Context

The Bureau of Labor Statistics (BLS) is a unit of the United States Department of Labor. It is the principal fact-finding agency for the U.S. government in the broad field of labor economics and statistics and serves as a principal agency of the U.S. Federal Statistical System. The BLS is a governmental statistical agency that collects, processes, analyzes, and disseminates essential statistical data to the American public, the U.S. Congress, other Federal agencies, State and local governments, business, and labor representatives. Source: https://en.wikipedia.org/wiki/Bureau_of_Labor_Statistics

Content

Bureau of Labor Statistics including CPI (inflation), employment, unemployment, and wage data.

Update Frequency: Monthly

Querying BigQuery Tables

Fork this kernel to get started.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:bls

https://cloud.google.com/bigquery/public-data/bureau-of-labor-statistics

Dataset Source: http://www.bls.gov/data/

This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by Clark Young from Unsplash.

Inspiration

What is the average annual inflation across all US Cities? What was the monthly unemployment rate (U3) in 2016? What are the top 10 hourly-waged types of work in Pittsburgh, PA for 2016?

Graph and download economic data for Total wages and salaries, BLS: Adjustment for misreporting on employment tax returns (BA07RC1A027NBEA) from 1982 to 2023 about return, adjusted, salaries, tax, wages, employment, GDP, and USA.

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 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.

Graph and download economic data for Employed full time: Wage and salary workers: Financial analysts occupations: 16 years and over (LEU0254476000A) from 2000 to 2019 about analysts, occupation, full-time, salaries, workers, financial, 16 years +, wages, employment, and USA.

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 TAC provides advice to the Bureau of Labor Statistics on technical aspects of data collection and the formulation of economic measures and makes recommendations on areas of research. On some technical issues, there are differing views and receiving feedback at public meetings provides BLS with the opportunity to consider all viewpoints.

The Committee consists of approximately 16 members who serve as Special Government Employees. Members are appointed by the BLS and are approved by the Secretary of Labor. Committee members are experts in economics, statistics, data science, and survey design. Members typically have Ph.D.s in their field and have significant experience. They are prominent experts in their fields and recognized for their professional achievements and objectivity. The economic experts will have research experience with technical issues related to BLS data and will be familiar with employment and unemployment statistics, price index numbers, compensation measures, productivity measures, occupational and health statistics, or other topics relevant to BLS data series. The statistical experts will have experience with sample design, data analysis, computationally intensive statistical methods, non-sampling errors or other areas which are relevant to BLS work. The data science experts will have experience compiling, modeling, analyzing, and interpreting large sets of structured and unstructured data. The survey design experts will have experience with questionnaire design, usability, or other areas of survey development. Collectively, the members will provide a balance of expertise in all of these areas.

BLS invites persons interested in serving on the TAC to submit their names for consideration for committee membership. Typically, TAC members are appointed to three-year terms and serve as unpaid Special Government Employees.

The Bureau often faces highly technical issues while developing and maintaining the accuracy and relevancy of its data on employment and unemployment, prices, productivity, and compensation and working conditions. These issues range from how to develop new measures to how to make sure that existing measures account for the ever-changing economy. BLS presents issues and then draws on the specialized expertise of Committee members representing specialized fields within the academic disciplines of economics, statistics and data science, and survey design. Committee members are also invited to bring to the attention of BLS issues that have been identified in the academic literature or in their own research.

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.

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.

About this Dataset

This dataset combines automation probability data with a breakdown of the number of jobs and salary in each occupation by state within the USA. Automation probability was acquired from the work of Carl Benedikt Freyand Michael A. Osborne; State employment data is from the Bureau of Labor Statistics. Note that for simplicity of analysis, all jobs where data was not available or there were less than 10 employees were marked as zero.

How to Cite this Dataset

If you use this dataset in your research, please credit the authors.

Salary Data

@misc{u.s. bureau of labor statistics, title={Occupational Employment Statistics}, url={https://www.bls.gov/oes/current/oes_nat.htm}, journal={U.S. BUREAU OF LABOR STATISTICS}}

Automation Data

@article{frey_osborne_2017, title={The future of employment: How susceptible are jobs to computerisation?}, volume={114}, DOI={10.1016/j.techfore.2016.08.019}, journal={Technological Forecasting and Social Change}, author={Frey, Carl Benedikt and Osborne, Michael A.}, year={2017}, pages={254–280}}

License

License was not specified at the source.

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