, EFTA countries, and EU candidate countries. The data are derived from various stages of the criminal justice system, including:  Police: Data on recorded offences, victims, and suspects.  Prosecution Services: Information on prosecuted individuals.  Courts: Records of convicted and acquitted persons, as well as the number of legal cases processed.  Prison Departments: Statistics on prisoners and prison capacity.  All Departments: Data on personnel involved in the criminal justice system.
Frameworks	International Classification of Crime for Statistical Purposes (ICCS)
More metadata	Link to more metadata from Eurostat: https://ec.europa.eu/eurostat/cache/metadata/en/crim_sims.htm Access indicator data from Eurostat

Short name	Victimization rate (7.2)
Long name	Share of households reporting crime, violence, or vandalism in the area
Dimension	Physical Safety
Definition	The percentage of households that report experiencing or perceiving issues related to crime, violence, or vandalism in their local area, based on self-reported survey responses.
Use	To assess community perceptions of safety, monitor the social impact of crime and vandalism, and guide policies aimed at improving neighbourhood security and quality of life.
Type	Subjective
Unit of measurement	Percentage
Data sources	The recommended data source is the household survey. The EU-SILC survey includes a specific variable (HS190) that captures respondents' perceptions of crime, violence, or vandalism in their area. Respondents indicate whether these issues are considered a problem in their local environment.
Frameworks	Eurostat Quality of Life Framework
More metadata	Eurostat’s data is collected through the European Union Statistics on Income and Living Conditions (EU-SILC) survey: https://ec.europa.eu/eurostat/cache/metadata/en/ilc_sieusilc.htm Access indicator data from Eurostat

Short name	Homicide rate (7.3)
Long name	Number of intentional homicides per 100,000 population
Dimension	Physical Safety
Definition	Homicides are defined as deaths resulting from assault, measured as a rate per 100,000 population.
Use	To monitor levels of violent crime, assess the effectiveness of public safety and justice systems, and guide policies aimed at reducing violence and ensuring public safety.
Type	Objective
Unit of measurement	Rate per 100,000 population (country)
Data sources	Administrative registers, where deaths are medically certified and classified according to causes. These data are compiled by national authorities and subsequently aggregated by international organizations to facilitate global comparisons. Two organizations that collect data are: World Health Organization (WHO): collects mortality data from member states' civil registration systems, focusing on medically certified causes of death. This information is used to compile global health statistics, including homicide rates. United Nations Office on Drugs and Crime (UNODC): compiles homicide statistics from criminal justice data sources, providing insights into global and regional homicide trends.
Frameworks	OECD How’s Life? Eurostat Quality of Life Framework Sustainable Development Goals
More metadata	Link to more metadata in SDG Database: https://unstats.un.org/sdgs/dataportal/SDMXMetadataPage?16.1.1-VC_IHR_PSRC Access indicator data from Eurostat SDG Database (SDG 16.1.1), OECD

Short name	Sexual violence (7.4)
Long name	Share of population subjected to sexual violence in the previous 12 months.
Dimension	Physical Safety
Definition	The percentage of the population who have experienced sexual violence within the previous 12 months. As defined by the International Classification of Crime for Statistical Purposes (ICCS), sexual violence encompasses unwanted sexual acts or attempts to obtain a sexual act without valid consent or with consent obtained through intimidation, force, fraud, coercion, threat, deception, use of drugs or alcohol, or abuse of power or vulnerability. This includes rape and other forms of sexual assault but excludes non-physical sexual assault (e.g., sexual harassment). Such acts may be committed by casual partners, acquaintances, strangers, or within formalized intimate partnerships, including marriages.
Use	To measure the prevalence of sexual violence, monitor trends over time, and inform policies and programs aimed at prevention, victim support, and justice for survivors.
Type	Objective
Unit of measurement	Percentage of population
Data sources	National Crime Victimization surveys that inquire about respondents' experiences with different types of violence, including sexual violence, over the past 12 months. The surveys should adhere to definitions provided by the International Classification of Crime for Statistical Purposes (ICCS), ensuring consistency in what constitutes sexual violence across different contexts.
Frameworks	Sustainable Development Goals
More metadata	Link to more metadata in SDG Database: https://unstats.un.org/sdgs/dataportal/SDMXMetadataPage?16.1.3-VC_VOV_SEXL Access indicator data from the SDG Database (SDG 16.1.3)

