Small cities face greater impact from automation

A Northwestern University and MIT study of automation and unemployment risks suggests that small cities will be less resilient than large cities in weathering the impacts of automation. Cities with fewer than 100,000 inhabitants will experience more disruption.

Smaller cities have proportional more jobs that are routine clerical work, such as cashier and food service jobs, which are more susceptible.

The city has proven to be the most successful form of human agglomeration and provides wide employment opportunities for its dwellers. As advances in robotics and artificial intelligence revive concerns about the impact of automation on jobs, a question looms: How will automation affect employment in cities? Here, they provide a comparative picture of the impact of automation across U.S. urban areas. Small cities will undertake greater adjustments, such as worker displacement and job content substitutions. They demonstrate that large cities exhibit increased occupational and skill specialization due to increased abundance of managerial and technical professions. These occupations are not easily automatable, and, thus, reduce the potential impact of automation in large cities. Their results pass several robustness checks including potential errors in the estimation of occupational automation and sub-sampling of occupations. Their study provides the first empirical law connecting two societal forces: urban agglomeration and automation’s impact on employment.

To construct a comparative picture of automation in cities, their first challenge is to get reliable estimates of how automation impacts workers. Existing estimates are wide ranging. Frey and Osborne estimate that 47% of U.S. employment is at “high risk of computerization” in the foreseeable future, while an alternative OECD study concludes a more modest 9% of employment is at risk. Note that these results do not tell us about the impact of automation in cities as they are presented at a national level. Differences in these predictions arise from discrepancies over two main skill dynamics: the substitution of routine skills, and complementarity of non-routine and communication skills. Additionally, technology-driven efficiency may redefine the skill requirements of occupations and actually increase employment in low-skilled jobs. Nevertheless, even if we take current estimates of the absolute risk of computerization of jobs with skepticism, these estimates can provide useful guidance about relative risk to different cities that is robust to errors in the estimates.

Large cities have more unique occupations and industries but distribute employment less uniformly across those occupations. This juxtaposition of both diversity and specialization in large cities is reconcilable through the division of labor theory.

What do large cities specialize in and why? The division of labor encourages worker modularity, which has the potential to impact whole groups of workers who are competing with automation technology. Therefore, specialization alone is not enough to explain the resilience to automation impact that we observe across cities. For example, Detroit, which is famous for its specialization in automotive manufacturing, has experienced economic down turn, while the San Francisco Bay area, epicenter of the information technology industry, continues to flourish despite the dot-com bubble (perhaps due to its support of a “creative class” of workers). Their analysis highlights specific occupations, such as Mathematician and Chemist, as well as specific types of skills, such as Computational/Analytical skill, that explain the increased resilience of large cities. These occupations and skills may inform policy makers in small cities as they identify new industries and design worker retraining programs to mitigate the negative effects of automation on employment. By quantifying relative impact, we provide an upper bound for technological unemployment in cities. Changing labor demands produce systemic effects, which make it difficult to precisely predict employment loss. For example, the introduction of Automated Teller Machines (ATMs) suggested a likely decrease in human bank teller employment. However, contrary to this prediction, ATM technology cut the cost to banks for opening and operating new branches, and, as a result, national bank teller employment increased. However, these bank tellers performed different tasks, such as relationship management and investment advice, which required very different skills. Hence, by impact, they refer to the magnitude of the skill substitution shocks that cities just respond to.
The actual technological unemployment in a city will be shaped both by free market dynamics (e.g. shifts in supply and demand curves) and by economic and educational policy (e.g. worker re-training, or skilled migration).

Despite being seemingly unrelated societal forces, they uncover a positive interplay between urbanization and automation. Larger cities not only tend to be more innovative but also harbor the workers who are prepared to both use and improve cutting-edge technology. In turn, these workers are more specialized in their workplace skills and less likely to be replaced by automated methods in the foreseeable future. These findings open the door for more controlled investigations with input from policy makers.

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