A new study confirms that automation does indeed affect many workers. Each year, about 9% of the workers in the sample are employed at firms that make major investments in automation. Yet relatively few workers are adversely affected. Only about 2% of tenured workers at automating firms leave the year of the automation event as a result of automation; after five years, 8.5% will have left, cumulatively. Nevertheless, those who do leave suffer significant economic costs, largely due to spells of unemployment. Surprisingly, this burden falls more frequently on highly-educated and highly-paid workers. Contrary to conventional wisdom, they are more likely to leave as a result of automation, although they also seem to find new jobs faster. In other words, highly-paid workers are more commonly affected, but the effects are more severe for less well-paid workers.
New AI and robotics technologies are increasingly automating work tasks. How much of a threat does automation pose to workers? A new study by one of us (James Bessen), along with Maarten Goos, Anna Salomons, and Wiljan van den Berge, provides the first large-scale quantitative evidence of how automation affects individual workers, using government data from 2000-2016 for 36,000 firms in the Netherlands, covering about 5 million workers each year.
We found that automation does indeed affect many workers. Each year, about 9% of the workers in the sample are employed at firms that make major investments in automation. Yet relatively few workers are adversely affected. Only about 2% of tenured workers at automating firms leave the year of the automation event as a result of automation; after five years, 8.5% will have left, cumulatively. (We can’t differentiate between those who choose to leave and those who are let go or fired.)
Nevertheless, those who do leave suffer significant economic costs, largely due to spells of unemployment. This affects both their economic prospects and their overall wellbeing. And though welfare programs like unemployment insurance are often framed as the way to address these costs, our data confirm that they don’t nearly make up for the income that workers lose.
Surprisingly, this burden falls more frequently on highly-educated and highly-paid workers. Contrary to conventional wisdom, they are more likely to leave as a result of automation, although they also seem to find new jobs faster. In other words, highly-paid workers are more commonly affected, but the effects are more severe for less well-paid workers.
To understand what happens when firms introduce automation on a large scale, the paper examines “automation spikes,” which we define as a year in which a firm makes expenditures on automation that are at least three times its average spending on automation in all other years. Comparing workers who experience such a spike in a given year with a control group that experiences spikes later enables analysis of how the spikes affect workers. The paper looks at both tenured workers (three years or more at the firm) and recently hired workers.
Although many commentators liken the introduction of automation in a workplace to a mass layoff or a plant closing, the study shows that that comparison is not particularly apt and suggests those fears are overblown. The data shows that workers do experience loss of both earnings and work following a spike, but that these losses are substantially less than that experienced by workers following a mass layoff. In the sample, only 0.7% of all workers on average leave their employers each year due to automation.
In contrast, in the Netherlands, about 3.5%-7.2% lose their jobs each year in mass layoffs, typically defined as a layoff of 30% or more of the workforce. (The comparable rate is 4.4% in the U.S.) The risk of anyone losing a job due to automation is thus much smaller than the risk of a mass layoff. Moreover, while plant closings or mass layoffs affect a large number of workers all at once, the effects from automation happen more gradually, giving workers more time to react and adjust.
The real impact of automation is on income and time spent unemployed. The data show that after a spike, tenured workers cumulatively lose about 3,800 Euros in wage and salary earnings over five years on average (about 9% of one year’s income). More recent hires also experience a negative impact, but only about 3% of one year’s income, which perhaps reflects their adaptability and flexibility as newer hires. The main reason for these losses is that workers who leave the firm experience longer periods of non-employment. Conditional on leaving, incumbent workers are employed 43 fewer days over five years following the automation event. Only about 12% of these losses are made up by unemployment, welfare, or disability payments, which is comparable to what workers receive after a mass layoff. (The Netherlands is slightly below average among OECD countries in its total social spending.)
How does income change for those workers who don’t leave the firm? We found that wage rates don’t change much for workers who stay at the firm or those who leave and are re-employed elsewhere. The effects of automation on income are concentrated among employees who lose their jobs or depart for other reasons.
The study also looks at differences across firms and workers with different characteristics. In a previous study, Carl Benedikt Frey and Michael A. Osborne of Oxford contend that low-wage occupations will be hardest hit by future automation. The data reject this common assumption, but we do find that lower-paid workers suffer longer unemployment after leaving. Among tenured workers, the probability of leaving employment does not vary much by age or gender, but among recent hires, older workers are more likely to leave. Also, while the losses are more severe in manufacturing industries, the impacts are seen across all industry sectors studied.
The study paints a picture of automation today that does not support the most alarmist views. The burden that automation places on workers is less than the burden created by mass layoffs and plant closings that arise from things like declining demand or bankruptcies. Nevertheless, the burden placed on affected workers is substantial, and existing safety net programs are not providing these workers much economic security. And, of course, the impact of automation might worsen in the future. Further research will show what happens to net employment after automation, and to the workers hired after the automation event.
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