Niet beschikbaar in het Nederlands
Sónia Cabral
- 6 July 2016
- WORKING PAPER SERIES - No. 1931Details
- Abstract
- Global Value Chains (GVCs) became the paradigm for the production of most goods and services around the world. Hence, interconnections among countries can no longer be adequately assessed through standard bilateral gross trade flows and new methods of analysis are needed. In this paper, we compute measures of network analysis and apply visualisation tools to value added trade flows in order to understand the nature and dynamics of GVCs. The paper uses data on the bilateral foreign value added in exports for the period 1995-2011 and, in each year, GVCs are represented as directed networks of nodes (countries) and edges (value added flows). The analysis is extended beyond total trade flows to discuss the distinct roles of goods and services in GVCs. Moreover, the differences between Germany, the US, China and Russia as major suppliers of value added in GVCs are also examined.
- JEL Code
- F12 : International Economics→Trade→Models of Trade with Imperfect Competition and Scale Economies, Fragmentation
F14 : International Economics→Trade→Empirical Studies of Trade
C67 : Mathematical and Quantitative Methods→Mathematical Methods, Programming Models, Mathematical and Simulation Modeling→Input?Output Models - Network
- Competitiveness Research Network
- 24 October 2014
- WORKING PAPER SERIES - No. 1739Details
- Abstract
- The production of most goods and services is nowadays vertically fragmented across different countries, as global value chains (GVCs) emerged as the current paradigm for the international organisation of production. This paper surveys part of the growing empirical literature on GVCs, starting by discussing the main driving forces of GVCs in recent decades. Next, it surveys the indicators used to map and measure this phenomenon, accounting for their different scopes and required datasets.
- JEL Code
- F60 : International Economics→Economic Impacts of Globalization→General
- Network
- Competitiveness Research Network