High Profile Layoffs
Recently we heard a lot about high profile layoffs – how does this impact the economy, individuals and firms?
Recently, there have been many high profile layoff announcements by tech firms.
As a share of the wider economy, however, these layoffs are not a large proportion.
Firms conduct layoffs believing it will improve their performance or increase their stock price. The evidence suggests that layoffs during worsening economic times do the exact opposite.
Layoffs have significant impacts on workers that are impacted by them: their lifetime earnings drop, they have more unemployment spells, their health deteriorates, and outcomes for their families are worse.
Layoffs also impact local markets leading to an outflow of people out of the local labor force – people either quit working or migrate away.
Overall, research on layoff impacts is limited and more needs to be done.
Over the last few months, many companies in the technology industry in the US have undertaken rounds of layoffs. These decisions made major headlines – in 2023, over 77,000 workers from US based tech firms have been laid off or announced to be laid off. The list of companies includes Yahoo, Zoom, Dell, Spotify, Google, Microsoft. It is worth putting the scope of these layoffs in the context of the US Economy – weekly, around 100,000 to 300,000 new people file for unemployment, suggesting these tech layoffs do not meaningfully impact unemployment figures. However, due to the market capitalization of these companies, these decisions were significantly reported on and discussed, with a lot of focus on what impact these layoffs will have. This is a question that can be addressed by economic methods. Below, I will go over the impacts of these layoffs on the wider economy, the individuals affected (which includes both laid off workers and workers that were not laid off), and the company making the decision to conduct layoffs. I’ll start off from the latter.
Firms and Layoffs
The decision to layoff employees is an independent choice by the management of the company. Therefore, we would expect that this decision should bring some positive impact to the company, preferably in a quantifiable way. Answering this question is not as easy as it seems due to the fact that it is difficult to establish the counterfactual, i.e. what would have been had the company not laid off employees. Furthermore, as each company’s circumstances are unique, undertaking a ‘natural experiment’1 is not straightforward. It is difficult to argue that the company that did a layoff is similar to a company that did not do a layoff. However, attempts to address these issues and develop some insights have been made.
Research on this topic has been undertaken by academics in the Management field.2 Carriger (2016) summarized the research on the impact of downsizing (layoffs). There are two key ways to look at how downsizing might impact a firm – a management perspective, which focuses on how metrics such as profitability and return on equity or assets react to downsizing, and a finance perspective, which focuses on how a company’s publicly traded stock responds to the downsizing.
From a management perspective:
De Meuse et al. (2004) found that the financial metrics of downsizing firms worsened in the years after a layoff event, but over time, improved back to match the financial performance of non–downsizing companies;
They also found that companies that did fewer layoff events performed financially better than companies that had more layoffs;
Guthrie and Datta (2009) found that downsizing reduces firm profitability, and is especially worse in industries characterized by large research & development investment, high growth, and low usage of tangible capital, such as machines (note: interestingly, this seems to describe the characteristics of the tech sector);
Chalos and Chen (2003) found that the cause of downsizing matters. If the reason to downsize is cost-cutting (a reactive downsizing), then firms have worse financial performance; if it is revenue-refocusing (i.e. pro-active downsizing in response to future market trends), then financial performance improves.
Even with the caveat that it is difficult to ascertain what would have happened had these firms not downsized, which would allow us to establish a causal link, most research shows that there is generally no evidence or correlations suggesting downsizing for cost-cutting purposes will improve financial performance. Turning to the financial perspective:
Chen et al. (2001) found that on the layoff announcement day, stock prices respond negatively, especially if the cause behind the layoffs is lowering demand for the company’s goods or services. They do find that profitability and labor productivity increases for companies that conduct layoffs. However, these results occur for companies that have been underperforming to their industry peers for the prior three years. Three years after the layoff decision, operational metrics of firms that conducted layoffs outperform industry peers, while employment reverts back to pre-layoff levels. Interestingly, this improvement does not get reflected in stock market performance – the stock performance three years after the layoff period remains similar to its peers. Hiller et al. (2007) find similar results;
Brookman et al. (2007) found that companies in which CEO’s pay is linked to the performance of the stock are more likely to announce layoffs; this layoff decision does generate positive returns to shareholders. They find that this is potentially due to CEOs “adopting operating strategies that improve operating profits and stock performance”. Furthermore, similar to the Chen et al. (2001) study, Brookman et al. (2007) found that company internal performance metrics improved within two years of the layoff announcement;
Marshall et al. (2012) found that stock prices respond positively to layoffs in good times, but fall in bad times;
Capelle-Blanchard and Couderc (2007) conducted an overview of the entire literature of stock prices reacting to layoff announcements and found that generally stocks respond negatively to these announcements. As with research mentioned above, if the layoffs are driven by ‘defensive’ reasons (the firm is facing a difficulty), then the stock performance is even worse.
