Graph and download economic data for Real Median Personal Income in the United States (MEPAINUSA672N) from 1974 to 2023 about personal income, personal, median, income, real, and USA.

Wages in the United States increased to 31.34 USD/Hour in July from 31.26 USD/Hour in June of 2025. This dataset provides - United States Average Hourly Wages - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Wages in the United States increased 5.35 percent in July of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United States Wages and Salaries Growth - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas, annual.

Average weekly earnings at sector level headline estimates, Great Britain, monthly, seasonally adjusted. Monthly Wages and Salaries Survey.

Average and median market, total and after-tax income of individuals by visible minority group, Indigenous group and immigration status, Canada and provinces.

This layer shows median household income by race and by age of householder. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Median income and income source is based on income in past 12 months of survey. This layer is symbolized to show median household income. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B19013B, B19013C, B19013D, B19013E, B19013F, B19013G, B19013H, B19013I, B19049, B19053Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

The wages on the Job Bank website are specific to an occupation and provide information on the earnings of workers at the regional level. Wages for most occupations are also provided at the national and provincial level. In Canada, all jobs are associated with one specific occupational grouping which is determined by the National Occupational Classification. For most occupations, a minimum, median and maximum wage estimates are displayed. They are update annually. If you have comments or questions regarding the wage information, please contact the Labour Market Information Division at: NC-LMI-IMT-GD@hrsdc-rhdcc.gc.ca

Average hourly and weekly wage rate, and median hourly and weekly wage rate by North American Industry Classification System (NAICS), type of work, gender, and age group.

Annual estimates of paid hours worked and earnings for UK employees by sex, and full-time and part-time.

This dataset provides information about earnings of employees who are working in an area, who are on adult rates and whose pay for the survey pay-period was not affected by absence. Tables provided here include total hourly earnings (excluding overtime), with breakdowns by gender, and full-time/part-time status, with median, mean and earnings distribution for the London region. These are provided annually in nominal terms dating from 1997. The earnings information presented relates to pay before tax, National Insurance or other deductions. These National Statistics are produced to high professional standards and released according to the arrangements approved by the UK Statistics Authority. The Annual Survey of Hours and Earnings (ASHE) provides information about the levels, distribution and make-up of earnings and hours worked for employees in all industries and occupations. ASHE is based on a 1% sample of employee jobs taken from HM Revenue & Customs (HMRC) Pay As You Earn (PAYE) records. Information on earnings and hours is obtained from employers and treated confidentially. ASHE does not cover the self-employed nor does it cover employees not paid during the reference period. Further information is available on the ONS website.

Average hourly and weekly wage rate, and median hourly and weekly wage rate by National Occupational Classification (NOC), type of work, gender, and age group.

Pakistan Average Monthly Wages data was reported at 24,028.000 PKR in 2021. This records an increase from the previous number of 21,326.000 PKR for 2019. Pakistan Average Monthly Wages data is updated yearly, averaging 12,636.500 PKR from Jun 2008 (Median) to 2021, with 10 observations. The data reached an all-time high of 24,028.000 PKR in 2021 and a record low of 6,612.000 PKR in 2008. Pakistan Average Monthly Wages data remains active status in CEIC and is reported by Pakistan Bureau of Statistics. The data is categorized under Global Database’s Pakistan – Table PK.G004: Average Monthly Wages: by Industry. No data for 2016-2017 as per source. Labour Force Survey has not been conducted for these two years due to Population Census.

Wages in Manufacturing in the United States remained unchanged at 28.96 USD/Hour in July. This dataset provides - United States Average Hourly Wages in Manufacturing - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Annual Survey of Hours and Earnings (ASHE) is one of the largest surveys of the earnings of individuals in the UK. Data on the wages, paid hours of work, and pensions arrangements of nearly one per cent of the working population are collected. Other variables relating to age, occupation and industrial classification are also available. The ASHE sample is drawn from National Insurance records for working individuals, and the survey forms are sent to their respective employers to complete.

While limited in terms of personal characteristics compared to surveys such as the Labour Force Survey, the ASHE is useful not only because of its larger sample size, but also the responses regarding wages and hours are considered to be more accurate, since the responses are provided by employers rather than from employees themselves. A further advantage of the ASHE is that data for the same individuals are collected year after year. It is therefore possible to construct a panel dataset of responses for each individual running back as far as 1997, and to track how occupations, earnings and working hours change for individuals over time. Furthermore, using the unique business identifiers, it is possible to combine ASHE data with data from other business surveys, such as the Annual Business Survey (UK Data Archive SN 7451).