Short name	Feeling safe in the area where they live (7.5)
Long name	Share of people declaring that they feel safe when walking alone at night in the city or area where they live.
Dimension	Physical Safety
Definition	Feeling safe at night is measured by the percentage of people answering ‘yes’ to a (yes/no) question: ‘Do you feel safe walking alone at night in the city or area where you live?’
Use	To assess perceptions of personal safety, monitor community well-being, and guide policies aimed at improving security and reducing fear of crime in residential areas.
Type	Subjective
Unit of measurement	Percentage of population (country average)
Data sources	Household surveys or targeted surveys. International sources include: Gallup World Poll, SDG Global Database (16.1.4) and OECD's How's Life? reports and accompanying databases. For this indicator, the OECD sources data from Gallup World Poll: https://gallup.com/analytics/232838/world-poll.aspx.
Frameworks	OECD How’s Life? Sustainable Development Goals
More metadata	Link to more metadata in SDG Database: https://unstats.un.org/sdgs/dataportal/SDMXMetadataPage?16.1.4-VC_SNS_WALN_DRK Access indicator data from the links below: SDG Database (SDG 16.1.4) OECD

Short name	Road fatalities (7.6)
Long name	Road deaths, rate per 100,000 population
Dimension	Physical Safety
Definition	Road deaths (rate per 100 000 population) refer to persons killed immediately or dying within 30 days because of a road accident, excluding suicides.
Use	To monitor road safety, evaluate the effectiveness of traffic safety measures, and inform policies aimed at reducing road traffic fatalities and improving transportation systems.
Type	Objective
Unit of measurement	Rate per 100,000 population
Data sources	Administrative registers. International sources include: European Commission - Directorate-General for Mobility and Transport (MOVE): This body supplies data to Eurostat, offering insights into road traffic deaths across European countries. Sustainable Development Goals (SDGs), particularly SDG 3.6.1, which focuses on reducing road traffic injuries and deaths. Further metadata and country-specific data can be accessed through the SDG Global Database.
Frameworks	OECD How’s Life? Sustainable Development Goals
More metadata	Eurostat’s data source: European Commission - Directorate-General for Mobility and Transport (MOVE). Link to more metadata in SDG Database: https://unstats.un.org/sdgs/dataportal/SDMXMetadataPage?3.6.1-SH_STA_TRAF Access indicator data from the links below: Eurostat SDG Database (SDG 3.6.1) OECD

Short name	Frequency of social contacts (8.1)
Long name	Share of the population having contact with family (relatives) or friends, by frequency of contact
Dimension	Social connections
Definition	The percentage of the population who report having contact with family and friends, categorized by the frequency of contact (e.g., daily, weekly, monthly, or less often).
Use	To monitor the frequency of social interactions, evaluate social connectedness, and inform policies aimed at reducing social isolation and fostering stronger community ties.
Type	Objective
Unit of measurement	Percentage of population
Data sources	Household surveys or Time Use Surveys. Administrative registers may collect information about social support services, which may indicate levels of contact with family or friends. International sources include EU-SILC and the European Social Survey (ESS).
Frameworks	CES Recommendations – Guidelines on producing leading, composite and sentiment indicators Eurostat Quality of Life Framework OECD How’s Life? (The OECD Well-being Database considers the time spent interacting with friends and family as a primary activity, hours per week, which is drawn from time use surveys.)
More metadata	Eurostat’s data is collected through the European Union Statistics on Income and Living Conditions (EU-SILC) survey: https://ec.europa.eu/eurostat/cache/metadata/en/ilc_sieusilc.htm Access indicator data on self-reported frequency of contacts from Eurostat Access indicator data on time spent in social interactions in hours from OECD

