This dataset provides values for WAGES IN MANUFACTURING reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset provides values for WAGE GROWTH reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

In April 2025, the UK minimum wage for adults over the age of 21 in will be 12.21 pounds per hour. For the 2025/26 financial year, there will be four minimum wage categories, three of which are based on age and one for apprentice workers. Apprentices, and workers under the age of 18 will have a minimum wage of 7.55 pounds an hour, increasing to ten pounds for those aged 18 to 20. When the minimum wage was first introduced in 1999, there were just two age categories; 18 to 21, and 22 and over. This increased to three categories in 2004, four in 2010, and five between 2016 and 2023, before being reduced down to four in the most recent year. The living wage The living wage is an alternative minimum wage amount that employers in the UK can voluntarily pay their employees. It is calculated independently of the legal minimum wage and results in a higher value figure. In 2023/24, for example, the living wage was twelve pounds an hour for the UK as a whole and 13.15 for workers in London, where the cost of living is typically higher. This living wage is different from what the UK government has named the national living wage, which was 10.42 in the same financial year. Between 2011/12 and 2023/24, the living wage has increased by 4.80 pounds, while the London living wage has grown by 4.85 pounds. Wage growth cancelled-out by high inflation 2021-2023 For a long period between the middle of 2021 and late 2023, average wage growth in the UK was unable to keep up with record inflation levels, resulting in the biggest fall in disposable income since 1956. Although the UK government attempted to mitigate the impact of falling living standards through a series of cost of living payments, the situation has still been very difficult for households. After peaking at 11.1 percent in October 2022, the UK's inflation rate remained in double figures until March 2023, and did not fall to the preferred rate of two percent until May 2024. As of November 2024, regular weekly pay in the UK was growing by 5.6 percent in nominal terms, and 2.5 percent when adjusted for inflation.

As of 2023, the average annual wage of Germany was 48,301 euros per year, a growth of almost 6,000 Euros when compared with 2000. From 2000 until 2007, wages rose by less than a thousand euros, with wage growth accelerating mainly in the period after 2010. Comparisons with rest of the EU Within the European Union Luxembourg had an average annual salary of almost 80 thousand Euros, with Germany having an annual salary comparable to other large European Countries, such as the United Kingdom and France. In neighboring Poland, the average annual salary was just over 39 thousand U.S dollars, meaning that German’s earned, on average, 20 percent more than what their Polish counterparts did. German economy slowing in 2023 While Germany initially had one of the strongest recoveries from the 2008 financial crash and as of 2020 had the largest economy in Europe its economy has started to slow in recent years. For 2023 the German economy is contracted by 0.26 percent, and while 2024 marked a slight improvement, the expectations are that 2025 remains a year of slow growth.

This provides replication data for "Incentives to Comply with the Minimum Wage in the US and UK", Anna Stansbury, ILR Review 2024. Please read the README file for instructions about how each figure, table, and statistic in the data is constructed.

This study contains the final dataset used in 'Distribution regression with sample selection and UK wage decomposition, by Victor Chernozhukov, Ivan Fernandez-Val and Siyi Luo.*

The data come from the UK Family Expenditure Survey (FES) for the years 1978 to 2001, Expenditure and Food Survey (EFS) for the years 2002 to 2007, and Living Costs and Food Survey (LCFS) for 2008-2013, supplemented with variables constructed with the tax and welfare-benefit model (TAXBEN) by the Institute for Fiscal Studies (IFS).

*(See: Chernozhukov, V., Fernández-Val, I. and Luo, S. (2023) 'Distribution regression with sample selection and UK wage decomposition', CEMMAP working paper CWP09/23, Institute for Fiscal Studies/Department of Economics, UCL.)

The data are available to download in Stata format only, with an accompanying Stata .do file and supplementary documentation.

