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2024 | OriginalPaper | Chapter

Economic Growth in Sub-Saharan Africa: An Analysis of the Technical Efficiency of Natural Resource Rent and Institutions Based on a Copula Stochastic Frontier Model

Authors : Augustine Kwabena Sowu, Chanamart Intapan, Chukiat Chaiboonsri, Emmanuel Mensaklo

Published in: Applications of Optimal Transport to Economics and Related Topics

Publisher: Springer Nature Switzerland

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Abstract

This study employs a copula-based stochastic frontier modeling (copula-SFM) framework to empirically assess resource-rich countries’ efficiency in utilizing resource rent within Sub-Saharan Africa. The primary objective is to provide robust scientific evidence to aid these nations in maximizing their mineral resource utilization. Model selection criteria, specifically the Bayesian Information Criterion (BIC) and Akaike Information Criterion (AIC), identify the Normal copula-SFM as the superior choice. The findings emphasize the pivotal role of government effectiveness and population growth as strong, positive determinants of per capita income in the selected countries. In terms of average technical efficiency (TE) scores, Equatorial Guinea leads the rankings with an impressive score of 0.9907, while Tanzania trails with the lowest average TE score of 0.8177. These results underscore the potential for enhancing resource-rich countries’ economic performance through effective governance and managing population growth.

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previous chapter Household Socio-Economic Characteristics of NEETs in Thailand
next chapter Analysis of Agricultural Production in China and Measurement of Technical Efficiency Using Copula-Based Stochastic Frontier Model
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Metadata
Title
Economic Growth in Sub-Saharan Africa: An Analysis of the Technical Efficiency of Natural Resource Rent and Institutions Based on a Copula Stochastic Frontier Model
Authors
Augustine Kwabena Sowu
Chanamart Intapan
Chukiat Chaiboonsri
Emmanuel Mensaklo
Copyright Year
2024
Book
Applications of Optimal Transport to Economics and Related Topics

Print ISBN: 978-3-031-67769-4

Electronic ISBN: 978-3-031-67770-0

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-3-031-67770-0_30

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