Konferenzbeitrag

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 aver- age, within manufacturing sectors, an increase by one robot per 1000 workers is associated with a 3.5% 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.

Sprache
Englisch

Erschienen in
Series: Beiträge zur Jahrestagung des Vereins für Socialpolitik 2019: 30 Jahre Mauerfall - Demokratie und Marktwirtschaft - Session: International Trade and Trade Reforms III ; No. E08-V1

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
Strulik, Holger
Prettner, Klaus
Ereignis
Veröffentlichung
(wer)
ZBW - Leibniz-Informationszentrum Wirtschaft
(wo)
Kiel, Hamburg
(wann)
2019

Handle
Letzte Aktualisierung
10.03.2025, 11:41 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

  • Konferenzbeitrag

Beteiligte

  • Krenz, Astrid
  • Strulik, Holger
  • Prettner, Klaus
  • ZBW - Leibniz-Informationszentrum Wirtschaft

Entstanden

  • 2019

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