Value chains are increasingly fragmented globally, and companies and governments struggle with understanding where value is added. Both scholars and practitioners developed models, but recent challenges are calling for original approaches to develop instruments to map and evaluate global value chains (GVCs) footprint. We carried out a structured literature review (SLR) to summarise the existing academic knowledge about GVCs mapping and also examined the related practitioners’ materials. We then investigated what data sources are currently available to collect data about global trade flows, and involved practitioners in the discussion to collect insights that could improve the current understanding. We aim at offering guidance in this process, highlighting what future directions should be pursued to increase the models’ descriptive and explanatory power. For example, customs data is largely available. Original models could be developed, and GVCs could be studied leveraging rich and granular customs data rather than traditional macro-economic data.
Can we increase the granularity in understanding global value chains An integration of academic and practice perspectives to enhance future developments
Prataviera, LB
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2024-01-01
Abstract
Value chains are increasingly fragmented globally, and companies and governments struggle with understanding where value is added. Both scholars and practitioners developed models, but recent challenges are calling for original approaches to develop instruments to map and evaluate global value chains (GVCs) footprint. We carried out a structured literature review (SLR) to summarise the existing academic knowledge about GVCs mapping and also examined the related practitioners’ materials. We then investigated what data sources are currently available to collect data about global trade flows, and involved practitioners in the discussion to collect insights that could improve the current understanding. We aim at offering guidance in this process, highlighting what future directions should be pursued to increase the models’ descriptive and explanatory power. For example, customs data is largely available. Original models could be developed, and GVCs could be studied leveraging rich and granular customs data rather than traditional macro-economic data.File | Dimensione | Formato | |
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