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ABSTRACT
Since digitalization and Industry 4.0 have been recognized as a key issue for future economic development, prosperity and wealth distribution, several studies have emerged on the potential threats of new technology on workforce development. The consensus is that jobs may fall away, while some new jobs will be created, with a different skills profile and a new set of qualifications that are required. This paper examines the effects of three main indicators: the impact of skills, industrial sector dominance and product complexity on workforce reduction. Based on metropolitan data from the US (Census) and Europe (Eurostat), the author develops a metropolitan typology based on industrial sectors in each metro and analyses the systematic relationship between regional variations of automation, local skills and economic sector variations, finding that automation exposure in Europe is significantly lower than in the US and that medium-skilled manufacturing jobs in the US are increasingly threatened and low-skill service jobs remain relatively safe from automation – leading to a decreasing middle class. This also shows how metropolitan areas are at risk of developing polarized effects: some facing economic upturn and continuous prosperity, and a majority of others either stagnant or with extreme downturn and high unemployment rates.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1 A citizen-oriented evaluation of the effects of digital infrastructure and artificial intelligence in metropolitan areas can be found in Tomer (Citation2019), including the many ways in which data circulation affect and facilitate human decision making, privacy concerns and an entirely new facet to structural inequity biases that are enforced and intensified by technology on an everyday basis.
2 A more comprehensive industry and occupation-based analysis of the effects of digitalization is Muro et al. (Citation2017). The report evaluates the risk of jobs, occupations and specific industries and found that while digitalization has been an ongoing trend in the US, the economy has experienced especially strong digitalization trends in the past 10 years and is continuously accelerating.
3 See also Lin, Shyu, and Ding (Citation2017), Seet et al. (Citation2018), Tessarini and Saltorato (Citation2018), Ehlers (Citation2020).
4 See, for example Buhr (Citation2017), who lays open three potential automation scenarios with different worker displacement consequences for the German economy.
5 A simple comparison of the average personal debts for adults in the US with private and Germany with public higher education shows the drastic impact: with more than USD 90,000 per adult in the US, Germans only carry about USD 30,000 of debt (Experian 2020).
6 As the concrete research methods of the paper from Muro, Maxim, and Whiton (Citation2019) were not available, there is a discrepancy of 5 metropolises between their generation of the top 100 list and the one used in this paper. For this reason, the author will use the thereby generated top 95 list for the analysis going further.
7 0.6 percent above average for high-tech employees and 3.8 percent above average for patents.