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.

This statistic displays the level of self-care of adults in England, by gender, according to the EQ-5D dimension test in 2012. In this year, 96 percent of men and 95 percent of women had no problems with self-care.

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.

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.

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.

Abstract

In 2001, the World Bank in co-operation with the Republika Srpska Institute of Statistics (RSIS), the Federal Institute of Statistics (FOS) and the Agency for Statistics of BiH (BHAS), carried out a Living Standards Measurement Survey (LSMS).

The Living Standard Measurement Survey LSMS, in addition to collecting the information necessary to obtain a comprehensive as possible measure of the basic dimensions of household living standards, has three basic objectives, as follows:

1. To provide the public sector, government, the business community, scientific institutions, international donor organizations and social organizations with information on different indicators of the population's living conditions, as well as on available resources for satisfying basic needs.

2. To provide information for the evaluation of the results of different forms of government policy and programs developed with the aim to improve the population's living standard. The survey will enable the analysis of the relations between and among different aspects of living standards (housing, consumption, education, health, labor) at a given time, as well as within a household.

3. To provide key contributions for development of government's Poverty Reduction Strategy Paper, based on analyzed data.

The Department for International Development, UK (DFID) contributed funding to the LSMS and provided funding for a further three years of data collection for a panel survey, known as the Household Survey Panel Series (HSPS) – and more popularly known as Living in BiH (LiBiH). Birks Sinclair & Associates Ltd. in cooperation with the Independent Bureau for Humanitarian Issues (IBHI) were responsible for the management of the HSPS with technical advice and support provided by the Institute for Social and Economic Research (ISER), University of Essex, UK.

The panel survey provides longitudinal data through re-interviewing approximately half the LSMS respondents for three years following the LSMS, in the autumns of 2002 and 2003 and the winter of 2004. The LSMS constitutes Wave 1 of the panel survey so there are four years of panel data available for analysis. For the purposes of this documentation we are using the following convention to describe the different rounds of the panel survey: - Wave 1 LSMS conducted in 2001 forms the baseline survey for the panel - Wave 2 Second interview of 50% of LSMS respondents in Autumn/Winter 2002 - Wave 3 Third interview with sub-sample respondents in Autumn/Winter 2003 - Wave 4 Fourth interview with sub-sample respondents in Winter 2004

The panel data allows the analysis of key transitions and events over this period such as labour market or geographical mobility and observations on the consequent outcomes for the well-being of individuals and households in the survey. The panel data provides information on income and labour market dynamics within FBiH and RS. A key policy area is developing strategies for the reduction of poverty within FBiH and RS. The panel will provide information on the extent to which continuous poverty and movements in an out of poverty are experienced by different types of households and individuals over the four year period. Most importantly, the co-variates associated with moves into and out of poverty and the relative risks of poverty for different people can be assessed. As such, the panel aims to provide data, which will inform the policy debates within BiH at a time of social reform and rapid change.

In order to develop base line (2004) data on poverty, incomes and socio-economic conditions, and to begin to monitor and evaluate the implementation of the BiH MTDS, EPPU commissioned this modified fourth round of the LiBiH Panel Survey.

Geographic coverage

National coverage. Domains: Urban/rural/mixed; Federation; Republic

Kind of data

Sample survey data [ssd]

Sampling procedure

The Wave 4 sample comprised of 2882 households interviewed at Wave 3 (1309 in the RS and 1573 in FBiH). As at previous waves, sample households could not be replaced with any other households.

Panel design

Eligibility for inclusion

The household and household membership definitions assume the same standard definitions used at Wave 3. While the sample membership, status and eligibility for interview are as follows: i) All members of households interviewed at Wave 3 have been designated as original sample members (OSMs). OSMs include children within households even if they are too young for interview, i.e. younger than 15 years. ii) Any new members joining a household containing at least one OSM, are eligible for inclusion and are designated as new sample members (NSMs). iii) At each wave, all OSMs and NSMs are eligible for inclusion, apart from those who move outof-scope (see discussion below). iv) All household members aged 15 or over are eligible for interview, including OSMs and NSMs.

Following rules

The panel design provides that sample members who move from their previous wave address must be traced and followed to their new address for interview. In some cases the whole household will move together but in other cases an individual member may move away from their previous wave household and form a new "split-off" household of their own. All sample members, OSMs and NSMs, are followed at each wave and an interview attempted. This method has the benefits of maintaining the maximum number of respondents within the panel and being relatively straightforward to implement in the field.

Definition of 'out-of-scope'

It is important to maintain movers within the sample to maintain sample sizes and reduce attrition and also for substantive research on patterns of geographical mobility and migration. The rules for determining when a respondent is 'out-of-scope' are:

i. Movers out of the country altogether i.e. outside BiH This category of mover is clear. Sample members moving to another country outside BiH will be out-of-scope for that year of the survey and ineligible for interview.

ii. Movers between entities Respondents moving between entities are followed for interview. Personal details of "movers" are passed between the statistical institutes and an interviewer assigned in that entity.

iii. Movers into institutions Although institutional addresses were not included in the original LSMS sample, Wave 4 individuals who have subsequently moved into some institutions are followed. The definitions for which institutions are included are found in the Supervisor Instructions.

iv. Movers into the district of Brcko
Are followed for interview. When coding, Brcko is treated as the entity from which the household moved.