Short name	Having someone to rely on (8.2)
Long name	Share of people who report having friends or relatives whom they can count on in times of trouble (social support)
Dimension	Social connections
Definition	Social support is measured by the percentage of people answering ‘yes’ to a (yes/no) question: ‘If you were in trouble, do you have relatives or friends you can count on to help you whenever you need them, or not?’.
Use	To measure social support networks, assess community resilience, and inform policies aimed at strengthening social cohesion and individual well-being.
Type	Subjective
Unit of measurement	Percentage of population aged 15 years or over
Data sources	Household surveys or social surveys. International sources include the Gallup World Poll, the OECD (which draws on the Gallup World Poll) and the upcoming OECD measurement guidance on social connectedness.
Frameworks	OECD How’s Life? Eurostat Quality of Life Framework
More metadata	Access indicator data from OECD Eurostat’s data is collected through the European Union Statistics on Income and Living Conditions (EU-SILC) survey: https://ec.europa.eu/eurostat/cache/metadata/en/ilc_sieusilc.htm Access data from Eurostat

Short name	Satisfaction with personal relationships (8.3)
Long name	Mean average satisfaction with personal relationships on a scale from 0 (not at all satisfied) to 10 (completely satisfied).
Dimension	Social connections
Definition	Satisfaction with personal relationships is measured as the mean score of survey respondents rating their satisfaction with personal relationships on an 11-point scale, ranging from 0 (not at all satisfied) to 10 (completely satisfied). This indicator reflects the respondent's subjective assessment of their overall satisfaction with personal relationships, including those with relatives, friends, and colleagues. Respondents are asked to provide a broad, reflective evaluation of their relationships at a specific point in time (e.g., ‘these days’).
Use	To assess the quality of personal relationships, monitor social well-being, and inform policies aimed at enhancing interpersonal connections and support networks.
Type	Subjective
Unit of measurement	Mean average satisfaction on a scale from 0 to 10.
Data sources	Household surveys or targeted social surveys International sources include the EU-SILC survey.
Frameworks	OECD How’s Life? Eurostat Quality of Life Framework
More metadata	The OECD Well-being Database includes Eurostat data as well as additional data on satisfaction with personal relationships for non-European OECD member countries. Access indicator data from OECD Eurostat’s data is collected through the European Union Statistics on Income and Living Conditions (EU-SILC) survey: https://ec.europa.eu/eurostat/cache/metadata/en/ilc_sieusilc.htm Access indicator data from Eurostat

Short name	Experience of discrimination (8.4)
Long name	Share of the population reporting feelings of discrimination or unfair treatment based on personal characteristics, such as race, gender, religion, or socioeconomic status.
Dimension	Social connections
Definition	This indicator represents the proportion of adults who self-report experiencing discrimination or harassment within the past 12 months based on grounds prohibited by international human rights law (IHRL). IHRL encompasses the body of international legal instruments designed to promote and protect human rights, including the Universal Declaration of Human Rights (UDHR) and subsequent treaties adopted by the United Nations (UN).
Use	To monitor experiences of discrimination, evaluate social equity and inclusion, and guide policies aimed at reducing prejudice and promoting equal treatment across all societal groups.
Type	Subjective
Unit of measurement	Percentage of the population
Data sources	Social surveys. For reference, household surveys, such as Multiple Indicator Cluster Surveys (MICS), victimisation surveys and other social surveys are the main data sources for this indicator. Data are available in the SDG Database (SDG 10.3.1)
Frameworks	Sustainable Development Goals Eurostat Quality of Life Framework
More metadata	Access SDG metadata (indicator 10.3.1) https://unstats.un.org/sdgs/dataportal/SDMXMetadataPage?10.3.1-VC_VOV_GDSD

