Marek Giebel

@marekgiebel.bsky.social

Associate Professor at the Department of Economics, Copenhagen Business School Research interests: Industrial Organization, Law and Economics, Intellectual Property Rights, and Finance http://www.marekgiebel.com/

Providing the first large-scale economic evidence on the frequency and purpose of workplace meetings, using administrative data from Norway, from David J. Deming, Katrine V. Løken, Alexander Willén, and Yaling Xu www.nber.org/papers/w35706

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Eric S Rosengren & Niccolò Comati argue that the risks that led to the collapse of Silicon Valley Bank were visible the whole time, and that the collapse is less a story of hidden danger than of a supervisory system that polices process rather than risk. cepr.org/voxeu/column... #EconSky

Figure shows the cumulative asset growth of Silicon Valley Bank compared to the FDIC system.

For its entire 15-year life as a regional bank until its collapse in 2023, Silicon Valley Bank held the same risky bet. This column argues that the risks were visible the whole time. The bank experienced rapid asset and stock price growth and maintained a high-risk profile with long-term securities funded by largely uninsured demand deposits. However, supervisors reacted only once unrecognised held-to-maturity losses materialised following the 2022-2023 interest rate increases. Thus, Silicon Valley Bank’s collapse is less a story of hidden danger than of a supervisory system that polices process rather than risk.

Providing a guide on imperfect competition in labor markets, focusing on the firm-specific labor supply elasticity as the definition of a firm’s labor market power, from Sydnee Caldwell, Arindrajit Dube, and Suresh Naidu www.nber.org/papers/w35608

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#econsky 🚨Reminder: You have 2 weeks left to submit your work! We’re looking forward to receiving your submissions!

Applied Young Economists' Webinar@ayew.bsky.social · 2mo ago

🚨CALL FOR PAPERS Hey #EconSky #EconTwitter! We are now accepting submissions for Season 13 of the Applied Young Economists Webinar! Deadline: Friday, 21 Aug 2026 Submission: forms.gle/5jsqgxLG1Jyvj8… (Email us your paper if you cannot access it) Details below⬇️

Analysis of 11 million US patents shows inventions have spread out in idea space since 1836, helping explain weaker spillovers and lower research productivity, from Ina Ganguli, Jeffrey Lin, Vitaly Meursault, and Nicholas F. Reynolds www.nber.org/papers/w35499

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Data from 380 trillion tokens of AI consumption across more than 400 models (2 percent of global AI consumption) shows detailed effects of AI on firms, markets, and workers, from Nicola Borri, Aleh Tsyvinski, and Yukun Liu www.nber.org/papers/w35451

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T Choukhmane, T de Silva, W Lin, & M Akuzawa show that AI financial advice is broadly consistent with what economists would prescribe. However, it also varies by how prompts are written, with different advice by gender, financial literacy, and prior AI experience. cepr.org/voxeu/column... #EconSky

Figure shows observed behaviour versus LLM-recommended behaviour over the life cycle. Red: survey respondents’ reported behaviour; green: one-shot LLM advice at current circumstances; blue: full life cycle simulation following LLM advice each year.

The rise of generative AI has raised hopes that high-quality, personalised financial advice might finally become cheap and universally accessible. This column develops a method to quantify the impact of following AI financial advice over a lifetime. The authors find that on the decisions that matter most for long-run wealth, such as investing in diversified equity or building a savings buffer, today’s leading models give advice broadly consistent with what economists would prescribe. But what people get from AI depends as much on the model's ability as on how they write their prompts, with the advice varying by gender, financial literacy, and prior AI experience.

More evidence, from a large-scale study in China, that using AI hurts learning if it undermines mental effort. When homework time drops due to AI use, so do test scores. Across studies, there is a clear theme: AI tutoring in support of classes is good, using AI to "help" with homework is bad.

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This paper finds that economists whose job market papers report (marginally) statistically significant results are more likely to secure academic jobs. Hiring committees prefer statistical significance; this creates incentives for researchers to p-hack.

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The five largest US tech firms are nearly doubling capex—from $380B in 2025 to $755B in 2026. They face bankruptcy unless productivity booms commensurately, from Jessica Wachter and Jonathan Wachter www.nber.org/papers/w35290

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