The Occupational Employment Statistics (OES) program conducts a semiannual survey designed to produce estimates of employment and wages for specific occupations. The OES program collects data on wage and salary workers in nonfarm establishments in order to produce employment and wage estimates for about 800 occupations. Data from self-employed persons are not collected and are not included in the estimates. The OES program produces these occupational estimates for the nation as a whole, by state, by metropolitan or nonmetropolitan area, and by industry or ownership. The Bureau of Labor Statistics produces occupational employment and wage estimates for approximately 415 industry classifications at the national level. The industry classifications correspond to the sector, 3-, 4-, and selected 5- and 6-digit North American Industry Classification System (NAICS) industrial groups. The OES program surveys approximately 200,000 establishments per panel (every six months), taking three years to fully collect the sample of 1.2 million establishments. To reduce respondent burden, the collection is on a three-year survey cycle that ensures that establishments are surveyed at most once every three years. The estimates for occupations in nonfarm establishments are based on OES data collected for the reference months of May and November. The OES survey is a federal-state cooperative program between the Bureau of Labor Statistics (BLS) and State Workforce Agencies (SWAs). BLS provides the procedures and technical support, draws the sample, and produces the survey materials, while the SWAs collect the data. SWAs from all fifty states, plus the District of Columbia, Puerto Rico, Guam, and the Virgin Islands participate in the survey. Occupational employment and wage rate estimates at the national level are produced by BLS using data from the fifty states and the District of Columbia. Employers who respond to states' requests to participate in the OES survey make these estimates possible. The OES features several arts-related occupations, particularly in the Arts, Design, Entertainment, Sports, and Media Occupations group (Standard Occupational Classification (SOC) code 27-0000). Several featured occupation groups include the following: Art and Design Workers (SOC 27-1000) Art Directors Fine Artists, including Painters, Sculptors, and Illustrators Multimedia Artists and Animators Fashion Designers Graphic Designers Set and Exhibit Designers Entertainers and Performers, Sports and Related Workers (SOC 27-2000) Actors Producers and Directors Athletes Coaches and Scouts Dancers Choreographers Music Directors and Composers Musicians and Singers Media and Communication Workers (SOC 27-3000) Radio and Television Announcers Reports and Correspondents Public Relations Specialists Writers and Authors Data for years 1997 through the latest release and can be found on the OES Data page. Also, see OES News Releases sections for current estimates and news releases. Users can analyze the data for the nation as a whole, by state, by metropolitan or nonmetropolitan area, and by industry or ownership. As well, OES Charts are available. Users may also explore data using OES Maps. If preferred, data can also be accessed via the Multi-Screen Data Search or Text Files using the OES Databases page.

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.

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.

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.

Allows users to search nonfatal and fatal data for the nation and for States from the most current Survey of Occupational Injuries and Illnesses and the Census of Fatal Occupational Injuries programs. Users can search by industry, demographic characteristics, and case characteristics. Historical data for years prior to the current year. More information and details about the data provided can be found at http://bls.gov/iif/Data.htm.

Allows users to search nonfatal and fatal data for the nation and for States from the most current Survey of Occupational Injuries and Illnesses and the Census of Fatal Occupational Injuries programs. Users can search by industry, demographic characteristics, and case characteristics. Historical data for years prior to the current year. More information and details about the data provided can be found at http://bls.gov/iif/Data.htm.

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This web map powers the Public Safety Coverage webmap application that enables users to search various GIS data. Information is updated on a weekly basis. Please contact gisadmin@co.crawford.pa.us for any questions, edits, or issues with this application.Additional maps can be found at our GIS landing page.

Graph and download economic data for Producer Price Index by Industry: Internet Publishing and Web Search Portals: Internet Publishing and Web Search Portals - Search and Textual Advertising Sales (PCU519130519130101) from Dec 2009 to Oct 2020 about advertisement, internet, printing, sales, PPI, industry, inflation, price index, indexes, price, and USA.

