Graph and download economic data for All Employees: Information: Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services in Dallas-Plano-Irving, TX (MD) (SMU48191245051800001SA) from Jan 1990 to Aug 2025 about information, services, employment, and USA.

Graph and download economic data for All Employees, Web Search Portals, Libraries, Archives, and Other Information Services (CES5051900001) from Jan 1990 to Sep 2025 about information, establishment survey, services, employment, and USA.

Context

A striking graph from the Social Security Administration (https://www.ssa.gov/policy/docs/factsheets/at-a-glance/earnings-men-1988-2018.html) shows that median annual earnings for all men above the age of 20 have decreased since 1988: https://www.ssa.gov/policy/docs/factsheets/at-a-glance/earnings-men-1988-2018.svg" alt="">

I wanted to better understand how educational attainment has played a role in the above trend, and to come up with a model to forecast the future trend for earnings by educational attainment.

As I began looking at the data from the Bureau of Labor Statistics website, there was a striking trend: the median weekly earnings for all groups of people who did not have a bachelors degree or higher had decreased from 1979 levels, in constant 2020 dollars.

Content

I collated data from the US Bureau of Labor Statistics (https://www.bls.gov/webapps/legacy/cpsatab4.htm) and (https://www.bls.gov/cps/cpswktabs.htm) and the US Census Bureau (https://www.census.gov/data/tables/time-series/demo/income-poverty/historical-income-people.html) to create this dataset.

I have omitted details of gender and race, to solely look at the correlation between educational attainment and median weekly earnings over the years. All of the data is for ages 25 and higher unless otherwise stated in the column header.

An important note is that all the earnings data are in constant base 2020 dollars. This removes the effects of inflation and makes it possible to compare the numbers over the years.

The data starts at the year 1960, but unfortunately only overall labor force data, and population percentages of persons with a high school graduation (HSG) and persons with a Bachelors or Higher Degree are available. Median weekly earnings data categorized by educational attainment is available from 1979 onwards, while labor force data i.e., labor force level, labor force participation rate and the employment level by educational attainment is available only from 1992 onwards.

The only columns that have data from 1960 onwards are: (i) overall labor force level, (ii) civilian non-institutional population level, (iii) overall labor force participation rate, (iv) overall employment level, (v) overall percentage of high school graduates, and (vi) overall percentage of persons with a bachelors degree or higher.

Some of the columns can be calculated from other columns, for instance the civilian non-institutional population level can be calculated from the labor force participation rate.

Acknowledgements

All of this data is from the Bureau of Labor Statistics, and the Census Bureau: https://www.bls.gov/webapps/legacy/cpsatab4.htm , https://www.bls.gov/cps/cpswktabs.htm and https://www.census.gov/data/tables/time-series/demo/income-poverty/historical-income-people.html .

A big thank you to all those who worked so hard to collect and organize this data.

Inspiration

The main question is: what is the best way to generate forecasts for median weekly earnings for each educational attainment level?

2004 to 2021 Virginia Employment Status of the Civilian Non-Institutional Population by Sex, by Race, Hispanic or Latino ethnicity, and detailed by Age, by Year. Annual averages, numbers in thousands.

U.S. Bureau of Labor Statistics; Local Area Unemployment Statistics, Expanded State Employment Status Demographic Data Data accessed from the Bureau of Labor Statistics website (https://www.bls.gov/lau/ex14tables.htm)

Statewide data on the demographic and economic characteristics of the labor force are published on an annual-average basis from the Current Population Survey (CPS), the sample survey of households used to calculate the U.S. unemployment rate (https://www.bls.gov/cps/home.htm). For each state and the District of Columbia, employment status data are tabulated for 67 sex, race, Hispanic or Latino ethnicity, marital status, and detailed age categories and evaluated against a minimum base, calculated to reflect an expected maximum coefficient of variation (CV) of 50 percent, to determine reliability for publication.

