Global value chains, labor productivity, and inclusive growth in Africa: empirical evidence from heterogeneous panel met
- PDF / 1,074,874 Bytes
- 23 Pages / 439.37 x 666.142 pts Page_size
- 96 Downloads / 194 Views
Global value chains, labor productivity, and inclusive growth in Africa: empirical evidence from heterogeneous panel methods Jeffrey Kouton1 · Sulpice Amonle1
© Institute for Social and Economic Change 2020
Abstract This paper analyzes the dynamic impact of global value chains (GVCs) on inclusive growth in Africa. In this regard, we use both a panel autoregressive distributed lags (ARDL) model and a cross-section augmented ARDL approach for a sample of thirty-five (35) African countries. The analysis used data from 1991 to 2018. Prior to the estimation of the parameters of the models, a set of tests are implemented namely: slope heterogeneity and cross-sectional dependence tests, as well as panel unit root test that permits crosssectional dependence and slope heterogeneity. The study focuses on labor productivity as an important measure of inclusiveness and shows that the participation of countries at different stages of GVCs affect labor productivity and thus contributes to the achievement of inclusive growth. However, labor productivity gains from GVCs are not automatic and are observed in a more significant manner in the long run. While distinguishing between upstream participation and downstream participation, the paper found that in the long run, downstream participation (backward linkages) and upstream participation (forward linkages) equally impact labor productivity. In the short run, upstream participation has a positive and significant effect on labor productivity, while downstream participation does not significantly affect labor productivity. In light of our findings, African countries could be better integrated into GVCs and have enough room to achieve higher labour productivity through their participation in GVCs. Keywords Africa · Cross-sectional dependence · Global value chain · Heterogeneous panel data models · Inclusive growth · Labor productivity JEL Classification C33 · E24 · F13 · F14 · J21 · O47 · O55
* Jeffrey Kouton [email protected] 1
Ecole Nationale Supérieure de Statistique et d’Economie Appliquée, Abidjan, Ivory Coast
13
Vol.:(0123456789)
Journal of Social and Economic Development
Introduction Recent decades have been characterized as an era of hyperglobalization and technological improvement, and among others, global value chains (henceforth GVCs) have become an essential feature of international trade. By their nature, GVCs affect economies in several ways including socioeconomic indicators such as sustainable and inclusive economic growth, employment, poverty, inequality, and labor productivity. Value chains refer to a range of activities that firms and workers perform to bring a product from its conception to its end use (Gereffi and Fernandez-Stark 2011). The activities that comprise a value chain can be contained within a single firm or distributed across different firms. GVCs represent value chain-related activities that have been carried out in inter-firm networks on a global scale. GVCs can also be defined as a geographically dispersed production process
Data Loading...