This collection provides data on labor force activity for the week prior to the survey. Comprehensive data are available on the employment status, occupation, and industry of persons aged 15 and over. Also shown are personal characteristics such as age, sex, race, marital status, veteran status, household relationship, educational background, and Hispanic origin. The Contingent Work Supplement questions were asked of all applicable persons aged 15 and older. The file contains information on contingent or temporary work done without expecting continuing employment. Also included is information about each worker's expectation of continuing employment, satisfaction with their current employment arrangement, transition into the current employment arrangement, current job history, search for other employment, employee benefits, and earnings. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR -- https://doi.org/10.3886/ICPSR04311.v2. We highly recommend using the ICPSR version as they made this dataset available in multiple data formats.

The Producer Price Index (PPI) is a family of indexes that measures the average change over time in selling prices received by domestic producers of goods and services. PPIs measure price change from the perspective of the seller. This contrasts with other measures, such as the Consumer Price Index (CPI), that measure price change from the purchaser's perspective. Sellers' and purchasers' prices may differ due to government subsidies, sales and excise taxes, and distribution costs. There are three main PPI classification structures which draw from the same pool of price information provided to the BLS by cooperating company reporters: Industry classification. A Producer Price Index for an industry is a measure of changes in prices received for the industry's output sold outside the industry (that is, its net output). The PPI publishes approximately 535 industry price indexes in combination with over 4,000 specific product line and product category sub-indexes, as well as, roughly 500 indexes for groupings of industries. North American Industry Classification System (NAICS) index codes provide comparability with a wide assortment of industry-based data for other economic programs, including productivity, production, employment, wages, and earnings. Commodity classification. The commodity classification structure of the PPI organizes products and services by similarity or material composition, regardless of the industry classification of the producing establishment. This system is unique to the PPI and does not match any other standard coding structure. In all, PPI publishes more than 3,700 commodity price indexes for goods and about 800 for services (seasonally adjusted and not seasonally adjusted), organized by product, service, and end use. Commodity-based Final Demand-Intermediate Demand (FD-ID) System. Commodity-based FD-ID price indexes regroup commodity indexes for goods, services, and construction at the subproduct class (six-digit) level, according to the type of buyer and the amount of physical processing or assembling the products have undergone. The PPI publishes over 600 FD-ID indexes (seasonally adjusted and not seasonally adjusted) measuring price change for goods, services, and construction sold to final demand and to intermediate demand. The FD-ID system replaced the PPI stage-of-processing (SOP) system as PPI's primary aggregation model with the release of data for January 2014. The FD-ID system expands coverage in its aggregate measures beyond that of the SOP system by incorporating indexes for services, construction, exports, and government purchases. For more information, visit: https://www.bls.gov/ppi

