U.S. citizens with a professional degree had the highest median household income in 2023, at 172,100 U.S. dollars. In comparison, those with less than a 9th grade education made significantly less money, at 35,690 U.S. dollars. Household income The median household income in the United States has fluctuated since 1990, but rose to around 70,000 U.S. dollars in 2021. Maryland had the highest median household income in the United States in 2021. Maryland’s high levels of wealth is due to several reasons, and includes the state's proximity to the nation's capital. Household income and ethnicity The median income of white non-Hispanic households in the United States had been on the rise since 1990, but declining since 2019. While income has also been on the rise, the median income of Hispanic households was much lower than those of white, non-Hispanic private households. However, the median income of Black households is even lower than Hispanic households. Income inequality is a problem without an easy solution in the United States, especially since ethnicity is a contributing factor. Systemic racism contributes to the non-White population suffering from income inequality, which causes the opportunity for growth to stagnate.

Wages in the United States increased 4.46 percent in January 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.

This statistic shows the median household income in the United States from 1990 to 2023 in 2023 U.S. dollars. The median household income was 80,610 U.S. dollars in 2023, an increase from the previous year. Household incomeThe median household income depicts the income of households, including the income of the householder and all other individuals aged 15 years or over living in the household. Income includes wages and salaries, unemployment insurance, disability payments, child support payments received, regular rental receipts, as well as any personal business, investment, or other kinds of income received routinely. The median household income in the United States varies from state to state. In 2020, the median household income was 86,725 U.S. dollars in Massachusetts, while the median household income in Mississippi was approximately 44,966 U.S. dollars at that time. Household income is also used to determine the poverty line in the United States. In 2021, about 11.6 percent of the U.S. population was living in poverty. The child poverty rate, which represents people under the age of 18 living in poverty, has been growing steadily over the first decade since the turn of the century, from 16.2 percent of the children living below the poverty line in year 2000 to 22 percent in 2010. In 2021, it had lowered to 15.3 percent. The state with the widest gap between the rich and the poor was New York, with a Gini coefficient score of 0.51 in 2019. The Gini coefficient is calculated by looking at average income rates. A score of zero would reflect perfect income equality and a score of one indicates a society where one person would have all the money and all other people have nothing.

The Quarterly Census of Employment and Wages (QCEW) Program is a Federal-State cooperative program between the U.S. Department of Labor’s Bureau of Labor Statistics (BLS) and the California EDD’s Labor Market Information Division (LMID). The QCEW program produces a comprehensive tabulation of employment and wage information for workers covered by California Unemployment Insurance (UI) laws and Federal workers covered by the Unemployment Compensation for Federal Employees (UCFE) program. The QCEW program serves as a near census of monthly employment and quarterly wage information by 6-digit industry codes from the North American Industry Classification System (NAICS) at the national, state, and county levels. At the national level, the QCEW program publishes employment and wage data for nearly every NAICS industry. At the state and local area level, the QCEW program publishes employment and wage data down to the 6-digit NAICS industry level, if disclosure restrictions are met. In accordance with the BLS policy, data provided to the Bureau in confidence are used only for specified statistical purposes and are not published. The BLS withholds publication of Unemployment Insurance law-covered employment and wage data for any industry level when necessary to protect the identity of cooperating employers. Data from the QCEW program serve as an important input to many BLS programs. The Current Employment Statistics and the Occupational Employment Statistics programs use the QCEW data as the benchmark source for employment. The UI administrative records collected under the QCEW program serve as a sampling frame for the BLS establishment surveys. In addition, the data serve as an input to other federal and state programs. The Bureau of Economic Analysis (BEA) of the Department of Commerce uses the QCEW data as the base for developing the wage and salary component of personal income. The U.S. Department of Labor’s Employment and Training Administration (ETA) and California's EDD use the QCEW data to administer the Unemployment Insurance program. The QCEW data accurately reflect the extent of coverage of California’s UI laws and are used to measure UI revenues; national, state and local area employment; and total and UI taxable wage trends. The U.S. Department of Labor’s Bureau of Labor Statistics publishes new QCEW data in its County Employment and Wages news release on a quarterly basis. The BLS also publishes a subset of its quarterly data through the Create Customized Tables system, and full quarterly industry detail data at all geographic levels.

VITAL SIGNS INDICATOR Jobs by Wage Level (EQ1)

FULL MEASURE NAME Distribution of jobs by low-, middle-, and high-wage occupations

LAST UPDATED January 2019

DESCRIPTION Jobs by wage level refers to the distribution of jobs by low-, middle- and high-wage occupations. In the San Francisco Bay Area, low-wage occupations have a median hourly wage of less than 80% of the regional median wage; median wages for middle-wage occupations range from 80% to 120% of the regional median wage, and high-wage occupations have a median hourly wage above 120% of the regional median wage.

