Eduard Storm
edthestorm.bsky.social
Eduard Storm
@edthestorm.bsky.social
Obacht: Summer Child here. ☝

Who: Labor Economist who likes to employ ML & NLP techniques to tame Big Data.

Where: PostDoc Researcher & Head of Junior Research Group @ihs.ac.at in #VividVienna

https://sites.google.com/view/eduardstorm/home
November 24, 2025 at 8:50 PM
Not properly captured in our paper, but: broader lesson for GenAI era❓

👉 gains depend not only on what AI can do (automation), but especially if workers can step into expanded task spaces that create new work (augmentation)

👉 People need to build and update skills that 🤝 with AI

12/12
November 24, 2025 at 8:47 PM
Key takeaways:

How does AI skill demand affect workers’ earnings and employment stability?

• No broad earnings and employment responses

• Gains mostly modest and concentrated among expert workers

• Certain inequality concerns, but also job-augmenting potential

11/12
November 24, 2025 at 8:47 PM
Results suggest: high-skilled benefit, lesser-skilled not so much ➡️ Implications for Inequality?

Estimates vary sharply across earnings distribution:

• Bottom deciles: -8 days, earnings −3.9%
• Top decile: +5 days, earnings +2.5%

👉 Suggestive evidence: AI could widen existing inequalities

10/N
November 24, 2025 at 8:46 PM
AI exposure expands analytic + interactive tasks, and reduces manual ones.

These task expansions translate into measurable earnings gains, especially through analytic work.

👉 Early AI technologies seem to induce task shifts, consistent with reinstatement effects.

9/N
November 24, 2025 at 8:46 PM
Clear pattern: expert workers gain modestly, others not.

• Experts: +0.7% earnings (~400€), small gains in days worked in response to doubling in local AI demand

• Lesser-skilled workers: small declines

👉 Job-specific expertise matters (more than formal education or other skill proxies).

8/N
November 24, 2025 at 8:46 PM
On average, rising AI demand does not change workers’ employment stability or annual earnings.

👉 Early AI neither caused broad job loss nor generated large productivity gains.

👉 But: these zero results mask considerable skill heterogeneity...

7/N
November 24, 2025 at 8:45 PM
Identification Strategy:

OLS likely biased

👉 We use a leave-one-out instrument: national AI demand within occupations (excluding worker’s own region).

This approach helps to isolate broad tech shifts from local conditions (see paper for technical details).

6/N
November 24, 2025 at 8:45 PM
AI Exposure rises with skill levels:

👉 Experts face more AI vacancies than helpers, professionals, or specialists

👉 Similar insights by formal education and occupational task structures

Sets the stage for distinct insights by skill groups (more on that later).

5/N
November 24, 2025 at 8:45 PM
AI demand across local labor markets: occupations × regions

Most local labor markets show little AI skill demand, others experienced notable increases.

(e.g.: in 2017 only 9% of local labor markets displayed AI demand, by 2023 ca. 16%)

👉 Key variation: changes in AI skill demand over time.

4/N
November 24, 2025 at 8:44 PM
Stylized Facts on AI Skill Demand (2017–23, Germany)

• Modest in aggregate terms, fluctuates between 1 – 1.5% of all job postings.

• Most demand on unspecified AI skills, #machinelearning, and other technologies popularized prior to the emergence of #GenAI.

3/N
November 24, 2025 at 8:44 PM
How does AI skill demand affect individual workers?

#AI can:

1. displace tasks
2. boost productivity
3. create new tasks

👉 Explore channels in context of longer-term dynamics of the early AI wave (2017-2023).

Data: Online Job Postings + German worker-level admin data (@iabnews.bsky.social)

2/N
November 24, 2025 at 8:43 PM
November 24, 2025 at 8:39 PM
November 24, 2025 at 8:18 PM
Key takeaways:

How does AI skill demand affect workers’ earnings and employment stability?

• No broad earnings and employment responses

• Gains mostly modest and concentrated among expert workers

• Certain inequality concerns, but also job-augmenting potential

11/12
November 24, 2025 at 8:11 PM
Identification Strategy:

OLS likely biased

👉 We use a leave-one-out instrument: national AI demand within occupations (excluding worker’s own region).

This approach helps to isolate broad tech shifts from local conditions (see paper for technical details).

6/N
November 24, 2025 at 8:03 PM
PS: No teaching load, lots of freedom for your own projects, and an interdisciplinary team that values both rigor and collegiality - and all of that in the heart of one of the most liveable cities worldwide.
November 12, 2025 at 5:30 PM
Link to paper: www.nber.org/system/files...

Link to non-technical summary: openai.com/index/how-pe...
www.nber.org
October 3, 2025 at 6:46 AM