Revisiting economic diversification in Africa's largest resource-rich nation: Empirical insights from unsupervised machine learning

Abstract

Nigeria, as the heartbeat of Africa and the largest resource-rich nation in the continent has been economically bedridden in recent times. This is mainly due to the lack of diversification of its economy and resources. This paper deals with the analysis of the Nigerian economy with a view of determining the constituents of its economic growth using cluster analysis-(an unsupervised machine learning) approach. Results from the two cluster analysis approaches used-(K-Means and Hierarchical Clustering) indicates that crude oil contributes mainly to the Nigerian economy in terms of revenue and it is clearly distinct from other resources/sectors which makes up the components of revenue generation in Nigeria. Spearman's correlation analysis shows that all the economic indicators considered are highly correlated with each other except with Petroleum and Solid Minerals. This is not unconnected with the fact that the Nigerian economy has been largely dependent on oil revenue over the years, besides the fact that sectorial linkages are limited. Policies aimed at the diversification of the Nigerian economy and promoting value chain across sectors is sine qua non to economic progress. The need to ensure that other sectors contribute meaningfully and tangibly to the Nigerian economy in the post-pandemic era should be revisited.