DATA SOURCE California Employment Development Department OES (2001-2017) http://www.labormarketinfo.edd.ca.gov/data/oes-employment-and-wages.html

American Community Survey (2001-2017) http://api.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Jobs are determined to be low-, middle-, or high-wage based on the median hourly wage of their occupational classification in the most recent year. Low-wage jobs are those that pay below 80% of the regional median wage. Middle-wage jobs are those that pay between 80% and 120% of the regional median wage. High-wage jobs are those that pay above 120% of the regional median wage. Regional median hourly wages are estimated from the American Community Survey and are published on the Vital Signs Income indicator page. For the national context analysis, occupation wage classifications are unique to each metro area. A low-wage job in New York, for instance, may be a middle-wage job in Miami. For the Bay Area in 2017, the median hourly wage for low-wage occupations was less than $20.86 per hour. For middle-wage jobs, the median ranged from $20.86 to $31.30 per hour; and for high-wage jobs, the median wage was above $31.30 per hour.

Occupational employment and wage information comes from the Occupational Employment Statistics (OES) program. Regional and subregional data is published by the California Employment Development Department. Metro data is published by the Bureau of Labor Statistics. The OES program collects data on wage and salary workers in nonfarm establishments to produce employment and wage estimates for some 800 occupations. Data from non-incorporated self-employed persons are not collected, and are not included in these estimates. Wage estimates represent a three-year rolling average.

Due to changes in reporting during the analysis period, subregion data from the EDD OES have been aggregated to produce geographies that can be compared over time. West Bay is San Mateo, San Francisco, and Marin counties. North Bay is Sonoma, Solano and Napa counties. East Bay is Alameda and Contra Costa counties. South Bay is Santa Clara County from 2001-2004 and Santa Clara and San Benito counties from 2005-2017.

Due to changes in occupation classifications during the analysis period, all occupations have been reassigned to 2010 SOC codes. For pre-2009 reporting years, all employment in occupations that were split into two or more 2010 SOC occupations are assigned to the first 2010 SOC occupation listed in the crosswalk table provided by the Census Bureau. This method assumes these occupations always fall in the same wage category, and sensitivity analysis of this reassignment method shows this is true in most cases.

In order to use OES data for time series analysis, several steps were taken to handle missing wage or employment data. For some occupations, such as airline pilots and flight attendants, no wage information was provided and these were removed from the analysis. Other occupations did not record a median hourly wage (mostly due to irregular work hours) but did record an annual average wage. Nearly all these occupations were in education (i.e. teachers). In this case, a 2080 hour-work year was assumed and [annual average wage/2080] was used as a proxy for median income. Most of these occupations were classified as high-wage, thus dispelling concern of underestimating a median wage for a teaching occupation that requires less than 2080 hours of work a year (equivalent to 12 months fulltime). Finally, the OES has missing employment data for occupations across the time series. To make the employment data comparable between years, gaps in employment data for occupations are ‘filled-in’ using linear interpolation if there are at least two years of employment data found in OES. Occupations with less than two years of employment data were dropped from the analysis. Over 80% of interpolated cells represent missing employment data for just one year in the time series. While this interpolating technique may impact year-over-year comparisons, the long-term trends represented in the analysis generally are accurate.

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.

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.

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

Graph and download economic data for Personal income per capita (A792RC0A052NBEA) from 1929 to 2024 about personal income, per capita, personal, income, GDP, and USA.

Sweden SE: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 11.100 % in 2021. This records an increase from the previous number of 10.100 % for 2020. Sweden SE: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 8.900 % from Dec 1975 (Median) to 2021, with 27 observations. The data reached an all-time high of 11.100 % in 2021 and a record low of 5.200 % in 1987. Sweden SE: Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sweden – Table SE.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

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.

Dominican Republic DO: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 14.300 % in 2022. This records an increase from the previous number of 12.900 % for 2021. Dominican Republic DO: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 18.900 % from Dec 1986 (Median) to 2022, with 28 observations. The data reached an all-time high of 22.900 % in 1986 and a record low of 12.900 % in 2021. Dominican Republic DO: Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Dominican Republic – Table DO.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

In 2023, the real median household income in the state of Alabama was 60,660 U.S. dollars. The state with the highest median household income was Massachusetts, which was 106,500 U.S. dollars in 2023. The average median household income in the United States was at 80,610 U.S. dollars.

