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.

Disclaimer: For information regarding future updates or preliminary/final data releases, please refer to the Bureau of Labor Statistics Release Calendar: https://www.bls.gov/cew/release-calendar.htm

Graph and download economic data for Employed full time: Wage and salary workers: Information security analysts occupations: 16 years and over: Men (LEU0257861100A) from 2011 to 2024 about analysts, occupation, information, full-time, males, salaries, workers, 16 years +, securities, wages, employment, and USA.

Graph and download economic data for Total wages and salaries, BLS: Adjustment for wages and salaries not covered or not fully covered by unemployment insurance (W873RC1A027NBEA) from 1982 to 2024 about covered, adjusted, insurance, salaries, wages, unemployment, GDP, and USA.

Introduction

As a part of the Google Data Analytics Professional Certificate Program, this case study serves as a data analytics adventure and a way to dive into something personal. While many face the difficulty of finding employment out of college, it became especially tedious to do so due to the COVID-19 pandemic. As such, this case study revolves around unemployment trends from 2021 using data sourced from the United States Bureau of Labor Statistics. I used datasets surrounding unemployment and employment trends in 2021 to answer the following:

Questions

1. What methods for job searching were the most prevalent across age ranges? Across gender/race/Hispanic-Latino ethnicity?
2. What trends exist between and within the most prevalent venues for job searching among the unemployed?
3. What job sector(s) does the majority of the population comprise? What trends exist within and between the most popular job sector and the least popular job sector? What relationship do these factors have with race/gender/Hispanic-Latino ethnicity?
4. How does information about prevalent job searching influence the job market and the applicants in the job search phase?

Insights (see the data section below for charts, graphs, and the .Rmd file I utilized)

• In 2021, the unemployed, with ages ranging from 16-65, preferred resumes and applications as their method for seeking out jobs. This method was especially prevalent in the age range 16-34, where, the highest bracket of job seekers were 24-35 years old. A close second was contacting an employer directly, primarily used by 45-64-year-olds. When considering gender/ethnicity/race, however, compared to their male counterparts, white women and women of color were the highest users of the resumes and applications method. However, white males and men of color were the highest users of the contacting employers directly method.
• Among the unemployed resumes were overall the most prevalent method of applying for jobs in 2021, where, people aged 16--34 and women regardless of ethnicity/race were the most likely to utilize this method to search for jobs.
• The majority of the population resides in the "Management, Professional, and related occupations" job sector, with the least popular form of occupations being in the "Farming, Fishing, and Forestry" sector. This sentiment can be found almost across all genders/races/ethnicities, though, some other job sectors like "Production, transportation, and material moving occupations" and "Natural resources, construction, and maintenance occupations" were more prevalent concerning the Black/African American men, Hispanic/Latino women, and Hispanic/Latino men respectively.
• This information is highly useful for job industries, specifically, those in the "Management, Professional, and related occupations" sector. With this, industries in this job sector can project what their incoming job applicant pool may look like and how to prepare for making the application process more accessible. This information can also serve to reinforce fairness and inclusivity in the job application process and in the work environment.

** Overall**

Using this information a company can project in 2022-2023 the majority of applicants will either apply to jobs using resumes/applications, the majority of these applicants may be 16-34 years old, and women regardless of ethnicity and race. They can also look out for applicants who are older, 45-64 years old, and applicants who are men regardless of ethnicity and race, being more likely to contact them as an employer directly. If an employer prefers to be directly contacted, they should make sure to consider the difficulties that people of different race/ethnic/and gender identities may have done so, and, either should either make the job positing more welcoming and inclusive to do so or, be sure to include a process of hiring via resumes/applications in order to better represent the unemployed population seeking jobs.

The Work Stoppages program provides monthly and annual data and analysis of major work stoppages involving 1,000 or more workers lasting one full shift or longer. The monthly and annual data show the establishment and union(s) involved in the work stoppage along with the location, the number of workers and the days of idleness. The monthly data list all work stoppages involving 1,000 or more workers that occurred during the full calendar month for each month of the year. The annualized data provide statistics, analysis and details of each work stoppage of 1,000 or more workers that occurred during the year. The work stoppages data are gathered from public news sources, such as newspapers and the Internet. The BLS does not distinguish between strikes and lock-outs in the data; both are included in the term "work stoppages". For more information and data visit: https://www.bls.gov/wsp/

The size of the Board Level Shielding (BLS) market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX% during the forecast period.

