Let's read the paper instead: https://digitaleconomy.stanford.edu/wp-content/uploads/2025/...
It presents a difference-in-differences (https://en.wikipedia.org/wiki/Difference_in_differences) design that exploits staggered adoption of generative AI to estimate the causal effect on productivity. It compares headcount over time by age group across several occupations, showing significant differentials across age groups.
Page 3: "We test for a class of such confounders by controlling for firm-time effects in an event study regression, absorbing aggregate firm shocks that impact all workers at a firm regardless of AI exposure. For workers aged 22-25, we find a 12 log-point decline in relative employment for the most AI-exposed quintiles compared to the least exposed quintile, a large and statistically significant effect."
I appreciate the link to differences in differences, I didn't know what to call this method.
The OP's point could still be valid: it’s still possible that macro factors like inflation, interest rates, or tariffs land harder on the exact group they label ‘AI-exposed.’ That makes the attribution messy.
Those fixed effects are estimated separately for each age group, controlling for that.
pg. 19, "We run this regression separately for each age group."
Interesting technique, that DID. But it assumes the non treatment factors would affect both the treatment group and control group equally, that the effect would scale linearly. If the treatment group was more exposed to the non-treatment factors, then an increase could account for a larger difference than the one seem at time 1. Idk which other industry they used as the controll group but interest rates could have a superlinear effect on tech as compared to on that, so the difference of difference would be explained by the non-treatment factor too
Were entry level jobs the first to go in earlier developer downturns?
Is AI being used to attempt to mitigate that effect?
I don't think their methods or any statistical method could decouple a perfectly correlated signal.
Without AI, would junior jobs have grown as quickly as other?
I'm not trying to be clever here. I'm trying to be publicly stupid in an effort to understand.