Ben Hyman
@revisenretweet.bsky.social
Economist @UCLA / @CAPolicyLab, formerly @NYFedResearch. @Wharton PhD. Research: Labor, PF, urban/spatial. Futbol: @OL, @ChelseaFC. W: benhyman.com
Although WIOA training participants are mostly low income, they are highly AI exposed. The modal participant displaces from the top quintile of occups in AI-exposure ('5' on the x-axis). Many workers in this quintile were cashiers or customer service reps before training. 10/🧵
August 31, 2025 at 2:17 PM
Although WIOA training participants are mostly low income, they are highly AI exposed. The modal participant displaces from the top quintile of occups in AI-exposure ('5' on the x-axis). Many workers in this quintile were cashiers or customer service reps before training. 10/🧵
More descriptives: high earnings returns are concentrated in the most recent years when labor mrkts were exceptionally tight ➡️ training may carry stronger signal value when firms have to reach deeper into the skill market. Or it may reflect changes in AI—an open question! 9/🧵
August 31, 2025 at 2:17 PM
More descriptives: high earnings returns are concentrated in the most recent years when labor mrkts were exceptionally tight ➡️ training may carry stronger signal value when firms have to reach deeper into the skill market. Or it may reflect changes in AI—an open question! 9/🧵
We find that 25-40% of occupations are “AI retrainable” (high sahre!). Some occupations (e.g. paralegals) rank higher b/c workers earn more when moving to higher AI-content work, and others (e.g. customer service reps), rank lower because workers are forced to move down in AI-content. 8/🧵
August 31, 2025 at 2:17 PM
We find that 25-40% of occupations are “AI retrainable” (high sahre!). Some occupations (e.g. paralegals) rank higher b/c workers earn more when moving to higher AI-content work, and others (e.g. customer service reps), rank lower because workers are forced to move down in AI-content. 8/🧵
Using our matched sample, we construct an AI Retrainability (AIR) index ranking occups by the share of retrained workers earning ⬆️ wages despite moving into AI-intensive roles. We then ask whether AIR is driven by earnings gains holding AI skills constant, or AI upskilling. 7/🧵
August 31, 2025 at 2:17 PM
Using our matched sample, we construct an AI Retrainability (AIR) index ranking occups by the share of retrained workers earning ⬆️ wages despite moving into AI-intensive roles. We then ask whether AIR is driven by earnings gains holding AI skills constant, or AI upskilling. 7/🧵
Our main finding is that while workers leaving AI‑exposed occups see strong earnings returns from training (~$1500/qtr, large estimates!), AI‑specific retraining delivers 29% lower returns vs. general training ➡️ frictions in acquiring skills used by AI-intensive occups. 6/🧵
August 31, 2025 at 2:17 PM
Our main finding is that while workers leaving AI‑exposed occups see strong earnings returns from training (~$1500/qtr, large estimates!), AI‑specific retraining delivers 29% lower returns vs. general training ➡️ frictions in acquiring skills used by AI-intensive occups. 6/🧵
This paper shines early light on the effectiveness of job training for workers in occs. exposed to AI before job loss, and for those who target AI-intensive occs. in their next jobs (presumably by acquiring AI-compatible skills that safeguard against future job loss). See ⬇️ 3/🧵
August 31, 2025 at 2:17 PM
This paper shines early light on the effectiveness of job training for workers in occs. exposed to AI before job loss, and for those who target AI-intensive occs. in their next jobs (presumably by acquiring AI-compatible skills that safeguard against future job loss). See ⬇️ 3/🧵
While much attention has been paid to the potential impact of AI on headcounts, there is next to no evidence on the adjustment margin firms most commonly pursue to accommodate AI technology: reskilling workers for AI. (See e.g., firm survey results w/ Fed colleagues below). 2/🧵
August 31, 2025 at 2:17 PM
While much attention has been paid to the potential impact of AI on headcounts, there is next to no evidence on the adjustment margin firms most commonly pursue to accommodate AI technology: reskilling workers for AI. (See e.g., firm survey results w/ Fed colleagues below). 2/🧵
CA spends three times as much on film tax credits as it does on discretionary business incentives for all remaining industries, ***combined***. One day, my paper on film tax credits will come out...
May 6, 2025 at 5:50 PM
CA spends three times as much on film tax credits as it does on discretionary business incentives for all remaining industries, ***combined***. One day, my paper on film tax credits will come out...