In 2023, London had a gross domestic product of over 569 billion British pounds, by far the most of any region of the United Kingdom. The region of South East England which surrounds London had the second-highest GDP in this year, at over 360 billion pounds. North West England, which includes the major cities of Manchester and Liverpool, had the third-largest GDP among UK regions, at almost 250 billion pounds. Levelling Up the UK London’s economic dominance of the UK can clearly be seen when compared to the other regions of the country. In terms of GDP per capita, the gap between London and the rest of the country is striking, standing at over 63,600 pounds per person in the UK capital, compared with just over 37,100 pounds in the rest of the country. To address the economic imbalance, successive UK governments have tried to implement "levelling-up policies", which aim to boost investment and productivity in neglected areas of the country. The success of these programs going forward may depend on their scale, as it will likely take high levels of investment to reverse economic neglect regions have faced in the recent past. Overall UK GDP The gross domestic product for the whole of the United Kingdom amounted to 2.56 trillion British pounds in 2024. During this year, GDP grew by 0.9 percent, following a growth rate of 0.4 percent in 2023. Due to the overall population of the UK growing faster than the economy, however, GDP per capita in the UK fell in both 2023 and 2024. Nevertheless, the UK remains one of the world’s biggest economies, with just five countries (the United States, China, Japan, Germany, and India) having larger economies. It is it likely that several other countries will overtake the UK economy in the coming years, with Indonesia, Brazil, Russia, and Mexico all expected to have larger economies than Britain by 2050.

According to the Hurun Global Rich List 2025, the United States housed the highest number of billionaires worldwide in 2025. In detail, there were *** billionaires living in the United States as of January that year. By comparison, *** billionaires resided in China. India, the United Kingdom, and Germany were also the homes of a significant number of billionaires that year. United States has regained its first place As the founder and exporter of consumer capitalism, it is no surprise that the United States is home to a large number of billionaires. Although China had briefly overtaken the U.S. recently, the United States has reclaimed its position as the country with the most billionaires in the world. Moreover, North America leads the way in terms of the highest number of ultra high net worth individuals – those with a net worth of more than ***** million U.S. dollars. The prominence of Europe and North America is a reflection of the higher degree of economic development in those states. However, this may also change as China and other emerging economies continue developing. Female billionaires Moreover, the small proportion of female billionaires does little to counter critics claiming the global economy is dominated by an elite comprised mainly of men. On the list of the richest people in the world, only *** were women. Moreover, recent political discourse has put a great amount of attention on the wealth held by the super-rich, with the wealth distribution of the global population being heavily unequal.

Estimates of UK financial claims by instrument, for 1900, 1910, 1920, 1927, 1937 and 1948 from Revell (1967).

In 2023, the gross domestic product per capita in London was 63,618 British pounds, compared with 37,135 pounds per capita for the United Kingdom as a whole. Apart from London, the only other region of the UK that had a greater GDP per capita than the UK average was South East England, at 38,004 pounds per capita. By contrast, North East England had the lowest GDP per capita among UK regions, at 26,347 pounds. Regional imbalance in the UK economy? London's overall GDP in 2022 was over 508 billion British pounds, which accounted for almost a quarter of the overall GDP of the United Kingdom. South East England had the second-largest regional economy in the country, with a GDP of almost 341.7 billion British pounds. Furthermore, these two regions were the only ones that had higher levels of productivity (as measured by output per hour worked) than the UK average. While recent governments have recognized regional inequality as a major challenge facing the country, it may take several years for any initiatives to bear fruit. The creation of regional metro mayors across England is one of the earliest attempts at giving regions and cities in particular more power over spending in their regions than they currently have. UK economy growth slow in late 2024 After ending 2023 with two quarters of negative growth, the UK economy grew at the reasonable rate of 0.8 percent and 0.4 percent in the first and second quarters of the year. This was, however, followed by zero growth in the third quarter, and by just 0.1 percent in the last quarter of the year. Other economic indicators, such as the inflation rate, fell within the expected range in 2024, but have started to rise again, with a rate of three percent recorded in January 2025. While unemployment has witnessed a slight uptick since 2022, it is still at quite low levels compared with previous years.

