Related variety and employment growth: a spatial dynamic model for Brazilian microregions

Abstract

This paper assesses the effects of economic externalities on the local employment growth in the Brazilian microregions. We present new evidence of the effects of industrial related variety, unrelated variety and productive specialization on regional performance for a developing country. We use a comprehensive labour market database covering the period between 1996 and 2016 for 558 microregions and an econometric method that includes both the temporal and spatial elements in the analysis, by means of a spatial dynamic panel estimated by a generalized method of moments (GMM) system. The results suggest that local employment growth is positively influenced by related industrial variety however, it is not related to unrelated variety.

How do gatekeepers in networks influence the production of new knowledge? Evidence from Brazilian co‐invention networks

“…In our case, these results seem to extend to an individual’s creative output. The related variety implies that sectors cognitively close enable more knowledge spillovers in a given region (Da Silva et al, 2020). However, the reasons why non‐proximity cognitive activities seem to influence the individual’s performance are rather unclear, suggesting the need for further research on the topic.…”

Unpacking the U‐shaped relationship between related variety and firm sales: Evidence from Japan

“…Our paper applies entropy to quantify related variety. Entropy is a popular indicator that has appeared in many studies on related variety (e.g., Content et al, 2019; da Silva et al, 2020; Fritsch & Kublina, 2018). However, Neffke and Henning (2013) noted that entropy is not a perfect indicator because it does not necessarily have relationships to those four‐digit sectors in the same two‐digit industry.…”

“…Accordingly, Frenken et al, (2007) based their related variety perspective on industrial classification system, regardless of whether it is the North American Industrial Classification System (NAICS), Standard Industrial Classification (SIC), the NACE system, or another system. For example, Content et al (2019) assumed that four‐digit sectors (other studies have used five‐digit sectors (e.g., Aarstad et al, 2016) or three‐digit sectors (da Silva et al, 2020, but this is not overly important) belonging to the same two‐digit industry share the same or similar knowledge base; therefore, such four‐digit sectors have high related variety. Content et al’s method fits people's intuition.…”

Regional heterogeneity and the effects of the related and unrelated varieties on innovation

“…Previous studies claim that it is important to consider the conditioning local factors that facilitate more related or unrelated diversification in regions (Boschma, 2017; Quatraro & Usai, 2017), including less‐developed and peripheral regions (Asheim et al, 2019; Mascarini et al, 2023). Third, our empirical analysis is applied to a developing country, a topic that has mostly been analysed in developed countries, with few exceptions (Silva et al, 2020). Different institutional environments have different effects on the ways in which new knowledge is generated and disseminated in regions, which requires new empirical evidence (Asheim et al, 2019; Trippl et al, 2019).…”