Mariola Sánchez

This paper explores the evolution of global value chains (GVCs) from 1995 to 2020 to assess the changes that have taken place during the last decades. Using country-sector data from the OECD’s TiVA database, we apply network techniques and graph theory to characterize the structure of the GVC network. We are interested in comparing the network at different moments in time to assess how its structure has evolved, and which countries and sectors, and how, participate in the network. To do so, we compute centrality metrics to identify countries and country-sectors’ position within these global production networks. Using the weighted hyperlink-induced topic search (HITS) algorithm, we identify key hubs and authorities in the GVC network: a hub is relevant supplier that is connected with relevant buyers, while an authority is a relevant buyer which is itself connected with relevant suppliers. Additionally, we also examine if the position of country-sectors in the GVC network matters: we conduct panel regressions to assess if network centrality has an impact on upgrading in GVCs, as measured by changes in the domestic value-added content of gross exports. Our results suggest that centrality plays an important role, especially as a buyer: according to our estimates, a one standard deviation increase in a country-sector’s authority score is associated with an increase in the rate of growth of domestic value added embodied in exports of 0.52 percentage points, whereas the same increase in the hub score boosts domestic value added in exports by 0.29 pp. Being well connected to the sources of value-added seems to play a more important role in benefiting from GVC participation.

This paper explores the evolution of global value chains (GVCs) from 1995 to 2020 to assess the changes that have taken place during the last decades. Using country-sector data from the OECD’s TiVA database, we apply network techniques and graph theory to characterize the structure of the GVC network. We are interested in comparing the network at different moments in time to assess how its structure has evolved, and which countries and sectors -and how- participate in the network. To do so, we compute centrality metrics to identify countries and country-sectors’ position within these global production networks. Using the weighted hyperlink-induced topic search (HITS) algorithm, we identify key hubs and authorities in the GVC network: a hub is relevant supplier that is connected with relevant buyers, while an authority is a relevant buyer which is itself connected with relevant suppliers. Additionally, we also examine if the position of country-sectors in the GVC network matters: we conduct panel regressions to assess if changes in network centrality have an impact on upgrading in GVCs, as measured by changes in the domestic value-added content of gross exports. Our results suggest that centrality plays an important role, especially as a buyer: according to our preferred estimation, a 1 percentage point increase in a country-sector’s authority score leads to an increase in the rate of growth of domestic value added embodied in exports of 0.39 percentage points, whereas increases in the hub score boost domestic value added in exports by 0.14 pp. Being well connected to the sources of value-added seems to play a more important role in benefiting from GVC participation. JEL codes: F14, F60

Social learning is the main policy-design mechanism that involves interactions between agents. This study provides an overview of the research on policy design using social learning. Descriptive and co-citation analyses were used to identify emerging research lines and thematic similarities between scientific publications. The database used for the bibliometric analysis contained 271 articles published between 1979 and 2022 in 152 journals indexed by the SSCI. We propose a study based on the origins and the future research agenda of social learning for policy design. The results reveal that “environment”, “governance”, and “social” represent the knowledge base. These topics have evolved over time and have become established as a consolidated intellectual structure. In addition, a new topic called “media and news” has emerged, focusing on the challenges of spreading fake news and learning manipulation in a post-truth world. The cluster “Media and news” is gaining significance due to its impact on the dissemination of information and the shaping of opinions in contemporary society.

R&D policies are usually designed to enhance firms’ internal capabilities, but do not explicitly target R&D cooperation. In this research, we propose that R&D programs can be a suitable instrument when it comes to fostering informal collaborative networking. We focus on a regional level, the cluster, and establish that firms can use their R&D subsidies not only to become more innovative, i. e. input-output additionality, but also to develop R&D informal collaborations, i. e. behavior additionality. To test this hypothesis, relational data from a biotechnological cluster in Alicante (Spain) have been analyzed. Results from ERGM confirm that promoting internal R&D efforts prompts the formation of knowledge-based relationships at the cluster level. Policy makers should consider this unforeseen behavior when designing and evaluating non-collaborative R&D support programs. New evidence on the role of distant and diverse non-local linkages on local network dynamics is also provided.