Short name	Loneliness (8.5)
Long name	Share of population aged 16+ feeling lonely most or all of the time in the past four weeks.
Dimension	Social connections
Definition	Loneliness refers to the percentage of people who report feeling lonely ‘all of the time’ and ‘most of the time’ in the past four weeks. The following question is asked to the household reference person: ‘How much of the time over the past four weeks have you been feeling lonely?’, with response categories: ‘all of the time’, ‘most of the time’, ‘some of the time’, ‘a little of the time’, and ‘none of the time’.
Use	To assess social isolation, monitor mental well-being trends, and guide interventions aimed at reducing loneliness and fostering social connectedness.
Type	Subjective
Unit of measurement	Percentage of population aged 16+
Data sources	Household surveys or social surveys. International sources include the EU-SILC.
Frameworks	OECD How’s Life?
More metadata	Eurostat’s data is collected through the European Union Statistics on Income and Living Conditions (EU-SILC) survey: https://ec.europa.eu/eurostat/cache/metadata/en/ilc_sieusilc.htm The OECD Well-being Database includes Eurostat data as well as additional data on loneliness for non-European OECD member countries. Access indicator data from OECD

Short name	Trust in other people (8.6)
Long name	Mean average, on a scale from 0 (you do not trust any other person) to 10 (most people can be trusted)
Dimension	Social connections
Definition	Trust in others is based on the survey question: ‘In general, how much do you trust most people?’. Respondents answer using an 11-point scale, ranging from 0 (‘Not at all’) to 10 (‘Completely’). This follows the OECD Guidelines on Measuring Trust.
Use	To measure social cohesion, evaluate the level of interpersonal trust within a society, and inform policies aimed at fostering community resilience and social capital.
Type	Subjective
Unit of measurement	Mean average, on a scale from 0 to 10
Data sources	Household surveys or social services. International sources include: EU-SILC (survey) and OECD's How's Life? Database (future well-being dataflow).
Frameworks	OECD How’s Life? Eurostat Quality of Life Framework CES Recommendations on measuring sustainable development in 2014.
More metadata	OECD Guidelines on Measuring Trust https://www.oecd.org/en/publications/oecd-guidelines-on-measuring-trust_9789264278219-en.html The OECD Well-being Database includes Eurostat data as well as additional data on trust in other people for non-European OECD member countries. Access indicator data from OECD Access indicator data from Eurostat Eurostat’s data is collected through the European Union Statistics on Income and Living Conditions (EU-SILC) survey: https://ec.europa.eu/eurostat/cache/metadata/en/ilc_sieusilc.htm

Short name	Participation in cultural activities (8.7)
Long name	Share of population not participating in cultural or sport activities in the last 12 months
Dimension	Social connections
Definition	The percentage of individuals who did not engage in any cultural or sports activities over the past 12 months. Cultural activities include attending events such as concerts, theatre performances, or museum visits, while sports activities refer to participation in physical exercises or organized sports. This is typically measured through self-reported survey data.
Use	To assess barriers to cultural engagement, monitor trends in cultural participation, and inform policies aimed at promoting access to and inclusion in cultural activities.
Type	Objective
Unit of measurement	Percentage of the population
Data sources	Recommended sources are household or social surveys and Time Use Surveys. International sources include EU-SILC Survey and OECE guidelines.
Frameworks	Eurostat Quality of Life Framework
More metadata	Eurostat’s data is collected through the European Union Statistics on Income and Living Conditions (EU-SILC) survey: https://ec.europa.eu/eurostat/cache/metadata/en/ilc_sieusilc.htm

