In 2022/23 the mean disposable income per household in the United Kingdom was ****** British pounds, while the median disposable income for households was ****** pounds

In 2023, the median hourly earnings of wage and salary workers in the United States was 19.24 U.S. dollars. This is an increase from 1979, when median hourly earnings were at 4.44 U.S. dollars. Hourly Workers The United States national minimum wage is 7.25 U.S. dollars per hour, which has been the minimum wage since 2009. However, each state has the agency to set their state minimum wage. Furthermore, some cities are able to create their minimum wage. Many argue that the minimum wage is too low and should be raised, because it is not considered a living wage. There has been a movement to raise the minimum wage to 15 U.S. dollars per hour, called “Fight for 15” which began in the early 2010s. While there has been no movement at the federal level, some states have moved to increase their minimum wages, with at least three states and the District of Columbia setting minimum wage rates at or above 15 dollars per hour. More recently, some proponents of increasing the minimum wage say that 15 dollars is too low, and lawmakers should strive toward a higher goal, especially given that a 2021 analysis found that the minimum wage in the U.S. should be 22.88 U.S. dollars if it grew at the same rate as economic productivity. Salary Workers On the other hand, salary workers in the United States do not get paid on an hourly basis. The median weekly earnings of salary workers have significantly increased since 1979. Asian salary workers had the highest hourly earnings in the U.S. in 2021. Among female salary workers, those ages 45 to 54 years old had the highest median hourly earnings in 2021, likewise for male salary workers.

Graph and download economic data for Median Household Income in the United States (MEHOINUSA646N) from 1984 to 2023 about households, median, income, and USA.

Abstract (en): The Recent College Graduates (RCG) survey estimates the potential supply of newly qualified teachers in the United States and explores the immediate post-degree employment and education experiences of individuals obtaining bachelor's or master's degrees from American colleges and universities. The RCG survey, which focuses heavily, but not exclusively, on those graduates qualified to teach at the elementary and secondary levels, is designed to meet the following objectives: (1) to determine how many graduates become eligible or qualified to teach for the first time and how many are employed as teachers in the year following graduation, by teaching field, (2) to examine the relationships among courses taken, student achievement, and occupational outcomes, and (3) to monitor unemployment rates and average salaries of graduates by field of study. The RCG survey collects information on education and employment of all graduates (date of graduation, field of study, whether newly qualified to teach, further enrollment, financial aid, employment status, and teacher employment characteristics) as well as standard demographic characteristics such as earnings, age, marital status, sex, and race/ethnicity. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Students within one year of attaining a bachelor's or a master's degree from an American college or university. A two-stage stratified sampling approach was employed. The first stage consisted of drawing a sample of bachelor's and master's degree-granting institutions from Higher Education General Information Survey (HEGIS)/Integrated Postsecondary Education Data System (IPEDS) completions files. Institutions were stratified by control (public or private), by region, and by the proportion of degrees awarded in the field of education (over or under a specified number). Within each of these strata, institutions were selected according to size (size being measured by the sum of bachelor's and master's degrees awarded that year). The second stage consisted of the selection of a core sample of graduates (bachelor's and master's degree recipients) who received their degrees from the sampled institutions during the 1976-1977 academic year. Sampling rates of graduates differed by major field of study. The institution sample consisted of 300 institutions of which 30 were Historically Black Colleges (HBCs). The graduate sample was stratified by degree received and major field of study (vocational education, special education, other education, and noneducation). Data are representative at the national level. 2001-01-05 SAS and SPSS data definition statements have been created for this collection. Also, the codebook and data collection instrument were converted to a PDF file. The codebook and data collection instrument are provided by ICPSR as a Portable Document Format (PDF) file. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using PDF reader software, such as the Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided on the ICPSR Web site.

Household data are collected as of March.

As stated in the Census's "Source and Accuracy of Estimates for Income, Poverty, and Health Insurance Coverage in the United States: 2011" (http://www.census.gov/hhes/www/p60_243sa.pdf):

Estimation of Median Incomes. The Census Bureau has changed the methodology for computing median income over time. The Census Bureau has computed medians using either Pareto interpolation or linear interpolation. Currently, we are using linear interpolation to estimate all medians. Pareto interpolation assumes a decreasing density of population within an income interval, whereas linear interpolation assumes a constant density of population within an income interval. The Census Bureau calculated estimates of median income and associated standard errors for 1979 through 1987 using Pareto interpolation if the estimate was larger than $20,000 for people or $40,000 for families and households. This is because the width of the income interval containing the estimate is greater than $2,500.

