Foreign direct investment, human capital and export diversification in Africa: A panel smooth transition regression (PSTR) model analysis

Abstract

AbstractThis paper investigates the role of local human capital in facilitating the export diversification improvement effect of foreign direct investment (FDI) in African countries. To this end, we use a panel smooth transition regression (PSTR) model which is able to deal with the heterogeneity issue associated with the cross-country data. Based on a sample of 30 African countries over the period 1996–2019, the results show that there is a minimum threshold of human capital beyond which the export diversification enhancing effect of FDI is unlocked in African countries. In other words, only countries located above a certain threshold of human capital benefit from the positive effect of FDI on export diversification. These results suggest that policymakers in African countries should focus on improving the conditions for acquiring local human capital (education and health) in order to extract economic gains from FDI.KEYWORDS: Foreign direct investmentexport diversificationhuman capitalpanel smooth transition regressionAfricaJEL Classifications: F21C23O55 Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are available in the various databases: UNCTAD, World Development Indicators (WDI), World Bank's World Governance indicators (WGI), Penn World Table, and Conference Board Total Economy Database.Notes1 Compiled in WIR, 2018 from UNCTAD, FDI/MNE database.2 Coffee, cocoa, oil from Côte d'Ivoire; gold, cocoa and oil from Ghana; uranium from Niger; gold and cotton from Mali and Burkina Faso; bauxite from Guinea; oil from Nigeria, Angola, Congo, Gabon, Chad; diamonds from Botswana, Sierra Leone etc. … .3 OLI Ownership (the company's specific asset holdings), Location (the advantages or conditions offered by host countries) and Integration (the comparison between internationalization and exporting in terms of costs and benefits for the relocating company).4 The main determinants identified are: size, resource wealth, trade, market access, trade costs, FDI, human capital, public investment and spending, exchange rate misalignment, terms of trade, financial market development, infrastructure, and institutional quality5 Hj=(∑i=1N⁡(Xi,j/Xj)2−1/N/1−1/N) where Hj is the export product concentration index for country j, Xi,j is the value of exports of product i by country j, Xj is the value of global exports by country j, and N is the total number of products exported.6 The six institutional quality indicators used include the rule of law (LAW), regulatory quality (REQ), governance effectiveness (GOV), corruption control (CORR), political stability (POS) and voice and accountability (VOA)7 According to Blundell and Bond (Citation1998), the GMM in-difference estimator can be non-convergent and biased, as the application of the estimator's moment conditions poses a number of problems, namely: the weakness of the instruments chosen, and the elimination of inter-country variations by first differentiation. To solve these problems, Blundell and Bond (Citation1998) developed the GMM system estimator, with additional moment conditions. The GMM estimators developed by Arellano and Bover (Citation1995) and Blundell and Bond (Citation1998) address endogeneity problems and allow for dynamic specification.