Abstract
In response to recent exponential advancements in computer technology (such as artificial intelligence, machine learning, and robotics), there have been several recent studies trying to assess the impacts on employment. Whereas most recent studies have focused on either the net effect on employment or the effect on particular occupation groups, this study extends this research by focusing on the social impacts in terms of the likely effect of various population groups. Employing a human capital and intersectionality lens, and a moderated-mediation analysis of Canadian 2016 Census data, this study finds that the likely effects of automation differ significantly depending on the intersections of income level, gender, and visible minority status, differences that for the most part are explained (or mediated) by human capital, especially education. However, a portion of the relationship between our intersecting variables and automatability is not explained by human capital variables, suggesting alternative explanations. We discuss the public policy implications, including the individual, employer, or government responsibilities for addressing the employability, labor market, human capital, and macro-level talent management challenges.
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