Poland PL: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 8.700 % in 2021. This records a decrease from the previous number of 9.200 % for 2020. Poland PL: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 12.900 % from Dec 2004 (Median) to 2021, with 18 observations. The data reached an all-time high of 17.000 % in 2004 and a record low of 8.700 % in 2021. Poland PL: Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Poland – Table PL.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

India Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 9.800 % in 2021. This records a decrease from the previous number of 10.000 % for 2020. India Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 6.200 % from Dec 1977 (Median) to 2021, with 14 observations. The data reached an all-time high of 10.300 % in 2019 and a record low of 5.100 % in 2004. India Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Turkey TR: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 17.800 % in 2021. This records an increase from the previous number of 17.300 % for 2020. Turkey TR: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 17.400 % from Dec 2017 (Median) to 2021, with 5 observations. The data reached an all-time high of 17.800 % in 2021 and a record low of 17.300 % in 2020. Turkey TR: Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Turkey – Table TR.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

analyze the survey of consumer finances (scf) with r the survey of consumer finances (scf) tracks the wealth of american families. every three years, more than five thousand households answer a battery of questions about income, net worth, credit card debt, pensions, mortgages, even the lease on their cars. plenty of surveys collect annual income, only the survey of consumer finances captures such detailed asset data. responses are at the primary economic unit-level (peu) - the economically dominant, financially interdependent family members within a sampled household. norc at the university of chicago administers the data collection, but the board of governors of the federal reserve pay the bills and therefore call the shots. if you were so brazen as to open up the microdata and run a simple weighted median, you'd get the wrong answer. the five to six thousand respondents actually gobble up twenty-five to thirty thousand records in the final pub lic use files. why oh why? well, those tables contain not one, not two, but five records for each peu. wherever missing, these data are multiply-imputed, meaning answers to the same question for the same household might vary across implicates. each analysis must account for all that, lest your confidence intervals be too tight. to calculate the correct statistics, you'll need to break the single file into five, necessarily complicating your life. this can be accomplished with the meanit sas macro buried in the 2004 scf codebook (search for meanit - you'll need the sas iml add-on). or you might blow the dust off this website referred to in the 2010 codebook as the home of an alternative multiple imputation technique, but all i found were broken links. perhaps it's time for plan c, and by c, i mean free. read the imputation section of the latest codebook (search for imputation), then give these scripts a whirl. they've got that new r smell. the lion's share of the respondents in the survey of consumer finances get drawn from a pretty standard sample of american dwellings - no nursing homes, no active-duty military. then there's this secondary sample of richer households to even out the statistical noise at the higher end of the i ncome and assets spectrum. you can read more if you like, but at the end of the day the weights just generalize to civilian, non-institutional american households. one last thing before you start your engine: read everything you always wanted to know about the scf. my favorite part of that title is the word always. this new github repository contains t hree scripts: 1989-2010 download all microdata.R initiate a function to download and import any survey of consumer finances zipped stata file (.dta) loop through each year specified by the user (starting at the 1989 re-vamp) to download the main, extract, and replicate weight files, then import each into r break the main file into five implicates (each containing one record per peu) and merge the appropriate extract data onto each implicate save the five implicates and replicate weights to an r data file (.rda) for rapid future loading 2010 analysis examples.R prepare two survey of consumer finances-flavored multiply-imputed survey analysis functions load the r data files (.rda) necessary to create a multiply-imputed, replicate-weighted survey design demonstrate how to access the properties of a multiply-imput ed survey design object cook up some descriptive statistics and export examples, calculated with scf-centric variance quirks run a quick t-test and regression, but only because you asked nicely replicate FRB SAS output.R reproduce each and every statistic pr ovided by the friendly folks at the federal reserve create a multiply-imputed, replicate-weighted survey design object re-reproduce (and yes, i said/meant what i meant/said) each of those statistics, now using the multiply-imputed survey design object to highlight the statistically-theoretically-irrelevant differences click here to view these three scripts for more detail about the survey of consumer finances (scf), visit: the federal reserve board of governors' survey of consumer finances homepage the latest scf chartbook, to browse what's possible. (spoiler alert: everything.) the survey of consumer finances wikipedia entry the official frequently asked questions notes: nationally-representative statistics on the financial health, wealth, and assets of american hous eholds might not be monopolized by the survey of consumer finances, but there isn't much competition aside from the assets topical module of the survey of income and program participation (sipp). on one hand, the scf interview questions contain more detail than sipp. on the other hand, scf's smaller sample precludes analyses of acute subpopulations. and for any three-handed martians in the audience, ther e's also a few biases between these two data sources that you ought to consider. the survey methodologists at the federal reserve take their job...

The NBA and WNBA are the two top leagues for basketball in the United States for men and women, respectively. In the NBA, players took home an average annual salary of over 12 million U.S. dollars for the 2023/24 season, with the league's minimum salary set at 1.16 million U.S. dollars that year. In comparison, players in the WNBA received an average annual pay of 119.59 thousand U.S. dollars, with the highest-earning players in the WNBA receiving around 252 thousand U.S. dollars annually.