From the above, it appears that layoffs have negative impacts in the short-term on company stock prices. If the layoffs are ‘defensive’ in nature, most evidence points that both company stock performance and internal performance will suffer. This does not necessarily determine causality – i.e. do layoffs make the firm worse, or was the firm going to be faring poorly regardless of layoff decision. If the layoffs are done as a strategic decision, evidence suggests that companies fare somewhat better after them. Again, the same causality issue applies as above. To further address some of these contradictory results, Carriger (2016) tracked companies for a longer period of time during the 2008 financial crisis. He partitioned companies into 4 groups by two categories: financially healthy and unhealthy companies based on the amount of available cash they were generating, and whether companies downsized in staff or not, specifically in 2008.
He found that generally, companies that downsized in 2008 did worse in the period 2008-2014 than non-downsizing companies, regardless of financial health. However, any difference between these two groups became negligible in 2014. Furthermore, companies that downsized in 2008, immediately began re-hiring, and within 3 years were back to their pre-downsize employee count.
One important issue with this analysis is “Survivorship Bias”. Survivorship bias is the fact that when doing data analysis, we can only use data points of companies that existed (i.e. survived) throughout the data analysis period (in this case, from 2008 to 2014). More specifically, in the case of this particular study on layoffs and firms, we do not observe firms that ceased to exist in 2014. Thus, we do not have data points on companies that either chose or didn’t choose to downsize, but went bankrupt in that time frame. For example, a company that downsized in 2008 and in 2014, appeared to have poor financial performance, maybe would have gone entirely bankrupt if it had not downsized.3 In this instance, survivorship bias may make downsizing and layoffs seem like a less beneficial tool to the firm than it really is.
Layoffs and Individuals
Layoffs impact specific people. Economists are very interested in this impact because layoffs can potentially create many costs. For example, being laid off destroys accumulated human capital4 that was specific to the firm (i.e. by working at a firm, you already know how everything works and because this knowledge is specific to the firm, upon getting laid off, this knowledge is wasted). Furthermore, there are potentially many impacts on laid off workers, including changes to long-run career prospects, health outcomes, and even impacts on their families.
Davis and von Wachter (2011) summarize the research around the various impacts of job displacement, an unexpected job loss initiated by the firm usually involving multiple people being laid off. In their own research, they find that for laid off workers during mass layoff events, lifetime earnings of displaced workers drops by approximately 1.4 years of pre-displacement earnings (i.e. the drop in lifetime earnings is equivalent to you not working for 1.4 years, regardless of length of unemployment). This effect is twice as large if the unemployment rate is high, above 8%. There is debate, whether the act of layoffs in itself results in lower earnings, or is there a different underlying cause. Fallick et al (2021) find that it is the joblessness duration after the layoff that explains all the drop in earnings – the longer one is unemployed, the worse their future earnings outcome. There are several hypotheses why duration of unemployment may have a significant impact on future earnings. The mains ones are: 1) worker differences (there is something inherently different about workers that stay unemployed longer, which results in them earning less in the future); 2) human capital depreciation (the longer one is unemployed, the more they ‘forget’ about the job and become less effective and thus, receive a lower wage); 3) changes to local labor markets (if many of those laid off are in one market, there is an over-supply of workers, leading to a reduction in earnings); and 4) moving to a worse firm (this is called the job ladder model: good companies hire from worse companies, but worse companies hire from the unemployed). The authors find that the most likely reason is number 4. Workers that stay unemployed for longer than four quarters see significant earning reductions because they end up at generally worse firms.
Browning and Crossley (2001), using Canadian job loss data, find that consumption drops significantly for laid off individuals who are in the lower of the income distribution, as they do not usually possess savings to use for consumption during unemployment spells. Stevens (1997) finds that job displacement lowers job stability, increases earnings instability, increases job and industry switching, as well as makes future unemployment spells more likely. These effects are very persistent, lasting 10 years after the layoff, resulting in earnings 9% below expected levels. If, however, a displaced worker is able to find a job in the same industry very soon after being laid-off, they experience an increase in earnings in comparison to their previous job.
