Eduard Storm

📢 PhD Workshop in Labor and Behavioral Economics 2026 📢

The next edition will take place at CEBI, University of Copenhagen (June 8–9, 2026).

Keynote speakers: Ingar Haaland (NHH) & Benjamin Schoefer (UC Berkeley).

Call for Papers: sites.google.com/view/behavio...
Deadline: Jan 31, 2026

@economist.com @spiegel.de @faznet.bsky.social @szde.bsky.social @derstandard.at @diepressecom.bsky.social

@ihs.ac.at @rwi.bsky.social

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

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

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

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

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

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

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

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

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

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

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

📢New WP - AI in Demand: How Expertise Shapes its (Early) Impact on Workers

In a nutshell:
• No broad impact on earnings & employment
• Gains concentrated among expert workers
• Inequality concerns, but also job-augmenting potential

Paper: irihs.ihs.ac.at/id/eprint/73...

Below a short 🧵 #EconSky

#econsky ;)

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@ihs.ac.at @rwi.bsky.social

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

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

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.

🏛️ Join us in vidid Vienna! 🏛️

We at @ihs.ac.at are hiring Postdocs in Econ, with a focus on #LaborEcon #EducationEcon #PublicFinance #SurveyResearch.

🕐 Apply via #EconJobMarket by Dec 2.

Feel free to share this post - or just come join us yourself!

👉 Link: econjobmarket.org/positions/12...

🆕Working Paper🚨

Training or Retiring? How Labor Markets Adjust to Trade and Technology Shocks📒
w/ A.Bertermann, @dauthecon.bsky.social & @suedekum.bsky.social

🤖 Robots ➡️ ⬆️training & ⬆️early retirement
🌏 Imports ➡️ ⬇️training & ⬆️early r.
🌎 Exports ➡️ ⬆️training & ⬇️e.r.

www.ifo.de/DocDL/cesifo...

🧵1/9

It was a pleasure to host Oliver Schlenker (ifo & @iabnews.bsky.social ) today @ihs.ac.at Vienna.

Oliver presented "The Deadly Consequences of Labor Scarcity: Evidence from Hospitals".

Setting: DE–CH border region, but w/ many lessons beyond. Very timely paper!

👉 Check it out: lnkd.in/eNyxXHNS

German Reunification Day invites both gratitude and reflection.
Gratitude, because the peaceful revolution of 1989 was nothing short of a miracle — a bloodless dismantling of a repressive regime.
Reflection, because the wounds of the transition still mark the country —and because

Link to paper: www.nber.org/system/files...

Link to non-technical summary: openai.com/index/how-pe...

But: the consultant role (“how can I do this?”) tends to produce higher-quality output. Note: [educated] users in high-paying jobs lean more on the ChatGPT-as-consultant role, esp. for decision support.

👉 It’s not just if you use ChatGPT, but HOW you use it that shapes the value you get from it.

1️⃣ ChatGPT is increasingly used for personal tasks – not just for work.

2️⃣ By July 2025, 56% of users employ ChatGPT as a “personal assistant” (e.g. “please edit this text”).

How do people use ChatGPT?

A new NBER WP by researchers from OpenAI, Harvard & Duke analyzes millions of ChatGPT conversations since its launch, showing how usage patterns have shifted across work & personal contexts.

There are many interesting insights in here, but two findings stood out to me:

War mit eine Freude ein paar Einschätzungen zum Artikel beitragen zu dürfen, insb. zum Spannungsfeld zwischen österreichischen und globalen Trends.