Skills-biased technological change occurs when technology increases the productivity, and hence the value created, by workers with higher skills, compared to those with lower skills. One of the canonical examples is computers, which have made skilled white-collar workers more productive, but automated away the jobs of some lower-skilled workers.
As noted in yesterday's post, something similar may be happening with AI. In this case though, generative AI is making more experienced (senior) workers more productive, while at the same time automating away the jobs of entry-level (junior) workers. Think of this as seniority-biased technological change. At least, that's what this new working paper by Seyed Hosseini and Guy Lichtinger (both Harvard University) calls it. Their explanation goes like this:
In many such [high-skill, white-collar] jobs, workers begin at the bottom of the career ladder performing intellectually mundane tasks, i.e., routine yet cognitively demanding activities such as debugging code or reviewing legal documents, which are likely to be especially exposed to recent advances in GenAI. As these workers gain experience, they typically move up the career ladder to more senior roles that involve more complex problem-solving or managerial responsibilities... If GenAI disproportionately substitutes for entry-level tasks, the lower rungs of these career ladders may be eroding...
Hosseini and Lichtinger use data from Revelio Labs, which is drawn from public LinkedIn profiles. Importantly:
A key feature of the dataset is the standardized seniority level variable for each position, constructed by Revelio through an ensemble modeling approach based on multiple sources of information.
Hosseini and Lichtinger group the standardised positions into juniors (Entry and Junior levels) and seniors (Associate and above). They also use data on job postings from Revelio. The resulting dataset is huge, and:
...covers 284,974 firms that were successfully matched to both employee position data and job postings, and that were actively hiring between January 2021 and March 2025... For these firms, we observe 156,765,776 positions dating back to 2015 and 198,773,384 job postings since 2021...
The raw data shows a pattern that is very similar to the pattern from the Brynjolfsson et al. paper I discussed yesterday. Figure 1 from the Hosseini and Lichtinger paper charts the change in employment compared with January 2015, for juniors and seniors (and overall):
Notice that junior and senior employment follow similar trends until 2020, at which point they diverge, with senior employment continuing to grow, while junior employment does not (and even starts to decline after 2023). Brynjolfsson et al. showed that entry-level employment started declining from 2022, so these results are similar (although the point of departure is somewhat different).
Brynjolfsson et al. weren't able to show definitively that generative AI was the cause of the divergence (although they were able to eliminate general trends in their robustness checks). Hosseini and Lichtinger use the raw description data for each job posting, and identify "GenAI integrator" positions - "those reflecting an active attempt to recruit workers tasked with adopting or implementing GenAI in the firm’s workflows". They then:
...define a firm as a GenAI adopter if it has posted at least one GenAI integrator vacancy. By this criterion, 10,599 firms qualify as adopters. While they make up only 3.72 percent of the 284,974 firms in our sample, adopters are disproportionately large... and account for 17.3 percent of the employment (positions) in our dataset.
Hosseini and Lichtinger then compare employment changes between GenAI adopter firms and 'non-adopters', between the period before the first quarter of 2023 and the period after, in a 'difference-in-differences' analysis. They find that:
...junior employment in adopting firms fell by 7.7 percent relative to controls six quarters after the diffusion of generative AI. By contrast, coefficients for senior workers show a persistent upward trajectory throughout the sample, suggesting that adopting firms expanded senior employment more strongly than non-adopters over the last decade.
Hosseini and Lichtinger then extend their analysis to a triple-difference-in-differences analysis, comparing the difference in employment between juniors and seniors, in GenAI adopter and non-adopter firms, before and after the first quarter of 2023. In this more strenuous analysis, they find that:
Aside from a brief dip in early 2021, the coefficients are essentially flat through 2022Q4. Starting in 2023Q1, however, the coefficients decline sharply, reaching roughly a 10 percent drop after six quarters.
That means that juniors in GenAI adopting firms had a 10 percent greater decrease in employment relative to seniors than juniors in non-adopter firms, between the time before and after the first quarter of 2023 (phew!). This is strong evidence in favour of seniority-biased technological change arising from the adoption of generative AI.
Hosseini and Lichtinger go on to show similar effects using an event study research design, and similar effects comparing juniors in occupations that are more exposed to generative AI (compared with those in less exposed occupations). The latter is similar in nature to the results of Brynjolfsson et al.
Hosseini and Lichtinger then look at whether the change arises from a decrease in hiring of junior employees, an increase in job separation, or a change in promotion, finding that:
...that the sharp contraction in junior employment among adopters is driven primarily by a slowdown in hiring, rather than by increased exits. Specifically, the coefficient on Hiring implies that, relative to non-adopters, GenAI-adopting firms hired on average 5.0 fewer junior workers per quarter after 2023Q1... For senior employees, by contrast, hiring shows little change, while separations rise modestly, leading to a small net decline in senior headcount.
So, again the news is not good for new graduates moving into the workforce. Firms that have adopted generative AI are employing fewer junior employees, and that's because they are hiring fewer junior employees. The effects for new graduates are somewhat heterogeneous though, as Hosseini and Lichtinger also find that:
Juniors from tier-3 and tier-4 universities experienced the steepest relative declines in employment, while juniors from tiers 1, 2 and 5 also saw reductions, but of smaller magnitude.
'Tier-3 universities' are "strong national or regional institutions", while 'tier-4 universities' are "lower-tier but standard institutions". It's easy to see why they might be more affected than 'tier-1 universities' (the Ivy League and elite universities), and 'tier-2 universities' (highly respected international institutions). Signals still matter in education. However, it is hard to see why "tier-5 universities", which are "weak or diploma-mill-type institutions" are less affected. Perhaps students from the lowest quality universities select into occupations that are less likely to be affected by generative AI? Hosseini and Lichtinger don't control for the specific occupation in that analysis, but that might provide an answer.
Unlike Brynjolfsson et al., Hosseini and Lichtinger don't try to end their paper on a positive note. Instead, they conclude that:
GenAI adoption appears to shift work away from entry-level tasks, narrowing the bottom rungs of internal career ladders. Because early-career jobs are central to lifetime wage growth and mobility, such shifts may have lasting consequences for inequality and the college wage premium. Taken together, our evidence suggests that GenAI diffusion constitutes a form of seniority-biased technological change, with far-reaching implications for how careers begin, how firms cultivate talent, and how the gains from new technologies are distributed.
I prefer the more upbeat conclusion of Brynjolfsson et al., which is that the labour market will eventually adjust, and the workers who are disadvantaged now, will end up redeployed into other jobs that open up as a result of generative AI. I guess we will find out as this technological change plays out in real time!
[HT: Marginal Revolution]
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