Qing Chang will present research on “Upward mobility in the green sector for frontline fossil fuel workers”—joined by Michaël Aklin, Morgan Frank, and Shanti Gamper-Rabindran.

Register NOW to join live seminar on Zoom:
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Policy, workers, and firms who make decisions with any one study's exposure scores are likely to get it wrong! These studies employ different definitions of "AI" and perform variably depending on the US region or sector. User beware!

So which exposure studies are the most predictive of job loss? Individually, exposure scores from Frey/Osborne, Brynjolfsson, Felten et al, and the recent Eloundou et al studies are not predictive of unemployment risk. However, when combined, they are very predictive!

How could we even test this? Commonly-used employment and wage data don't directly capture job loss. So we instead use data from each state's unemployment insurance program to calculate workers' unemployment risk based on their occupation and local labor market

AI exposure scores can predict workers' unemployment risk! While individual AI exposure models aren't predictive of job losses, combining them creates a powerful predictive tool. #AIandJobs #FutureOfWork
New work with @yyahn.bsky.social & @estebanmoro.bsky.social academic.oup.com/pnasnexus/ad...

In a fast-changing economy, we need dynamic solutions for communicating and verifying our skills, knowledge, and abilities. Come have an early look at our solution! @youngjinyoo.bsky.social @kerrilemoie.bsky.social

New @naturehumbehav.bsky.social paper tracks mobility to reveal surprisingly long-distance dependencies between businesses. These insights could improve policy to bolster neighborhood economies and help business owners see how closures elsewhere might impact them

www.nature.com/articles/s41...

A wonderful postdoc opportunity to work with @estebanmoro.bsky.social who is the best collaborator and a thoughtful, dedicated mentor. I have had so much fun working with him and I learn so much from him. You will too! Apply!
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