Luxembourg stands out as the European leader in quality of life for 2025, achieving a score of 220 on the Quality of Life Index. The Netherlands follows closely behind with 211 points, while Albania and Ukraine rank at the bottom with scores of 104 and 115 respectively. This index provides a thorough assessment of living conditions across Europe, reflecting various factors that shape the overall well-being of populations and extending beyond purely economic metrics. Understanding the quality of life index The quality of life index is a multifaceted measure that incorporates factors such as purchasing power, pollution levels, housing affordability, cost of living, safety, healthcare quality, traffic conditions, and climate, to measure the overall quality of life of a Country. Higher overall index scores indicate better living conditions. However, in subindexes such as pollution, cost of living, and traffic commute time, lower values correspond to improved quality of life. Challenges affecting life satisfaction Despite the fact that European countries register high levels of life quality by for example leading the ranking of happiest countries in the world, life satisfaction across the European Union has been on a downward trend since 2018. The EU's overall life satisfaction score dropped from 7.3 out of 10 in 2018 to 7.1 in 2022. This decline can be attributed to various factors, including the COVID-19 pandemic and economic challenges such as high inflation. Rising housing costs, in particular, have emerged as a critical concern, significantly affecting quality of life. This issue has played a central role in shaping voter priorities for the European Parliamentary Elections in 2024 and becoming one of the most pressing challenges for Europeans, profoundly influencing both daily experiences and long-term well-being.

As of June 2025, the average score for how happy people felt in the UK was 7.1 out of ten, people aged 70 and over reporting an average score of 7.7, the highest among the provided demographics.

This collection consists of the syntax (using R) for generating individual and household-level data on 7 dimensions and 14 indicators of multdimensional work quality from a new UK Quality of Work (QoW) index using Understanding Society and the Labour Force Survey. It also contains detailed data on workers' personal, family and household circumstances - conceptualised using the Capability Approach as their Conversion Factors and Capability Set.

This will potentially be useful for researchers interested in the working conditions and work quality of paid workers in the UK, and also the circumstances under which they access this work - such as the commitments they have to manage alongside paid work (Conversion Factors); and estimates of the choice (Capabilities) they have over alternative work and non-work activities.

The data covers all regions and nations of the UK from Waves 4 (2012-13), Waves 6, Waves 8, Waves 10, and Waves 12 (2020-21) of Understanding Society. Everyone in paid work, or away from a paid job they usually do at the time interviewed, is included in the index, so the data release contains an unweighted (non-independent) total of 108,973 observations. Both employees and self-employed workers are included in the index, with three of the indicators also capturing data on all workers' jobs and not just their main job. The data can be analysed at individual- or household-level, and will be useful for both cross-sectional and longitudinal analysis of the quality of working life in the UK.

The UK QoW index captures many aspects of peoples' work quality. The seven dimensions are Earnings, Insurance, Security, Autonomy & Voice, Work-life balance, Prospects and Health & Safety. Within these dimensions, respondents' job quality is coded using a mix of binary, categorical and continuous indicators: Earnings Equity and Earnings Sufficiency (Earnings dimension); Pensions (Insurance); Continuous Employment and Composite Security (Security); task Autonomy and Collective Voice (Autonomy & Voice); Employee Flexibility and Excessive Hours (Work-life balance); Managerial Duties, Short-term Prospects and Long-term Prospects (Prospects); and Work Fatalities, Work Accidents and Work Illnesss (Health & Safety). A set of alternative weighting methods for aggregating the indicators into an index is also included in the data release, alongside imputed and non-imputed data for missing cases in each wave. Whilst most of the data is drawn from survey answers to Understanding Society, I also introduce information on workers' job prospects and health and safety into the index by matching with data on workers' Standard Industrial Classifications (SICs) and Standard Occupational Classifications (SICs) from the Labour Force Survey and Health and Safety Executive, and from Department for Education Working Futures surveys, respectively. I also take advantage of the longitudinal nature of Understanding Society to create an indicator of workers' length of continuous employment with the same employer.

In addition, the deposit includes a rich amount of detail on the wider circumstances of this same sample of workers. Using household and family-level data, a set of Conversion Factors are computed calculating workers' wider family and life commitments which they have to manage alongside paid work - including their health, caring responsibilities, disabilities and. A set of measures of workers' economic, social and cultural & human capital - including their home ownership, assets, skills and social connections are also included. These are conceptualised as proxies for workers' Capability Set - i.e. the choice workers have over alternative opportunities inside and outside the labour market, other than their chosen work activity.

