Abstract
How digital technologies will affect skills and training needs is one of the conundrums of the evolving Industry 4.0. Important answers have been provided over the past 20 years by a new perspective: the task-based approach within labour economics. This approach interprets a job as a set of tasks, and it exploits employee survey data to measure and compare jobs in terms of skill needs. This chapter presents insights gained from the extant literature within this approach, including our own research, and identifies groups of employees whose job prospects and training opportunities are especially vulnerable. In particular, the evidence suggests that digital technologies often replace human employees who perform routine tasks. The remaining jobs will tend to include more sophisticated tasks such as directing and planning. Employees in jobs involving more routine tasks will therefore most likely need considerable retraining and upskilling. For various reasons, employers are unlikely to support this group of employees in terms of training and retraining. At the same time, routine jobs are often highly specific—their sets of tasks differ from those of most available jobs. Therefore, employees with such skill profiles are less likely to easily find alternative jobs. In other words, we identify a triple risk of digitalisation for some workers, namely those who conduct routine tasks, have a very specific skill profile that limits their ability to move to other jobs, and receive little training and retraining. Our approach and findings have important managerial and policy implications. Based on the framework we suggest, employers could identify and support employees with considerable risk by tracking their skills in a competence management system, i.e., by examining the digital twins of employees in terms of skills. If employers are unlikely to offer enough further training and retraining, an appropriate public policy response would be to reorganise initial vocational training—more than in the existing system, young employees need to be equipped with an even more general set of tasks, which would allow them to switch jobs within (and perhaps across) occupations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Acemoglu, D. (2002). Technical change, inequality, and the labor market. Journal of Economic Literature, 40(1), 7–72. https://doi.org/10.1257/0022051026976
Acemoglu, D., & Autor, D. (2011). Skills, tasks and technologies: Implications for employment and earnings. Handbook of Labor Economics, 4, 1043–1171. https://doi.org/10.1016/S0169-7218(11)02410-5
Acemoglu, D., & Restrepo, P. (2018). The race between man and machine: Implications of technology for growth, factor shares, and employment. American Economic Review, 108(6), 1488–1542. https://doi.org/10.1257/aer.20160696
Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3–30. https://doi.org/10.1257/jep.33.2.3
Arntz, M., Gregory, T., Jansen, S., & Zierahn, U. (2016). Tätigkeitswandel und Weiterbildungsbedarf in der digitalen Transformation. Mannheim: ZEW-Gutachten und Forschungsberichte.
Arntz, M., Gregory, T., & Zierahn, U. (2017). Revisiting the risk of automation. Economics Letters, 159, 157–160. https://doi.org/10.1016/j.econlet.2017.07.001
Arntz, M., Gregory, T., & Zierahn, U. (2019). Digitization and the future of work: Macroeconomic consequences. In K. F. Zimmermann (Ed.), Handbook of Labor, Human Resources and Population Economics, Springer eBook Collection (pp. 1–29). Cham: Springer. https://doi.org/10.1007/978-3-319-57365-6_11-1
Autor, D. H., Levy, F., & Murnane, R. J. (2003). The skill content of recent technological change: An empirical exploration. The Quarterly Journal of Economics, 118(4), 1279–1333. https://doi.org/10.1162/003355303322552801
Backes-Gellner, U., & Mure, J. (2004). The skill-weights approach on firm specific human capital: Empirical results for Germany. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.710441
Bechichi, N., & Jamet, S. (2018). Moving between jobs: An analysis of occupation distances and skill needs. OECD Science, Technology and Industry Policy Papers, 52. https://doi.org/10.1787/d35017ee-en
Bechichi, N., Jamet, S., Kenedi, G., Grundke, R., & Squicciarini, M. (2019). Occupational mobility, skills and training needs. OECD Science, Technology and Industry Policy Papers, 70. https://doi.org/10.1787/23074957
Becker, G. S. (1975). Human capital: A theoretical and empirical analysis, with special reference to education, Human behavior and social institutions (2nd ed., Vol. 5). New York, NY: Columbia University Press.
Bilger, F., Behringer, F., Kuper, H., Schrader, J., et al. (2017). Weiterbildungsverhalten in Deutschland 2016: Ergebnisse des Adult Education Survey (AES). W. Bertelsmann Verlag.
