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Modular structure in labour networks reveals skill basins

Author

Listed:
  • Neave O'Clery
  • Stephen Kinsella
Abstract
There is an emerging consensus in the literature that locally embedded capabilities and industrial know-how are key determinants of growth and diversification processes. In order to model these dynamics as a branching process, whereby industries grow as a function of the availability of related or relevant skills, industry networks are typically employed. These networks, sometimes referred to as industry spaces, describe the complex structure of the capability or skill overlap between industry pairs, measured here via inter-industry labour flows. Existing models typically deploy a local or 'nearest neighbour' approach to capture the size of the labour pool available to an industry in related sectors. This approach, however, ignores higher order interactions in the network, and the presence of industry clusters or groups of industries which exhibit high internal skill overlap. We argue that these clusters represent skill basins in which workers circulate and diffuse knowledge, and delineate the size of the skilled labour force available to an industry. By applying a multi-scale community detection algorithm to this network of flows, we identify industry clusters on a range of scales, from many small clusters to few large groupings. We construct a new variable, cluster employment, which captures the workforce available to an industry within its own cluster. Using UK data we show that this variable is predictive of industry-city employment growth and, exploiting the multi-scale nature of the industrial clusters detected, propose a methodology to uncover the optimal scale at which labour pooling operates.

Suggested Citation

  • Neave O'Clery & Stephen Kinsella, 2019. "Modular structure in labour networks reveals skill basins," Papers 1909.03379, arXiv.org, revised Jan 2022.
    • Handle: RePEc:arx:papers:1909.03379
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    3. Li, Yang & Neffke, Frank M.H., 2024. "Evaluating the principle of relatedness: Estimation, drivers and implications for policy," Research Policy, Elsevier, vol. 53(3).
    4. Sandor Juhasz & Zoltan Elekes & Virag Ilyes & Frank Neffke, 2024. "Colocation of skill related suppliers – Revisiting coagglomeration using firm-to-firm network data," Papers in Evolutionary Economic Geography (PEEG) 2416, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised May 2024.
    5. Mattie Landman & Sanna Ojanperä & Stephen Kinsella & Neave O’Clery, 2023. "The role of relatedness and strategic linkages between domestic and MNE sectors in regional branching and resilience," The Journal of Technology Transfer, Springer, vol. 48(2), pages 515-559, April.
    6. Yang Li & Frank Neffke, 2022. "Relatedness in regional development: in search of the right specification," Papers in Evolutionary Economic Geography (PEEG) 2208, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Apr 2022.
    7. Mattie Susan Landman & Neave O'Clery, 2020. "The impact of the Employment Equity Act on female inter-industry labour mobility and the gender wage gap in South Africa," WIDER Working Paper Series wp-2020-52, World Institute for Development Economic Research (UNU-WIDER).
    8. Sabrina Aufiero & Giordano De Marzo & Angelica Sbardella & Andrea Zaccaria, 2023. "Mapping job complexity and skills into wages," Papers 2304.05251, arXiv.org.
    9. Max Sina Knicker & Karl Naumann-Woleske & Michael Benzaquen, 2024. "The Structure of Occupational Mobility in France," Papers 2407.14179, arXiv.org, revised Nov 2024.
    10. Modic, Dolores & Suklan, Jana, 2022. "Multidimensional experience and performance of highly skilled administrative staff: Evidence from a technology transfer office," Research Policy, Elsevier, vol. 51(10).
    11. Zoltán Elekes & Anna Baranowska-Rataj & Rikard Eriksson, 2023. "Regional diversification and labour market upgrading: local access to skill-related high-income jobs helps workers escaping low-wage employment," Cambridge Journal of Regions, Economy and Society, Cambridge Political Economy Society, vol. 16(3), pages 417-430.

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    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • N9 - Economic History - - Regional and Urban History

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