The Dynamic Resilience of Urban Labour Networks

Feng, Xiangnan; Rutherford, Alex

doi:10.48550/arxiv.2202.12856

Cited by 2 publications

(2 citation statements)

References 45 publications

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“…Indeed, given the existing data constraints, our analysis of the job and skill spaces is fixed in time. However, the skill content of jobs may change over time, across space 70,71 or even between firms 72 . Therefore, provided the appropriate information-e.g., online job advertisement data as done by Deming and co-authors 70 may open new possibilities for understanding the changing network structure of the occupational demand for skills, both in the time and space dimension.…”

Section: Discussionmentioning

confidence: 99%

Mapping job fitness and skill coherence into wages: an economic complexity analysis

Aufiero,

De Marzo,

Sbardella

et al. 2024

Sci Rep

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Leveraging the discrete skill and knowledge worker requirements of each occupation provided by O*NET, our empirical approach employs network-based tools from the Economic Complexity framework to characterize the US occupational network. This approach provides insights into the interplay between wages and the complexity or relatedness of the skill sets within each occupation, complementing conventional human capital frameworks. Our empirical strategy is threefold. First, we construct the Job and Skill Progression Networks, where nodes represent jobs (skills) and a link between two jobs (skills) indicates statistically significant co-occurrence of skills required to carry out those two jobs, that can be useful tools to identify job-switching paths and skill complementarities Second, by harnessing the Fitness and Complexity algorithm, we define a data-driven skill-based complexity measure of jobs that positively maps, but with interesting deviations, into wages and in the bottom–up and broad abstract/manual and routine/non-routine job characterisations, however providing a continuous and endogenous metric to assess the degree of complexity of each occupational skill-set. Third, building on relatedness and corporate coherence metrics, we introduce a measure of each job’s skill coherence, that negatively maps into wages. Our findings may inform policymakers and employers on designing more effective labour market policies and training schemes, that, rather than fostering hyper-specialization, should favor the acquisition of complex and “uncoherent” skill sets, enabling workers to more easily move throughout the job and skill progression networks and make informed career choices decisions while unlocking higher wage opportunities.

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Section: Discussionmentioning

confidence: 99%

Mapping job fitness and skill coherence into wages: an economic complexity analysis

Aufiero,

De Marzo,

Sbardella

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Linkage prediction methods are applied in economics to predict the evolution of future economic networks to guide policymakers. Authors use these methods to predict possible linkages in the future labor market [38]. However, these methods use adjacency matrix perturbations, for example, but without capitalizing on the data within the graphs [39].…”

Section: Economics Graph Learningmentioning

confidence: 99%

Product Space Clustering with Graph Learning for Diversifying Industrial Production

Cortial,

Albouy-Kissi,

Chausse

2024

Applied Sciences

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During economic crises, diversifying industrial production emerges as a critical strategy to address societal challenges. The Product Space, a graph representing industrial knowledge proximity, acts as a valuable tool for recommending diversified product offerings. These recommendations rely on the edges of the graph to identify suitable products. They can be improved by grouping similar products together, which results in more precise suggestions. Unlike the topology, the textual data in nodes of the Product Space graph are typically unutilized in graph clustering methods. In this context, we propose a novel approach for economic graph learning that incorporates learning node data alongside network topology. By applying this method to the Product Space dataset, we demonstrate how recommendations have been improved by presenting real-life applications. Our research employing a graph neural network demonstrates superior performance compared to methods like Louvain and I-Louvain. Our contribution introduces a node data-based deep graph clustering graph neural network that significantly advances the macroeconomic literature and addresses the imperative of diversifying industrial production. We discuss both the advantages and limitations of deep graph learning models in economics, laying the groundwork for future research.

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The Dynamic Resilience of Urban Labour Networks

Cited by 2 publications

References 45 publications

Mapping job fitness and skill coherence into wages: an economic complexity analysis

Mapping job fitness and skill coherence into wages: an economic complexity analysis

Product Space Clustering with Graph Learning for Diversifying Industrial Production

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