The layoff hangover begins

Suddenly your recruiting team is scrambling to manage an explosion of new job applications. Why?

Analyzing layoff benefits provides a clue:

Severance periods are ending and impacted employees are flooding the job market, doubling to ~106,000 in April.

It’s a classic labor supply shock. Combined with tightening job openings, we can expect “prices” (offers) to fall in the short-term until the market stabilizes.

Analysis details below, and you can view my Google Sheet here.

Analysis

For severance package info, I relied on the Compa Layoff Benefits Tracker which tracks publicly disclosed layoff benefits from CEO memos.

For number of employees laid off, I relied on Layoffs.fyi and various public disclosures. I limited the analysis to US-headquartered tech companies that disclosed 300 or more impacted employees beginning in June 2022.

Key assumptions:

• For companies that did not disclose severance details, I estimated the median period of 14 weeks
• I assume the minimum severance period, the most common type of disclosure — most companies offered additional severance for longer-tenured employees
• For companies with multiple layoffs, I assume the same severance period
• Where % of workforce impacted was disclosed, not #, I used the best publicly available estimate of total headcount to calculate the impacted #
• My weakest assumption: I assume that people only begin job seeking near the end of their severance periods. Many people look for jobs right away, especially if they received severance in a lump sum payment; but there’s some evidence that others wait…

The resulting analysis included 107 layoff events, of which 39 disclosed severance periods.

I initially looked at just the data for companies that disclosed benefits:

It seemed clear that the supply shock would kick into gear starting in March or April, but I worried the low n-count skewed the dataset.

Expanding to all 107 layoff events smoothed out some lumps, but the picture is essentially the same:

This is partly because this dataset is, like so many things in life, Pareto-distributed.

That is, the top 20% of layoff events contribute the vast majority (~70%) of impacted employees.

View my analysis here.