Replication files for "Job-to-Job Mobility and Inflation" Authors: Renato Faccini and Leonardo Melosi Review of Economics and Statistics Date: February 2, 2023 -------------------------------------------------------------------------------------------- ORDERS OF TOPICS .Section 1. We explain the code to replicate all the figures in the paper (except Figure 6) .Section 2. We explain how Figure 6 is constructed .Section 3. We explain how the data are constructed SECTION 1 Replication_Main.m is used to reproduce all the figures of the paper except Figure 6. All the primitive variables are defined in the code and all the steps are commented in code to facilitate the replication of our results. Replication_Main.m, should be run in Matlab. The authors tested it on a DELL XPS 15 7590 laptop wih the follwoing characteristics: -------------------------------------------------------------------------------------------- Processor Intel(R) Core(TM) i9-9980HK CPU @ 2.40GHz 2.40 GHz Installed RAM 64.0 GB System type 64-bit operating system, x64-based processor -------------------------------------------------------------------------------------------- It took 2 minutes and 57 seconds for this machine to construct Figures 1, 2, 3, 4a, 4b, 5, 7a, and 7b. The following version of Matlab and Matlab toolboxes has been used for the test: -------------------------------------------------------------------------------------------- MATLAB Version: 9.7.0.1190202 (R2019b) MATLAB License Number: 363305 Operating System: Microsoft Windows 10 Enterprise Version 10.0 (Build 19045) Java Version: Java 1.8.0_202-b08 with Oracle Corporation Java HotSpot(TM) 64-Bit Server VM mixed mode -------------------------------------------------------------------------------------------- MATLAB Version 9.7 (R2019b) Financial Toolbox Version 5.14 (R2019b) Optimization Toolbox Version 8.4 (R2019b) Statistics and Machine Learning Toolbox Version 11.6 (R2019b) Symbolic Math Toolbox Version 8.4 (R2019b) -------------------------------------------------------------------------------------------- The replication code uses auxiliary files and save the pictures in various subfolders: \JL_models: It contains the equations describing the model including the observation equations and routine used to solve the model. To do so, the routine in this folder calls other routines located in some fo the subfolders below. \gensystoama: It contains a set of codes that allow us to solve linear rational expectations models. We use the AMA solver. More information are provided in the file AMASOLVE.m. The codes in this subfolder have been developed by Alejandro Justiniano. \filters: it contains the Kalman filter augmented with a routine to make sure that the zero lower bound constraint for the nominal interest rate is satisfied in every period in our sample. \SteadyStateSolver: It contains a set of routines that are used to solved the steady state of the model numerically. \NLEquations: It contains some of the equations of the model that are log-linearized using the symbolic toolbox of matlab. \NberDates: It contains a set of routines that allows to add shaded area to graphs to denote NBER recessions. \Graphics: It contains useful codes enabling features to construct some of the graphs in the paper. \Data: it contains the data set used in the paper. \Params: It contains a spreadsheet with the values attributes to the model parameters. \VAR_Estimation: It contains the forecasts implied by the Bayesian VAR model of Section 2. The output of Replication_Main.m are the figures of the paper that are stored in the subfolder \Figures SECTION 2 The Excel file "Figure-6.xlsx" is used to create the charts in Figure 6. All three panels of the charts (A, B, and C) plot a measure of unexpected wage inflation against the unemployment rate, then fits separate linear regressions for the periods 1960-1985,1986-2007, and 2008-2009. Unexpected wage inflation is given by the difference between wage growth and a measure of expected wage growth. In all three panels, the unemployment rate used is the civilian unemployment rate (UNRATE), seasonally adjusted, from the BLS. The sheet "Panel A" uses quarterly manufacturing sector average hourly earnings growth data, seasonally adjusted (CES3000000008), from the Bureau of Labor Statistics (BLS) Employment Situation report as the measure of wage inflation. The unexpected wage inflation is given by the difference between earnings growth at time t and the average of earnings growth across the previous four months. Growth rates are annualized quarterly values. The sheet "Panel B" uses quarterly Nonfarm Business Sector Compensation Per Hour, seasonally adjusted (COMPNFB), from the BLS Productivity and Costs report as its measure of wage inflation. As in Panel A, expected wage inflation is given by the... Visit https://dataone.org/datasets/sha256%3A44c88fe82380bfff217866cac93f85483766eb9364f66cfa03f1ebdaa0408335 for complete metadata about this dataset.

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 features 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 2017, and were made available on September 11, 2018. The unpublished integrated CE data tables produced by the BLS are available to download through NADAC (click on "Excel" in the Dataset(s) section). Also, see Featured CE Tables and Economic News Releases sections on the CE home page for current data tables and news release. The 2017 public-use microdata is the most recent and was released on September 11, 2018.

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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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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.

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Graph and download economic data for Producer Price Index by Commodity: Employment Services: Executive Search Services (WPU462) from Dec 2008 to Sep 2025 about services, commodities, employment, PPI, inflation, price index, indexes, price, and USA.

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No description is available. Visit https://dataone.org/datasets/13e6f8e10a7a283c3c3e232d8ddcb431 for complete metadata about this dataset.