We would like to request the details of all positions that have been available through NHS Jobs: • At an NHS Band 4 and 5 • Available within 40 miles of S75 2JG • Between 20 April 2023 to date We should be grateful if you would kindly include the information set out below in any table complied: • Name of organisation • Vacancy reference number • Job title • Staff group • Salary • AfC banding • Date published • Date closed • Duration (i.e. permanent/fixed term for X months) • Vacancy site (i.e. the hospital/site the position was held) Should you require any further information in order to complete this request, please do not hesitate to contact us. On the 30 May 2024 you clarified the following: We are interested in job vacancies in the region of South Yorkshire. Response A copy of the information is attached. Search terms Pay Band - Band 4&5 Date Range - 20/04/2023 – 28/05/2024 The post code field confirms the location of the vacancy. The information provided is limited to what the recruiting organisation supplies us with. For more detail on each position you may wish to write to each trust directly. A directory of trusts and Integrated Care Boards (ICBs) (including the ones provided in the data) can be found at the below web link: https://www.nhs.uk/nhs-services/find-your-local-integrated-care-board/ If you have any queries regarding the data provided, or if you plan on publishing, the data please contact foirequests@nhsbsa.nhs.uk ensuring you quote the above reference. This is important to ensure that the figures are not misunderstood or misrepresented. If you plan on producing a press or broadcast story based upon the data please contact communicationsteam@nhsbsa.nhs.uk. This is important to ensure that the figures are not misunderstood or misrepresented.

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What you should know about this indicator This GDP per capita indicator provides information on economic growth and income levels in the very long run. Some country estimates are available as far back as 1 CE and regional estimates as far back as 1820 CE. This data is adjusted for inflation and for differences in the cost of living between countries. This data is expressed in international-$ at 2011 prices, using a combination of 2011 and 1990 PPPs for historical data. Time series for former countries and territories are calculated forward in time by estimating values based on their last official borders. For more regularly updated estimates of GDP per capita, see the World Bank's indicator.

Real GDP per capita in 2011$

In two ways, this analysis leads to departures from the original Maddison approach and closer to the multiple benchmark approach as developed by the PWT. There is, to begin with, no doubt that the 2011 PPPs and the related estimates of GDP per capita reflect the relative levels of GDP per capita in the world economy today better than the combination of the 1990 benchmark and growth rates of GDP per capita according to national accounts. This information should be taken into account. At the same time, the underlying rule within the current Maddison Database is that economic growth rates of countries in the dataset should be identical or as close as possible to growth rates according to the national accounts (which is also the case for the pre 1990 period). For the post-1990 period we therefore decided to integrate the 2011 benchmarks by adapting the growth rates of GDP per capita in the period 1990–2011 to align the two (1990 and 2011) benchmarks. We estimated the difference between the combination of the 1990 benchmark and the growth rates of GDP (per capita) between 1990 and 2011 according to the national accounts, and annual growth rate from the 1990 benchmark to the 2011 benchmark. This difference is then evenly distributed to the growth rate of GDP per capita between 1990 and 2011; in other words, we added a country specific correction (constant for all years between 1990 and 2011) to the annual national account rate of growth to connect the 1990 benchmark to the 2011 benchmark. Growth after 2011 is, in the current update, exclusively based on the growth rates of GDP per capita according to national accounts.

We also use the collected set of historical benchmark estimates to fine tune the dataset for the pre-1940 period, but only in those cases where the quality of the benchmark was high and there were multiple benchmarks to support a revision. The most important correction concerns the US/UK comparison. The conventional picture, based on the original 1990 Maddison estimates, indicated that the US overtook the UK as the world leader in the early years of the 20th century. This finding was first criticized by Ward and Devereux (2003), who argued, based on alternative measures of PPP-adjusted benchmarks between 1870 and 1930, that the United States was already leading the United Kingdom in terms of GDP per capita in the 1870s. This conclusion was criticized by Broadberry (2003).

New evidence, however, suggests a more complex picture: in the 18th century, real incomes in the US (settler colonies only, not including indigenous populations) were probably higher than those in the UK (Lindert & Williamson, 2016a). Until about 1870, growth was both exten- sive (incorporating newly settled territory) and intensive (considering the growth of cities and industry at the east coast), but on balance, the US may—in terms of real income—have lagged behind the UK. After 1870, intensive growth becomes more important, and the US slowly gets the upper hand. This pattern is consistent with direct benchmark comparison of the income of both countries for the period 1907–1909 (Woltjer, 2015). This shows that GDP per capita for the United States in those years was 26% higher than in the United Kingdom. We have used Woltjer’s (2015) benchmark to correct the GDP series of the two countries. Projecting this benchmark into the 19th century with the series of GDP per capita of both countries results in the two countries achieving parity in 1880. This is close to Prados de la Escosura’s conjecture based on his short- cut method (Prados de la Escosura, 2000), and even closer to the Lindert and Williamson (2016a) results.