This code has been developed as part of an ESRC-funded PhD thesis on 'Work and Wellbeing in Modern Britain: An Application of the Capability Approach' (2025). This R code is therefore provided open access (CC BY SA 4.0). However to replicate this analysis, researchers will need to download Understanding Society from the UK Data Service, and re-run the R syntax - this data is safeguarded and so not supplied in this data deposit, and is only available subject to signing up to UKDA's conditions for data access. For the dimensions on Health and Safety and long-term prospects, users will also need to access the Labour Force Survey through the UK Data Service and link to the API of Working Futures via 'LMI for All', respectively.

An ESRC funded PhD research project based at the Department of Social Policy and the Centre for Analysis of Social Exclusion at the LSE, to apply the Capability Approach to the way we understand and measure labour market advantage and disadvantage.

A list of some key resources for comparing London with other world cities.

European Union/Eurostat, Urban Audit

Arcadis, Sustainable cities index

AT Kearney, Global Cities Index

McKinsey, Urban world: Mapping the economic power of cities

Knight Frank, Wealth report

OECD, Better Life Index

UNODC, Statistics on drugs, crime and criminal justice at the international level

Economist, Hot Spots

Economist, Global Liveability Ranking and Report August 2014

Mercer, Quality of Living Reports

PWC, Cities of opportunity

BCG, Decoding Global Talent

Forbes, World's most influential cities

Mastercard, Global Destination Cities Index

Numbeo, Database of user contributed data

The average for 2024 based on 138 countries was 5.56 points. The highest value was in Finland: 7.74 points and the lowest value was in Afghanistan: 1.72 points. The indicator is available from 2013 to 2024. Below is a chart for all countries where data are available.

Estimates of life satisfaction, feeling that the things done in life are worthwhile, happiness and anxiety at the UK, country, regional, county, local and unitary authority level.

Monthly time series of simulated de-trended/de-seasonalised biological indicators at 86 bio-monitoring sites in England and Wales based on the modelled response of these indicators to discharge (represented by a standardised streamflow index, SSI) at 76 paired gauging stations. The biological indicators include: (i) Average Score per Taxon (ASPT) (ii) Lotic-invertebrate Index for Flow Evaluation (LIFE) calculated at family-level (LIFE Family) (iii) LIFE calculated at species-level (LIFE Species). The simulation spans the period 1964-2012.

People living with dementia experience higher levels of sleep dysfunction compared to healthy older people. Poor sleep is common in Alzheimer’s disease (AD) and dementia with Lewy Bodies (DLB); two common causes of neurodegenerative dementia comprising of approximately 70% of diagnoses. Sleep dysfunction in dementia has been attributed as a significant contributing factor to early admittance into care. (Sloan, 2015, Saheed, 2017, Figuerio et al, 2015, Forbes et al, 2014). Sleep is important for quality of life, health and well-being and when the sleep of both the person with dementia and their caregiver is affected, supporting individuals to live independently at home becomes more challenging. A significant contributing factor to a move out of the home prematurely into institutional care is sleep dysfunction in the person with dementia, resulting in caregiver exhaustion and burnout. Given the complexity of sleep problems, there is a need for tools which can evaluate poor sleep in populations living with dementia.

The Nurolight study sought to explore the impact of poor sleep on people living with dementia and their care partners.

Using the Pittsburgh Sleep Quality Index; a tool designed to evaluate sleep disturbances in populations. It comprises Comprising of 19 self-reported items belonging to one of seven subcategories: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. An additional section of 5 questions relates to partner/roommate reporting and are not scored.

The Nurolight assessed 11 participants (M=6, F=5). The component scores are summed to produce a global score (range 0 to 21). Higher scores indicate poor sleep quality, with a score greater than 5 suggesting significant sleep difficulties. Findings from this study indicate that 81% participants were considered to have significant sleep difficulties.

The Index of Multiple Deprivation, which was produced at LSOA level in 2007 and 2004, combines seven distinct domains of deprivation together to give an overall impression of the level of deprivation experienced by an area. The Living Environment domain combines 4 indicators to give an overall score for the level of deprivation in the quality of the local environment. The indicators used in the latest update of this domain are; - Social and private housing in poor condition - Houses without central heating - Air quality - Road traffic accidents involving injury to pedestrians and cyclists More information about this domain can be found in Chapter 2, Section 8 of the English Indices of Deprivation 2007 report http://webarchive.nationalarchives.gov.uk/20120919132719/http://www.communities.gov.uk/documents/communities/pdf/733520.pdf

This dataset provides detailed information on the 2019 Index of Multiple Deprivation (IMD) for Birmingham, UK. The data is available at the postcode level and includes the Lower Layer Super Output Area (LSOA) information.Data is provided at the LSOA 2011 Census geography.The decile score ranges from 1-10 with decile 1 representing the most deprived 10% of areas while decile 10 representing the least deprived 10% of areas.The IMD rank and decile score is allocated to the LSOA and all postcodes within it at the time of creation (2019).Note that some postcodes cross over LSOA boundaries. The Office for National Statistics sets boundaries for LSOAs and allocates every postcode to one LSOA only: this is the one which contains the majority of residents in that postcode area (as at 2011 Census).

