Maximizing the Collective Learning Effects in Regional Economic Development

TL;DR

This paper investigates how to optimize collective learning in regional economic development by balancing industry diversification and regional connectivity, using network models based on Brazilian labor data.

Contribution

It introduces a simple propagation model to identify optimal strategies for maximizing collective learning effects in regional economic development.

Findings

Nearby random strategies enhance collective learning effects

Balancing core and periphery industries improves inter-regional learning

Optimal spatial connection strategies depend on regional network structure

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.