The CPS sample was redesigned in 2014–15 to reflect the distribution of the population as of the 2010 Census. At the same time, BLS developed improved techniques for calculating minimum bases. These changes resulted in generally higher minimum bases of unemployment, leading to the publication of fewer state-demographic groups beginning in 2015. The most notable impact was on the detailed age categories, particularly the teenage and age 65 and older groups. In an effort to extend coverage, BLS introduced a version of the expanded state employment status demographic table with intermediate age categories, collapsing the seven categories historically included down to three. Ages 16–19 and 20–24 were combined into a 16–24 year-old category, ages 25–34, 35–44, and 45–54 were combined into a 25–54 year-old category, and ages 55–64 and 65 and older were combined into a 55-years-and-older category. These intermediate age data are tabulated for the total population, as well as the four race and ethnicity groups, and then are evaluated against the unemployment minimum bases. The more detailed age categories continue to be available in the main version of the expanded table, where the minimum base was met.

Additional information on the uses and limitations of statewide data from the CPS can be found in the document Notes on Using Current Population Survey (https://www.bls.gov/lau/notescps.htm) Subnational Data and in Appendix B of the bulletin Geographic Profile of Employment and Unemployment (https://www.bls.gov/opub/geographic-profile/home.htm).

1990 to present (approximate 2 month lag) Virginia Labor Force and Unemployment estimates by Month by County.

Special data considerations: Period values of "M01-M12" represent Months of Year; "M13" is the Annual Average.

U.S. Bureau of Labor Statistics; Local Area Unemployment Statistics, table la.data.54.Virginia Data accessed from the Bureau of Labor Statistics public database LABSTAT (https://download.bls.gov/pub/time.series/la/)

Supporting documentation can be found on the U.S. Bureau of Labor Statistics website under Local Area Unemployment Statistics, Handbook of Methods (https://www.bls.gov/opub/hom/lau/home.htm)

Survey Description: Labor force and unemployment estimates for States and local areas are developed by State workforce agencies to measure local labor market conditions under a Federal-State cooperative program. The Department of Labor develops the concepts, definitions, and technical procedures which are used by State agencies for preparation of labor force and unemployment estimates.

These estimates are derived from a variety of sources, including the Current Population Survey, the Current Employment Statistics survey, the Quarterly Census of Employment and Wages, various programs at the Census Bureau, and unemployment insurance claims data from the State workforce agencies.

To establish uniform labor force concepts and definitions in all States and areas consistent with those used for the U.S. as a whole, monthly national estimates of employment and unemployment from the Current Population Survey are used as controls (benchmarks) for the State labor force statistics.

Summary Data Available: Monthly labor force and unemployment series are available for approximately 7,500 geographic areas, including cities over 25,000 population, counties, metropolitan areas, States, and other areas.

For each area, the following measures are presented by place of residence:

• Total civilian labor force,
• Total number of people employed,
• Total number of people unemployed, and
• Unemployment rate

Data Characteristics: Rates are expressed as percents with one decimal place. Levels are measured as individual persons (not thousands) and are stored with no decimal places.

Context

This is a dataset that I built by scraping the United States Department of Labor's Bureau of Labor Statistics. I was looking for county-level unemployment data and realized that there was a data source for this, but the data set itself hadn't existed yet, so I decided to write a scraper and build it out myself.

Content

This data represents the Local Area Unemployment Statistics from 1990-2016, broken down by state and month. The data itself is pulled from this mapping site:

https://data.bls.gov/map/MapToolServlet?survey=la&map=county&seasonal=u

Further, the ever-evolving and ever-improving codebase that pulled this data is available here:

https://github.com/jayrav13/bls_local_area_unemployment

Acknowledgements

Of course, a huge shoutout to bls.gov and their open and transparent data. I've certainly been inspired to dive into US-related data recently and having this data open further enables my curiosities.

Inspiration

I was excited about building this data set out because I was pretty sure something similar didn't exist - curious to see what folks can do with it once they run with it! A curious question I had was surrounding Unemployment vs 2016 Presidential Election outcome down to the county level. A comparison can probably lead to interesting questions and discoveries such as trends in local elections that led to their most recent election outcome, etc.

