Digitalization and the future of work: macroeconomic consequences
Computing power continues to grow at an enormous rate. Simultaneously, more and better data is increasingly available and Machine Learning methods have seen significant breakthroughs in the recent past. All this pushes further the boundary of what machines can do. Nowadays increasingly complex tasks...
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| Main Authors: | , , |
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| Format: | Book/Monograph Working Paper |
| Language: | English |
| Published: |
Mannheim, Germany
ZEW - Leibniz-Zentrum für Europäische Wirtschaftsforschung GmbH Mannheim
2019
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| Series: | Discussion paper
no. 19, 024 (06/2019) |
| In: |
Discussion paper (no. 19, 024 (06/2019))
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| Subjects: | |
| Online Access: | Resolving-System, kostenfrei, Volltext: http://hdl.handle.net/10419/200063 Verlag, kostenfrei, Volltext: https://www.zew.de/de/publikationen/digitalization-and-the-future-of-work-macroeconomic-consequences/?cHash=d75f9477e32c7e3decfe7ae3020db35b Verlag, kostenfrei, Volltext: http://ftp.zew.de/pub/zew-docs/dp/dp19024.pdf Verlag, kostenfrei: https://madoc.bib.uni-mannheim.de/52488 
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| Author Notes: | Melanie Arntz, Terry Gregory, and Ulrich Zierahn |
MARC
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| 490 | 1 | |a Discussion paper / ZEW |v no. 19, 024 (06/2019) | |
| 520 | |a Computing power continues to grow at an enormous rate. Simultaneously, more and better data is increasingly available and Machine Learning methods have seen significant breakthroughs in the recent past. All this pushes further the boundary of what machines can do. Nowadays increasingly complex tasks are automatable at a precision which seemed infeasible only few years ago. The examples range from voice and image recognition, playing Go, to self-driving vehicles. Machines are able to perform more and more manual and also cognitive tasks that previously only humans could do. As a result of these developments, some argue that large shares of jobs are "at risk of automation", spurring public fears of massive job-losses and technological unemployment. This chapter discusses how new digital technologies might affect the labor market in the near future. First, the chapter discusses estimates of automation potentials, showing that many estimates are severely upward biased because they ignore that workers in seemingly automatable occupations already take over hard-to-automate tasks. Secondly, it highlights that these numbers only refer to what theoretically could be automated and that this must not be equated with job-losses or employment effects - a mistake that is done often in the public debate. Thirdly, the chapter develops scenarios on how digitalization is likely to affect the German labor market in the next five years and derives implications for policy makers on how to shape the future of work. Germany is an interesting case to study, as it is a developed country at the technological frontier. In particular, the main challenge will not be the number, but the structure of jobs and the corresponding need for supply side adjustments to meet the shift in demand both within and between occupations and sectors. | ||
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| 650 | 4 | |a Digitalization |7 (dpeaa)DE-206 | |
| 650 | 4 | |a Unemployment |7 (dpeaa)DE-206 | |
| 650 | 4 | |a Inequality |7 (dpeaa)DE-206 | |
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