United Kingdom UK: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider, Richest 60%: % of Population Aged 15+ data was reported at 97.620 % in 2017. This records a decrease from the previous number of 99.490 % for 2014. United Kingdom UK: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider, Richest 60%: % of Population Aged 15+ data is updated yearly, averaging 97.620 % from Dec 2011 (Median) to 2017, with 3 observations. The data reached an all-time high of 99.490 % in 2014 and a record low of 97.599 % in 2011. United Kingdom UK: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider, Richest 60%: % of Population Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Bank Account Ownership. Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (richest 60%, share of population ages 15+).; ; Demirguc-Kunt et al., 2018, Global Financial Inclusion Database, World Bank.; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

United Kingdom UK: Oil Rents: % of GDP data was reported at 0.291 % in 2016. This records an increase from the previous number of 0.223 % for 2015. United Kingdom UK: Oil Rents: % of GDP data is updated yearly, averaging 0.585 % from Dec 1971 (Median) to 2016, with 46 observations. The data reached an all-time high of 2.664 % in 1984 and a record low of 0.000 % in 1971. United Kingdom UK: Oil Rents: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Land Use, Protected Areas and National Wealth. Oil rents are the difference between the value of crude oil production at regional prices and total costs of production.; ; World Bank staff estimates based on sources and methods described in 'The Changing Wealth of Nations 2018: Building a Sustainable Future' (Lange et al 2018).; Weighted average;

With a Gross Domestic Product of over 4.3 trillion Euros, the German economy was by far the largest in Europe in 2024. The similarly sized economies of the United Kingdom and France were the second and third largest economies in Europe during this year, followed by Italy and Spain. The smallest economy in this statistic is that of the small Balkan nation of Montenegro, which had a GDP of 7.4 billion Euros. In this year, the combined GDP of the 27 member states that compose the European Union amounted to approximately 17.95 trillion Euros. The big five Germany’s economy has consistently had the largest economy in Europe since 1980, even before the reunification of West and East Germany. The United Kingdom, by contrast, has had mixed fortunes during the same period and had a smaller economy than Italy in the late 1980s. The UK also suffered more than the other major economies during the recession of the late 2000s, meaning the French economy was the second largest on the continent for some time afterward. The Spanish economy was continually the fifth-largest in Europe in this 38-year period, and from 2004 onwards, has been worth more than one trillion Euros. The smallest GDP, the highest economic growth in Europe Despite having the smallerst GDP of Europe, Montenegro emerged as the fastest growing economy in the continent, achieving an impressive annual growth rate of 4.5 percent, surpassing Turkey's growth rate of 4 percent. Overall,this Balkan nation has shown a remarkable economic recovery since the 2010 financial crisis, with its GDP projected to grow by 28.71 percent between 2024 and 2029. Contributing to this positive trend are successful tourism seasons in recent years, along with increased private consumption and rising imports. Europe's economic stagnation Malta, Albania, Iceland, and Croatia were among the countries reporting some of the highest growth rates this year. However, Europe's overall performance reflected a general slowdown in growth compared to the trend seen in 2021, during the post-pandemic recovery. Estonia experienced the sharpest negative growth in 2023, with its economy shrinking by 2.3% compared to 2022, primarily due to the negative impact of sanctions placed on its large neighbor, Russia. Other nations, including Sweden, Germany, and Finland, also recorded slight negative growth.

Dataset of long-run data on wealth inequality drawn from existing sources and compiled into a single country-year dataset.