Context

To contribute towards the research and analysis on COVID-19 and it's impact on the human life, I have made this data available in usable format for analysis.

Content

1. This dataset contains time series data of unemployment rate in US from January 2010 to Present. It contains unemployment rate records based on education qualification, race, and gender of adults.
2. This dataset also contains state-wise unemployment rate data for year 2020. The data set is clean and doesn't require much pre-processing. Dataset can be used with BI tools like Tableau as well.

Acknowledgements

I would like to thank "U.S. BUREAU OF LABOR STATISTICS" for making the data available. URL: https://data.bls.gov/cgi-bin/surveymost?ln

Inspiration

1. Combine with COVID-19 dataset to understand the impact on Unemployment rate in the United States.
2. Perform Individual study on Unemployment in United States.

Official monthly employment data from BLS Employment Situation and JOLTS reports tracking hiring trends, job openings, and unemployment rates.

Official monthly employment data from Bureau of Labor Statistics (BLS) Employment Situation and JOLTS reports covering 2025

This data collection constitutes a reorganization of data from the Interview Survey component of the Consumer Expenditure Surveys produced by the Bureau of Labor Statistics (BLS) for the years 1980-1989. The Interview Surveys collect data on the expenditures, household characteristics, and income of a sample of consumer units. Interviews are conducted quarterly for a period of 15 months. While the original files are ordered by calendar quarter and calendar month, the reorganized files in this collection use the consumer unit (equivalent to a family or household) as the unit of analysis. The reorganization facilitates analysis of expenditure patterns of individual consumer units. Two kinds of files are presented in this collection: detailed and summary. The detailed files, Consumer Unit (CU), BLS Aggregated Data (BLS), Member Data (MEM), and Expenditure Tabulations (MT) files, retain almost all of the information from the original Interview Survey files (FMLY, MEMB, and MTAB). The detailed files are named according to the calendar year in which the consumer unit's fifth interview took place. Expenditures are expressed as monthly or quarterly totals in 472 categories. The summary files, Sum of Quarterly Expenditures by Consumer Price Index Aggregation (SUMQ), Summary (SUMMARY), and Aggregated Quarterly Expenditures, 1984-1989 (BLSSUM), aggregate expenditures by type and by quarter or year. The SUMQ files (one for each year) contain information on expenditures aggregated over interview quarters in approximately 70 aggregate categories. The SUMMARY file contains annual expenditures in the same 70 categories, along with selected demographic variables, for those consumer units that participated in the survey for a full year. For convenience, two files containing United States city average Consumer Price Indices corresponding to the aggregate goods categories by month and by year are provided. The BLSSUM file contains quarterly summed expenditures for all consumer units from 1984 on, using the aggregation scheme followed by the BLS files.

The size of the BLS Training Manikins market was valued at USD XXX million in 2023 and is projected to reach USD XXX million by 2032, with an expected CAGR of XX% during the forecast period.

The Basic Life Support (BLS) Training Manikin market has seen significant growth in recent years as the demand for effective training tools increases within the healthcare and emergency response sectors. These manikins are integral for teaching vital lifesaving skills, including cardiopulmonary resuscitation (CPR) a

The Board Level Shielding (BLS) market plays a crucial role in electronic device manufacturing by providing electromagnetic interference (EMI) shielding for circuit boards, ensuring optimal performance and reliability. BLS solutions are particularly vital in industries including telecommunications, aerospace and def

Graph and download economic data for Employed full time: Wage and salary workers: Compensation, benefits, and job analysis specialists occupations: 16 years and over (LEU0257856200A) from 2011 to 2024 about occupation, compensation, jobs, benefits, full-time, salaries, workers, 16 years +, wages, employment, and USA.

