Abstract

This study examines how technology implementation within workplaces impacts job ending among employees. We advance the literature on the labor market consequences of new technologies by focusing on their impact within workplaces where they are implemented, rather than inferring from aggregate labor structural changes. We also address how the impact of technology differs depending on workers education, organizational tenure and age. Using large-scale Dutch matched employer-employee panel data directly measuring technology implementation, we find that technology implementation is associated with an overall decrease in the probability of job ending. In line with the skill biased technological change hypothesis, higher educational attainment is associated with lower probabilities of job ending. Furthermore, we find older workers (around 50+) and workers with longer organizational tenure (around 12+ years) to have a higher probability of job ending when technology is implemented. Finally, we do not find the effects of technology implementation to differ depending on the union density of the industry in which an enterprise operates.

Highlights

  • Concerns about technological advancement and the future of work greatly increased in the past decades

  • Looking at the effects of technology implementation on the likelihood of job ending in the baseline model 1, we find that technology implementation is associated with a signifi­ cantly lower probability of job ending in both models

  • Estimating the overall probability of job ending between the two conditions, we find that under technology implementation the chances of job ending are 23.24 %, which is 1,69 % lower than in times without technological innovation (24.93 %)

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Summary

Introduction

Concerns about technological advancement and the future of work greatly increased in the past decades. Such worries have been strengthened by studies examining the feasibility of replacing human jobs by soon-to-be realized technologies in robotics, machine learning and artificial intelligence. Frey and Osborne (2017) estimate that, in the coming decades, 47 % of all jobs in the U.S are at risk of being auto­ mated. The World Bank (2016) comes to a similar estimate: almost 60 % of jobs in the OECD are susceptible to automation in the near future. Labor eco­ nomic theorizing reiterates this labor replacing potential of technolog­ ical advancement (Goos, Manning, & Salomons, 2014; Acemoglu & Autor, 2011; Autor, Levy, & Murnane, 2003; Goos & Manning, 2007; Goos, Manning, & Salomons, 2009)

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