Analysis of job change data for the white-collar workforce
Executive layoffs made up almost two times the share of total layoffs versus the previous 5-year average.
From our sample, manager-level or higher layoffs made up almost half of all observed layoffs in 2023. Up an average of 57.6% versus the previous 5-year average.
Explore the annual breakdown of layoffs by job level.
A sample of 6.5M+ job changes for white-collar employees from 2018 to 2023.
It is worth noting that Live Data Technologies is primarily focused on monitoring employment status and job change events for white-collar employees in North America.
Live Data Technologies observes millions of job change events every year (both company changes and internal changes). We sampled 6.5M+ of these observed job changes where the individual left their previous company (external) and then spent more than 60 days without starting a new job. If the individual left a company and did not start a new job within 60 days, we classified this job change as involuntary or a “layoff”.
Our best proxy for determining layoffs requires someone to stay laid off for 60 days. While the majority of white-collar professionals who are laid off take at least this much time to find their next professional role, some people are hired within 60 days.
Live Data Technologies uses an ML solution for title-to-level classification. The analysis separated workers into three job level categories: staff, manager, and executive. Across the entirety of our 88M+ person dataset, we see an average of 63% staff-level employees (including those currently unemployed), 23% manager-level employees, and 14% executives.
For this analysis, we used the level classification for the the role that any given individual left before entering the greater than 60-day period of unemployment. For each month in our sample, we looked at the percentage of the total observed layoffs by level.
Live Data Technologies's standard process (our patented IP) is generally referred to as SERP analysis. We query the major search engines (Baidu, Yandex, Bing, Google, etc.) for information on people and their employment. A shorthand way of thinking of this is that we are prompt engineering the search engines.
All of our data is sourced through these sources and is publicly available. We assess ALL the info that comes back from each query to the search engines. We use a proprietary process to monitor the current company and title for 88M+ people on at least a twice-monthly basis, and as such pick up a lot of job changes monthly. This means we have a) the most recent employment data for the white-collar workforce and b) a continuous stream of job change events. This allows us to report on movement at the person, title, function, job level, company, and industry levels.