We calculated estimates of median income and associated standard errors for 1976, 1977, and 1978 using Pareto interpolation if the estimate was larger than $12,000 for people or $18,000 for families and households. This is because the width of the income interval containing the estimate is greater than $1,000. All other estimates of median income and associated standard errors for 1976 through 2011 (2012 ASEC) and almost all of the estimates of median income and associated standard errors for 1975 and earlier were calculated using linear interpolation.

Thus, use caution when comparing median incomes above $12,000 for people or $18,000 for families and households for different years. Median incomes below those levels are more comparable from year to year since they have always been calculated using linear interpolation. For an indication of the comparability of medians calculated using Pareto interpolation with medians calculated using linear interpolation, see Series P-60, Number 114, Money Income in 1976 of Families and Persons in the United States (www2.census.gov/prod2/popscan/p60-114.pdf).

In 2022/23, the top quintile of earners in the United Kingdom had an average household disposable income of approximately ****** British pounds, compared with ****** for the bottom quintile.

The study constructs new series of nominal wages in industry and crafts as well as a new consumer goods price index for the period 1850-1889; the coefficient of the two series gives the real wage. While such information was collected and published by government agencies from the late 1880s onwards, the decades before are part of the pre-statistical age. After all, information is available from municipal authorities, from branches of territorial state authorities and from individual companies. For the construction of a new nominal wage series, the current study refer to Kuczynski´s material (1961/62), supplements it with information from individual studies of the past 50 years, and constructs wage indices for the heavy ironware, machine construction, mining, printing, and municipal construction industries on this basis by means of unbalanced panel regression with fixed effects. Of the 38 individual wage series on which these sector indices are based, 27 come from Kuczynski, the remainder from more recent studies. Wages in the textile sector are represented by those in the cotton industry. The study uses the wage series published by Kirchhain (1977). Weighted according to employment figures, all these sector-specific series (excluding miners´ wages) are aggregated into a Fisher index of nominal wages in industry and crafts. Both this index and the indices at sector level are linked in 1888/89 with the series by Hoffmann (1965); the resulting values denote annual earnings in Marks. The sector indices differ little from those of Kuczynski and Hoffmann despite the expansion of the database and the different methodology of index construction, but the aggregated index shows a stronger growth rate than that of Kuczynski; the latter index is obviously erroneous (Pfister 2018, 576). The consumer goods price index is based on five sub-indices for (1) food, (2) beverages and luxury foods, (3) rent, (4) furniture, household goods and heating, and (5) clothing. The sub-indices for food and rent are new, the other three are from Hoffmann (1965). Weights are determined for 1848/49 and 1889 on the basis of research literature, values in between are interpolated linearly. Both the sub-index of food prices and the overall index are constructed as Fisher indices. Both the rental index and the food prices rise more strongly in the long term than the two corresponding Hoffmann indices (Pfister 2018, 578 and 582). Hoffmann constructs the rental price index only indirectly by multiplying the estimated building capital by an assumed interest rate. The rent index of the current study is based on data from three major cities. Only if it is assumed that large cities are completely unrepresentative for the entire real estate market should Hoffmann´s series still be considered. In the case of food prices, the comparatively stronger long-term increase - compared to previous research - results from the higher weight of prices from the southern parts of the country far from the sea in the new sub-index. Here, the price dampening effect of growing imports of American grain had a weaker effect than in the coastal regions in the north. Thus, one of the main findings of the study is that the assessment of the development of the living standards of urban workers from the 1850s to 1880s strongly depends on how one determines the effect of the first wave of modern globalization on the German price structure. The greater consideration given in this study to food prices in areas distant from the sea results in a more pessimistic view of the development of real wages during this period than has been the case with some previous research. To the data: 1. individual wage series (table set A.01) This set of tables contains wage series from six branches at the level of regions, cities, individual enterprises and in one case (cotton industry) an entire branch. Only series containing data for at least 15 years were taken into account. In detail, the series are the following:Heavy IronwareBochum 1869-1889: Average annual income of the workers of the Bochumer Verein (steelworks) in Mark; Däbritz (1934, Annex Table 4).Essen 1848-1889: Average annual income of the workers of the Krupp works in Mark; Kuczynksi (1961-62, vol. I, 377, vol. II, 227, vol. III, 426).Ruhr 1855-1889: Average annual income of the workers at the blast furnaces in the Ruhr district in Mark; banks (2000, Table A59).Saar 1869-1889: Day wage of workers at the blast furnaces of the Burbach Ironworks in Mark; Kuczynksi (1961-62, vol. III, 426).Silesia 1869-1889: Average annual income of workers at the blast furnaces in Silesia in Mark; banks (2000, Table A59). Machine constructionAugsburg 1851-1889: Average annual income of the workers of the Machine Factory Augsburg in Mark; Vol. II, 227; Kuczynski (1961-62, Vol. III, 426).Chemnitz 1860-1887: Weekly wage of machinists in Mark; Kuczynski (1961-62, vol. II, 227; vol. III, 426).Esslingen 1848-1889: Average annual income of workers at the Ess...

