The Impact of Job Automation Is A Product of Automatibility and Skill Transferability

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Job automatibility is not a direct indicator of job security as people are not just their occupations, but also their skills. While some occupations are likely to be automated, individuals in those roles often possess transferable skills that allow them to transition to other growing occupations. Conversely, occupations that are not likely to be automated may still face challenges due to oversupply and may not offer the same opportunities for skill transfer. The impact of job automation is complex and cannot be determined solely based on the likelihood of automatibility. The network analysis reveals that the situation is not as straightforward as assuming automatable occupations will suffer and non-automatable occupations will grow; rather, it lies somewhere in between and requires a comprehensive understanding of the network to fully grasp.

Since the first Industrial Revolution, most people have responded in one of two ways to the threat of technological unemployment: either a general blanket fear that the machines are coming for us all, or an equally uncritical dismissal of the issue. But history shows otherwise: the labor market changes over time in adaptation to the complex and nonlinear ways automation eats economies. Some jobs are easier to lose but teach skills that translate to other more secure jobs; other kinds of work elude mechanization but are comparably easier for humans, and thus don’t provide the kind of job security one might suppose. By analyzing labor networks — studying the landscapes of how skillsets intersect with labor markets and these systems mutate under pressure from a changing technological milieu — researchers can make deeper and more practical quantitative models for how our world will shift along with evolutions in robotics and AI. Dispelling Chicken Little fears and challenging the sanguine techno-optimists, these models start to tell a story of a future not unlike the past: one in which Big Changes will disrupt the world we know, arrive unevenly, reshape terrains of privilege and hardship, and reward those who can dedicate themselves to lifelong learning.

This week’s guest is R. Maria del Rio-Chanona, a Mathematics PhD student supervised by SFI External Professor Doyne Farmer at the University of Oxford. Before starting her PhD, Maria did her BSc in Physics at Universidad Nacional Autónoma de México and was a research intern at the International Monetary Fund, where she studied global financial contagion in multilayer networks. We met at the 2019 New Complexity Economics Symposium to discuss the use of agent-based models in economics, how the labor market changes in response to technological disruption, and the future of work.

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Maria’s Website & Links to Papers.

Maria’s Google Scholar Page.

Andrew McAfee & Erik Brynjolfsson on Technological Unemployment.

Carl Benedikt Frey & Michael A. Osborne on Technological Unemployment.

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