A Bayesian Approach to the Determinants of Structural Transformation in Sub‐Saharan Africa
Aronda Tchamsé
What the paper says
ABSTRACT The literature on structural transformation reveals a large number of variables, models and a wide range of recommendations (sometimes divergent or conflicting). This creates a certain amount of confusion as to the best‐suited model to analyse this phenomenon and as to variables that are the main determinants of its process. This paper aims to dispel that confusion in sub‐Saharan Africa by attempting to identify (i) the best‐suited model to analyse the structural transformation of the region, and (ii) the variables that are its main determinants. From a panel of 43 countries over the period of 1991–2019, a two‐step methodological approach is used. First, shift‐share decomposition, to assess the annual performances of countries in structural transformation, and then, Bayesian approaches, to select the ‘best‐suited model’ and the ‘main determinants’ of the process from a list of 58 variables that run through the literature. The results show that none of the models significantly dominates the models' space. However, they ranked 12 variables as key determinants of structural transformation in the region. Findings suggest three key policy axes for stimulating structural transformation in sub‐Saharan Africa: (i) the improvement of governance, the eradication of corruption and respect for the fundamental rights and freedoms of individuals; (ii) the increase in human capital, mainly through secondary education; and (iii) subregional and regional integration of African countries. The value of this paper lies in its success in identifying the main determinants of structural transformation in sub‐Saharan Africa, by introducing Bayesian approaches to this field of study.
2 citations
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.25 × 0.4 = 0.10 |
| M · momentum | 0.55 × 0.15 = 0.08 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.