This data file includes the Gini coefficient calculated for different wealth welfare aggregates constructed for all Luxembourg Wealth Study (LWS) datasets in all waves (as of March 2022). It includes Gini coefficients calculated on: • Disposable Net Worth • Value of Principal residence • Financial Assets

This project sought to renew the ESRC's invaluable financial support to LIS (formerly the Luxembourg Income Study) for a period of five more years. LIS is an independent, non-profit cross-national data archive and research institute located in Luxembourg. LIS relies on financial contributions from national science foundations, other research institutions and consortia, data-providing agencies, and supranational organisations to support data harmonisation and enable free and unlimited data access to researchers in the participating countries and to students world-wide. LIS' primary activity is to make harmonised household microdata available to researchers, thus enabling cross-national, interdisciplinary primary research into socio-economic outcomes and their determinants. Users of the Luxembourg Income Study Database and Luxembourg Wealth Study Database come from countries around the globe, including the UK. LIS has four goals: 1) to harmonise microdatasets from high- and middle-income countries that include data on income, wealth, employment, and demography; 2) to provide a secure method for researchers to query data that would otherwise be unavailable due to country-specific privacy restrictions; 3) to create and maintain a remote-execution system that sends research query results quickly back to users at off-site locations; and 4) to enable, facilitate, promote and conduct crossnational comparative research on the social and economic wellbeing of populations across countries. LIS contains the Luxembourg Income Study (LIS) Database, which includes income data, and the Luxembourg Wealth Study (LWS) Database, which focuses on wealth data. LIS currently includes microdata from 46 countries in Europe, the Americas, Africa, Asia and Australasia. LIS contains over 250 datasets, organised into eight time "waves," spanning the years 1968 to 2011. Since 2007, seventeen more countries have been added to LIS, including the BRICS countries (Brazil, Russia, India, China, South Africa), Japan, South Korea and a number of other Latin American countries. LWS contains 20 wealth datasets from 12 countries, including the UK, and covers the period 1994 to 2007. All told, LIS and LWS datasets together cover 86% of world GDP and 64% of world population. Users submit statistical queries to the microdatabases using a Java-based job submission interface or standard email. The databases are especially valuable for primary research in that they offer access to cross-national data at the micro-level - at the level of households and persons. Users are economists, sociologists, political scientists, and policy analysts, among others, and they employ a range of statistical approaches and methods. LIS also provides extensive documentation - metadata - for both LIS and LWS, concerning technical aspects of the survey data, the harmonisation process, and the social institutions of income and wealth provision in participating countries. In the next five years, for which support is sought, LIS will: - expand LIS, adding Waves IX (2013) and X (2016), and add new middle-income countries; - develop LWS, adding another wave of datasets to existing countries; acquire new wealth datasets for 14 more countries in cooperation with the European Central Bank (based on the Household Finance and Consumption Survey); - create a state-of-the-art metadata search and storage system; - maintain international standards in data security and data infrastructure systems; - provide high-quality harmonised household microdata to researchers around the world; - enable interdisciplinary cross-national social science research covering 45+ countries, including the UK; - aim to broaden its reach and impact in academic and non-academic circles through focused communications strategies and collaborations.

United Kingdom CG: RC: SIA: Current Taxes on Income, Wealth, etc. data was reported at 56,226.000 GBP mn in Jun 2018. This records a decrease from the previous number of 85,694.000 GBP mn for Mar 2018. United Kingdom CG: RC: SIA: Current Taxes on Income, Wealth, etc. data is updated quarterly, averaging 39,047.000 GBP mn from Mar 1987 (Median) to Jun 2018, with 126 observations. The data reached an all-time high of 85,694.000 GBP mn in Mar 2018 and a record low of 12,314.000 GBP mn in Jun 1987. United Kingdom CG: RC: SIA: Current Taxes on Income, Wealth, etc. data remains active status in CEIC and is reported by Office for National Statistics. The data is categorized under Global Database’s United Kingdom – Table UK.AB042: ESA10: Resources and Uses: Central Government: Secondary Income.

