The factors that improve job resiliency in North American cities have been identified

Researchers have analysed two cities in the US - Burlington (Vermont) and Bloomi

Researchers have analysed two cities in the US - Burlington (Vermont) and Bloomington (Indiana) - which are the same size and have the same percentage of job occupations but are differently prepared for the future job shock that automation will cause. Bloomington has a less-diverse network and, therefore, workers who are displaced by automation will have more problems finding work in the city, they predict.

“Job connectivity? (the possibility of finding a similar job) is a key factor for the recovery of local economies in the face of crises, according to a study published recently by researchers from the Universidad Carlos III de Madrid (UC3M), the Massachusetts Institute of Technology (MIT), the Max Planck Society and the University of Pittsburgh. The researchers in this study reached this conclusion by drawing on network modelling research and mapped the job landscapes in cities across the United States during economic crises.

Knowing and understanding which factors contribute to the health of job markets is interesting as it can help promote faster recovery after a crisis, such as a major economic recession or the current COVID pandemic. Traditional studies perceive the worker as someone linked to a specific job in a sector. However, in the real-world professionals often end up working in other sectors that require similar skills. In this sense, researchers consider job markets as being something similar to ecosystems, where organisms are linked in a complex network of interactions.

In this context, an effective job market depends on many aspects, such as diversity and the number of job offers or training opportunities that workers have in order to acquire new skills, for example. In this scientific study, researchers have found that cities where all of these factors are very similar respond differently in regard to recovering from an economic crisis. Why? “We have discovered that the difference comes, in part, from the jobs ’map’, a network that tells us how jobs within a city are related, according to the similarity of the skills required to perform those jobs,’ explains Esteban Moro, an associate professor at the UC3M’s Department of Mathematics and co-author of the study, who is currently a visiting professor at the MIT Media Lab.

“When that map is extremely limited, in other words, when there is very little chance of finding another similar job (what we call “job connectivity?), cities are less prepared for a job crisis. In contrast, when that map offers lots of possibilities of moving from one job to another similar one, the city is better prepared. It also has an effect on workers’ wages: workers in cities that have a more diverse network earn more than those in the same occupation in cities where this network is more limited,’ adds Esteban Moro.

Ecology, complex networks and job connectivity

In ecology and other domains where complex networks are present, resilience has been closely linked to the “connectivity? of the networks. In nature, for example, ecosystems with lots of connections have proven to be more resistant to certain shocks (such as changes in acidity or temperature) than those with fewer connections. Inspired by this idea and drawing on previous network modelling research, the authors of the study modelled the relationships between jobs in several cities across the United States. Just as connectivity in nature fosters resilience, they predicted that cities with jobs connected by overlapping skills and geography would fare better in the face of economic shock than those without such networks.

In order to validate this, the researchers examined data from the Bureau of Labor Statistics for all metropolitan areas in the US from the beginning to the end of the Great Recession (2008-2014). Based on this data, they created maps of the job landscape in each area, including the number of specific jobs, their geographical distribution, and the extent to which the skills they required overlapped with other jobs in the area. The size of a given city, as well as its employment diversity, played a role in resilience, with bigger, more diverse cities obtaining better results than smaller and less-diverse ones. However, by controlling size and diversity and taking job connectivity into account, predictions of peak unemployment rates during the recession improved significantly. In other words, cities where job connectivity was higher before the crash were significantly more resilient and recovered faster than those with less-connected markets.

Even in the absence of temporary crises like the Great Recession or the COVID pandemic, phenomena, such as automation, might radically change the job landscape in many areas in the coming years. How can cities prepare for this disruption? The researchers in this study extended their model to predict how job markets would behave when facing job loss due to automation. They found that while cities of similar sizes would be affected similarly in the early stages of automation shocks, those with well-connected job networks would provide better opportunities for displaced workers to find other jobs. This prevents widespread unemployment and, in some cases, even leads to more jobs being created as a result of the initial automation shock.

The findings of this study suggest that policymakers should consider job connectivity when planning for the future of employment in their regions, especially where automation is expected to replace a large number of jobs. Furthermore, increased connectivity does not just result in lower unemployment, it also contributes to a rise in overall wages. These results provide a new perspective on discussions about the future of employment and may help guide and complement current decisions about where to invest in job creation and training programmes, say researchers.

Bibliography: Moro, E., Frank, M.R., Pentland, A. et al. Universal resilience patterns in labor markets. Nat Commun 12, 1972 (2021). doi.org/10.1038/s414­67-021-22086-3

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