Graph and download economic data for Consumer Price Index for All Urban Wage Earners and Clerical Workers: Hospital and Related Services in U.S. City Average (CWUR0000SEMD) from Dec 1977 to May 2025 about clerical workers, hospitals, urban, wages, services, CPI, inflation, price index, indexes, price, and USA.

Graph and download economic data for Consumer Price Index for All Urban Wage Earners and Clerical Workers: Miscellaneous Personal Services in U.S. City Average (CWUR0000SEGD) from Dec 1977 to May 2025 about clerical workers, miscellaneous, urban, wages, services, CPI, inflation, price index, indexes, price, and USA.

Household data are collected as of March.

As stated in the Census's "Source and Accuracy of Estimates for Income, Poverty, and Health Insurance Coverage in the United States: 2011" (http://www.census.gov/hhes/www/p60_243sa.pdf):

Estimation of Median Incomes. The Census Bureau has changed the methodology for computing median income over time. The Census Bureau has computed medians using either Pareto interpolation or linear interpolation. Currently, we are using linear interpolation to estimate all medians. Pareto interpolation assumes a decreasing density of population within an income interval, whereas linear interpolation assumes a constant density of population within an income interval. The Census Bureau calculated estimates of median income and associated standard errors for 1979 through 1987 using Pareto interpolation if the estimate was larger than $20,000 for people or $40,000 for families and households. This is because the width of the income interval containing the estimate is greater than $2,500.

We calculated estimates of median income and associated standard errors for 1976, 1977, and 1978 using Pareto interpolation if the estimate was larger than $12,000 for people or $18,000 for families and households. This is because the width of the income interval containing the estimate is greater than $1,000. All other estimates of median income and associated standard errors for 1976 through 2011 (2012 ASEC) and almost all of the estimates of median income and associated standard errors for 1975 and earlier were calculated using linear interpolation.

Thus, use caution when comparing median incomes above $12,000 for people or $18,000 for families and households for different years. Median incomes below those levels are more comparable from year to year since they have always been calculated using linear interpolation. For an indication of the comparability of medians calculated using Pareto interpolation with medians calculated using linear interpolation, see Series P-60, Number 114, Money Income in 1976 of Families and Persons in the United States (www2.census.gov/prod2/popscan/p60-114.pdf).

Main tables from the Effects of taxes and benefits on household income publication from 1977, including average incomes, taxes and benefits and household characteristics of all, retired and non-retired individuals and households in the UK by quintile and decile groups.

Poland PL: Wages Index data was reported at 130.794 2010=100 in 2017. This records an increase from the previous number of 123.884 2010=100 for 2016. Poland PL: Wages Index data is updated yearly, averaging 42.476 2010=100 from Dec 1977 (Median) to 2017, with 41 observations. The data reached an all-time high of 130.794 2010=100 in 2017 and a record low of 0.021 2010=100 in 1977. Poland PL: Wages Index data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Poland – Table PL.IMF.IFS: Wages, Labour Cost and Employment Index: Annual.

Thailand Daily Minimum Wage: Northern: Mae Hong Son data was reported at 310.000 THB in Oct 2018. This stayed constant from the previous number of 310.000 THB for Sep 2018. Thailand Daily Minimum Wage: Northern: Mae Hong Son data is updated monthly, averaging 128.000 THB from Oct 1974 (Median) to Oct 2018, with 529 observations. The data reached an all-time high of 310.000 THB in Oct 2018 and a record low of 16.000 THB in Sep 1977. Thailand Daily Minimum Wage: Northern: Mae Hong Son data remains active status in CEIC and is reported by Ministry of Labour. The data is categorized under Global Database’s Thailand – Table TH.G008: Daily Minimum Wage.

Thailand Daily Minimum Wage: Southern: Yala data was reported at 308.000 THB in Nov 2018. This stayed constant from the previous number of 308.000 THB for Oct 2018. Thailand Daily Minimum Wage: Southern: Yala data is updated monthly, averaging 128.000 THB from Oct 1974 (Median) to Nov 2018, with 530 observations. The data reached an all-time high of 308.000 THB in Nov 2018 and a record low of 18.000 THB in Sep 1977. Thailand Daily Minimum Wage: Southern: Yala data remains active status in CEIC and is reported by Ministry of Labour. The data is categorized under Global Database’s Thailand – Table TH.G008: Daily Minimum Wage.

