Inferring Networks of Interdependent Labor Skills to Illuminate Urban Economic Structure

Abstract

Cities are among the best examples of complex systems. The adaptive components of a city, such as its people, firms, institutions, and physical structures, form intricate and often non-intuitive interdependencies with one another. These interdependencies can be quantified and represented as links of a network that give visibility to otherwise cryptic structural elements of urban systems. Here, we use aspects of information theory to elucidate the interdependence network among labor skills, illuminating parts of the hidden economic structure of cities. Using pairwise interdependencies we compute an aggregate, skills-based measure of system "tightness" of a city's labor force, capturing the degree of integration or internal connectedness of a city's economy. We find that urban economies with higher tightness tend to be more productive in terms of higher GDP per capita. However, related work has shown that cities with higher system tightness are also more negatively affected by shocks. Thus, our skills-based metric may offer additional insights into a city's resilience. Finally, we demonstrate how viewing the web of interdependent skills as a weighted network can lead to additional insights about cities and their economies.

Keywords: Panarchy; cities; co-occurrence; information theory; interdependence; regional science; resilience; urban science; workforce.

Figure 1

Skill interdependence networks constructed from O*Net (A) elements and (B) individual work activities (IWAs). Nodes are skills and proximity between skills is a function of pairwise interdependencies. Non-normalized edge weights are displayed. All edges greater than zero are displayed. Node colors are determined using the Kamada–Kawai community detection algorithm. In the element-based skills network (A), yellow nodes = Sensory-physical skills, green nodes = Socio-Cognitive: Technical skills, and red nodes = Socio-Cognitive: General skills. In the IWA-based skills network (B), four communities emerged (colored differently) but they form unintuitive groupings of skills. Insets show distribution of normalized link weights in each network.

Figure 2

Locations of three representative metropolitan statistical areas (MSAs) within the national skills (elements) interdependence network. (A) Seattle is located almost exclusively within the socio-cognitive cluster of skills, while (B) Chicago is more balanced across lobes and (C) Indianapolis is largely within the sensory-physical cluster. Normalized tightness scores of each city are shown in parenthesis.

Figure 3

Frequency distribution of economic tightness T across MSAs. (A) T derived using O*Net elements and (B) O*Net individual work activities (IWAs). Distributions based on 2018 occupational employment data, N = 395 MSAs in both panels.

Figure 4

Spatial distribution and autocorrelation of MSA tightness values. (A) IWA Tightness and (B) Anselin’s Local Moran’s I (LISA). Clusters significant at a confidence level of 0.05 using k-nearest neighbors, k = 4.

Figure 5

A conceptualized tradeoff between occupation-based and skills-based tightness. (A) stylized tradeoff model with the red arc representing a Pareto frontier indicative of a tradeoff. (B) actual data for 395 MSAs using 2018 labor data. MSAs labeled a, b and c correspond to example city networks in Figure 2. (C) plots the tightness centroids of cities grouped into five bins based on population size (bin sizes shown next to points), while (D) does the same for cities grouped into bins based on 5-year GDP growth (growth bins shown next to points). Tightness measures have been normalized as z-scores with the y-axis inverted in all cases.