The English Indices of Deprivation 2019 provide detailed measures of relative deprivation across small areas in England. The Living Environment Deprivation dataset is a key component of this index, assessing the quality of the local environment. This dataset includes indicators such as housing quality, air quality, and road traffic accidents. It helps identify areas where the living environment is poor, guiding policy interventions and resource allocation to improve housing conditions, reduce pollution, and enhance overall living standards.

In 2023, Norway ranked first with a health index score of 83, followed by Iceland and Sweden. The health index score is calculated by evaluating various indicators that assess the health of the population, and access to the services required to sustain good health, including health outcomes, health systems, sickness and risk factors, and mortality rates. The statistic shows the health and health systems ranking of European countries in 2023, by their health index score.

The Organisation for Economic Co-operation and Development (OECD) Social and Welfare Statistics (previously Social Expenditure Database) available via the UK Data Service includes the following databases:

The OECD Social Expenditure Database (SOCX) has been developed in order to serve a growing need for indicators of social policy. It includes reliable and internationally comparable statistics on public and mandatory and voluntary private social expenditure at programme level. SOCX provides a unique tool for monitoring trends in aggregate social expenditure and analysing changes in its composition. The main social policy areas are as follows: old age, survivors, incapacity-related benefits, health, family, active labour market programmes, unemployment, housing, and other social policy areas.

The Income Distribution database contains comparable data on the distribution of household income, providing both a point of reference for judging the performance of any country and an opportunity to assess the role of common drivers as well as drivers that are country-specific. They also allow governments to draw on the experience of different countries in order to learn "what works best" in narrowing income disparities and poverty. But achieving comparability in this field is also difficult, as national practices differ widely in terms of concepts, measures, and statistical sources.

The Child Wellbeing dataset compare 21 policy-focussed measures of child well-being in six areas, chosen to cover the major aspects of children’s lives: material well being; housing and environment; education; health and safety; risk behaviours; and quality of school life.

The Better Life Index: There is more to life than the cold numbers of GDP and economic statistics. This Index allows you to compare well-being across countries, based on 11 topics the OECD has identified as essential, in the areas of material living conditions and quality of life.

The Social Expenditure data were first provided by the UK Data Service in March 2004.

ID 2004 Outdoors Living Environment Subdomain (air quality and road safety) Source: Office of the Deputy Prime Minister (ODPM): ID 2004 Publisher: Communities and Local Government (CLG) Geographies: Lower Layer Super Output Area (LSOA) Geographic coverage: England Time coverage: 2004 (using 2001 data) Type of data: Modelled data

In 2023, Switzerland led the ranking of countries with the highest average wealth per adult, with approximately ******* U.S. dollars per person. Luxembourg was ranked second with an average wealth of around ******* U.S. dollars per adult, followed by Hong Kong SAR. However, the figures do not show the actual distribution of wealth. The Gini index shows wealth disparities in countries worldwide. Does wealth guarantee a longer life? As the old adage goes, “money can’t buy you happiness”, yet wealth and income are continuously correlated to the quality of life of individuals in different countries around the world. While greater levels of wealth may not guarantee a higher quality of life, it certainly increases an individual’s chances of having a longer one. Although they do not show the whole picture, life expectancy at birth is higher in the wealthier world regions. Does money bring happiness? A number of the world’s happiest nations also feature in the list of those countries for which average income was highest. Finland, however, which was the happiest country worldwide in 2022, is missing from the list of the top twenty countries with the highest wealth per adult. As such, the explanation for this may be the fact that the larger proportion of the population has access to a high income relative to global levels. Measures of quality of life Criticism of the use of income or wealth as a proxy for quality of life led to the creation of the United Nations’ Human Development Index. Although income is included within the index, it also has other factors taken into account, such as health and education. As such, the countries with the highest human development index can be correlated to those with the highest income levels. That said, none of the above measures seek to assess the physical and mental environmental impact of a high quality of life sourced through high incomes. The happy planet index demonstrates that the inclusion of experienced well-being and ecological footprint in place of income and other proxies for quality of life results in many of the world’s materially poorer nations being included in the happiest.