Feed-forward

Details of the address at which respondents were found in the previous wave, together with a listing of household members found in each household at the last wave were fed-forward as the starting point for Wave 4 fieldwork. This "feed-forward" data also includes key variables required for correctly identifying individual sample members and includes the following: - For each household: Household ID (IDD); Full address details and phone number - For each Original Sample Member: Name; Person number (ID); unique personal identifier (LID); Sex; Date of birth

The sample details are held in an Access database and in order to ensure the confidentiality of respondents, personal details, names and addresses are held separately from the survey data collected during fieldwork. The IDD, LID and ID are the key linking variables between the two databases i.e. the name and address database and the survey database.

Mode of data collection

Face-to-face [f2f]

Research instrument

Approximately 70% of the questionnaire was based on the Wave 3 questionnaire, carrying forward core measures in order to measure change over time. However in order to develop base line (2004) data on poverty, incomes and socio-economic conditions, and to begin to monitor and evaluate the implementation of the BiHDS the Wave 4 questionnaire additionally contained the Wave 1 Consumption module and a few other LSMS items to allow direct comparability with the Wave 1 data.

Cleaning operations

Dat entry

As at previous waves, CSPro was the chosen data entry software. The CSPro program consists of two main features intended to reduce the number of keying errors and to reduce the editing required following data entry:
- Data entry screens that included all skip patterns. - Range checks for each question (allowing three exceptions for inappropriate, don't know and missing codes).

The Wave 4 data entry program had similar checks to the Wave 3 program - and DE staff were instructed to clear all anomalies with SIG fieldwork members. The program was tested prior to the commencement of data entry. Twelve data entry staff were employed in each Field Office, as all had worked on previous waves training was not undertaken.

Editing

Instructions for editing were provided in the Supervisors Instructions. At Wave 4 supervisors were asked to take more time to edit every questionnaire returned by their interviewers. The SIG Fieldwork Managers examined every Control Form.

Response rate

The level of cases that were unable to be traced is extremely low as are the whole household refusal or non-contact rates. In total, 9128 individuals (including children) were enumerated within the sample households at Wave 4, 5019 individuals in the FBiH and 4109 in the RS. Within in the 2875 eligible households, 7603 individuals aged 15 or over were eligible for interview with 7116 (93.6%) being successfully interviewed. Within co-operating households (where there was at least one interview) the interview rate was

In June 2024, the household cost inflation rate (HCI) for low-income households in the United Kingdom was 1.7 percent, compared with 2.3 percent for middle-income households, and 3.3 percent for high-income households. Unlike other measures of inflation such as the consumer price index (CPI) the HCI isn't based on a fixed basket of goods, but is weighted to show how price changes affect different households by their economic status.

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.

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.

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.

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.

In 2024, the average life expectancy in the world was 71 years for men and 76 years for women. The lowest life expectancies were found in Africa, while Oceania and Europe had the highest. What is life expectancy?Life expectancy is defined as a statistical measure of how long a person may live, based on demographic factors such as gender, current age, and most importantly the year of their birth. The most commonly used measure of life expectancy is life expectancy at birth or at age zero. The calculation is based on the assumption that mortality rates at each age were to remain constant in the future. Life expectancy has changed drastically over time, especially during the past 200 years. In the early 20th century, the average life expectancy at birth in the developed world stood at 31 years. It has grown to an average of 70 and 75 years for males and females respectively, and is expected to keep on growing with advances in medical treatment and living standards continuing. Highest and lowest life expectancy worldwide Life expectancy still varies greatly between different regions and countries of the world. The biggest impact on life expectancy is the quality of public health, medical care, and diet. As of 2022, the countries with the highest life expectancy were Japan, Liechtenstein, Switzerland, and Australia, all at 84–83 years. Most of the countries with the lowest life expectancy are mostly African countries. The ranking was led by the Chad, Nigeria, and Lesotho with 53–54 years.

The housing costs inflation rate for low-income households in the United Kingdom was noticeably higher than that of high-income ones between April 2022 and April 2023, during a serious cost of living crisis in the UK. As of June 2024, however, the inflation rate for high-income households was higher than that of middle or low incomes ones.