This table presents income shares, thresholds, tax shares, and total counts of individual Canadian tax filers, with a focus on high income individuals (95% income threshold, 99% threshold, etc.). Income thresholds are based on national threshold values, regardless of selected geography; for example, the number of Nova Scotians in the top 1% will be calculated as the number of taxfiling Nova Scotians whose total income exceeded the 99% national income threshold. Different definitions of income are available in the table namely market, total, and after-tax income, both with and without capital gains.

Abstract copyright UK Data Service and data collection copyright owner. Carried out every four years, the European Quality of Life Survey (EQLS) examines both the objective circumstances of European citizens' lives and how they feel about those circumstances and their lives in general. It collects data on a range of issues, such as employment, income, education, housing, family, health and work-life balance. It also looks at subjective topics, such as people's levels of happiness, life satisfaction, and perceived quality of society. By running the survey regularly, it has also become possible to track key trends in the quality of people's lives over time. Previous surveys have shown, for instance, that people are having greater difficulty making ends meet since the economic crisis began. In many countries, they also feel that there is now more tension between people from different ethnic groups. And across Europe, people now trust their governments less than they did before. However, people still continue to get the greatest satisfaction from their family life and personal relationships. Over the years, the EQLS has developed into a valuable set of indicators which complements traditional indicators of economic growth and living standard such as GDP or income. The EQLS indicators are more inclusive of environmental and social aspects of progress and therefore are easily integrated into the decision-making process and taken up by public debate at EU and national levels in the European Union. In each wave a sample of adult population has been selected randomly for a face to face interview. In view of the prospective European enlargements the geographical coverage of the survey has expanded over time from 28 countries in 2003 to 34 countries in 2011-12. Further information about the survey can be found on the European Foundation for the Improvement of Living and Working Conditions (Eurofound) EQLS web pages. Main Topics: The survey examines a range of issues, such as employment, income, education, housing, family, health, work-life balance, life satisfaction and perceived quality of society. Multi-stage stratified random sample See documentation for details Face-to-face interview 2003 AGE AGRICULTURE ATTITUDES Austria BASIC NEEDS Belgium Bulgaria CARE OF DEPENDANTS CAREER DEVELOPMENT CHARITABLE ORGANIZA... CHIEF INCOME EARNERS CHILD CARE CHILDREN COMMUTING COMPUTERS CONSUMER GOODS Cyprus Czech Republic DEBILITATIVE ILLNESS DEBTS DECISION MAKING DISABILITIES DISADVANTAGED GROUPS DOMESTIC RESPONSIBI... Denmark ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL COURSES EMPLOYMENT EMPLOYMENT HISTORY ENGLISH LANGUAGE EVERYDAY LIFE EXPECTATION EXPOSURE TO NOISE Estonia European Union Coun... FAMILY LIFE FINANCIAL RESOURCES FOOD AND NUTRITION FURNITURE Finland France GENDER GENERAL PRACTITIONERS Germany October 1990 Greece HEALTH CONSULTATIONS HEALTH SERVICES HOURS OF WORK HOUSEHOLD BUDGETS HOUSEHOLD HEAD S EC... HOUSEHOLD HEAD S OC... HOUSEHOLDS HOUSEWORK HOUSING CONDITIONS Hungary INCOME INTERGROUP CONFLICT Ireland Italy JOB SATISFACTION LABOUR FORCE LABOUR RELATIONS LAND OWNERSHIP LEISURE GOODS LIFE SATISFACTION LIFE STYLES LIVING CONDITIONS LOCAL COMMUNITY FAC... LUCK Latvia Lithuania Luxembourg MEALS MOTOR VEHICLES Malta NEIGHBOURHOODS Netherlands OCCUPATIONAL SAFETY OCCUPATIONAL STATUS OCCUPATIONS PARENTS PERSONAL CONTACT POLITICAL PARTICIPA... POLLUTION POVERTY Poland Portugal QUALIFICATIONS QUALITY OF LIFE RECREATIONAL FACILI... RELIGIOUS ATTENDANCE ROOMS RURAL AREAS Romania SATISFACTION SOCIAL ATTITUDES SOCIAL EXCLUSION SOCIAL INDICATORS SOCIAL LIFE SOCIAL SECURITY BEN... SOCIAL SUPPORT STANDARD OF LIVING STATE RETIREMENT PE... STRESS PSYCHOLOGICAL SUBSIDIARY EMPLOYMENT SUPERVISORY STATUS Slovakia Slovenia Social behaviour an... Social conditions a... Sweden TERMINATION OF SERVICE TRAINING TRUST Turkey URBAN AREAS United Kingdom VOLUNTARY WORK WAGES WATER PROPERTIES WORK ATTITUDE WORKING CONDITIONS