Primary Outcomes (explanation)
Firms with an investment motif are firms that lose money on training apprentices during the apprenticeship itself (but do it for the chance to hire trained workers). Firms with a profit motif are firms that already make a profit during the apprenticeship from offering the apprenticeship. We will use firm characteristics to classify firms. For example, smaller firms are more likely to operate under the investment motif.
The idea of displacement risk for jobs exposed to digital technology is not new and for some occupations, experts have stressed automation prospects for a long time now (see, e.g., Arntz et al, 2016; Frey & Osborne, 2017). However, ChatGTP had a profound impact on how automation risk is perceived (Goller, Gschwent & Wolter, 2023) and evaluations of exposure and potential automation risk post-ChatGTP (e.g., Eloundou et al., 2023; Felten et al., 2023; Hui, Reshef & Zhou, 2023) come to different conclusions than earlier work such as Frey & Osborne (2017). We hypothesize that firms that offer training for newly affected occupations have had less time to adapt to a situation with relevant exposure to new AI technologies and are therefore more responsive to the different scenarios. Moreover, Goller, Gschwendt & Wolter (2023) find on the supply side of apprenticeship-labor that the introduction of ChatGTP makes prospective apprentices search less for apprenticeships that focus on cognitive tasks and have high language requirements. We therefore hypothesize to find a matching heterogeneity on the demand side – again because firms have not had much time to adapt to the type of exposure caused by ChatGPT and similar AI technologies.
Arntz, M., Gregory, T., & Zierahn, U. (2016). The risk of automation for jobs in OECD countries: A comparative analysis.
Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023). GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models. arXiv:2303.10130v4
Felten, E. W., Raj, M., & Seamans, R. (2023). Occupational heterogeneity in exposure to generative ai. Available at SSRN 4414065.
Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254-280.
Goller, D., Gschwendt, C. & Wolter, S. C. (2023). “This Time It’s Different” – Generative Artificial Intelligence and Occupational Choice. IZA Discussion Paper Series, No. 16638
Hui, X., Reshef, O., & Zhou, L. (2023). The Short-Term Effects of Generative Artificial Intelligence on Employment: Evidence from an Online Labor Market. CESifo Working Paper, No. 10601.