Do industrialization, trade openness, and labor force participation enhance Africa’s productive capacity?

ABSTRACT

We investigated the effects of industrialization, trade openness, and labor force participation on Africa’s productive capacity. We also examined how industrialization moderates the link between trade openness, labor force participation, and productive capacity. We adopted the pooled OLS, the dynamic system GMM, and the bias-corrected least squares dummy variable (LSDV) estimators for 49 African economies between 2000 and 2018. Our findings indicate that industrialization, trade openness, and labor force participation are significantly enhancing Africa’s productive capacity. Notably, industrialization significantly moderates the positive effects of trade openness and labor force participation on productive capacity in Africa. Our results also indicate that human capital development, foreign direct investment inflow, and institutional quality are significant drivers of productive capacity, while infrastructural development and natural resource endowment have predominantly negative impacts. The policy implications of these findings include the creation of an enabling environment that promotes industrialization, international trade, and increased labor force participation.

KEYWORDS:

• Industrialization
• trade openness
• labor force participation
• productive capacity
• system GMM regression

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 It should be noted that while existing studies (such as Shahid 2014; Bokosi 2022; Bunje, Abendin, and Wang 2022) have indicated that industrialization, trade openness and labor force participation enhance economic growth, there is no such empirical evidence for productive capacity, particularly in Africa. In fact, this study emphasizes that there is a distinction between productive capacity and economic growth. Productive capacity refers to the maximum potential output an economy can achieve, while economic growth refers to the actual increase in output over time (UNCTAD 2021).

2 Agenda 2063 is the African Union's long-term development plan aiming to transform Africa into a prosperous, integrated, and peaceful continent through inclusive economic growth, regional integration, good governance, cultural preservation, and global competitiveness over the next 50 years.

3 In this study, the institution variable is the composite indicator obtained from principal component analysis (PCA). This allowed us to consider all the dimensions of world governance indicators at the same time.

4 The countries are: Algeria, Angola, Benin, Botswana, Burkina Faso, Burundi, Cabo Verde, Cameroon, Congo Dem. Rep, Congo Republic, Comoros, Cote d'Ivoire, Chad, Djibouti, Egypt, Eritrea, Eswatini, Ethiopia, Gabon, Ghana, Gambia, Guinea, Guinea-Bissau, Kenya, Lesotho, Libya, Liberia, Madagascar, Mali, Mauritania, Mauritius, Morocco, Mozambique, Namibia, Niger, Nigeria, Rwanda, Sao Tome, Senegal, Sierra Lone, Seychelles, South Africa, Sudan, Tanzania, Togo, Tunisia, Uganda, Zambia, and Zimbabwe.

5 Due to the closely intertwined relationship between economic growth and the Cobb-Douglas production function, empirical studies on economic growth and development often adopt the Cobb-Douglas production function as their theoretical framework (see for example, Bunje, Abendin, and Wang 2022; Doan 2019; Duodu and Baidoo 2020). However, given the close relationship and subtle difference between economic growth and productive capacity as already established earlier in this study, we have adopted the neoclassical growth model as the theoretical framework, and we have implemented it through Cobb-Douglas production function.

6 The literature has addressed the issue of endogeneity by employing either instrumental variable techniques or the generalized method of moments (GMM) approach. In this study, we have chosen to utilize the GMM approach over the instrumental variable two-stage least squares (2SLS) method. This decision is based on the recognition that while the instrumental variable approach has been effective in addressing the problem of reverse causality (Farhadi, Islam, and Moslehi 2015; Kamguia, Ndjakwa, and Tadadjeu 2023), it faces challenges in identifying purely exogenous external instruments. Furthermore, the method often overlooks the potential endogeneity of other explanatory variables.