Abstract
In the past twenty years, production has been increasingly unbundled and shared across many countries at different levels of development. The common perception is that Sub-Saharan Africa (SSA) has neither been able so far to intercept the main changes in trade patterns nor enter massively into global production networks. This chapter uses the EORA Input-Output Tables and it applies to this data the gross exports decomposition method provided by Wang et al. (2013). The Global Value Chain (GVC) participation and position of SSA countries is then analysed with a focus on agricultural and food sectors. Results show a light and shadow picture. On the one hand, despite low trade shares at the global level, SSA agricultural sector turns out to be deeply involved in GVC participation and the relevance of its international linkages is increasing over time. On the other hand, SSA involvement in GVC is still limited to upstream production stages of the chain and mainly driven by the European market. This suggests a need for a new multi-stakeholder agenda to foster the capacity of SSA to take advantage of GVCs as drivers for their structural transformation, going beyond the simple narrative of upgrading.
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Notes
- 1.
The use of EORA database is the only option to look at the issue for a comprehensive set of countries in Sub-Saharan Africa so far. None of the other similar efforts, such as the Asian IO tables (IDE‑Jetro), the GTAP project, the OECD-WTO TiVA initiative and the WIOD project has the same extension in terms of country coverage and the same level of detail for end-use categories in Sub-Saharan Africa. Notwithstanding the growing use of the EORA database to carry out GVC studies (see, among others Caliendo et al. 2016; Del Prete et al. 2018), we made additional sensitivity analysis by comparing EORA and WIOD for overlapping sectors and countries. This highlighted consistent trends and a slight upward bias from WIOD (both at the country level and at the world level) likely due to the fact that the latter includes an artificial ‘Rest of the World’ country whose I–O matrix has been derived through a proportionality assumption based on an ‘average’ world technology. As pointed by the UNCTAD (2013) this assumption could yield a downward bias in the computed world FVA, as the world average I–O includes by definition large, relatively close, countries, while most excluded countries in the ‘Rest of the World’ aggregate tend to be small, relatively more open, economies.
- 2.
- 3.
The DVX component includes also the returned value added (RDV), that is the portion of domestic value added that is initially exported but ultimately returned home by being embedded in the imports from other countries and consumed at home.
- 4.
Note that the reported measures tend to be inflated by intermediate flows between countries of the same region. This inserts a bias in favor of the EU relative to other large single countries or smaller regional groups (e.g. NAFTA).
- 5.
In some cases, such as for Uganda, Zambia and Niger, the EU absorbs almost 80% of the DVX despite the main destination for their gross exports is Africa.
- 6.
It is called “Rotterdam effect” the fact that trade in goods with the Netherlands is artificially inflated by those goods dispatched from or arriving in Rotterdam despite the ultimate destination or country of origin being located elsewhere.
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Balié, J., Del Prete, D., Magrini, E., Montalbano, P., Nenci, S. (2019). Food and Agriculture Global Value Chains: New Evidence from Sub-Saharan Africa. In: Elhiraika, A., Ibrahim, G., Davis, W. (eds) Governance for Structural Transformation in Africa. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-03964-6_8
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