Maximizing the Collective Learning Effects in Regional Economic Development
For policymakers and economists, it provides actionable strategies to optimize regional economic development through collective learning, though the findings are based on a single country's data.
This paper investigates how to maximize collective learning effects in regional economic development using a propagation model on Brazilian labor data, finding optimal strategies that balance core-periphery industries and nearby-distant regions to enhance diversification.
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.