An in-depth dataset with statistics and insights related to career changes, including frequency, reasons, age-based trends, industry shifts, and psychological drivers for switching careers.

Introduction

Job Satisfaction Statistics: Companies use the term "job contentment" or "employee satisfaction " to measure how happy or unhappy workers are with their jobs. Companies that want to get the best results can use job satisfaction data because it is closely connected to things like employee performance, retention, and overall happiness at work.

Employers who want to attract and keep the best employees need to understand how important job satisfaction is. In this article, we will look at the key Job Satisfaction Statistics.

This dataset represents the CHANGE in the number of jobs per industry category and sub-category from the previous month, not the raw counts of actual jobs. The data behind these monthly change values is from the Bureau of Labor Statistics (BLS) Current Employment Statistics (CES) program. CES data represents businesses and government agencies, providing detailed industry data on employment on nonfarm payrolls.

The Job Openings and Labor Turnover Survey (JOLTS) program provides national estimates of rates and levels for job openings, hires, and total separations. Total separations are further broken out into quits, layoffs and discharges, and other separations. Unadjusted counts and rates of all data elements are published by supersector and select sector based on the North American Industry Classification System (NAICS). The number of unfilled jobs—used to calculate the job openings rate—is an important measure of the unmet demand for labor. With that statistic, it is possible to paint a more complete picture of the U.S. labor market than by looking solely at the unemployment rate, a measure of the excess supply of labor. Information on labor turnover is valuable in the proper analysis and interpretation of labor market developments and as a complement to the unemployment rate. For more information and data visit: https://www.bls.gov/jlt/

Between 2023 and 2027, the majority of companies surveyed worldwide expect big data to have a more positive than negative impact on the global job market and employment, with ** percent of the companies reporting the technology will create jobs and * percent expecting the technology to displace jobs. Meanwhile, artificial intelligence (AI) is expected to result in more significant labor market disruptions, with ** percent of organizations expecting the technology to displace jobs and ** percent expecting AI to create jobs.

Introduction

Career Change Statistics: If you've been thinking about leaving your job to focus more than focusing on your work, you're not the only one. Since the pandemic, nearly half of us have thought about changing careers. The idea that there's something better out there is growing quickly, with over 60% of workers planning to switch jobs this year. The days of staying in one job forever are gone.

Whether it's because of burnout or feeling stuck, the numbers don't lie – it's a big shift in how we think about our careers, and you might be the next one to break free from the corporate routine. We shall shed more light on the Career Change Statistics through this article.

By the last business day of September 2024, there were about 7.44 million job openings in the United States. This is a decrease from the previous month, when there were 7.86 million job openings. The data are seasonally adjusted. Seasonal adjustment is a statistical method for removing the seasonal component of a time series that is used when analyzing non-seasonal trends.

Data from the Bureau of Labor Statistics (BLS) Current Employment Statistics (CES) program. CES data represents businesses and government agencies, providing detailed industry data on employment on nonfarm payrolls.

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.

The statistic shows the share of jobs at high risk of automation by region and industry sector. By 2030, **** percent of jobs in the energy, utilities and mining industry in North America are at high risk of automation.

39.8% of workers from the Indian ethnic group were in 'professional' jobs in 2021 – the highest percentage out of all ethnic groups in this role.

The jobactive Job Placements table provides data on jobactive job placements. The table includes provider information, vacancy details, job seeker characteristics at the time of the job placement and job placement to outcome conversion denominators and numerators. The lowest data grain in the dataset is JOB_PLACEMENT_ID, a unique code generated each time a job seeker is referred to a vacancy. The dataset was extracted on 5 August 2018, however is based on job placements confirmed between 1 July 2016 and 30 June 2017.
Please note that the time period of the dataset has been restricted to mitigate any potential sensitivity risks and this may limit certain analyses.

The chart provides an insightful analysis of the estimated sales amounts for Jobs & Education stores across various platforms. WooCommerce stands out, generating a significant portion of sales with an estimated amount of $3.83B, which is 48.16% of the total sales in this category. Following closely, Custom Cart accounts for $2.82B in sales, making up 35.46% of the total. Magento also shows notable performance, contributing $395.50M to the total sales, representing 4.97%. This data highlights the sales dynamics and the varying impact of each platform on the Jobs & Education market.

Graph and download economic data for Job Openings: Total Nonfarm (JTSJOL) from Dec 2000 to May 2025 about job openings, vacancy, nonfarm, and USA.

This chart offers a detailed view of the estimated sales amounts for Jobs & Education stores across different regions. In United States, the sales figures are particularly impressive, with the region generating $3.20B, which accounts for 40.19% of the total sales in this category. United Kingdom follows with robust sales, totaling $919.73M and representing 11.56% of the overall sales. Vietnam also contributes significantly to the market with sales amounting to $443.64M, making up 5.57% of the total. These numbers not only illustrate the economic vitality of each region in the Jobs & Education market but also highlight regional consumer preferences and spending power.

Graph and download economic data for Multiple Jobholders, Primary Job Full Time, Secondary Job Part Time (LNU02026625) from Jan 1994 to Jun 2025 about multiple jobholders, part-time, full-time, 16 years +, household survey, employment, and USA.

Graph and download economic data for Job Openings: Retail Trade (JTS4400JOL) from Dec 2000 to Apr 2025 about job openings, vacancy, retail trade, sales, retail, and USA.

Our data sheds light on the distribution of Jobs & Education stores across different online platforms. WooCommerce leads with a substantial number of stores, holding 118.71K stores, which accounts for 50.99% of the total in this category. Custom Cart follows with 24.93K stores, making up 10.71% of the Jobs & Education market. Meanwhile, Wix offers a significant presence as well, with 16.44K stores, or 7.06% of the total. This chart gives a clear picture of how stores within the Jobs & Education sector are spread across these key platforms.

Introducing Job Posting Datasets: Uncover labor market insights!

Elevate your recruitment strategies, forecast future labor industry trends, and unearth investment opportunities with Job Posting Datasets.

Job Posting Datasets Source:

1. Indeed: Access datasets from Indeed, a leading employment website known for its comprehensive job listings.

2. Glassdoor: Receive ready-to-use employee reviews, salary ranges, and job openings from Glassdoor.

3. StackShare: Access StackShare datasets to make data-driven technology decisions.

Job Posting Datasets provide meticulously acquired and parsed data, freeing you to focus on analysis. You'll receive clean, structured, ready-to-use job posting data, including job titles, company names, seniority levels, industries, locations, salaries, and employment types.

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Feature Articles on Employment and Labour - Statistics on Job Vacancies