United Kingdom UK: Forest Rents: % of GDP data was reported at 0.005 % in 2016. This records an increase from the previous number of 0.005 % for 2015. United Kingdom UK: Forest Rents: % of GDP data is updated yearly, averaging 0.008 % from Dec 1970 (Median) to 2016, with 47 observations. The data reached an all-time high of 0.019 % in 1976 and a record low of 0.004 % in 2005. United Kingdom UK: Forest Rents: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Land Use, Protected Areas and National Wealth. Forest rents are roundwood harvest times the product of regional prices and a regional rental rate.; ; World Bank staff estimates based on sources and methods described in 'The Changing Wealth of Nations 2018: Building a Sustainable Future' (Lange et al 2018).; Weighted average;

Abstract

Young Lives: An International Study of Childhood Poverty is a collaborative project investigating the changing nature of childhood poverty in selected developing countries. The UK’s Department for International Development (DFID) is funding the first three-year phase of the project.

Young Lives involves collaboration between Non Governmental Organisations (NGOs) and the academic sector. In the UK, the project is being run by Save the Children-UK together with an academic consortium that comprises the University of Reading, London School of Hygiene and Tropical Medicine, South Bank University, the Institute of Development Studies at Sussex University and the South African Medical Research Council.

The study is being conducted in Ethiopia, India (in Andhra Pradesh), Peru and Vietnam. These countries were selected because they reflect a range of cultural, geographical and social contexts and experience differing issues facing the developing world; high debt burden, emergence from conflict, and vulnerability to environmental conditions such as drought and flood.

Objectives of the study The Young Lives study has three broad objectives: • producing good quality panel data about the changing nature of the lives of children in poverty. • trace linkages between key policy changes and child poverty • informing and responding to the needs of policy makers, planners and other stakeholders There will also be a strong education and media element, both in the countries where the project takes place, and in the UK.

The study takes a broad approach to child poverty, exploring not only household economic indicators such as assets and wealth, but also child centred poverty measures such as the child’s physical and mental health, growth, development and education. These child centred measures are age specific so the information collected by the study will change as the children get older.

Further information about the survey, including publications, can be downloaded from the Young Lives website.

Geographic coverage

Young Lives is an international study of childhood poverty, involving 12,000 children in 4 countries. - Ethiopia (20 communities in Addis Ababa, Amhara, Oromia, and Southern National, Nationalities and People's Regions) - India (20 sites across Andhra Pradesh and Telangana) - Peru (74 communities across Peru) - Vietnam (20 communities in the communes of Lao Cai in the north-west, Hung Yen province in the Red River Delta, the city of Danang on the coast, Phu Yen province from the South Central Coast and Ben Tre province on the Mekong River Delta)

Analysis unit

Individuals; Families/households

Universe

Cross-national; Subnational

Children aged approximately 5 years old and their households, and children aged 12 years old and their households, in Ethiopia, India (Andhra Pradesh), Peru and Vietnam, in 2006-2007. These children were originally interviewed in Round 1 of the study. See documentation for details of the exact regions covered in each country.

Kind of data

Sample survey data [ssd]

Sampling procedure

Purposive selection/case studies

A key need for the study's objectives was to obtain data at different levels - the children, their households, the community in which they resided, as well as at regional and national levels. This need thus determined that children should be selected in geographic clusters rather than randomly selected across the country. There was, however, a much more important reason for recruiting children in clusters - the sites are also intended to provide suitable settings for a range of complementary thematic studies. For example, one or a few sites may be used for a qualitative study designed to achieve a deeper level of understanding of some social issues, either because they are important in that particular place, or because the sites are appropriate locales to investigate a more general concern. The quantitative panel study is seen as the foundation upon which a coherent and interesting range of linked studies can be set up.

Thus the design was decided, in each country, comprising 20 geographic clusters with 100 children sampled in each cluster.

Sampling deviation

Ethiopia: 1,912 (5-year-olds), 980 (12-year-olds); India: 1,950 (5-year-olds), 994 (12-year-olds); Peru: 1,963 (5-year-olds), 685 (12-year-olds); Vietnam: 1,970 (5-year-olds), 990 (12-year-olds)

Mode of data collection

Face-to-face interview

Research instrument

Every questionnaire used in the study consists of a 'core' element and a country-specific element, which focuses on issues important for that country.

