Stop Worrying and Love the Robot: An Activity-Based Approach to Assess the Impact of Robotization on Employment Dynamics

Abstract

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Can Machines Learn Creativity Needs? An Approach Based on Matrix Completion

“…Therefore, one can distinguish between routine and non-routine tasks (e.g., in the case of manual tasks): while the latter exhibit a complementary relationship to digital technologies, cognitive routine tasks (which are typical of clerical workers and administrative jobs) are potentially highly exposed to automation, due to the introduction of digital technologies (Autor et al 2003). 5 To conclude, the literature on the task-based approach is rapidly expanding, and many papers have appeared recently, including recent contributions related to Italy as a case study (see, e.g., Bonacini et al 2021;Carbonero and Scicchitano 2021;Caselli et al 2021;Cetrulo et al 2020;Esposito and Scicchitano 2022;Vannutelli et al 2021).…”

The reassuring effect of firms' technological innovations on workers' job insecurity

“…The second dataset employed allows us to account for the task-content of jobs and their potential exposure to robotization. Following the methodology developed by Caselli et al (2021), we use ICP to identify the occupations characterized by tasks and activities that could be performed by specific robot applications (as defined by the International Federation of Robotics). This survey, whose last wave was released in 2013, is run by INAPP on about 16,000 workers, and it covers about 800 occupations according to the 5-digit CP2011 classification (the Italian equivalent of the ISCO-08 classification by the International Labour Organization).…”

The composite link between technological change and employment: A survey of the literature

“…Caselli et al. (2021) observe that the industry shift‐share approach does not account for the high degree of firms and workers' heterogeneity within an industry (as it implicitly assumes that every worker in every firm in an industry faces the same level of robot exposure and that the distribution of robots within an industry is uniform across regions conditional on the local employment shares), nor does it allow for distinguishing occupations that are complementary to robot adoption from those that are exposed (and those that are not). In their analysis of the employment consequences of robot penetration in the Italian local labor markets, Caselli et al.…”

“…In this respect, Aghion et al (2020), who study the labor implications of automation technologies at different levels of aggregation (see Section 3.2.3), postulate that the heterogeneity of these studies in terms of main findings may be partly attributable to different levels of exposure to trade (for instance, Germany relies heavily on exports, while, in the US, domestic firms have a larger domestic market and are less exposed to international competition). Caselli et al (2021) observe that the industry shift-share approach does not account for the high degree of firms and workers' heterogeneity within an industry (as it implicitly assumes that every worker in every firm in an industry faces the same level of robot exposure and that the distribution of robots within an industry is uniform across regions conditional on the local employment shares), nor does it allow for distinguishing occupations that are complementary to robot adoption from those that are exposed (and those that are not). In their analysis of the employment consequences of robot penetration in the Italian local labor markets, Caselli et al introduce two major novelties, namely, they match occupations and robots based on the tasks characterizing both the professions and the robots and focus on the evolution of local employment in the occupations exposed to robots, rather than resorting to broad occupational groups (e.g., skilled/unskilled, routine/non-routine, and the like).…”