Correspondence: raquel.ortega-argiles@manchester.ac.uk

Summary of the Session’s Theme and Objectives

Understanding how knowledge diffuses within and between regions is crucial for explaining persistent productivity differentials and designing effective development policies. While evolutionary economic geography and related variety theories highlight the path-dependent nature of regional growth, the specific mechanisms driving knowledge transmission and its impact remain multifaceted. This session delves into the intricate dynamics of knowledge diffusion by exploring diverse pathways – labour mobility, skill configurations, technological recombination, and refined industrial relatedness – and their distinct contributions to regional productivity.

Bringing together novel empirical research primarily focused on the UK and EU regions, this session will offer fresh perspectives beyond traditional frameworks. We seek papers employing a range of methodologies – including analysis of large-scale administrative and novel datasets (employee surveys, job vacancies, patents, firm-level trade), network analysis, economic complexity metrics, and advanced relatedness indices – to provide a nuanced understanding of how different facets of regional knowledge ecosystems drive productivity. The papers will offer critical insights for policymakers aiming to foster innovation and unlock new growth trajectories by leveraging the diverse channels of knowledge diffusion.

List of Topics to Be Presented in the Special Session

1. Knowledge on the Move: How does the mobility of employees between firms, industries, and regions act as a vector for knowledge diffusion
2. What combinations of in-demand skills characterise high-performing regional economies
3. The productivity effects of skills demand and supply mismatches
4. The productivity effects of changes in regional industrial structures
5. The association between skills in demand and regional productivity
6. The short- and long-term productivity impacts of regions engaging in unconventional technological combinations
7. Industrial relatedness and its impact on regional productivity
8. The use of machine learning techniques and language processing models to analyse regional and local industrial transition

Key References

1. Ciarli, T., Kenney, M., Massini, S., & Piscitello, L. (2021). Digital technologies, innovation, and skills: Emerging trajectories and challenges. Research Policy, 50(7), 104289
2. Cicerone, G., McCann, P., & Venhorst, V. (2020). Promoting Regional Growth and Innovation: Relatedness, Revealed Comparative Advantage and the Product Space. Journal of Economic Geography, 20(1), 293-316 8.
3. Criscuolo, C., Gal, P., Leidecker, T., & Nicoletti, G. (2021). The human side of productivity. https://www.oecd-ilibrary.org/content/paper/5f391ba9-en
4. Hidalgo, C. A., & Hausmann, R. (2009). The building blocks of economic complexity. Proceedings of the National Academy of Sciences, 106(26), 10570–10575 9.
5. Molloy, R., Smith, C.L., Wozniak, A. (2017). Job Changing and the Decline in Long-Distance Migration in the United States. Demography; 54 (2)
6. Nesta, L., & Saviotti, P.P. (2005). Coherence of the knowledge base and the firm’s innovative performance: Evidence from the US pharmaceutical industry. The Journal of Industrial Economics, 53(1), 123–142 10.
7. Rocchetta, S., Ortega-Argilés, R., & Kogler, D.F. (2022). The Non-Linear Effect of Technological Diversification on Regional Productivity: Implications for Growth and Smart Specialisation Strategies. Regional Studies, 56(9), 1480-1495 11.
8. Storper, M., Kemeny, T., Makarem, N., & Osman, T. (2015). The rise and fall of urban economies: Lessons from San Francisco and Los Angeles. Stanford University Press.
9. Tacchella, A., Cristelli, M., Caldarelli, G., Gabrielli, A., & Pietronero, L. (2012). A New Metrics for Countries’ Fitness and Products’ Complexity. Scientific Reports, 2(723)
10. Uzzi, B., Mukherjee, S., Stringer, M. and Jones, B. (2013). ‘Atypical combinations and scientific impact’, Science, vol. 342(6157), pp. 468–472