Next Steps

Version 1 of this is as a massive JSON blob, normalized by year / month / state. I intend to transform this into a CSV in the future as well.

The series comes from the 'Current Employment Statistics (Establishment Survey).' The source code is: CES0500000003

The Average Hourly Earnings of All Private Employees is a measure of the average hourly earnings of all private employees on a “gross” basis, including premium pay for overtime and late-shift work. These differ from wage rates in that average hourly earnings measure the actual return to a worker for a set period of time, rather than the amount contracted for a unit of work, the wage rate. This measure excludes benefits, irregular bonuses, retroactive pay, and payroll taxes paid by the employer.

Average Hourly Earnings are collected in the Current Employment Statistics (CES) program and published by the BLS. It is provided on a monthly basis, so this data is used in part by macroeconomists as an initial economic indicator of current trends. Progressions in earnings specifically help policy makers understand some of the pressures driving inflation.

It is important to note that this series measures the average hourly earnings of the pool of workers in each period. Thus, changes in average hourly earnings can be due to either changes in the set of workers observed in a given period, or due to changes in earnings. For instance, in recessions that lead to the disproportionate increase of unemployment in lower-wage jobs, average hourly earnings can increase due to changes in the pool of workers rather than due to the widespread increase of hourly earnings at the worker-level.

For more information, see: U.S. Bureau of Labor Statistics, CES Overview (https://www.bls.gov/web/empsit/cesprog.htm) U.S. Bureau of Labor Statistics, BLS Handbook of Methods: Chapter 2. Employment, Hours, and Earnings from the Establishment Survey (https://www.bls.gov/opub/hom/pdf/ces-20110307.pdf)

Graph and download economic data for All Employees: Information: Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services in Texas (SMU48000005051800001SA) from Jan 1990 to Aug 2025 about information, TX, services, employment, and USA.

The Consumer Expenditure Survey (CE) program consists of two surveys: the quarterly Interview survey and the annual Diary survey. Combined, these two surveys provide information on the buying habits of American consumers, including data on their expenditures, income, and consumer unit (families and single consumers) characteristics. The survey data are collected for the U.S. Bureau of Labor Statistics (BLS) by the U.S. Census Bureau. The CE collects all on all spending components including food, housing, apparel and services, transportation, entertainment, and out-of-pocket health care costs. The CE tables are an easy-to-use tool for obtaining arts-related spending estimates. They feature several arts-related spending categories, including the following items: Spending on Admissions Plays, theater, opera, and concerts Movies, parks, and museums Spending on Reading Newspapers and magazines Books Digital book readers Spending on Other Arts-Related Items Musical instruments Photographic equipment Audio-visual equipment Toys, games, arts and crafts The CE is important because it is the only Federal survey to provide information on the complete range of consumers' expenditures and incomes, as well as the characteristics of those consumers. It is used by economic policymakers examining the impact of policy changes on economic groups, by the Census Bureau as the source of thresholds for the Supplemental Poverty Measure, by businesses and academic researchers studying consumers' spending habits and trends, by other Federal agencies, and, perhaps most importantly, to regularly revise the Consumer Price Index market basket of goods and services and their relative importance. The most recent data tables are for 2022 and include: 1) Detailed tables with the most granular level of expenditure data available, along with variances and percent reporting for each expenditure item, for all consumer units (listed as "Other" in the Download menu); and 2) Tables with calendar year aggregate shares by demographic characteristics that provide annual aggregate expenditures and shares across demographic groups (listed as "Excel" in the Download menu). Also, see Featured CE Tables and Economic News Releases sections on the CE home page for current data tables and news release. The 1980 through 2022 CE public-use microdata, including Interview Survey data, Diary Survey data, and paradata (information about the data collection process), are available on the CE website.

