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Book (stand-alone)The impact of changes in the fundamental drivers of trade – Productivity, trade costs, and trade policies
Background paper for The State of Agricultural Commodity Markets (SOCO) 2022
2022Also available in:
No results found.Countries’ varying natural resource endowments are vital in explaining international trade. Traditional trade theory suggests that differences in technology and factor endowments lead countries to specialize and export certain goods or services in which they have a comparative advantage. The computable general equilibrium (CGE) model simulations used in this paper aim to explain agricultural trade patterns and show how agricultural trade would change in response to productivity, infrastructure and institutions improvements and trade cost changes from regional and global trade liberalization. A set of nine scenarios is used to explore the effects of productivity, transport costs, non-tariff barriers (NTB), and border measures changes on agricultural and food trade and related welfare implications. Policies driving agricultural productivity growth such as investments in research and development, economic reforms that strengthen incentives for farmers, rural education and extension, and improved infrastructure are shown to reduce the yield gap and improve productivity. Lower trade costs help comparative advantage play out, resulting in gains from trade. Measures taken to increase trade integration in Africa and Asia will be important for economic growth and development in these regions. -
Book (stand-alone)The effects of global value chain (GVC) participation on the economic growth of the agricultural and food sectors
Background paper for The State of Agricultural Commodity Markets (SOCO) 2020
2020Also available in:
No results found.Trade liberalization has long been advocated as a means to foster growth and welfare. In developing countries, the expansion of global value chain (GVC) participation of agriculture and food sectors could support transformation from a subsistence-oriented and farm-centered system to a commercialized, productive and off-farm centered one. While empirical evidence examining the linkages between GVC participation and economic performance in the agricultural sector has traditionally relied on case studies at the product level, the availability of new aggregate data on trade in value added, now provides an unprecedented opportunity to carry out a global empirical assessment of the linkages. The present paper examines new measures of GVCs participation and positioning from the EORA panel data for the period 1995–2015 (Nenci, 2020) and tests their effects on changes in agriculture value added per worker. The results show that changes in GVC participation are, on average and ceteris paribus, positively associated with changes in agriculture value added per worker, net to time-invariant confounders, whereas mixed results are found on the effects of countries’ positioning along the value chain. In the conclusive remarks, the authors argue that import tariff and non-tariff barriers – including barriers to service trade – should be seen as the first obstacle to increase GVC participation and improve domestic value-added. The presence of signs of heterogeneity by geographical location confirms that general universal recipes do not exist. -
Book (stand-alone)Mapping global value chain (GVC) participation, positioning and vertical specialization in agriculture and food
Technical note for The State of Agricultural Commodity Markets (SOCO) 2020
2020Also available in:
No results found.This Technical Report includes: • A policy note with: 1) a short review of the literature on the state-of-art methodologies for computing indicators for global value chain (GVC) participation, positioning and vertical specialization; and 2) a mapping and short analysis of agriculture and food GVCs by world region. • A Data set, in Excel format, that includes time series of GVC indicators computed at country/industry level for the period from 1990 to 2015 for agriculture and food sectors for all countries with data in the EORA dataset.
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