Thailand Daily Minimum Wage: Northeastern: Nakhon Ratchasima data was reported at 315.000 THB in Jul 2018. This stayed constant from the previous number of 315.000 THB for Jun 2018. Thailand Daily Minimum Wage: Northeastern: Nakhon Ratchasima data is updated monthly, averaging 126.000 THB from Oct 1974 (Median) to Jul 2018, with 526 observations. The data reached an all-time high of 315.000 THB in Jul 2018 and a record low of 18.000 THB in Sep 1977. Thailand Daily Minimum Wage: Northeastern: Nakhon Ratchasima data remains active status in CEIC and is reported by Ministry of Labour. The data is categorized under Global Database’s Thailand – Table TH.G009: Daily Minimum Wage.

Household data are collected as of March.

As stated in the Census's "Source and Accuracy of Estimates for Income, Poverty, and Health Insurance Coverage in the United States: 2011" (http://www.census.gov/hhes/www/p60_243sa.pdf):

Estimation of Median Incomes. The Census Bureau has changed the methodology for computing median income over time. The Census Bureau has computed medians using either Pareto interpolation or linear interpolation. Currently, we are using linear interpolation to estimate all medians. Pareto interpolation assumes a decreasing density of population within an income interval, whereas linear interpolation assumes a constant density of population within an income interval. The Census Bureau calculated estimates of median income and associated standard errors for 1979 through 1987 using Pareto interpolation if the estimate was larger than $20,000 for people or $40,000 for families and households. This is because the width of the income interval containing the estimate is greater than $2,500.

We calculated estimates of median income and associated standard errors for 1976, 1977, and 1978 using Pareto interpolation if the estimate was larger than $12,000 for people or $18,000 for families and households. This is because the width of the income interval containing the estimate is greater than $1,000. All other estimates of median income and associated standard errors for 1976 through 2011 (2012 ASEC) and almost all of the estimates of median income and associated standard errors for 1975 and earlier were calculated using linear interpolation.

Thus, use caution when comparing median incomes above $12,000 for people or $18,000 for families and households for different years. Median incomes below those levels are more comparable from year to year since they have always been calculated using linear interpolation. For an indication of the comparability of medians calculated using Pareto interpolation with medians calculated using linear interpolation, see Series P-60, Number 114, Money Income in 1976 of Families and Persons in the United States (www2.census.gov/prod2/popscan/p60-114.pdf).

Thailand Daily Minimum Wage: Northeastern: Roi Et data was reported at 310.000 THB in Oct 2018. This stayed constant from the previous number of 310.000 THB for Sep 2018. Thailand Daily Minimum Wage: Northeastern: Roi Et data is updated monthly, averaging 128.000 THB from Oct 1974 (Median) to Oct 2018, with 529 observations. The data reached an all-time high of 310.000 THB in Oct 2018 and a record low of 16.000 THB in Sep 1977. Thailand Daily Minimum Wage: Northeastern: Roi Et data remains active status in CEIC and is reported by Ministry of Labour. The data is categorized under Global Database’s Thailand – Table TH.G008: Daily Minimum Wage.

Graph and download economic data for Median Household Income in Georgia (MEHOINUSGAA646N) from 1984 to 2023 about GA, households, median, income, and USA.

Graph and download economic data for Consumer Price Index for All Urban Wage Earners and Clerical Workers: Motor Vehicle Parts and Equipment in U.S. City Average (CWUR0000SETC) from Dec 1977 to May 2025 about clerical workers, parts, vehicles, equipment, urban, wages, CPI, inflation, price index, indexes, price, and USA.

Thailand Daily Minimum Wage: Western: Samut Songkhram data was reported at 310.000 THB in Jul 2018. This stayed constant from the previous number of 310.000 THB for Jun 2018. Thailand Daily Minimum Wage: Western: Samut Songkhram data is updated monthly, averaging 118.000 THB from Oct 1974 (Median) to Jul 2018, with 526 observations. The data reached an all-time high of 310.000 THB in Jul 2018 and a record low of 18.000 THB in Sep 1977. Thailand Daily Minimum Wage: Western: Samut Songkhram data remains active status in CEIC and is reported by Ministry of Labour. The data is categorized under Global Database’s Thailand – Table TH.G009: Daily Minimum Wage.