The core element of the questionnaires consists of the following sections: Core 5 & 12 year old household questionnaire • Section 1: Parental background • Section 2: Household education • Section 3: Livelihoods and asset framework • Section 3a: Land & crops • Section 3b: Time allocation • Section 3c: Productive assets • Section 3d: Non-agricultural earnings • Section 3e: Transfers • Section 4: Consumption/Expenditure • Section 4a: Food consumption/expenditure • Section 4b: Non-food consumption/expenditure • Section 5: Social capital • Section 5a: Support networks • Section 5b: Family, group and political capital • Section 5c: Collective action and exclusion • Section 5d: Information networks • Section 6: Economic changes and recent life history • Section 7: Socio-economic status • Section 8: Child care, education & activities (blank in 12yr old household) • Section 9: Child health • Section 10: Child development (blank in 12yr old household) • Section 11: Anthropometry • Section 12: Caregiver perceptions & attitudes

Core 12 year old child questionnaire • Section 1: School and activities • Section 2: Child health • Section 3: Social networks, social skills and social support • Section 4: Feelings and attitudes • Section 5: Parents and household issues • Section 6: Perceptions of household wealth and future • Section 7: Child Development

The community questionnaire used in Ethiopia consists of the following sections: - MODULE 1 General Module • Section 1 General Community Characteristics • Section 2 Social Environment • Section 3 Access to Services • Section 4 Economy • Section 5 Local Prices - MODULE 2 Child-Specific Modules • Section 1 Educational Service (General) • Section 2 NOT INCLUDED IN ETHIOPIA CONTEXT INSTRUMENT • Section 3 Educational Services (Preschool, Primary, Secondary) • Section 4 Health Services • Section 5 Child Protection Services - MODULE 3 Country specific community level questions • Section 1 Conversion factors • Section 2 Migration • Section 3 Social protection program • Section 4 Equity and budget management in education and health

The community questionnaire used in India consists of the following sections: - MODULE 1 General Module • Section 1: General Community Characteristics • Section 2: Social Environment • Section 3: Access to Services • Section 4: Economy • Section 5; Local Prices - MODULE 2 Child-Specific Modules • Section 1: Educational Services (General) • Section 2: Child day care Services • Section 3: Educational Services (Preschool, Primary, Secondary) • Section 4: Health Services • Section 5: Child Protection Services

The community questionnaire used in Peru consists of the following sections: - MODULE 1 General Module • Section 1: General Community Characteristics • Section 2: Social Environment • Section 3: Access to Services • Section 4: Economy • Section 5: Local Prices - MODULE 2 Child-Specific Modules • Section 1: Educational Services (General) • Section 2: Child day care Services • Section 3: Educational Services (Preschool, Primary, Secondary) • Section 4: Health Services • Section 5: Child Protection Services

The community questionnaire used in Vietnam consists of the following sections: - MODULE 1 General Module • Section 1: General Community Characteristics • Section 2: Social Environment • Section 3: Access to Services • Section 4: Economy • Section 5: Local Prices • Section 6: Poverty Alleviation and Infrastructure Initiatives - MODULE 2 Child-Specific Module • Section 1: Educational Services (General and Country Specific) • Section 2: Child day care Services • Section 3: Educational Services (Preschool, Primary, Secondary) • Section 4: Health Services • Section 5: Child Protection Services

This dataset encompasses the foundations and findings of a study titled "Housing Wealth Distribution, Inequality, and Residential Satisfaction," highlighting the evolution of residential properties from mere consumption goods to significant assets for wealth accumulation. Since the 1980s, with financial market deregulation in the UK, there has been a noticeable shift in homeownership patterns and housing wealth's role. The liberalisation of the banking sector, particularly mortgage lending, facilitated a significant rise in homeownership rates from around 50% in the 1970s to over 70% in the early 2000s, stabilizing at 65% in recent years. Concurrently, housing wealth relative to household annual gross disposable income has seen a considerable increase, underscoring the growing importance of residential properties as investment goods.

