Structural and Institutional Determinants of Poverty in Sub-Saharan African Countries

Abstract

The conventional policy models designed to tackle poverty have not been able to address the peculiar socio-economic and institutional conditions of the country or region in perspective. Much of the literature focuses on the macroeconomic determinants of poverty, leaving out non-economic factors that could be more important. In this milieu, this study empirically examines the relationship between governance, physical infrastructure, and the level of poverty in sub-Saharan Africa. The estimations are based on a panel of 19 selected sub-Saharan African countries over the period 1990-2010 using the two-stage least-squares estimation techniques. The results from the estimations portray robust parameter estimates and suggest that governance and infrastructure are significant determinants of poverty in the region. Furthermore, the study tends to detect that a sustainable level of poverty could be attained at particular governance and infrastructure rating after controlling for the level of gross domestic product and other factors across the region. Therefore, countries with better governance and infrastructure ratings will achieve lower poverty levels, and poverty tends to converge as physical infrastructure improvement and better governance are pursued.

Short and long-term impact of trade openness on financial development in Sub-Saharan Africa

“…This section therefore evaluates the role of infrastructure in the financeopenness relationship. As argued by Calderón and Servén (2004) and Akanbi (2015), infrastructure is a complex concept. Consequently, to account for the various facets of infrastructure, we employ a broad measure from Akanbi (2015) to perform the analysis.…”

Impact of Macroeconomic, Institutional and Structural Factors on Inequality in South Africa

“…In this scenario, an instrumental variables regression is appropriate to derive robust estimates of the parameters in Equations (1) -(3). Following similar steps as in Akanbi (2013Akanbi ( , 2015, a two-stage least squares (TSLS) estimation method is adopted and in order to correct for possible omitted variables and error-in-variables, suitable instruments that are assumed to be highly correlated with the observed explanatory variables and uncorrelated with the error term are used (Wooldridge, 2010: 89-115). The instruments used are one to two period lag of all the dependent and independent variables in the estimations.…”

“…4 The governance scores range from −2.5 to 2.5 and in order to categorize governance at different levels: the range between −2.5 and −1 corresponds to 'poor governance'; between −1 and +0.5 corresponds to 'average governance'; and, between +0.5 and +2.5 corresponds to 'good governance. 5 Following the approach used in Akanbi (2013Akanbi ( , 2015, the physical infrastructure index (PII) derived for the South African economy ranges from 0.6 to 1.25. However, Calderón and Servén's (2010) derivation, which incorporates other regions such as India and the East Asia Pacific (EAP), found a range between −1.5 and +2.…”

Interpretive Structural Analysis of Interrelationships among the Elements of Characteristic Agriculture Development in Chinese Rural Poverty Alleviation

“…In a specific CAD project, natural, geographical, and cultural conditions limit the choice of remunerative crops and farming operations [60]. Basic strategies for upgrading the agro value chain are: focusing on the stages of production (e.g., delivering larger volumes and matching higher standard), processing (e.g., invoicing, reducing wastage), transport (e.g., building cool chain, employing air freight), and marketing (e.g., advertising, developing local food brand) [61][62][63][64][65]. Linking farmers and the NAME through contract farming is considered as a possible method to secure value chain participation [66].…”