The ASHE replaced the New Earnings Survey (NES, SN 6704) in 2004. NES was developed in the 1970s in response to the policy needs of the time. The survey had changed very little in its thirty-year history. ASHE datasets for the years 1997-2003 were derived using ASHE methodologies applied to NES data.

The ASHE improves on the NES in the following ways:

• the NES questionnaire allowed too much variation in employer responses, leading to wide variations in the data
• weightings have been introduced to take account of the population size (significant biases were a known problem in NES data)
• the significant numbers of employees who change jobs between the sample selection and survey reference dates are retained in the ASHE sample, whereas these were dropped from the NES

Linking to other business studies
These data contain Inter-Departmental Business Register (IDBR) reference numbers. These are anonymous but unique reference numbers assigned to business organisations. Their inclusion allows researchers to combine different business survey sources together. Researchers may consider applying for other business data to assist their research.

Observations from Northern Ireland
The ASHE data held by the UK Data Archive include very few observations from Northern Ireland. Users requiring access to Northern Ireland data are advised to contact the Northern Ireland Statistics and Research Agency, who administer this aspect of the survey.

Local unit reference variable, luref
The local unit reference variable 'luref', is generated to indicate multiple occurrences of the same local unit for disclosure checking purposes. It is inconsistent across years and is not an IDBR reference number. It should not be used to link ASHE with other business datasets.

For Secure Lab projects applying for access to this study as well as to SN 6697 Business Structure Database and/or SN 7683 Business Structure Database Longitudinal, only postcode-free versions of the data will be made available.

Latest Edition Information
For the twenty-sixth edition (February 2025), the data file 'ashegb_2023r_2024p_pc' has been added, along with the accompanying data dictionary.

Average weekly earnings, average hourly wage rate and average usual weekly hours by union status and type of work, last 5 years.

Abstract copyright UK Data Service and data collection copyright owner.The Family Expenditure Survey (FES), which closed in 2001, was a continuous survey with an annual sample of around 10,000 households. They provided information on household and personal incomes, certain payments that recurred regularly (e.g. rent, gas and electricity bills, telephone accounts, insurances, season tickets and hire purchase payments), and maintained a detailed expenditure record for 14 consecutive days. The original purpose of the FES was to provide information on spending patterns for the United Kingdom Retail Price Index (RPI). The survey was a cost-efficient way of collecting a variety of related data that the government departments required to correlate with income and expenditure at the household, tax unit and person levels. The annual FES began in 1957 (with an earlier large scale survey conducted in 1953/54) and was one of the first Department of Employment (DE) systems to be computerised in the early 1960s. The UKDA holds FES data from 1961-2001. The Northern Ireland Family Expenditure Survey (NIFES), which ran from 1967-1998, was identical to the UK FES and therefore used the same questionnaires and documentation. However, starting in 1988, a voluntary question on religious denomination was asked of those aged 16 and over in Northern Ireland. The UKDA holds NIFES data from 1968-1998, under GN 33240. Significant FES developments over time include: 1968: the survey was extended to include a sample drawn from the Northern Ireland FES and a new computer system was introduced which was used until 1985 1986: DE and the Office of Population Censuses and Surveys (OPCS) converted the FES into a new database system using the SIR package 1989: the Central Statistical Office (CSO) took over responsibility for the survey 1994: in April, computerised personal interviewing was introduced using lap-top computers, the database system changed to INGRES and the survey changed from a calendar year to financial year basis 1996: in April, OPCS and CSO were amalgamated into the Office for National Statistics (ONS), who assumed responsibility for the FES 1998: from April onwards information from expenditure diaries kept by children aged 7 to 15 was included in data, and grossing factors were made available on the database From 2001, the both the FES and the National Food Survey (NFS) (held at the UKDA under GN 33071) were completely replaced by a new survey, the Expenditure and Food Survey (EFS). Prior to the advent of the EFS, there had previously been considerable overlap between the FES and NFS, with both surveys asking respondents to keep a diary of expenditure. Thus, the 2000-2001 FES was the final one in the series. The design of the new EFS was based on the previous FES; further background to its development may be found in the 1999-2000 and 2000-2001 Family Spending reports. From 2008, the EFS became the Living Costs and Food Survey (LCF) (see under GN 33334). Main Topics:Household Schedule: This schedule was taken at the main interview. Information for most of the questions was obtained from the head of household or housewife, but certain questions of a more individual character were put to every spender aged 15 or over (or 16 or over from 1973 onwards). Until the introduction of the community charge, information on rateable value and rate poundage was obtained from the appropriate local authority, as was information on whether the address was within a smokeless zone. Information was collected about the household, the sex and age of each member, and also details about the type and size of the household accommodation. The main part of the questionnaire related to expenditure both of a household and individual nature, but the questions were mainly confined to expenses of a recurring nature, e.g.:Household: housing costs, payment to Gas and Electricity Boards or companies, telephone charges, licences and television rentalIndividual: motor vehicles, season tickets for transport, life and accident insurances, payments through a bank, instalments, refund of expenses by employer, expenditure claimed by self-employed persons as business expenses for tax purposes, welfare foods, education grants and feesIncome Schedule: Data were collected for each household spender. The schedule was concerned with income, national insurance contributions and income tax. Income of a child not classed as a spender was obtained from one or other of his parents and entered on the parent's questionnaire. Information collected included: employment status and recent absences from work, earnings of an employee, self-employed earnings, National Insurance contributions, pensions and other regular allowances, occasional benefits - social security benefits and other types, investment income, miscellaneous earnings of a 'once-only' character, tax paid directly to Inland Revenue or refunded, income of a child. Diary Records: The diary covered fourteen days. Each household member aged 15 or over (or 16 or over from 1973 onwards) was asked to record all expenditure made during the 14 days. Children aged between 7 and 15 were also asked to complete simplified diaries of their daily expenditure. Data from the children's diaries was included in the survey results for the first time in 1998-99. Multi-stage stratified random sample For specific details of the sampling procedures for individual years, please refer to the annual report. Face-to-face interview Diaries

