Exploring Universal Resilience Patterns in Labor Networks

Figure 1: Urban labor markets are complex ecologies of labor comprised of career opportunities based on workers’ skills and local employers. Quantifying bundles of skills can improve models of career mobility and clarify the economic resilience of the local workforce.

Cities are the innovation centers of the US economy, but many modern trends threaten to disrupt employment and career mobility. Examples in- clude technological change, the Great Recession, and the COVID-19 global pandemic. Given the diversity of these disruptions, policy makers must promote the jobs and skills that increase worker pay, create employment, and foster economic resilience in general.

To study, the economic resilience of urban labor markets, we adapt a resilience framework from ecosystems to model cities as ecologies of labor. Although it’s common to consider a city’s labor market using employment counts by occupation, it is also beneficial to consider the career mobility available to workers. We achieve this goal by mapping opportunities for job transitions according to the shared skill requirements of occupation pairs according to data from the US Department of Labor. Quantifying workers’ skills and their impact on career advancement offers a new perspective that connects labor disruptions to aggregate labor trends (e.g., advancements in computer vision may impact the demand for visual workplace activities, but likely does not entirely automate any occupation).

Mapping shared skill requirements among occupations in a labor market allows us to quantify the average career mobility available to local workers. That is, if a worker losses employment in an occupation that shares skill requirements with many other occupations, then that worker is better prepared to find new employment without significant re-skilling or up-skilling. Given a city, we can identify such occupations on the network by measuring the number of connections between that occupation and other network nodes. We say that occupations with many connections are embedded in their local labor market.

Figure 2: The occupation network of Boulder, CO, and Chicago, IL, as subsets of the US national occupation network. Occupations (nodes) are colored according to occupation type. Learn more about the network here.

Generalizing this notion of embeddedness, we can assess the overall economic resilience of a city’s labor market to disruption. That is, if a city’s occupation network is densely connected, then, in expectation, displaced workers have relevant skills that enable them to seek new employment within the same labor market. This individual career mobility on aggregate creates an adaptable labor market that is able to recover from disruptions.

Consider the occupation network for Boulder, CO, and Chicago, IL as examples. We first construct the occupation network for the entire US economy and then highlight the parts of the national network that represent each city. Comparing the two city networks, we first notice that Chicago’s network has more nodes than Boulder’s network. This corresponds to the fact that Chicago has more workers (i.e., more total employment) than Boulder. The second thing to consider is the density of connections between those occupations included in each city’s network; even after controlling for the number of occupations, Chicago’s network is more densely connected.

Figure 3: US cities colored by their occupation network connectivity. Explore the map and the city job network here.

Additionally, we find that embedded occupations enjoy a privileged and valuable position in their local labor market. In particular, we uncover an embeddedness wage premium where workers of a given occupation will earn higher wages in a city where that occupation is embedded compared to their peers with the same job title in other cities. For example, we observe a strong relationship between occupational embeddedness and annual wages for Financial Managers across US cities.

Figure 4: Financial Managers earn higher wages in cities where the occupation is more embedded. Learn more about the embedded occupations here.