- Céline Allard, Jorge Canales Kriljenko, Jesus Gonzalez-Garcia, Emmanouil Kitsios, Juan Trevino, and Wenjie Chen
- Published Date:
- March 2016
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This work was initiated in the context of the preparation of the IMF’s April 2015 Regional Economic Outlook for Sub-Saharan Africa. We would like to thank Domenico Fanizza, Anne-Marie Gulde, and Abebe Aemro Selassie for their useful comments, and Cleary Haines, George Rooney, and Fan Yang for outstanding research assistance.
We measure trade openness with export- and import-to-GDP ratio to document actual trade flows in sub-Saharan Africa as it has been commonly used in the literature; see, for example, Baldwin 2004. Alternatively, one could also study tariffs and impediments to trade, which would be more suited to an analysis of the effects of trade liberalization. The analysis on the determinants of trade openness includes tariff and other nontariff barrier measures in the later sections.
To address endogeneity issues, we either use lagged variables or we instrumentalize using a 3 Stage Least Squares (SLS) estimation methodology (see also Appendix 3.1).
Common language dummies indicate whether the pair of trading partners shares a common official language or a language that is spoken by at least 9 percent of the population in both countries (Mayer and Zignago 2011).
Cross-regional trading dummies are included, but not shown, in the specifications of columns 2 and 3.
The rule of law and infrastructure quality indicators are taken from the Global Competitiveness Indicators database provided by the World Economic Forum. Tariffs are computed as the averages of effectively applied rates weighted by the product import shares corresponding to each partner country. Credit availability refers to domestic credit provided by the financial sector in percent of GDP. Both variables were obtained from the World Development Indicators database from the World Bank.
Unrecorded flows across borders within sub-Saharan Africa are likely to be larger than elsewhere in the world, and the gaps are possibly overestimated as a consequence. Nonetheless, given the magnitude of the gaps estimated here, these would persist even with more comprehensive data coverage.
For the numerical calculations, we follow the mathematical calculations as described in UNCTAD 2014, pp. 26–9.
For instance, Maertens, Colen, and Swinnen (2011) find a positive effect of integration into agricultural GVCs on poverty reduction as it provides a source of formal and paid employment to largely informally employed agricultural workers who have low levels of education.
It is worth stressing that integration in GVCs in itself is not a guarantee of higher income, as countries participating in portions of the GVCs with low value added run the risk of being permanently confined to these segments. However, scaling up in the GVC—that is, increasing the share of FVA in one country’s exports—is indeed associated with better chances to accelerate structural transformation. The insertion into GVCs can also enhance positive spillovers into the domestic economy through backward linkages, if domestic sectors are competitive enough to contribute into the value chain. For instance, in vertical backward linkages that integrate local suppliers into production processes of global value chain firms, these domestic suppliers can benefit from knowledge and technology spillovers (Javorcik and Spatareanu 2008).