77-Layoffs, Expectations and the Economy

When we read the headlines, it sometimes seems that layoffs of the workforce when revenues decline in an industry are random. Sometimes they seem high. Other times low. But they usually have a logic. This blog shows some of the patterns you might see why you see them.

Posted 2/5/09

We are flirting with the highest levels of unemployment in a generation. Things feel bad and are bad. They could get a lot worse.

Consider some of the recent lay-offs. Macy’s laid off 4% of its workforce. U.S. Steel laid off 16%. Sun Microsystems announced plans to lay-off 15 to 18% of its workforce, Texas Instruments 12%, Sprint 14%. In most of these cases, the lay-offs seemed large, and they are.

But they are not as large as they may seem. In fact, an unscientific analysis suggests there may be a pattern here that contains a warning for the future. Most of the companies reporting in the last few weeks are laying off between 10 and 20% of their workforce. However, behind those lay-offs are reductions in current quarterly revenues of 20 to 30%. These companies are laying off at a slower pace than their revenues are falling. (See the Perspective, “Costs: The Last Consideration” on StrategyStreet.com.)

So, what does this tell us? Two things. First, the companies believe there will be a turn-around within a year or so. They are willing to see margins fall in order to hold experienced employees in anticipation of a rebound in demand. Second, the economy could be much worse than it is. If the average employer has laid off about 75% of the workers it would need to lay off to match the workforce with the current revenues, unemployment would be even higher than it already is. If the anticipated turn-around does not appear as expected, there would be a catch-up period as companies “right size” their organizations for the new, lower, revenue base.

Now there’s a scary thought for the politicians.

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Update 2022:

The layoffs in the early Covid economy were more extensive but somewhat less painful. The economy fell roughly 10% in a little over a month. During that period, roughly 15% of the workforce went on either temporary or permanent unemployment, exceeding the falloff in the economy. Small business revenue fell by 20%. The number of labor force participants not at work quadrupled from January to April 2020. As bad as these numbers were, federal and state assistance initiatives helped reduce the degree of pain suffered by the newly unemployed.

A cost reduction program must ensure that the company maintains its lead over competition, especially in market share, which is the ultimate source of low-cost. For more explanation of this idea see this perspective from the body of the blog above and also further thoughts HERE.

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Update 3/26

An analysis of rough available data says that in downturns some industries layoff people aggressively, layoff percentages are greater than the percentage falloff of revenues. Other industries follow the opposite tactic, laying off a smaller percentage of the workforce than the percentage falloff in revenues. Let’s look at several industries to understand what might be happening. Recall that we are using broad definitions of industries and often resort to using proxies to estimate percentage declines. Still, the results are worth considering.

We will measure the approaches to layoffs using a ratio: % layoff/ % revenue decline. A ratio above 1.0 says that the industry lays off more people as a percentage than revenue falls as a percentage, an aggressive approach to layoffs. A ratio below 1.0 suggests a much more conservative approach to layoffs.  The industry lays off fewer people as a percentage of the workforce than it suffers in percentage revenue declines.

We use the following downturns for our ratios of declines in revenues compared to declines in employment: 2008 with the great financial crisis, 2014 during the period of the oil crash, 2020 with the pandemic and 2022 to 23 during the tech mini recession.

We examine 10 industries, very broadly defined: technology, retail, transportation/warehousing, manufacturing, construction, energy/mining/logging, finance, healthcare/social services, leisure/hospitality, and professional services.

Here are the rough ratio groupings we have found over these downturns. Four industries showed aggressive layoff patterns, with ratios from 1 to 1.5: finance, professional services and manufacturing. Three industries demonstrated modest layoffs with ratios less than 1.0 down to .8: construction, leisure/hospitality and retail. The final three industries showed a greater tendency to retain employees, with ratios below .8: healthcare/social services, transportation/warehousing, and energy/mining/logging.

What forces cause the variations in these patterns? The following variables explain most of the variations: high fixed cost in the industry, industry expectations of prompt demand and revenue recovery, hoarding of highly specialized employees who are expensive to rehire, labor costs a high and variable percentage of revenues, and employees requiring limited training for positioning in multiple jobs.

The aggressive approach of finance and professional services may come as a surprise because of their highly trained workforce. An example may help to explain this result. Let’s assume for the moment that these industries have a ratio of 1.5. The workforce represents 80% or more of the revenues in these industries. Virtually all the other costs are fixed cash costs. These industries rely on high workforce utilization rates in order to maintain their margins. Utilization rates below 75% create cash losses for these industries. So, if a company sustains a 10% decline in revenues, it cannot reduce its workforce just by 10% and still maintain its margins. The layoffs must be greater than 10% in order to keep margins steady. While these firms will reduce partner distributions and freeze hiring, layoffs are the only answer in the face of significant revenue declines. Both industries have proven over time that they are able to re-staff with competent people as demand recovers.

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