Wages in Hungary increased to 704371 HUF/Month in June from 702798 HUF/Month in May of 2025. This dataset provides - Hungary Gross Average Wages - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Average hourly and weekly wage rate, and median hourly and weekly wage rate by National Occupational Classification (NOC), type of work, sex, and age group, 1997 to 2022.

Abstract

The Health Inequality Project uses big data to measure differences in life expectancy by income across areas and identify strategies to improve health outcomes for low-income Americans.

Section 7

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution. Both race-adjusted and unadjusted estimates are reported.

Source

Section 13

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution separately by year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 6

This dataset was created on 2020-01-10 18:53:00.508 by merging multiple datasets together. The source datasets for this version were:

Commuting Zone Life Expectancy Estimates by year: CZ-level by-year life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy: Commuting zone (CZ)-level life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy Trends: CZ-level estimates of trends in life expectancy for men and women, by income quartile

Commuting Zone Characteristics: CZ-level characteristics

Commuting Zone Life Expectancy for larger populations: CZ-level life expectancy estimates for men and women, by income ventile

Section 15

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by state of residence and year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 11

This table reports US mortality rates by gender, age, year and household income percentile. Household incomes are measured two years prior to the mortality rate for mortality rates at ages 40-63, and at age 61 for mortality rates at ages 64-76. The “lag” variable indicates the number of years between measurement of income and mortality.

Observations with 1 or 2 deaths have been masked: all mortality rates that reflect only 1 or 2 deaths have been recoded to reflect 3 deaths

Source

Section 3

This table reports coefficients and standard errors from regressions of life expectancy estimates for men and women at age 40 for each quartile of the national income distribution on calendar year by commuting zone of residence. Only the slope coefficient, representing the average increase or decrease in life expectancy per year, is reported. Trend estimates for both race-adjusted and unadjusted life expectancies are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 9

This table reports life expectancy estimates at age 40 for Males and Females for all countries. Source: World Health Organization, accessed at: http://apps.who.int/gho/athena/

Source

Section 10

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by county of residence. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for counties with populations larger than 25,000 only

Source

Section 2

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by commuting zone of residence and year. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 8

This table reports US population and death counts by age, year, and sex from various sources. Counts labelled “dm1” are derived from the Social Security Administration Data Master 1 file. Counts labelled “irs” are derived from tax data. Counts labelled “cdc” are derived from NCHS life tables.

Source

Section 12

This table reports numerous county characteristics, compiled from various sources. These characteristics are described in the county life expectancy table.

Two variables constructed by the Cen