Skip to main content

Robots at Work: Automatable and Non-automatable Jobs

Abstract

This chapter builds on Autor and Dorn’s (Am Econ Rev 103(5):1553–1597, 2013) classification of automatable work at the three-digit occupation code level to identify additional jobs that will be automatable over the next decade by drawing on patent data. Based on this new classification, the chapter provides estimates of the share of jobs that are expected to be automatable in the EU and across 25 individual countries. The chapter highlights that aspects of 47% of jobs will be automatable over the next decade, with 35% of all jobs being fully automatable. It also provides some evidence that “thinking” and “people” skills will become increasingly important for the fourth industrial revolution. The chapter puts emphasis on the fact that these estimates are based on static models. Assuming that some of the rents from labor technology will filter back into the economy, it is expected that other occupations will expand in number as people consume more goods and services.

This is a preview of subscription content, access via your institution.

References

  • Acemoglu D, Restrepo P (2018) The race between man and machine: implications of technology for growth, factor shares, and employment. Am Econ Rev 108(6):1488–1542

    CrossRef  Google Scholar 

  • Autor DH, Dorn D (2013) The growth of low-skill service jobs and the polarization of the US labour market. Am Econ Rev 103(5):1553–1597

    CrossRef  Google Scholar 

  • Autor D, Dorn D, Hanson G (2015) Untangling trade and technology: evidence from local labour markets. Econ J 125(584):621–646

    CrossRef  Google Scholar 

  • Bogliacino F, Piva M, Vivarelli M (2012) R&D and employment: an application of the LSDVC estimator using European data. Econ Lett 116:383–404

    CrossRef  Google Scholar 

  • Calvino F, Virgillito ME (2018) The innovation-employment nexus: a critical survey of theory and empirics. J Econ Surv 32:83–117

    CrossRef  Google Scholar 

  • Coad A, Rao R (2011) The firm-level employment effects of innovations in high-tech US manufacturing industries. J Evol Econ 21:255–283

    CrossRef  Google Scholar 

  • Dorn D (2009) Essays on inequality, spatial interaction, and the demand for skills. Dissertation, University of St. Gallen no. 3613, September

    Google Scholar 

  • Falk M, Hagsten E (2018) Employment impacts of market novelty sales: evidence for nine European countries. Eurasian Bus Rev 8:119–137

    CrossRef  Google Scholar 

  • Frey CB, Osborne MA (2017) The future of employment: how susceptible are jobs to computerisation? Technol Forecast Soc Chang 114:254–280

    CrossRef  Google Scholar 

  • Graetz G, Michaels G (2018) Robots at work. Rev Econ Stat 100(5):753–768

    CrossRef  Google Scholar 

  • Greenhalgh C, Longland M, Bosworth D (2001) Technological activity and employment in a panel of UK firms. Scott J Polit Econ 48(3):260

    CrossRef  Google Scholar 

  • Harrison R, Jaumandreu J, Mairesse J, Peters B (2014) Does innovation stimulate employment? A firm-level analysis using comparable micro-data from four European countries. Int J Ind Organ 35:29–43

    CrossRef  Google Scholar 

  • Heckman JJ, Kautz T (2014) Fostering and measuring skills: interventions that improve character and cognition. In: Heckman JJ, Humphries JE, Kautz T (eds) The myth of achievement tests: the GED and the role of character in American life. University of Chicago Press, Chicago, pp 341–430

    Google Scholar 

  • Kautz T, Heckman J, Diris R et al (2014) Fostering and measuring skill: improving cognitive and non-cognitive skills to promote lifetime success. NBER working paper, 20749. NBER, Cambridge

    CrossRef  Google Scholar 

  • Lachenmaier S, Rottmann H (2011) Effects of innovation on employment: a dynamic panel analysis. Int J Ind Organ 29:210–220

    CrossRef  Google Scholar 

  • Lordan G (2018) Robots at work: a report on automatable and non-automatable employment shares in Europe. LSE Research Online Documents on Economics 90500, London School of Economics and Political Science, LSE Library

    Google Scholar 

  • Lordan G (2019) People versus machines in the UK: minimum wages, labor reallocation and automatable jobs. PLoS One 14:e0224789

    CrossRef  Google Scholar 

  • Lordan G, McGuire A (2019) Widening the high school curriculum to include soft skill training: impacts on health, behaviour, emotional wellbeing and occupational aspirations. IZA Discussion Paper No. 12439. Available at SSRN: https://ssrn.com/abstract=3415785

  • Lordan G, Neumark D (2018) People versus machines: the impact of minimum wages in automatable jobs. Labour Econ 52:40–53

    CrossRef  Google Scholar 

  • Lordan G, Pischke JS (2016) Does Rosie like riveting? Male and female occupational choices. NBER working paper, 22495. National Bureau of Economic Research, Cambridge, MA

    CrossRef  Google Scholar 

  • Pianta M (2005) Innovation and employment. In: Fagerberg J, Mowery D, Nelson RR (eds) Handbook of innovation. Oxford University Press, Oxford, pp 568–598

    Google Scholar 

  • Van Roy V, Vertesy D, Vivarelli M (2018) Technology and employment: mass unemployment or job creation? Empirical evidence from European patenting firms. Res Policy 47:1762–1776

    CrossRef  Google Scholar 

  • Vivarelli M (2014) Innovation, employment, and skills in advanced and developing countries: a survey of the economic literature. J Econ Issues 48:123–154

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Grace Lordan .

Editor information

Editors and Affiliations

Section Editor information

Rights and permissions

Reprints and Permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Josten, C., Lordan, G. (2020). Robots at Work: Automatable and Non-automatable Jobs. In: Zimmermann, K. (eds) Handbook of Labor, Human Resources and Population Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-57365-6_10-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-57365-6_10-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57365-6

  • Online ISBN: 978-3-319-57365-6

  • eBook Packages: Springer Reference Economics and FinanceReference Module Humanities and Social Sciences