Blien, U., Dauth, W., & Roth, D. H. (2021). Occupational routine intensity and the costs of job loss: Evidence from mass layoffs. Labour Economics, 68(101953). https://doi.org/10.1016/j.labeco.2020.101953
Boothby, D., Dufour, A., & Tang, J. (2010). Technology adoption, training and productivity performance. Research Policy, 39(5), 650–661. https://doi.org/10.1016/j.respol.2010.02.011
Brynjolfsson, E., & Mitchell, T. (2017). What can machine learning do? Workforce implications. Science (New York, NY), 358(6370), 1530–1534. https://doi.org/10.1126/science.aap8062
Chiang, J. K. H., & Suen, H. Y. (2015). Self-presentation and hiring recommendations in online communities: Lessons from LinkedIn. Computers in Human Behavior, 48, 516–524. https://doi.org/10.1016/j.chb.2015.02.017
Dauth, C. (2020). Regional discontinuities and the effectiveness of further training subsidies for low-skilled employees. ILR Review, 73(5), 1147–1184. https://doi.org/10.1177/0019793919885109
Dauth, W., Findeisen, S., Suedekum, J., & Woessner, N. (2017). German robots—The impact of industrial robots on workers. IAB-Discussion Paper No. 30.
Dehnbostel, P. (2018). Lern- und kompetenzförderliche arbeitsgestaltung in der digitalisierten arbeitswelt. Arbeit, 27(4), 269–294. https://doi.org/10.1515/arbeit-2018-0022
Deming, D. J. (2017). The growing importance of social skills in the labor market*. The Quarterly Journal of Economics, 132(4), 1593–1640. https://doi.org/10.1093/qje/qjx022
Dengler, K., & Matthes, B. (2018). The impacts of digital transformation on the labour market: Substitution potentials of occupations in Germany. Technological Forecasting and Social Change, 137, 304–316. https://doi.org/10.1016/j.techfore.2018.09.024
Dutta, S. (2010). What’s your personal social media strategy? Harvard Business Review, 88(11):127–130, 151.
de Vries, G. J., Gentile, E., Miroudot, S., & Wacker, K. M. (2020). The rise of robots and the fall of routine jobs. Labour Economics, 66. https://doi.org/10.1016/j.labeco.2020.101885
Eggenberger, C., Rinawi, M., & Backes-Gellner, U. (2018). Occupational specificity: A new measurement based on training curricula and its effect on labor market outcomes. Labour Economics, 51, 97–107. https://doi.org/10.1016/j.labeco.2017.11.010
Eisele, S., & Schneider, M. R. (2020). What do unions do to work design? Computer use, union presence, and Tayloristic jobs in Britain. Industrial Relations: A Journal of Economy and Society, 59(4), 604–626. https://doi.org/10.1111/irel.12266
Eraut, M. (2004). Informal learning in the workplace. Studies in Continuing Education, 26(2), 247–273. https://doi.org/10.1080/158037042000225245
Feldmann, H. (2013). Technological unemployment in industrial countries. Journal of Evolutionary Economics, 23(5), 1099–1126. https://doi.org/10.1007/s00191-013-0308-6
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. https://doi.org/10.1016/j.techfore.2016.08.019
Gardberg, M., Heyman, F., Norbäck, P. J., & Persson, L. (2020). Digitization-based automation and occupational dynamics. Economics Letters, 189. https://doi.org/10.1016/j.econlet.2020.109032
Geel, R., & Backes-Gellner, U. (2011). Occupational mobility within and between skill clusters: An empirical analysis based on the skill-weights approach. Empirical Research in Vocational Education and Training, 3(1), 21–38. https://doi.org/10.1007/BF03546496
Geel, R., Mure, J., & Backes-Gellner, U. (2011). Specificity of occupational training and occupational mobility: An empirical study based on Lazear’s skill-weights approach. Education Economics, 19(5), 519–535. https://doi.org/10.1080/09645291003726483
Goos, M., Manning, A., & Salomons, A. (2009). Job polarization in Europe. American Economic Review, 99(2), 58–63. https://doi.org/10.1257/aer.99.2.58
Goos, M., Manning, A., & Salomons, A. (2014). Explaining job polarization: Routine-biased technological change and offshoring. American Economic Review, 104(8), 2509–2526. https://doi.org/10.1257/aer.104.8.2509
Haslberger, M. (2021). Routine-biased technological change does not always lead to polarisation: Evidence from 10 OECD countries, 1995–2013. Research in Social Stratification and Mobility, 74. https://doi.org/10.1016/j.rssm.2021.100623
Hellweg, T. (2021). Do employees with specific skill profiles receive more employer-funded training during technological change? Evidence from employer-employee data. In 19th ILERA World Congress, Lund, Sweden.
Heß, P., Janssen, S., & Leber, U. (2019). Digitalisierung und berufliche weiterbildung: Beschäftigte, deren tätigkeiten durch technologien ersetzbar sind, bilden sich seltener weiter, Technical report, IAB-Kurzbericht.