The study explores the multifaceted impact of housing wealth on various aspects of life, including retirement financing, intergenerational wealth transfer, health, consumption, energy conservation, and education. Residential satisfaction, defined as the overall experience and contentment with housing, emerges as a critical factor influencing subjective well-being and labor mobility. Despite the evident influence of housing characteristics, social environment, and demographic factors on residential satisfaction, the relationship between housing wealth and satisfaction remains underexplored.

To bridge this gap, the research meticulously assembles data from different surveys across the UK and the USA spanning 1970 to 2019, despite challenges such as data compatibility and measurement errors. Initial findings reveal no straightforward correlation between rising house prices and residential satisfaction, mirroring the Easterlin Paradox, which suggests that happiness levels do not necessarily increase with income growth. This paradox is dissected through the lenses of social comparison and adaptation, theorizing that relative income and the human tendency to adapt to changes might explain the stagnant satisfaction levels despite increased housing wealth.

Further analysis within the UK context supports the social comparison hypothesis, suggesting that disparities in housing wealth distribution can lead to varied satisfaction levels, potentially exacerbating societal inequality. This phenomenon is not isolated to developed nations but is also pertinent to developing countries experiencing rapid economic growth alongside widening income and wealth gaps. The study concludes by emphasizing the significance of considering housing wealth inequality in policy-making, aiming to mitigate its far-reaching implications on societal well-being.

Although China has almost eliminated urban poverty, the total number of Chinese citizens in poverty remains at 82 million, most of which are rural residents. The development of rural finance is essential to preventing the country from undergoing further polarization because of the significant potential of such development to facilitate resource interflows between rural and urban markets and to support sustainable development in the agricultural sector. However, rural finance is the weakest point in China's financial systems. Rural households are more constrained than their urban counterparts in terms of financial product availability, consumer protection, and asset accumulation. The development of the rural financial system faces resistance from both the demand and the supply sides.

The proposed project addresses this challenge by investigating the applications of a proven behavioural approach, namely, Libertarian Paternalism, in the development of rural financial systems in China. This approach promotes choice architectures to nudge people into optimal decisions without interfering with the freedom of choice. It has been rigorously tested and warmly received in the UK public policy domain. This approach also fits the political and cultural background in China, in which the central government needs to maintain a firm control over financial systems as the general public increasingly demands more freedom.

Existing behavioural studies have been heavily reliant on laboratory experiments. Although the use of field studies has been increasing, empirical evidence from the developing world is limited. Meanwhile, the applications of behavioural insights in rural economic development in China remains an uncharted territory. Rural finance studies on the household level are limited; evidence on the role of psychological and social factors in rural households' financial decisions is scarce. The proposed project will bridge this gap in the literature.

The overarching research question of this project is whether and how behavioural insights can be used to help rural residents in China make sound financial decisions, which will ultimately contribute to the sustainable economic development in China. The research will be conducted through field experiments in rural China. By relying on field evidences, the project team will develop policy tools and checklists for policy makers to help rural households make sound financial decisions. Two types of tools will be developed for policy makers, namely, "push" tools that aim to achieve short-term policy compliance among rural households so that they can break out of the persistent poverty cycle and "pull" tools that can reduce fraud, error, and debt among rural households to prevent them from falling back into poverty. Finally, the project team will also use the research activities and findings as vehicles to engage and educate rural residents, local governments, regulators, and financial institutions. Standard and good practice will be proposed to interested parties for the designs of good behavioural interventions; ethical guidelines will be provided to encourage good practice. This important step ensures that the findings of this project will benefit academia and practice, with long-lasting, positive impacts.

The findings will benefit researchers in behavioural finance and economics, rural economics, development economics, political sciences, and psychology. The findings of and the engagement in this project will help policy makers to develop cost-effective behavioural change policies. Rural households will benefit by being nudged into sound financial decisions and healthy financial habits. The project will provide insights on how to leverage behavioural insights to overcome persistent poverty in the developing world. Therefore, the research will be of interest to communities in China and internationally.