These data represent Socioeconomic Projections for the MAG Region by municipal planning area (MPA)*,adopted June 28, 2023, by the MAG Regional Council. An official set of projections is required to be used in transportation, air quality, and water quality management plans, as well as providing the base for all other regional planning activities. Current projections, therefore, are integral for managing future growth. The development of socioeconomic projections requires the collection and merging of a substantial amount of data from varying sources with differing data quality and resolution. These data include the following:Population and Housing: American Community Survey 5-year data (2017-2021),MAG Residential Completions database, County Property Assessment data, MAG/Arizona Department of Administration (ADOA) Annual Population Estimates.Group Quarters (Institutional and Non-institutional): MAG group quarters inventory.Detailed Population Characteristics: American Community Survey (ACS) Public Use Microdata Sample (PUMS) - 5-year data (2017-2021).Employment: MAG Employer Database, county level control totals developed from the Quarterly Census of Employment and Wages and Bureau of Labor Statistics (QCEW/BLS) data.Residential Completions: Current through 2022Q4, submitted and reviewed by MAG member agencies.Existing Land Use: Land use current as of December 2022, reviewed by MAG Population Technical Advisory Committee (POPTAC).Built Space: Maricopa County Assessor’s data current as of July 2022.Future Plans: General Plans current as of December 2022 or later, reviewed by MAG POPTAC.Development Data: data current as of 2023Q2 or later, reviewed by MAG POPTAC.TAZ system: TAZ2021b supplied by MAG Transportation Division.Educational institutions: Inventory of schools from Arizona Department of Education and post high school institutions.Mobile Home and RV Parks: Inventory of mobile home and RV parks.Retirement Areas: Age restricted communities reviewed by MAG POPTAC.Hotels/Motels/Resorts: Inventory of hotels/motels.For full documentation on the model process, please consult the Socioeconomic Projections Documentation: Data, Models, Methods, and Assumptions in the MAG Socioeconomic Projections 2023 on the MAG website at https://www.azmag.gov.These projections were adopted by the MAG Regional Council on June 28, 2023 for the MAG planning area. Areas outside of the MAG planning area are not adopted by the MAG Regional Council, but are prepared on behalf of Central Arizona Governments (CAG) and adopted separately.*Municipal planning areas are determined by the MAG member agencies in consultation with MAG staff. The MPAs identify the anticipated future corporate limits of a city or town.

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

Six alternative measures of labor underutilization have long been available on a monthly basis from the Current Population Survey (CPS) for the United States as a whole. They are published in the Bureau of Labor Statistics' monthly Employment Situation news release. (See table 15.) The official concept of unemployment (as measured in the CPS by U-3 in the U-1 to U-6 range of alternatives) includes all jobless persons who are available to take a job and have actively sought work in the past four weeks. This concept has been thoroughly reviewed and validated since the inception of the CPS in 1940. The other measures are provided to data users and analysts who want more narrowly (U-1 and U-2) or broadly (U-4 through U-6) defined measures.

BLS is committed to updating the alternative measures data for states on a 4-quarter moving-average basis. The use of 4-quarter averages increases the reliability of the CPS estimates, which are based on relatively small sample sizes at the state level, and eliminates seasonality. Due to the inclusion of lagged quarters, the state alternative measures may not fully reflect the current status of the labor market. The analysis that follows pertains to the 2023 annual averages. Data are also available for prior time periods back to 2003.

Excel spreadsheet containing, for all analysis methods, the proportion of articles mentioning an analysis method out of the total article count by year. The code for generating the bootstrapped confidence intervals in Fig 3 is provided at https://github.com/tsb46/stats_history. (XLSX)

Excel spreadsheet containing 4 tabs: (1) the total number of article counts per discipline; (2) the count of mentions for all analysis methods by year; (3) the top 50 journal counts by discipline; and (4) the embedding coordinates of each discipline in the MDS space (Fig 2B). MDS, multidimensional scaling. (XLSX)

The Basic Life Support (BLS) simulators market has emerged as a crucial segment within the healthcare training industry, particularly for professionals responsible for responding to cardiac emergencies. As the demand for effective training tools rises, BLS simulators provide an invaluable solution by offering realis