Technological employment: Evidence from worldwide robot adoption

Shahab Sharfaei and Jan Bittner

Technological Forecasting and Social Change, 2024, vol. 209, issue C

Abstract: Technological advances can automate many jobs, therby reducing the reliance on some workers, yet they can also create jobs. Determining the overall impact is not immediately evident and necessitaes empirical research. In this paper, we use data on worldwide robot adoption from 1993 to 2021 to explore whether robots increase labour productivity and employment in the 74 sampled economies. We find that robots lead to higher productivity and employment on a global scale. To gain more insights into the results, we also examine the effects both in the long run and the short run using labour market data from developed countries. Using error correction model, the findings show that robot adoption increases employment in the long run but has no effect on overall employment in the short run. This study argues that in spite of perpetual automation spanning many generations, there are still many occupational opportunities, a fact that is not likely to change with the increased robot use. In fact, robot adoption leads to more jobs in developed economies which by and large adopt robots to a greater degree, leading to ‘technological employment’. While economies of the developing countries which adopt robots to a lower degree, do not seem to experience a similar outcome.

Keywords: Technology; Robots; Employment; Labour market; Productivity; Technological employment (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0040162524005407
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:209:y:2024:i:c:s0040162524005407

DOI: 10.1016/j.techfore.2024.123742

Access Statistics for this article

Technological Forecasting and Social Change is currently edited by Fred Phillips

More articles in Technological Forecasting and Social Change from Elsevier
Bibliographic data for series maintained by Catherine Liu ().