The layoff hangover begins

April 20, 2023
Min Read
The layoff hangover begins

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

Key topics

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.


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 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.

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