Regarding health, workers that experience job loss have higher incidences of strokes and heart attacks (Burgard, Brand, and House, 2007). Sullivan and von Wachter (2010) use administrative and social security data that allowed them to track workers that were displaced and found that mortality rates significantly increased for this group. Based on their analysis, a job loss during a recession reduced life expectancy for middle-aged men by 1 to 1.5 years.
Besides impacts on the individual, job losses also spread to the family of the impacted workers. Stevens and Schaller (2011) established that parental job loss reduced children’s educational achievement, with children being 15% more likely to repeat a grade. Wightman (2009) further shows that both educational attainment and cognitive development of the children suffer after job loss. Other studies have shown that job loss increases divorce rates (Charles and Stephens, 2004), reduces fertility, and decreases homeownership (von Wachter and Handwerker, 2009).
Layoffs have a significant cost on employees. These costs are both direct with falling earnings and consumption, but also indirect, as health, life, and family outcomes are also adversely affected.
Layoffs and the Wider Economy
The prior two sections focused on the impact of layoffs on firms and individuals. These impacts, however, also have costs to the economy. From the firm side, layoffs might lead to lower profitability, which means we are generally using resources less efficiently, and the government has lower tax revenues. The negative consequences to the workers are also costly to the economy; as a society, we have less effective workers due to losses in health, human capital, and job match. Furthermore, government services are also additionally strained due to the adverse health and family outcomes of impacted workers, as they will seek assistance from government programs.
Beyond these economic costs, how do layoffs affect the communities and areas they are located in? Foote et al. (2018) studied mass layoff events from 2000 to 2011 to establish how impacted local labor markets respond to such events. They found that if 1% of a county’s population gets laid off, the county’s labor force drops by 0.19 percentage points. This is caused by two key channels: migration (that is local population moving to other parts of the US) and quitting the labor force (i.e. permanently not looking for a job, retiring, or going on disability). Typically migration was the main driver of local labor market adjustments to layoffs. However, the authors found that after the Great Recession of 2007, quitting the labor force grew substantially and became the predominant way local labor markets adjust to shocks. This is similar to the current situation in 2020-2023, during which labor force participation has dropped by 1 percentage point.
Regarding the tech layoffs, the migration channel will probably remain stronger, as generally higher income displaced workers are the ones that typically migrate. Thus, if the tech layoffs were concentrated in specific counties, the labor force and population would be expected to drop.
Research on layoffs and its impacts still appears to be in its infancy, in part due to data difficulties and assessing counterfactuals (i.e. what would have happened if the firm did or did not conduct layoffs). Current research suggests that layoffs may not be the best response during tougher economic times, as they’re neither beneficial to the firm nor the employees. The negative impacts could further spread into the local market, if the layoffs are geographically concentrated. Interestingly, the US unemployment insurance program requires firms that have undertaken layoffs to pay larger unemployment insurance taxes. The bigger the layoffs, the larger the tax. During the Great Recession of 2007-2009, this mechanism is estimated to have reduced layoffs by around 825,000.
Layoffs appear to have a significant human capital cost, which lowers the overall productivity of the economy. Thus, it is crucial to understand whether layoffs generate sufficient market efficiencies to offset the human capital loss. Further research is needed to understand these potential trade offs to determine whether layoffs are inherently a bad tool or if they have a place in the economy.
In the hard-sciences (e.g. biology, chemistry), an experiment is when we take two groups and treat one of them with an intervention (for example, a medicine) and argue that any difference of outcomes between the groups is due to the treatment. That is because there shouldn’t be any difference in the group prior to the treatment if the enrollment into the groups was random. In social sciences (e.g. economics, psychology), such experiments are usually not allowed for ethical reasons or feasibility. However, they tend to occur naturally due to laws and regulations that arbitrarily divide people into two groups. For example, two groups with no discernible difference between them: one that receives government intervention and one that doesn’t.
The reason this topic is less popular in economic journals is that economics as a field focuses more on the impact of layoffs on overall economic output rather than the focus on whether it is optimal for the firm.
A famous example of survivorship bias occurred during World War II with statistician Abraham Wald. Bombers that returned from flight missions were analyzed for where they had bullet holes from anti-aircraft fire in order to establish where to put additional armor on the planes. One common suggestion was to strengthen the parts that have many bullet holes. Wald, however, recommended adding armor to the areas of the plane that did not have any bullet holes, because the planes that were hit in those areas simply did not return. The planes that returned with bullet holes survived, meaning the areas they were hit were already well protected.
Human capital consists of personal attributes that are of use to the production process, including education, know-how, skills, talent, and health.