In case you're excited about labor economics and are considering a predoc: please come and work with my wonderful colleagues and me at the @Princeton @PrincetonEcon Industrial Relations Section. Please spread the word! 🙏👇 irs.princeton.edu/senior-res…

Come be my colleague! Cleveland Fed is hiring a research economist. Applied micro especially encouraged (by me) to apply: econjobmarket.org/positions/…

Researchers are using things like LinkedIn data to study labour markets. But an interesting new @nberpubs working paper out today shows people retroactively edit how they describe a previous job in LinkedIn. Here are three cool graphs from the paper

Inspired by the below piece, I plotted the nr of applicants to “The Knowledge” – the intense exam to become a London cab driver. Even though we now have Uber + Waymo’s being trained, it has never been higher!

New CEPR Discussion Paper - DP21699 Job Ads as Signals: Evidence from a Priced Amenity and Worker Beliefs Pawel Pawel Adrjan @indeed, Mária Balgová @bankofengland, Simon Jäger @simon_jaeger @PrincetonEcon @Princeton, Jonas Jessen @WZB_Berlin, Jason Sockin @CornellEcon @Cornell ow.ly/qc5w50ZkFya #CEPR_LE #EconTwitter

A small favour for my economist network - we're hiring an Economist (Dutch speaker) based in the Netherlands at Indeed Hiring Lab. If you know someone who'd be a great fit, please share this with your Dutch-speaking network! Application link below

It's funny how this essay about matchmakers feels like a sort of fable about technology and work—even AI and jobs Step 1. Online dating makes something measurable—matching—more efficient. Step 2. People predict that, obviously, this will put matchmakers out of a job. Step 3. But increasing the measurable thing, matches, also increases the annoying thing, bad matches, and that makes people frustrated, Step 4: Frustrated people with money pay matchmakers to improve the process. Ta-da: Technology to increase digital matchmaking ironically increases the demand for human matchmakers (cc @alexolegimas)

Excited to be co-organising the 3rd Workshop on “Text-as-Data” in Economics @UniofBath, 16–17 July 2026. We have two fantastic keynote speakers, Daniel Chen and Milena Djourelova, and welcome submissions on NLP, LLMs, ML, and related methods across economics and neighbouring fields. Deadline: 24 May — please share widely!

Is AI killing jobs? New data shows that, more than three years after the release of ChatGPT, there is no evidence for a significant impact of AI on overall employment in the UK. In our new report, we break down the labour force into different occupations and use four measures of AI exposure to determine how likely they are to be affected by the technology. Surprisingly, occupations with higher exposure to AI have grown faster than least-exposed ones, not slower. This holds across all four measures, and across two different data sources. The wage picture is different. Pay in AI-exposed occupations has lagged the rest of the labour market since 2019. But that gap opened three years before ChatGPT, which makes AI an unlikely candidate for the observed wage compression. This flattening of the wage structure is visible across the within-occupation distribution and strongest at the top quartile, which is consistent with labour market dynamics that predate generative AI.

Many of us are trying to figure out where the AI labor market transition may be going. But that's fundamentally unknowable. So instead of trying to predict the future we can instead look back to try to figure out what may be coming...by reading 19th c english literature 1/

When interest rates fall, do people spend more? And if so why? We argue monetary policy affects consumption mostly because house prices rise when rates fall, and households borrow against their homes. With superstar coauthors Angus Foulis, @AtifRMian and Belinda Tracey. 1/5

Please read our new research on “What makes hiring difficult” Published in European Economic Review 🔑 Findings: 1) search &training frictions discourage hirings as much as labour costs 2) mainly for younger &smaller firms 3) many firms prefer hiring employed to unemployed workers. Abstract We use a novel firm survey linked to Danish administrative data to examine the factors that shape hiring decisions. Our analysis reveals three key findings. First, search and training frictions are as influential as labor costs in discouraging hiring despite potential needs. Second, these frictions disproportionately constrain younger and smaller firms, while firms with high-wage policies are less likely to report labor costs as an obstacle. Third, employers’ beliefs play a critical role: many firms prefer hiring employed rather than unemployed workers, perceiving the latter as lower ability due to negative selection or skill depreciation. Firms holding such beliefs are also more likely to report that labor market frictions impede their hiring decisions.

This essay “The Last Temptation of Claude” by Harry Law is excellent. This part stuck out: de skilling doesn’t just happen. People dont make one choice—it’s hundreds and thousands of little choices. Off load a memo, off load a section of the paper, etc. they all seem small but then you wake up one day and hey that thing that used to be easy is now quite hard, or even impossible. The temptation is real, so as with all temptation, it’s important to be deliberate and have rules. Everyone will be different, I have mine (write everything on my own, read old fiction)—but they might sound dumb or not work for others. open.substack.com/pub/cosmos…

It’s a truth universally acknowledged—and you’re absolutely right to notice this—that a single man in possession of a good fortune must be not merely wealthy, but, also, and more precisely, in want of a wife. This isn’t about gossip—it’s about social incentives.

My New Year post is a letter to a young person trying to find their direction in a world disrupted by AI. My advice, in four words: take the messy job. I hope you enjoy it and find it useful. Happy New Year!

The model is aviation, and pilots specifically. The computer (mostly) flies the plane itself. The result is a much safer flying environment than if everything was 100% manual But pilots are essential to the process. They’re an offline module in case the electronics fail, as an authority figure, and as the human interface between the aircraft, its passengers and crew, and the rest of the world. Their day to day responsibilities involve monitoring the outputs of the increasingly brilliant systems. They are also there to take responsibility for what happens. The AI isn’t a responsible party Lawyers will be the same. They’ll be fine

Last month, Anthropic asked if I wanted to test its Claude-powered vending machine in our offices. One month later: The vending machine business is bankrupt, but morale is higher than ever, we have a free PlayStation—and a new pet fish!

🚨 Job Market Paper 🚨 Wealth Inequality and Labor Mobility: The Job Trap Does wealth affect workers’ ability to move to better jobs? Why do some remain stuck with low wages? My answer: The Job Security Premium Paper: elenapellegrini.github.io/JM… 1/12 #EconTwitter #EconJobMarket

What happens when online job applicants start using LLMs? It ain't good. 1. Pre-LLM, cover letter quality predicts your work quality, and a good cover gets you a job 2. LLMs wipe out the signal, and employer demand falls 3. Model suggests high ability workers lose the most 1/n