Short name	Volunteering (8.8)
Long name	Percentage of the population who declared having volunteered through an organisation in the past month
Dimension	Social connections
Definition	Volunteering through organizations is defined as the percentage of the population who report having volunteered their time with an organization in response to a binary question like: ‘In the past month, have you volunteered your time at an organization?’. This measure captures formal volunteer activity within structured groups or institutions.
Use	To measure civic engagement, assess the role of organized volunteerism in social cohesion, and guide policies promoting community involvement and active citizenship.
Type	Objective
Unit of measurement	Percentage of the population
Data sources	Recommended sources are household or social surveys, or Time Use Surveys. International sources include OECD (data are collected through Gallup World Poll, which the OECD Well-being Database draws on at the moment) and EU-SILC Survey.
Frameworks	OECD How’s Life? CES Recommendations Eurostat Quality of Life Framework
More metadata	https://www.oecd.org/content/dam/oecd/en/topics/policy-sub-issues/measuring-well-being-and-progress/oecd-well-being-database-definitions.pdf Eurostat’s data is collected through the European Union Statistics on Income and Living Conditions (EU-SILC) survey: https://ec.europa.eu/eurostat/cache/metadata/en/ilc_sieusilc.htm Access the indicator data from Eurostat.

Short name	People who feel they have a say in what the government does (9.1)
Long name	Share of respondents with a score equal to 6 or above, on a scale of 0 (not at all) to 10 (a great deal) when asked ‘How much would you say the political system in your country allows people like you to have a say in what the government does?’
Dimension	Civic engagement
Definition	Having a say in what the government does is measured through a question which asks respondents to rate on a 0-10 point scale, where 0 stands for ‘not at all’ and 10 for ‘a great deal’, what extent they agree with the statement, ‘How much would you say the political system in [COUNTRY] allows people like you to have a say in what the government does?’ Having a say in government refers to the percentage of respondents who gave a score of at least 6.
Use	To assess public perceptions of political efficacy, evaluate the inclusiveness of governance, and guide policies to enhance citizen engagement and trust in government decision-making.
Type	Subjective
Unit of measurement	Percentage of respondents (country average)
Data sources	Household surveys or targeted surveys. Leverage data and methodology from the OECD Survey on the Drivers of Trust in Public Institutions, which measures public perceptions of political influence and governance inclusiveness.
Frameworks	OECD How’s Life?
More metadata	OECD sources data from: OECD Trust Survey, https://www.oecd.org/governance/trust-in-government/ Access indicator data from OECD

Short name	Active citizenship (9.2)
Long name	Share of population who participate in civic and political activities
Dimension	Civil engagement
Definition	The proportion of the population actively engaging in civic and political activities, including actions such as voting, attending public meetings, joining protests, signing petitions, or participating in political parties, organizations, or community groups. This is typically measured through self-reported survey data.
Use	To measure civic engagement, promote democratic participation, and inform policies aimed at fostering active involvement in societal and political processes.
Type	Objective
Unit of measurement	Percentage of population (16 years and over)
Data sources	Household surveys or target surveys. Leverage data and methodology from EU-SILC survey; However, European Social Survey (ESS) also provides cross-national data on various dimensions of political engagement and civil participation.
Frameworks	Eurostat Quality of Life Framework
More metadata	Eurostat’s data is collected through the European Union Statistics on Income and Living Conditions (EU-SILC) survey: https://ec.europa.eu/eurostat/cache/metadata/en/ilc_sieusilc.htm Access indicator data from Eurostat

Short name	Voter turnout (9.3)
Long name	Share of votes cast among the population registered to vote
Dimension	Civic engagement
Definition	Voter turnout is calculated as the number of votes cast as a percentage of the population registered to vote, based on the electoral register. Data is collected from National Statistical Offices and electoral management bodies, compiled by the International Institute for Democracy and Electoral Assistance, and pertains to major national elections. National elections include presidential elections in countries such as Chile, Colombia, France, Korea, Lithuania, Mexico, Poland, the Russian Federation, Türkiye, and the United States, and parliamentary elections in other nations. In Australia, Belgium, Brazil, Luxembourg, and Türkiye, voting is compulsory. Distribution estimates of voter turnout by population group are derived from post-election self-reported survey data collected by the Comparative Study of Electoral Systems, using responses to a yes/no question: ‘Did the respondent cast a ballot in the current election?’
Use	To measure civic engagement, assess the strength of democratic participation, and inform policies aimed at increasing political inclusion and voter accessibility.
Type	Objective
Unit of measurement	Percentage of population registered to vote.
Data sources	Administrative records. Leverage data and methodology for country average data: Institute for Democracy and Electoral Assistance (IDEA) (database), and for horizontal inequality data: Comparative Study of Electoral Systems (database), https://cses.org/; and estimates provided by National Statistical Offices.
Frameworks	OECD How’s Life? Eurostat Quality of Life Framework CES Recommendations – Report on measuring sustainable development (2012)
More metadata	OECD data sources: for country average data, Institute for Democracy and Electoral Assistance (IDEA) (database), www.idea.int; and for horizontal inequality data, Comparative Study of Electoral Systems (database), www.cses.org; and estimates provided by NSOs. Access indicator data from OECD

