Segmented inter-occupation labour flows and the related diversification of regions
Abstract
This paper examines how labour market segmentation shapes inter-occupational labour flows and the related diversification of regions, contributing new evidence to the evolutionary economic geography (EEG) literature. Using Swedish matched employer-employee data (2002-2012), we construct segment-specific occupation networks by sex, education, and country of origin to uncover heterogeneity in revealed skill-relatedness. Applying a novel measure based on normalized mutual information, we compare the structural similarity of these networks and demonstrate that while occupations may appear related on average, the underlying connections are often segment-specific. Regional diversification processes may therefore unevenly align with the redeployment potential of different worker groups. This unevenness implies that policies promoting related diversification, central to frameworks such as Smart Specialisation, may inadvertently reproduce labour market segmentation by privileging capabilities concentrated in certain groups (e.g., men, Nordic-born, or medium-educated workers). Conceptually, the paper advances the EEG agenda by integrating network science with labour segmentation theory, revealing that relatedness is not uniform but socially embedded. Regarding policy, it calls for diversification strategies that explicitly assess (i) how local skill-related activities are distributed across worker segments and (ii) whether new specialisations reinforce or mitigate local segmentation. Doing so would allow regional innovation and industrial policies to better balance efficiency and inclusiveness. This is particularly relevant in the context of “just” and “green” transitions, where aligning emerging activities with the capabilities of diverse local workforces is critical to ensuring broad-based opportunity creation rather than deepening existing divides.
Fields of Study & Subjects
Regional Economics and Spatial Analysis
Regional resilience and development
Economic and Technological Innovation
Publication details
- Journal / source
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Publication date
- 1 January 2026
- Document type
- Preprint