Scandinavian Working Papers in Economics
Papers in Innovation Studies,
Lund University, CIRCLE - Centre for Innovation Research
No 2026/12: Multi-View Representation Learning for Regional Opportunity Spaces and Industrial Path Development
Philipp Stark (), Alexandros Sopasakis (), Ola Hall () and Markus Grillitsch ()
Additional contact information
Philipp Stark: Department of Human Geography, Lund University, Postal: Sölvegatan 10, 223 62 Lund, Sweden
Alexandros Sopasakis: Centre for Mathematical Sciences, Lund University, Postal: Sölvegatan 18, 223 62 Lund, Sweden
Ola Hall: Department of Human Geography, Lund University, Postal: Sölvegatan 10, 223 62 Lund, Sweden
Markus Grillitsch: Department of Human Geography, Lund University, Postal: Sölvegatan 16, 223 62 Lund, Sweden
Abstract: Regional opportunities for industrial development arise when local conditions favor particular economic activities. These conditions depend in part on a region’s existing industry profile and on the economic relationships among its activities. Heterogeneous data sources on mobility, production, and firm ownership provide complementary evidence about this underlying industrial structure. We therefore develop a multi-view representation-learning framework for industries that integrates these different sources into a shared latent representation. By using graph diffusion, the framework captures both direct and indirect connections between industries. Applied to Swedish industries over three decades and three classification versions, the learned representation places industries from different classification periods within a common latent space. To evaluate this operationalization of opportunity, we construct an opportunity-density measure by combining latent similarity with local industry size. Higher opportunity density is consistently associated with industry entry and startup formation and with lower industry exit. Across classification periods, a one-standard-deviation increase in opportunity density is associated with 5.3–6.2 percentage-point higher industry-entry probabilities (118–136% of baseline rates), 8.9–13.8 percentage-point lower industry-exit probabilities (147–209%), and 1.1–1.7 percentage-point higher startup-formation probabilities (131–187%). Opportunity density remains associated with these outcomes in 41 of 42 models after accounting for information contained in the individual data sources. Our machine-learning framework shows how heterogeneous data can be integrated into a common latent structure and used to operationalize regional opportunity spaces, providing a new approach to studying industrial path development.
Keywords: Regional opportunity spaces; Industrial path development; Industry relatedness; Multi-view representation learning; Regional diversification
Language: English
46 pages, October 13, 2026
Note: This work was supported by FORMAS (grant no. 2022-153657) and seed funding from the AI Lund Initiative at Lund University through the project “Temporal Harmonization of Industry Classifications with Swedish Register Data.” The work of A.S. was partially supported by eSSENCE (grant no. 156938) and the Swedish National Space Agency (grant no. 2025-00016). The computations were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS), partially funded by the Swedish Research Council through grant agreement no. 2022-06725.
Full text files
25887Full text
Questions (including download problems) about the papers in this series should be directed to Torben Schubert ()
Report other problems with accessing this service to Sune Karlsson ().
RePEc:hhs:lucirc:2026_012This page generated on 2026-10-13 08:34:25.