Automation, robots and wage inequality in Germany: A decomposition analysis

Abstract

We analyze how and through which channels wage inequality is affected by the rise in automation and robotization in the manufacturing sector in Germany from 1996 to 2017. Combining rich linked employer-employee data accounting for a variety of different individual, firm and industry characteristics with data on industrial robots and automation probabilities of occupations, we are able to disentangle different potential causes behind changes in wage inequality in Germany. We apply the recentered influence function (RIF) regression based Oaxaca-Blinder (OB) decomposition on several inequality indices and find evidence that besides personal characteristics like age and education the rise in automation and robotization contributes significantly to wage inequality in Germany. Structural shifts in the workforce composition towards occupations with lower or medium automation threat lead to higher wage inequality, which is observable over the whole considered time period. The effect of automation on the wage structure results in higher inequality in the 1990s and 2000s, while it has a significant decreasing inequality effect for the upper part of the wage distribution in the more recent time period.

Sustainable youth employment quality management: The impact of robotization in China

“…The potential reason may be that limiting factors like incongruous proportions of the sample in terms of gender and age obscure underlying correlations [40][41][42]. We refer to the treatment of Brall and Schmid (2023) and conduct a classification discussion based on individual heterogeneity [43], to further analyze the individual heterogeneity of robotization effect.…”

Robots and Wages: A Meta-Analysis1

“…Estimations with a measure of inequality (e.g. Brall and Schmid, 2020), skill premium (e.g. (Gravina and Foster-McGregor, 2020)), labor (income) share (e.g.…”