Remote Work Across Jobs, Companies and Space
Nicholas Bloom,
Steven J. Davis,
Stephen Hansen,
Peter John Lambert (),
Yabra Muvdi (),
Raffaella Sadun and
Bledi Taska ()
No 2629, Working Papers from Federal Reserve Bank of Dallas
Abstract:
The pandemic catalyzed an enduring shift to remote work. To measure this shift, we develop a large language model (LLM), fine tune and assess it using 30,000 human classifications, and apply it to nearly 600 million job vacancy postings across five English-speaking countries. Our model achieves a 98% classification accuracy, greatly outperforming dictionary-based approaches, and matching or surpassing the performance of frontier AI models at a fraction of the cost. From 2019 to 2026, the share of postings that indicate new employees can work remotely at least one day per week rose more than three-fold in the U.S. and by a factor of five or more in Australia, Canada, New Zealand and the U.K. These developments are highly non-uniform across and within cities, industries, occupations and companies. Even when focusing on employers in the same industry competing for talent in the same occupations, we find large differences in the share of job postings that explicitly offer remote work.
Keywords: remote work; hybrid work; work from home; job vacancies; text classifiers; large language models; pandemic impact; labour markets (search for similar items in EconPapers)
JEL-codes: C55 E24 M54 O33 R3 (search for similar items in EconPapers)
Date: 2026-08-19
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Related works:
Working Paper: Remote work across jobs, companies and space (2023) 
Working Paper: Remote work across jobs, companies and space (2023) 
Working Paper: Remote Work across Jobs, Companies, and Space (2023) 
Working Paper: Remote work across jobs, companies and space (2023) 
Working Paper: Remote Work across Jobs, Companies, and Space (2023) 
Working Paper: Remote Work across Jobs, Companies, and Space (2023) 
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DOI: 10.24149/wp2629
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