Automation and income inequality in Europe

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Date:
2026-02-13
Authors:
Karina Doorley
Jan Gromadzki
Piotr Lewandowski
Dora Tuda
Philippe Van Kerm
Publication year:
2026
Publications category:
Abstract:

This paper examines the impact of industrial robot adoption on household income inequality in 14 European countries between 2006 and 2018. Automation reduced relative wages, employment, and market incomes of more exposed demographic groups. However, feeding these automation-induced wage and employment shocks into the EUROMOD microsimulation model shows that their effect on inequality in disposable household income was small. Tax-benefit systems, particularly transfers, largely absorbed the disequalizing labor income shocks caused by automation. Household labour income diversification cushioned the automation-induced labour income shocks, but played a limited role for inequality.

Additional information:

We thank the editor and two anonymous reviewers for their constructive comments. We thank Karol Madoń and Artur Król for excellent research assistance. We thank Mikkel Barslund, Sydnee Caldwell, David Card, Cristiano Perugini, Vegard Skirbekk, and participants of the EALE Annual Conference 2023, ECINEQ Annual Meeting 2023, IEA Annual Meeting 2023, SOLE Annual Meeting 2023, TASKS VI conference, Untangled 2022 conference, VALURED 2024 workshop, and workshops in Oslo, Warsaw, and Trento for their comments. This paper uses Eurostat data. Eurostat has no responsibility for the results and the conclusions, which are those of the authors. The results presented here are based on EUROMOD version I3.86. Originally maintained, developed and managed by the Institute for Social and Economic Research (ISER), since 2021 EUROMOD is maintained, developed and managed by the Joint Research Centre (JRC) of the European Commission, in collaboration with Eurostat and national teams from the EU countries. We are indebted to the many people who have contributed to the development of EUROMOD. The results and their interpretation are the authors’ responsibility. This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 1001004776. Gromadzki acknowledges funding from the Polish-U.S. Fulbright Commission.

Published in:

Accepted in ILR Review

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