About this Research Topic
Developing economies increase productivity faster than developed economies thus producing so-called economic convergence. It is morally imperative in our global society to promote economic convergence because it is the most reliable path to lift people out of poverty and guarantee them decent standards of living. However, our modern society is characterized by a high degree of complexity in both the global market and in regional industrial activities. Consequently, before economic growth can be achieved an integrated understanding of the ecosystem of complementary actors, knowhow, and capital is required. Thus, one can conceptualize productivity as an emerging property of a complex system made by simpler interacting parts - the actors in a region’s economy. Complex systems are notoriously difficult to control but quantifying these interactions can identify the bottlenecks to growth and inform policy to bolster economic convergence. Using tools from economics, complex systems, and network science, we seek crucial insights that enable economic convergence.
The goal of this Research Topic is to collect contributions using complex network analysis to model economic systems and to gain insights into economic development which has proven to be a valuable scientific undertaking. Recent results on economic complexity, the principle of relatedness, and on the automation of workplace activities have shown how network analysis can uncover the pathways for economic development and highlight potential issues. For example, the Product Space analysis showed how a bipartite country-product network reveals economic complexity that is strongly correlated with diversified export portfolio and future GDP growth. The principle of relatedness – which is used to construct the Product Space – unveils hidden relationships between different industrial activities that can be leveraged to diversify an economy. Failure to exploit these opportunities highlights economic frictions and slows economic convergence. We think that there is much to add to this research, ranging from enhancing its spatial granularity (from global economics to the regional and even the intra-firm level), to exploring the complex dynamics of knowledge exchange (which is at the basis of the development of new skills and, therefore, of new economic activities), to applying similar techniques in new areas of economic research.
Building on the above, the aim of this Research Topic is to explore the potential applications of complex network analysis to foster our understanding of complex economic systems. We welcome contributions in the broad areas of:
• Mapping the relationship of complex economic activities to build Product and Industry Spaces at the global, regional, and local level;
• Tracking flows of knowhow in all its forms (business travels, social interrelationships between entrepreneurs, etc);
• Creating networks of related tasks and skills to estimate knockoff effects and productivity gains of automation;
• Investigating the dynamics of innovation via analysis of patents and inventions;
• Uncovering scaling laws and other growth trends able to describe the systemic increase in complexity of activities due to agglomeration, e.g. in cities;
• In general, any application of network analysis that can be used to further our understanding of economics.
The goal of this Research Topic is to collect contributions using complex network analysis to model economic systems and to gain insights into economic development which has proven to be a valuable scientific undertaking. Recent results on economic complexity, the principle of relatedness, and on the automation of workplace activities have shown how network analysis can uncover the pathways for economic development and highlight potential issues. For example, the Product Space analysis showed how a bipartite country-product network reveals economic complexity that is strongly correlated with diversified export portfolio and future GDP growth. The principle of relatedness – which is used to construct the Product Space – unveils hidden relationships between different industrial activities that can be leveraged to diversify an economy. Failure to exploit these opportunities highlights economic frictions and slows economic convergence. We think that there is much to add to this research, ranging from enhancing its spatial granularity (from global economics to the regional and even the intra-firm level), to exploring the complex dynamics of knowledge exchange (which is at the basis of the development of new skills and, therefore, of new economic activities), to applying similar techniques in new areas of economic research.
Building on the above, the aim of this Research Topic is to explore the potential applications of complex network analysis to foster our understanding of complex economic systems. We welcome contributions in the broad areas of:
• Mapping the relationship of complex economic activities to build Product and Industry Spaces at the global, regional, and local level;
• Tracking flows of knowhow in all its forms (business travels, social interrelationships between entrepreneurs, etc);
• Creating networks of related tasks and skills to estimate knockoff effects and productivity gains of automation;
• Investigating the dynamics of innovation via analysis of patents and inventions;
• Uncovering scaling laws and other growth trends able to describe the systemic increase in complexity of activities due to agglomeration, e.g. in cities;
• In general, any application of network analysis that can be used to further our understanding of economics.
Keywords: complex networks, economic complexity, economic development, economic systems, economic convergence, big data networks, network analysis
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