United Kingdom UK: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Latin America & The Caribbean data was reported at 1.461 % in 2016. This records a decrease from the previous number of 1.594 % for 2015. United Kingdom UK: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Latin America & The Caribbean data is updated yearly, averaging 2.035 % from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 6.578 % in 1960 and a record low of 1.423 % in 1990. United Kingdom UK: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Latin America & The Caribbean 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: Imports. Merchandise imports from low- and middle-income economies in Latin America and the Caribbean are the sum of merchandise imports by the reporting economy from low- and middle-income economies in the Latin America and the Caribbean region according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;

Quick Stats is the National Agricultural Statistics Service's (NASS) online, self-service tool to access complete results from the 1997, 2002, 2007, and 2012 Censuses of Agriculture as well as the best source of NASS survey published estimates. The census collects data on all commodities produced on U.S. farms and ranches, as well as detailed information on expenses, income, and operator characteristics. The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production.

This paper outlines a class of statistical procedures that permit testing of a broad range of multidimensional stochastic dominance hypotheses and, more generally, welfare hypotheses that rely upon multiple stochastic dominance conditions. We apply the procedures to data on income and leisure hours for individuals in Germany, the UK, and the USA. We find that no country first-order stochastically dominates the others in both dimensions for all years of comparison. Furthermore, while in general the USA stochastically dominates Germany and the UK with respect to income, in most periods Germany stochastically dominates with respect to leisure hours. Finally, we find evidence that bivariate poverty (which refers, for example, to the working poor, that is, those who have little leisure and low income) is lower in Germany than in either the UK or the USA. On the other hand, poverty comparisons between the UK and the USA are sensitive to the subpopulation of individuals considered.

The Gross Domestic Product per capita in the United Kingdom was last recorded at 47322.67 US dollars in 2023. The GDP per Capita in the United Kingdom is equivalent to 375 percent of the world's average. This dataset provides the latest reported value for - United Kingdom GDP per capita - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This deposit contains three do files which were constructed as part of the project “Intergenerational income mobility: Gender, Partnerships and Poverty in the UK”, UKRI grant number ES/P007899/1. The aim of the do files is to convert partnership, fertility, and labour market activity information provided with the age 55 wave of the National Child Development Study into monthly panel format. There are separate do files to do this for each of the three aspects.

This important new work looks to fill an 'evidence deficit' within the literature on intergenerational economic mobility by investigating intergenerational income mobility for two groups who are often overlooked in existing research: women and the poorest in society. To do this, the research will make two methodological advancements to previous work: First, moving to focus on the family unit in the second generation and total family resources rather than individual labour market earnings and second, looking across adulthood to observe partnership, fertility and poverty dynamics rather than a point-in-time static view of these important factors. Specifically it will ask four research questions: 1) What is the relationship between family incomes of parents in childhood and family incomes of daughters throughout adulthood? The majority of previous studies of intergenerational income mobility have focused on the relationship between parents' income in childhood and sons' prime-age labour market earnings. Women have therefore been consistently disregarded due to difficulties observing prime-age labour market earnings for women. This is because women often exit the labour market for fertility reasons, and the timing of this exit and the duration of the spell out of the labour market are related to both parental childhood income and current labour market earnings. This means that previous studies that have focused on employed women only are not representative of the entire population of women. By combining our two advancements, considering total family income and looking across adulthood for women, we can minimise these issues. The life course approach enables us to observe average resources across a long window of time, dealing with issues of temporary labour market withdrawal, while the use of total family income gives the most complete picture of resources available to the family unit including partner's earnings and income from other sources, including benefits. 2) What role do partnerships and assortative mating play in this process across the life course? The shift to focusing on the whole family unit emphasises the importance of partnerships including when they occur and breakdown and who people partner with in terms of education and current labour market earnings. Previous research on intergenerational income mobility in the UK has suggested an important role for who people partner with but has been limited to only focusing on those in partnerships. This work will advance our understanding of partnership dynamics by looking across adulthood at both those in partnerships and at the importance of family breakdown and lone parenthood in this relationship. 3) What is the extent of intergenerational poverty in the UK, and does this persist through adulthood? The previous focus on individuals' labour market earnings has often neglected to consider intergenerational income mobility for the poorest in society: those without labour market earnings for lengthy periods of time who rely on other income from transfers and benefits. The shift in focus to total family resources and the life course approach will allow us to assess whether those who grew up in poor households are more likely to experience persistent poverty themselves in adulthood. 4) What is the role of early skills, education and labour market experiences, including job tenure and progression, in driving these newly estimated relationships? Finally our proposed work will consider the potential mechanisms for these new estimates of intergenerational income mobility for women and the poorest in society for the first time and expand our understanding of potential mechanisms for men. While our previous work showed the importance of early skills and education in transmitting inequality across generations for males, this new work will also consider the role of labour market experiences including job tenure and promotions as part of the process.