Out of all OECD countries, Cost Rica had the highest poverty rate as of 2022, at over 20 percent. The country with the second highest poverty rate was the United States, with 18 percent. On the other end of the scale, Czechia had the lowest poverty rate at 6.4 percent, followed by Denmark.

The significance of the OECD

The OECD, or the Organisation for Economic Co-operation and Development, was founded in 1948 and is made up of 38 member countries. It seeks to improve the economic and social well-being of countries and their populations. The OECD looks at issues that impact people’s everyday lives and proposes policies that can help to improve the quality of life.

Poverty in the United States

In 2022, there were nearly 38 million people living below the poverty line in the U.S.. About one fourth of the Native American population lived in poverty in 2022, the most out of any ethnicity. In addition, the rate was higher among young women than young men. It is clear that poverty in the United States is a complex, multi-faceted issue that affects millions of people and is even more complex to solve.

• construct waves of accessible and well-documented panel data;
• provide these data in a convenient and timely fashion to the scientific and policy research community;
• describe health trajectories, disability and healthy life expectancy in a representative sample of the English population aged 50 and over;
• examine the relationship between economic position and health;
• investigate the determinants of economic position in older age;
• describe the timing of retirement and post-retirement labour market activity; and
• understand the relationships between social support, household structure and the transfer of assets.

• Primary Data from Wave 8 onwards (SN 8444) includes all the variables in the SL primary dataset (SN 8346) as well as day of birth, combined SIC 2003 code (5 digit), combined SOC 2000 code (4 digit), NS-SEC long version including and excluding unclassifiable and non-workers.
• Pension Age Data from Wave 8 onwards (SN 8445) includes all the variables in the SL pension age data (SN 8375) as well as year reached pension age variable.
• Detailed geographical identifier files for each wave, grouped by identifier held under SN 8423 (Index of Multiple Deprivation Score), SN 8424 (Local Authority District Pre-2009 Boundaries), SN 8438 (Local Authority District Post-2009 Boundaries), SN 8425 (Census 2001 Lower Layer Super Output Areas), SN 8434 (Census 2011 Lower Layer Super Output Areas), SN 8426(Census 2001 Middle Layer Super Output Areas), SN 8435 (Census 2011 Middle Layer Super Output Areas), SN 8427 (Population Density for Postcode Sectors), SN 8428 (Census 2001 Rural-Urban Indicators), SN 8436 (Census 2011 Rural-Urban Indicators).

• either SN 8424 (Local Authority District Pre-2009 Boundaries) or SN 8438 (Local Authority District Post-2009 Boundaries)
• either SN 8425 (Census 2001 Lower Layer Super Output Areas) or SN 8434 (Census 2011 Lower Layer Super Output Areas)
• either SN 8426 (Census 2001 Middle Layer Super Output Areas) or SN 8435 (Census 2011 Middle Layer Super Output Areas)
• either SN 8428 (Census 2001 Rural-Urban Indicators) or SN 8436 (Census 2011 Rural-Urban Indicators)

ObjectivesThis study aimed to test the reliability, validity and sensitivity of Chinese version of the EQ-5D preference weights in Chinese general people, examine the differences between the China value set and the UK, Japan and Korea value sets, and provide methods for evaluating and comparing the EQ-5D value sets of different countries.MethodsA random sample of 2984 community residents (15 years or older) were interviewed using a questionnaire including the EQ-5D scale. Level of agreement, convergent validity, known-groups validity and sensitivity of the EQ-5D China, United Kingdom (UK), Japan and Korea value sets were determined.ResultsThe mean EQ-5D index scores were significantly (P 0.75) and convergent validity (Pearson’s correlation coefficients > 0.95) were found between each paired schemes. The EQ-5D index scores discriminated equally well for the four versions between levels of 10 known-groups (P< 0.05). The effect size and the relative efficiency statistics showed that the China weights had better sensitivity.ConclusionsThe China EQ-5D preference weights show equivalent psychometric properties with those from the UK, Japan and Korea weights while slightly more sensitive to known group differences than those from the Japan and Korea weights. Considering both psychometric and sociocultural issues, the China scheme should be a priority as an EQ-5D based measure of the health related quality of life in Chinese general population.

Health-related quality of life of NAFLD patients by place of origin (Spain and UK) and NASH (absence and presence).

Abbreviations: GDP, gross domestic product; No, number; HRT, hormone replacement therapy; HS, health state, No, number; GP, general population; QoL, quality of life;UK, united Kingdom;ROK, Republic of Korea; US, United states;¶ mild or severe osteoarthritis;*EQ-5D described severity;^ CSM,general medical clinic,cerebral aneurysms;^^Knee osteoarthritis;€chronic prostatitisWTP per QALY compared to GDP per Capita.