Katz, L. F., & Murphy, K. M. (1992). Changes in relative wages, 1963–1987: Supply and demand factors. The Quarterly Journal of Economics, 107(1), 35–78. https://doi.org/10.2307/2118323
Lamo, A., Messina, J., & Wasmer, E. (2011). Are specific skills an obstacle to labor market adjustment? Labour Economics, 18(2), 240–256. https://doi.org/10.1016/j.labeco.2010.09.006
Lazear, E. P. (2009). Firm-specific human capital: A skill-weights approach. Journal of Political Economy, 117(5), 914–940. https://doi.org/10.1086/648671
Lindgren, H., & Schultze. (2004). Design principles for competence management systems: A synthesis of an action research study. MIS Quarterly, 28(3), 435–472. https://doi.org/10.2307/25148646
Lukowski, F., Baum, M., & Mohr, S. (2020). Technology, tasks and training—Evidence on the provision of employer-provided training in times of technological change in Germany. Studies in Continuing Education, 48, 1–22. https://doi.org/10.1080/0158037X.2020.1759525
Machin, S., & van Reenen, J. (1998). Technology and changes in skill structure: Evidence from seven OECD countries. The Quarterly Journal of Economics, 113(4), 1215–1244. https://doi.org/10.1162/003355398555883
Madia, S. A. (2011). Best practices for using social media as a recruitment strategy. Strategic HR Review, 10(6), 19–24. https://doi.org/10.1108/14754391111172788
Miranda, S., Orciuoli, F., Loia, V., & Sampson, D. (2017). An ontology-based model for competence management. Data & Knowledge Engineering, 107, 51–66. https://doi.org/10.1016/j.datak.2016.12.001
Mohr, S., Troltsch, K., & Gerhards, C. (2016). Job tasks and the participation of low-skilled employees in employer-provided continuing training in Germany. Journal of Education and Work, 29(5), 562–583. https://doi.org/10.1080/13639080.2015.1024640
Nedelkoska, L. (2013). Occupations at risk: Job tasks, job security, and wages. Industrial and Corporate Change, 22(6), 1587–1628. https://doi.org/10.1093/icc/dtt002
North, K., Reinhardt, K., & Sieber-Suter, B. (2018). Kompetenzmanagement in der Praxis: Mitarbeiterkompetenzen systematisch identifizieren, nutzen und entwickeln. Mit vielen Praxisbeispielen (3rd edn.) Wiesbaden: Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-16872-8
OECD. (2019). Education and training. In OECD (Ed.), Measuring the digital transformation: A roadmap for the future (pp. 172–173). OECD Publishing.
OECD. (2021). Continuing education and training in Germany. OECD Publishing.
Ore, O., & Sposato, M. (2021). Opportunities and risks of artificial intelligence in recruitment and selection. International Journal of Organizational Analysis. https://doi.org/10.1108/IJOA-07-2020-2291
Ouadahi, J. (2008). A qualitative analysis of factors associated with user acceptance and rejection of a new workplace information system in the public sector: A conceptual model. Canadian Journal of Administrative Sciences/Revue Canadienne des Sciences de l’Administration, 25(3), 201–213. https://doi.org/10.1002/cjas.65
Sebastian, R., & Biagi, F. (2018). The routine biased technical change hypothesis: A critical review. Joint Research Centre (Seville site), European Commission.
Seyda, S., Meinhard, D. B., & Placke, B. (2018). Weiterbildung 4.0 - digitalisierung als treiber und innovator betrieblicher weiterbildung. IW-Trends - Vierteljahresschrift zur empirischen Wirtschaftsforschung, 45(1):107–124.
Spitz-Oener, A. (2006). Technical change, job tasks, and rising educational demands: Looking outside the wage structure. Journal of Labor Economics, 24(2), 235–270. https://doi.org/10.1086/499972
Venkatesh, V., Speier, C., & Morris, M. G. (2002). User acceptance enablers in individual decision making about technology: Toward an integrated model. Decision Sciences, 33(2), 297–316. https://doi.org/10.1111/j.1540-5915.2002.tb01646.x
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Hellweg, T., Schneider, M. (2023). Which Types of Workers Are Adversely Affected by Digital Transformation? Insights from the Task-Based Approach. In: Gräßler, I., Maier, G.W., Steffen, E., Roesmann, D. (eds) The Digital Twin of Humans. Springer, Cham. https://doi.org/10.1007/978-3-031-26104-6_11
Download citation
DOI: https://doi.org/10.1007/978-3-031-26104-6_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-26103-9
Online ISBN: 978-3-031-26104-6
eBook Packages: Computer ScienceComputer Science (R0)