In the build up to the Second World War, the United States was the major power with the highest gross domestic product (GDP) per capita in the world. In 1938, the United States also had the highest overall GDP in the world, and by a significant margin, however differences in GDP per person were much smaller. Switzerland In terms of countries that played a notable economic role in the war, the neutral country of Switzerland had the highest GDP per capita in the world. A large part of this was due to the strength of Switzerland's financial system. Most major currencies abandoned the gold standard early in the Great Depression, however the Swiss Franc remained tied to it until late 1936. This meant that it was the most stable, freely convertible currency available as the world recovered from the Depression, and other major powers of the time sold large amounts of gold to Swiss banks in order to trade internationally. Switzerland was eventually surrounded on all sides by Axis territories and lived under the constant threat of invasion in the war's early years, however Swiss strategic military planning and economic leverage made an invasion potentially more expensive than it was worth. Switzerland maintained its neutrality throughout the war, trading with both sides, although its financial involvement in the Holocaust remains a point of controversy. Why look at GDP per capita? While overall GDP is a stronger indicator of a state's ability to fund its war effort, GDP per capita is more useful in giving context to a country's economic power in relation to its size and providing an insight into living standards and wealth distribution across societies. For example, Germany and the USSR had fairly similar GDPs in 1938, whereas Germany's per capita GDP was more than double that of the Soviet Union. Germany was much more industrialized and technologically advanced than the USSR, and its citizens generally had a greater quality of life. However these factors did not guarantee victory - the fact that the Soviet Union could better withstand the war of attrition and call upon its larger population to replenish its forces greatly contributed to its eventual victory over Germany in 1945.

In 2025, Luxembourg was the country with the highest gross domestic product per capita in the world. Of the 20 listed countries, 13 are in Europe and five are in Asia, alongside the U.S. and Australia. There are no African or Latin American countries among the top 20. Correlation with high living standards While GDP is a useful indicator for measuring the size or strength of an economy, GDP per capita is much more reflective of living standards. For example, when compared to life expectancy or indices such as the Human Development Index or the World Happiness Report, there is a strong overlap - 14 of the 20 countries on this list are also ranked among the 20 happiest countries in 2024, and all 20 have "very high" HDIs. Misleading metrics? GDP per capita figures, however, can be misleading, and to paint a fuller picture of a country's living standards then one must look at multiple metrics. GDP per capita figures can be skewed by inequalities in wealth distribution, and in countries such as those in the Middle East, a relatively large share of the population lives in poverty while a smaller number live affluent lifestyles.

The Retail Price Index (RPI) is one of the main measures of inflation used to calculate the change in the price of goods and services within the British economy. In the first quarter of 2025 the index value was 393.7, indicating that the price for a fixed basket of goods had increased by almost 294 percent since 1987. The RPI inflation rate for March 2025 was 3.2 percent, down from 3.4 percent in the previous month. Inflation and UK living standards For UK consumers, high inflation is one of the main drivers of the ongoing cost of living crisis. With wages struggling to keep up with the pace of inflation for a long period between 2021 and 2023, UK households saw their living standards fall significantly. In 2022/23, real household disposable income in the UK is estimated to have fallen by 2.1 percent, which was the biggest fall in living standards since 1956. While there have been some signals that the crisis eased somewhat in 2024, such as falling energy and food inflation, an increasing share of UK households have reported increasing living costs since Summer 2024. Additional inflation indicators Aside from the Retail Price Index, the UK also produces other inflation indices such as the Consumer Price Index (CPI) and the Consumer Price Index including owner occupiers' housing costs (CPIH). While these particular indices measure consumer price increases slightly differently, they both provide an overall picture of rising prices. More specific inflation rates, such as by sector, are also produced, while other indices omit certain items, such as core inflation, which excludes food and energy inflation, to provide a more stable measure of inflation.

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.

In 2024, gross domestic product per capita in the United Kingdom was 37,044 British pounds, compared with 37,033 pounds in the previous year. In general, while GDP per capita has grown quite consistently throughout this period, there are noticeable declines, especially between 2007 and 2009, and between 2019 and 2020, due to the Global Financial Crisis, and COVID-19 pandemic, respectively. Why is GDP per capita stagnating when the economy is growing? During the last two years that GDP per capita fell and then stagnated in the UK, the overall economy grew by 0.4 percent in 2023 and 1.1 percent in 2024. While the overall UK economy is therefore larger than it was in 2022, the UK's population has grown at a faster rate, resulting in the lower GDP per capita figure. The long-term slump in the UK's productivity, as measured by output per hour worked, has meant that the gap between GDP growth and GDP per capita growth has been widening for some time. Economy remains the main concern of UK voters As of February 2025, the economy was seen as the main issue facing the UK, just ahead of immigration, health, and several other problems in the country. While Brexit was seen as the most important issue before COVID-19, and concerns about health were dominant throughout 2020 and 2021, the economy has generally been the primary facing voters issue since 2022. The surge in inflation throughout 2022 and 2023, and the impact this had on wages and living standards, resulted in a very tough period for UK households. As of January 2025, 57 percent of households were still noticing rising living costs, although this is down from a peak of 91 percent in August 2022.

This statistic shows a ranking of the leading pets owned by households in the United Kingdom (UK) between 2016 and 2019, broken down by region. In London, 14 percent of people were cat owners, while 9 percent owned a dog, making this the only region were cats were more popular than dogs.

In total, 45 percent of the UK population own a pet . This figure has increased by five percent since 2016. Over 90 percent of pet owners in the UK say that owning a pet makes them feel happy and 88 percent feel that pet ownership improves their overall quality of life.

With such as high ownership of pets in the United Kingdom, this leads the path for retailers in a growing and dynamic market. Pet food has a key role to play, with dog and cat food alone estimated at 2.5 billion British pounds in 2017.