Short name	Trust in institutions (9.4)
Long name	Average score in trust in different institutions on a scale from 0 to 10
Dimension	Civic engagement
Definition	The average trust score in various institutions, reported on a scale from 0 (no trust) to 10 (complete trust), based on survey responses. Institutions may include, e.g., parliament, civil service, the legal system, and police.
Use	To measure public confidence in governance and service delivery, assess societal cohesion, and inform policies to strengthen institutional effectiveness and accountability.
Type	Subjective
Unit of measurement	Mean score on a scale from 0 (no trust at all) to 10 (complete trust)
Data sources	Household surveys or targeted surveys. Leverage data and methodology from three databases used to access data (1) OECD How's Life? Database (future well-being dataflow), (2) EU-SILC survey and (3) Gallup survey.
Frameworks	OECD How’s Life? Eurostat Quality of Life Framework
More metadata	OECD Guidelines on Measuring Trust https://www.oecd.org/en/publications/oecd-guidelines-on-measuring-trust_9789264278219-en.html OECD Trust Survey, https://www.oecd.org/governance/trust-in-government/ Until sufficient waves of the OECD Trust Survey have been conducted to guarantee time series, the OECD Well-being Database draws on a binary indicator of trust in the national government from the Gallup World Poll. Access indicator data from OECD Eurostat collected data for ‘trust in the police’, ‘trust in legal system’ and ‘trust in political system’ as a one-off in 2013 through the European Union Statistics on Income and Living Conditions (EU-SILC) survey: https://ec.europa.eu/eurostat/cache/metadata/en/ilc_sieusilc.htm

Short name	Air quality (10.1)
Long name	Share of population exposed to outdoor air pollution.
Dimension	Environmental conditions
Definition	Exposure to outdoor air pollution is defined as the percentage of the population living in areas where annual concentrations of fine particulate matter (PM2.5), particles less than 2.5 microns in diameter, exceed the 2021 WHO Air Quality Guideline limit of 5 micrograms per cubic meter.
Use	To assess air quality, monitor population exposure to harmful particulate matter, and guide policies to reduce air pollution and its health impacts.
Type	Objective
Unit of measurement	Percentage of population
Data sources	National administrative records, including official data collected by government agencies, such as air pollution monitoring stations, which apply to WHO data, are recommended. Countries could also source by combining satellite observation, ground monitoring and modelling to estimate air pollution exposure levels globally. Leverage data and methodology from linking Global Burden of Disease air pollutant data with Global Human Settlement Layer population data, as done in the OECD’s Exposure to air pollution database.
Frameworks	OECD How’s Life? CES Recommendations on measuring sustainable development UNECE’s Convention on long-range Transboundary Air Pollution.
More metadata	Access indicator data from OECD

