This study by Burcu Sevde Selvi and Owen Garling uses network analysis to explore the spatial and sectoral dimensions of productivity to better understand the underlying reasons for slow productivity growth in East Anglia.
Relatedness has become a key concept in regional studies, and argues that the growth of an industry in a region depends on the presence of related activities in the area. This study applies network tools to a novel dataset based on a new classification of data – Real Time Industrial Classifications – to map the sectoral relatedness in East Anglia. We identify seven sectoral family groups and find that the life sciences, net zero, and research & consulting-related sectors have a strong influence over other sectors in the region. The results provide evidence of how different sectors within a geographically defined location connect as a network, enabling knowledge and practices to flow between different sectors. We conclude by discussing some of the implications for regional policy.