United Kingdom UK: Total Natural Resources Rents: % of GDP data was reported at 0.392 % in 2016. This records an increase from the previous number of 0.338 % for 2015. United Kingdom UK: Total Natural Resources Rents: % of GDP data is updated yearly, averaging 0.827 % from Dec 1970 (Median) to 2016, with 47 observations. The data reached an all-time high of 3.057 % in 1984 and a record low of 0.070 % in 1972. United Kingdom UK: Total Natural Resources Rents: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Land Use, Protected Areas and National Wealth. Total natural resources rents are the sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, and forest rents.; ; World Bank staff estimates based on sources and methods described in 'The Changing Wealth of Nations 2018: Building a Sustainable Future' (Lange et al 2018).; Weighted average;

United Kingdom UK: Surface Area data was reported at 243,610.000 sq km in 2017. This stayed constant from the previous number of 243,610.000 sq km for 2016. United Kingdom UK: Surface Area data is updated yearly, averaging 243,610.000 sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 243,610.000 sq km in 2017 and a record low of 243,610.000 sq km in 2017. United Kingdom UK: Surface Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Land Use, Protected Areas and National Wealth. Surface area is a country's total area, including areas under inland bodies of water and some coastal waterways.; ; Food and Agriculture Organization, electronic files and web site.; Sum;

In 1938, the year before the Second World War, the United States had, by far, the largest economy in the world in terms of gross domestic product (GDP). The five Allied Great Powers that emerged victorious from the war, along with the three Axis Tripartite Pact countries that were ultimately defeated made up the eight largest independent economies in 1938.

When values are converted into 1990 international dollars, the U.S. GDP was over 800 billion dollars in 1938, which was more than double that of the second largest economy, the Soviet Union. Even the combined economies of the UK, its dominions, and colonies had a value of just over 680 billion 1990 dollars, showing that the United States had established itself as the world's leading economy during the interwar period (despite the Great Depression).

Interestingly, the British and Dutch colonies had larger combined GDPs than their respective metropoles, which was a key motivator for the Japanese invasion of these territories in East Asia during the war. Trade with neutral and non-belligerent countries also contributed greatly to the economic development of Allied and Axis powers throughout the war; for example, natural resources from Latin America were essential to the American war effort, while German manufacturing was often dependent on Swedish iron supplies.

United Kingdom UK: Land Area data was reported at 241,930.000 sq km in 2017. This stayed constant from the previous number of 241,930.000 sq km for 2016. United Kingdom UK: Land Area data is updated yearly, averaging 241,930.000 sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 241,930.000 sq km in 2017 and a record low of 241,930.000 sq km in 2017. United Kingdom UK: Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Land Use, Protected Areas and National Wealth. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.; ; Food and Agriculture Organization, electronic files and web site.; Sum;

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This is a dataset that results from extracting the terms "Adam Smith", "Theory of Moral Sentiments" and "Wealth of Nations" from a scraping exercise of the Hansard via the site TheyWorkForYou.com. The extracts were used to construct the Figure and for qualitative coding utilized in the following working paper. Raw notes by the authors in terms of the tone and the substance of the speech accompany the excel spreadsheet noting the specific mentions. The political party of the speaker also is a variable

United Kingdom PC: Current Receipts: Taxes on Income & Wealth data was reported at 24.000 GBP mn in Jun 2018. This records a decrease from the previous number of 25.000 GBP mn for Mar 2018. United Kingdom PC: Current Receipts: Taxes on Income & Wealth data is updated quarterly, averaging 2.000 GBP mn from Mar 1946 (Median) to Jun 2018, with 290 observations. The data reached an all-time high of 451.000 GBP mn in Mar 1991 and a record low of -13.000 GBP mn in Sep 1962. United Kingdom PC: Current Receipts: Taxes on Income & Wealth data remains active status in CEIC and is reported by Office for National Statistics. The data is categorized under Global Database’s United Kingdom – Table UK.F010: ESA 2010: Public Sector Finances: Transactions: Public Corporations.