Automatability of occupations, workers’ labor-market expectations, and willingness to train

Philipp Lergetporer (), Katharina Wedel () and Katharina Werner
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Philipp Lergetporer: Technical University of Munich, CESifo and IZA
Katharina Wedel: ifo Institute at the Ludwigs-Maximilians-University of Munich

Munich Papers in Political Economy from Munich School of Politics and Public Policy and the School of Management at the Technical University of Munich

Abstract: We study how beliefs about the automatability of workers’ occupation affect labor-market expectations and willingness to participate in further training. In our representative online survey, respondents on average underestimate the automation risk of their occupation, especially those in high-automatability occupations. Randomized information about their occupations’ automatability increases respondents’ concerns about their professional future, and expectations about future changes in their work environment. The information also increases willingness to participate in further training, especially among respondents in highly automatable occupation (+five percentage points). This uptick substantially narrows the gap in willingness to train between those in high- and low-automatability occupations.

Keywords: automation; further training; labor-market expectations; survey experiment; information (search for similar items in EconPapers)
JEL-codes: D83 I29 J24 O33 (search for similar items in EconPapers)
Pages: 51 pages
Date: 2023-12
New Economics Papers: this item is included in nep-dcm and nep-exp
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Related works:
Working Paper: Automatability of Occupations, Workers’ Labor-Market Expectations, and Willingness to Train (2023)
Working Paper: Automatability of Occupations, Workers' Labor-Market Expectations, and Willingness to Train (2023)
Working Paper: Automatability of Occupations, Workers' Labor-market Expectations, and Willingness to Train (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:aiw:wpaper:32

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