Quarterly Percent Change in 3rd Month Employment Level Data 1990 - Present

  Over-the-year percent change in the third month's employment level of a given quarter (Rounded to the tenths place). County, state, and MSA level, by industry, yearly from 1990 - present. About the BLS Unemployment Data including Current Population Survey Demographic Breakdowns: Links to several different datasets, including Current Population Survey results showing seasonally adjusted unemployment data broken out by ethnicity and age, reason for unemployment, and duration of employment prior to unemployment for years including 2017-2019.  Other datasets show over-the-year percent change in the third month's employment level and taxable wages by industry for a given quarter at the County, State, and MSA level yearly from 1990 - present.
  Geography Level: State, County, MSAItem Vintage: 1990-Present
  Update Frequency: YearlyAgency: BLSAvailable File Type: Website link to CSV/Excel/Legacy Flat files download 

  Return to Other Federal Agency Datasets Page

Graph and download economic data for All Employees: Information: Web Search Portals, Libraries, Archives, and Other Information Services in California (SMU06000005051900001SA) from Jan 1990 to Aug 2025 about information, CA, services, employment, and USA.

The paper examines how immediate and projected occupational restructuring affects workers’ mobility. The original worker mobility data can be downloaded from IPUMS CPS (https://cps.ipums.org/cps/). The original occupational restructuring data from the BLS's Occupational Employment and Wage Statistics, Employment Matrix, and Occupational Outlook Handbooks can be downloaded from the BLS website. The original ONET data can be downloaded from the ONET website. The integrated data can be downloaded from the replication files.

The Local Area Unemployment Statistics (LAUS) program produces monthly employment, unemployment, and labor force data for Census regions and divisions, States, counties, metropolitan areas, and many cities, by place of residence. The LAUS program is a federal-state cooperative endeavor in which states develop state and sub-state data using concepts, definitions, and technical procedures prescribed by the Bureau of Labor Statistics (BLS). A major source of labor force data estimates, the Current Population Survey (CPS) includes a sample of over 1,600 Connecticut households each month regarding the labor force status of their occupants

Further information from the CT Department of Labor is available here: https://www1.ctdol.state.ct.us/lmi/LAUS/default.asp

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Supplementary deposit of QCEW data from public websites of the vintage used in Guren and Greenwald (2025) "Do Credit Conditions Move House Prices" (AER).Readme: The data for 1990-2017 are available from the Bureau of Labor Statistics at https://www.bls.gov/cew/downloadable-data-files.htm. We accessed the data in May 2015 for all years prior to 2014, August 2015 for 2014, August 2016 for 2015, September 2017 for 2016, and August 2018 for 2017.

Abstract

The Consumer Expenditure Survey (CE) program provides a continuous and comprehensive flow of data on the buying habits of American consumers. These data are used widely in economic research and analysis, and in support of revisions of the Consumer Price Index. To meet the needs of users, the Bureau of Labor Statistics (BLS) produces population estimates (for consumer units or CUs) of average expenditures in news releases, reports, and articles. Tabulated CE data are also available on the internet (see Section XV. Appendix 4). The microdata are available on the public BLS website for free download. These microdata files present detailed expenditure and income data for the Diary component of the CE. They include weekly expenditure (EXPD), annual income (DTBD), and imputed income (DTID) files. The data in EXPD, DTBD, and DTID files are categorized by a Universal Classification Code (UCC). The advantage of the EXPD and DTBD files is that with the data classified in a standardized format, the user may perform comparative expenditure (income) analysis with relative ease. The FMLD and MEMD files contain data on the characteristics and demographics of CUs and CU members. The summary level expenditure and income information on the FMLD files permits the data user to link consumer spending, by general expenditure category, to household characteristics and demographics on one set of files. Estimates of average expenditures from the Diary survey, integrated with data from the Interview survey, are published online in the CE annual reports.. A number of recent publications containing data from the CE are available on the public website as well. The microdata files are in the public domain and, with appropriate credit, may be reproduced without permission. A suggested citation is: "U.S. Department of Labor, Bureau of Labor Statistics, Consumer Expenditure Survey, Diary Survey, 2012."