This data and code accompanies the paper Robson, O'Donnell and Van Ourti (2024), "Aversion to Health Inequality – Pure, Income-Related and Income-Caused", Journal of Health Economics. In the paper, we design and run an online experiment, with a sample of the UK population, to identify aversion to pure health inequality separately from aversion to income-related and income-caused health inequality. Here, we provide data and code to enable replication of the experiment and analysis, alongside a policy evaluation tool. The Overview.pdf provides an overview of all code and data. Please contact us if you have any questions.

Abstract copyright UK Data Service and data collection copyright owner.

The OECD Banking statistics database includes data from 1979 to 2009 on classification of bank assets and liabilities, income statement and balance sheet and structure of the financial system for OECD countries. The OECD have discontinued this dataset, so no further updates will be made.

The OECD Banking Statistics are presented in the following tables (some tables will include missing data):

Classification of bank assets and liabilities

This dataset provides the composition of bank assets and liabilities of residents and non-residents denominated in domestic and foreign currencies based on financial statements of banks in each OECD member country and Russia. Data are reported at current prices in millions of national currency and in millions of Euros for OECD countries. The data covers the years starting from 2005 extending until 2009. The countries covered are Austria, Belgium, Canada, Chile, Czech Republic, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, and Russian Federation.

Income statement and balance sheet

This comparative tables comprises statistics on country’s financial profiles by presenting their respective extensive income statements, balance sheets and capital adequacy by banking group that can be further analyzed by type of financial institution such as commercial banks, savings banks co-operative banks and other monetary institutions. This dataset provides information on income statements, balance sheets and capital adequacy by banking group. Data are reported at current prices in millions of national currency. The data covers the years starting from 1979 extending until 2009. The countries covered are Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, United States and Russian Federation.

Structure of the financial system

This dataset provides information on the overall structure of the financial system per country by type of institution and their components: Central banks, other monetary institutions, other financial institutions and insurance institutions. Data relate to number of institutions, number of branches, number of employees, total assets and liabilities and total financial assets. The data covers the years starting from 1979 extending until 2009. The countries covered are Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, United States and Russian Federation.

These data were first provided by the UK Data Service in December 2014.

Main Topics:

• Banking
• Financial statement
• Financial structure
• Financial system
• Monetary institutions
• Monetary system

The number of Income Support Allowance (IS) claimants by area for Glasgow. Income Support Allowance is a benefit payable to help people who are on low incomes who are not available for employment. It is claimed by those aged 16 or over not working or working under 16 hours per week (and/with partner who works under 24 hours. The links below download the data from the Office of National Statistics website (via an API). The datasets below cover the last two years but if you need additional historical data, contact us: data@glasgow.gov.uk Licence: None

Comparing the 130 selected regions regarding the gini index , South Africa is leading the ranking (0.63 points) and is followed by Namibia with 0.58 points. At the other end of the spectrum is Slovakia with 0.23 points, indicating a difference of 0.4 points to South Africa. The Gini coefficient here measures the degree of income inequality on a scale from 0 (=total equality of incomes) to one (=total inequality).The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).