The Insightful Leader
Sent to subscribers on March 6, 2024
The release of ChatGPT in 2022 brought to a boil a conversation that had been simmering for years: How and how much will new technology change the labor market?
While generative AI like ChatGPT represents a major advancement, it is certainly not the first time a new tool has sat poised to change (or even eliminate) certain jobs. And that previous experience may provide important lessons about what we can expect in the future.
This week, we’ll explore what new technology—including generative AI—could mean for workers and their earnings.
Winners and losers
In a new paper, Kellogg’s Dimitris Papanikolaou and Bryan Seegmiller (a professor and assistant professor of finance, respectively) and their colleagues explored what happened to workers when job-altering tools came on the scene.
Their analysis focused on the period from 1981 to 2016—a time span in which many new technologies were introduced across a variety of occupations, both blue- and white-collar.
By linking occupation descriptions with patent information, the researchers were able to determine whether a patented technology was labor-saving (that is, able to replace a worker performing a task) or labor-augmenting (something that would complement a worker performing a task). This information was used to compute measures of exposure to labor-saving and labor-augmenting technology for different occupations.
To determine the consequences of such exposure, the researchers gathered U.S. government data on workers in different occupations, including their earnings, ages, and levels of education from the years after the patents were granted.
Overall, for any given occupation, exposure to labor-saving technology predicted lower wages and lower employment. Exposure to labor-augmenting technology, meanwhile, predicted higher wages and higher employment for that job.
Punishing experts and lifting novices
But when the researchers shifted gears and began analyzing the effects of technology exposure at the worker level, rather than the occupation level, they discovered a more complicated story—particularly when it came to labor-augmenting technology.
Across all occupations, the average worker suddenly exposed to labor-augmenting technology saw a small decrease in earnings and a small increase in the likelihood of losing their job. These trends were even more pronounced among white-collar workers, older workers, and highly paid workers within an occupation.
This finding, combined with the knowledge that wages increased at the occupation level, “leads you to suggest that a lot of the benefits go to newly hired workers,” Papanikolaou says. In other words, workers who were used to doing things a certain way struggled to adapt when complementary technology arrived, while less-experienced workers could harness the power of these new tools.
The researchers also studied the potential ramifications of AI specifically on today’s workers. They found that “AI, as a technology, levels the playing field within an occupation,” Papanikolaou says. In other words, if everyone can code, a skilled and experienced coder will be less valuable in the job market. The upshot is that “it’s going to hurt workers that are better at their jobs.”
You can read more about the study in Kellogg Insight.
“While not every new idea is going to be good for consumers, the status quo is not acceptable. The more we encourage experimentation and encourage new ideas, the more we learn.”
— David Dranove, in the BBC, on why there’s room for startups to disrupt the American healthcare system.
See you next week!
Susie Allen, senior research editor
Kellogg Insight