The Diary survey PUMD are organized into five major data files for each quarter: 1. FMLD - a file with characteristics, income, and summary level expenditures for the household 2. MEMD - a file with characteristics and income for each member in the household
3. EXPD - a detailed weekly expenditure file categorized by UCC 4. DTBD - a detailed annual income file categorized by UCC
5. DTID - a household imputed income file categorized by UCC

Geographic coverage

National

Analysis unit

Consumer Unit

Universe

Eligible population includes all civilian non-institutional persons.

Kind of data

Sample survey data [ssd]

Sampling procedure

Samples for the CE are national probability samples of households designed to be representative of the total U. S. civilian population. Eligible population includes all civilian non-institutionalized persons. The first step in sampling is the selection of primary sampling units (PSUs), which consist of counties (or parts thereof) or groups of counties. The set of sample PSUs used for the 2012 sample is composed of 91 areas. The design classifies the PSUs into four categories: 21 "A" certainty PSUs are Metropolitan Statistical Areas (MSA's) with a population greater than 1.5 million. 38 "X" PSUs, are medium-sized MSAs. 16 "Y" PSUs are nonmetropolitan areas that are included in the CPI. 16 "Z" PSUs are nonmetropolitan areas where only the urban population data will be included in the CPI.

The sampling frame (that is, the list from which housing units were chosen) for the 2012 survey is generated from the 2000 Population Census file. The sampling frame is augmented by new construction permits and by techniques used to eliminate recognized deficiencies in census coverage. All Enumeration Districts (EDs) from the Census that fail to meet the criterion for good addresses for new construction, and all EDs in non-permit-issuing areas are grouped into the area segment frame. To the extent possible, an unclustered sample of units is selected within each PSU. This lack of clustering is desirable because the sample size of the Diary Survey is small relative to other surveys, while the intraclass correlations for expenditure characteristics are relatively large. This suggests that any clustering of the sample units could result in an unacceptable increase in the within-PSU variance and, as a result, the total variance. Each selected sample unit is requested to keep two 1-week diaries of expenditures over consecutive weeks. The earliest possible day for placing a diary with a household is predesignated with each day of the week having an equal chance to be the first of the reference week. The diaries are evenly spaced throughout the year.

Mode of data collection

Face-to-face [f2f]

A dataset that permits examination of health, economic, work, and retirement trajectories for a representative national sample of men from middle to old age. The original sample of 5,020 men, first interviewed in 1966, was re-interviewed periodically until 1983 under a contract with the US Department of Labor. The study provided a detailed longitudinal record of their labor market activity, health, financial status, family structure, and attitudes toward and experience in retirement. The NIA grant made possible a re-interview in 1990 with the surviving men and the widows (or other next-of-kin) of the decedents. The merging of the 1990 data includes death certificate information for the decedents, Blacks were over-represented in the original sample in a ratio of about three or four to one, resulting in about 500 surviving black men in the sample. Information on labor market activity, income, and assets also is available for a sample of about 1,350 widows, 90 percent of whom are between 60 and 89 years of age. This information can be linked to earlier data on the women''s health and work activity that was reported by their late husbands. Due to the original sample selection, other NLS cohorts contain wives and daughters of the older men. These other surveys also hold a wealth of detailed information on aging and retirement issues, especially on income transfers. * Dates of Study: 1966-1990 * Study Features: Longitudinal, Minority Oversamples * Sample Size: ** 1966: 5,020 men (baseline) ** 1990: 2,092 surviving men, 1,341 widows, 865 other next-of-kin Links: * BLS Website on NLS: http://www.bls.gov/nls/ * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/04675

Graph and download economic data for Unemployment Rate in Minnesota (LAUST270000000000003A) from 1976 to 2024 about MN, household survey, unemployment, rate, and USA.

Graph and download economic data for All Employees, Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services (CEU5051800001) from Jan 1990 to Sep 2025 about information, establishment survey, services, employment, and USA.

Graph and download economic data for All Employees: Information: Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services in Florida (SMU12000005051800001) from Jan 1990 to Aug 2025 about internet, information, FL, services, employment, and USA.

Graph and download economic data for All Employees: Information: Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services in Georgia (SMU13000005051800001) from Jan 1990 to Aug 2025 about internet, information, GA, services, employment, and USA.