Short name	Noise pollution (10.2)
Long name	Share of population reporting noise from neighbours or the street.
Dimension	Environmental conditions
Definition	The proportion of the population who report being regularly disturbed by noise from neighbours or street activities, as assessed through survey responses. This includes noise from traffic, construction, industrial activities, or other sources in their living environment.
Use	To monitor environmental noise pollution, assess its impact on quality of life, and guide policies aimed at reducing noise exposure in residential areas.
Type	Subjective
Unit of measurement	Percentage of population
Data sources	A household survey is recommended. Leverage data and methodology from EU-SILC Survey.
Frameworks	Eurostat Quality of Life Framework
More metadata	Data is collected through the European Union Statistics on Income and Living Conditions (EU-SILC) survey: https://ec.europa.eu/eurostat/cache/metadata/en/ilc_sieusilc.htm Access indicator data from Eurostat

Short name	Access to green space (10.3)
Long name	Share of urban population with access to green space within a 5 minutes walk
Dimension	Environmental conditions
Definition	Access to recreational green space in urban areas is defined as the proportion of the urban population with access to recreational green spaces within a 5-minute walking distance (approximately 400 meters). These green spaces include public areas primarily used for recreation, such as gardens, playgrounds, zoos, parks, castle parks, and cemeteries, as well as suburban natural areas managed as urban parks. Forests or green spaces extending into urban areas are also included when bordered by urban structures on at least two sides and showing signs of recreational use. The method involves mapping areas within a 5-minute walking distance (400 meters, based on an average walking speed of 5 km/h) from inhabited polygons defined in the European Urban Atlas.
Use	To evaluate urban liability, promote equitable access to green spaces, and inform urban planning and public health initiatives.
Type	Objective
Unit of measurement	Percentage of urban population
Data sources	Household surveys and administrative registers, including geospatial data, are recommended. Leverage data and methodology from OECD's How's Life? Database.
Frameworks	OECD How’s Life?
More metadata	OECD’s calculations are based on Poelman (2018), ‘A walk to the park? Assessing access to green areas in Europe’s cities, update using completed Copernicus urban atlas data’, European Commission, Regional and urban policy, https://ec.europa.eu/regional_policy/en/information/publications/working-papers/2018/a-walk-to-the-park-assessing-access-to-green-areas-in-europe-s-cities; and estimates provided by NSOs. Access indicator data from OECD.

Short name	Exposure to extreme temperatures (heat stress) (10.4)
Long name	Share of population exposed to hot days for at least two weeks a year
Dimension	Environmental conditions
Definition	Percentage of the population exposed to hot days (maximum temperature > 35°C) for a minimum of two weeks per year. Exposure to extreme heat has well-documented effects on health and overall well-being and is expected to rise due to climate change.
Use	To assess population vulnerability to extreme heat, monitor climate change impacts, and inform policies for climate adaptation and public health protection.
Type	Objective
Unit of measurement	Percentage of population
Data sources	Administrative registers, including geospatial data. Leverage data and methodology from OECD Exposure to extreme temperature (database), which draws on Copernicus Climate Data Store temperature data (ERA5) and Global Human Settlement Layer population grid data.
Frameworks	OECD How’s Life? Sustainable Development Goals (13.1)
More metadata	Access indicator data from OECD.

Short name	Clean drinking water (10.5)
Long name	Share of population using safely managed drinking water services (SDG 6.1.1)
Dimension	Environmental conditions
Definition	The proportion of the population using safely managed drinking water services is defined as the proportion of population using an improved drinking water source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved drinking water sources include piped supplies, boreholes and tubewells, protected dug wells, protected springs, rainwater, water kiosks, and packaged and delivered water.
Use	To track progress toward universal access to safe drinking water, identify disparities, and guide policies to improve water infrastructure and public health.
Type	Objective
Unit of measurement	Percentage of the population
Data sources	Household surveys and administrative registers. Several international organisations produce data on the indicator, including. WHO/UNICEF Joint Monitoring Programme (JMP), World Bank’s World Development Indicators (WDI) and MICS Surveys.
Frameworks	Sustainable Development Goals
More metadata	Data collected by WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) through household surveys such as MICS and DHS. More information available at https://washdata.org/ Link to more metadata in SDG Database: https://unstats.un.org/sdgs/dataportal/SDMXMetadataPage?6.1.1-SH_H2O_SAFE