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Maximizing the Collective Learning Effects in Regional Economic Development

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  • Jian Gao

Abstract

Collective learning in economic development has been revealed by recent empirical studies, however, investigations on how to benefit most from its effects remain still lacking. In this paper, we explore the maximization of the collective learning effects using a simple propagation model to study the diversification of industries on real networks built on Brazilian labor data. For the inter-regional learning, we find an optimal strategy that makes a balance between core and periphery industries in the initial activation, considering the core-periphery structure of the industry space--a network representation of the relatedness between industries. For the inter-regional learning, we find an optimal strategy that makes a balance between nearby and distant regions in establishing new spatial connections, considering the spatial structure of the integrated adjacent network that connects all regions. Our findings suggest that the near to by random strategies are likely to make the best use of the collective learning effects in advancing regional economic development practices.

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  • Jian Gao, 2017. "Maximizing the Collective Learning Effects in Regional Economic Development," Papers 1712.08876, arXiv.org.
    • Handle: RePEc:arx:papers:1712.08876
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    1. Frank Neffke & Martin Henning & Ron Boschma, 2011. "How Do Regions Diversify over Time? Industry Relatedness and the Development of New Growth Paths in Regions," Economic Geography, Taylor & Francis Journals, vol. 87(3), pages 237-265, July.
    2. Cesar A. Hidalgo & Ricardo Hausmann, 2009. "The Building Blocks of Economic Complexity," Papers 0909.3890, arXiv.org.
    3. C. A. Hidalgo & B. Klinger & A. -L. Barabasi & R. Hausmann, 2007. "The Product Space Conditions the Development of Nations," Papers 0708.2090, arXiv.org.
    4. Bogang Jun & Aamena Alshamsi & Jian Gao & Cesar A Hidalgo, 2017. "Relatedness, Knowledge Diffusion, and the Evolution of Bilateral Trade," Papers 1709.05392, arXiv.org.
    5. Liu, Jin-Hu & Wang, Jun & Shao, Junming & Zhou, Tao, 2016. "Online social activity reflects economic status," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 581-589.
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    1. Hidalgo, César A., 2023. "The policy implications of economic complexity," Research Policy, Elsevier, vol. 52(9).
    2. Yang, Xiao & Gao, Jian & Liu, Jin-Hu & Zhou, Tao, 2018. "Height conditions salary expectations: Evidence from large-scale data in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 86-97.

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