Arbeitspapier

Robots, Reshoring, and the Lot of Low-Skilled Workers

We propose a theoretical framework to analyze the offshoring and reshoring decisions of firms in the age of automation. Our theory suggests that increasing productivity in automation leads to a relocation of previously offshored production back to the home economy but without improving low-skilled wages and without creating jobs for low-skilled workers. Since it leads also to increasing wages for high-skilled workers, automation-induced reshoring is associated with an increasing skill premium and increasing inequality. We develop a measure for reshoring activity at the macro-level and, using data from the world input output table, we provide evidence for automation-driven reshoring. On average, within manufacturing sectors, an increase by one robot per 1000 workers is associated with a 3.5% increase of reshoring activity. Using robots in countries with similar sectoral structure as an instrument, we find that an increase by one robot per 1000 workers causes a 2.5% increase of reshoring activity. We also provide the first cross-country evidence that reshoring is positively associated with wages and employment for high-skilled labor but not for low-skilled labor and that tariffs increase the degree of reshoring.

Sprache
Englisch

Erschienen in
Series: GLO Discussion Paper ; No. 443

Klassifikation
Wirtschaft
Trade Policy; International Trade Organizations
Economic Impacts of Globalization: Macroeconomic Impacts
Wage Level and Structure; Wage Differentials
Technological Change: Choices and Consequences; Diffusion Processes
Thema
Automation
Reshoring
Employment
Wages
Inequality
Tariffs

Ereignis
Geistige Schöpfung
(wer)
Krenz, Astrid
Prettner, Klaus
Strulik, Holger
Ereignis
Veröffentlichung
(wer)
Global Labor Organization (GLO)
(wo)
Essen
(wann)
2020

Handle
Letzte Aktualisierung
10.03.2025, 11:45 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Krenz, Astrid
  • Prettner, Klaus
  • Strulik, Holger
  • Global Labor Organization (GLO)

Entstanden

  • 2020

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