Abstract
Agriculture plays an important role in the African continent’s growth. However, regions’ characteristics differences explain different types of production technologies use leading to a technological gap which delays these regions’ economic convergence. This article uses the stochastic metafrontier analysis based on a new approach for Technical Efficiency’s (TE) estimation and the technological gap ratios (TGR) of the agricultural production of the five African regions from 1980 to 2012. The results reveal a very high average TE score of 92.73% of the five regions whereas a low TGR score of 35.63% is noticed. The EAST region is the closest one to the best technology available with a 68.73% score. Besides, these results also show the existence of a catch-up phenomenon between low TGR level countries and those with higher TGR level. Zimbabwe has the highest catch-up score with a yearly average of 3%. Considering the agricultural sector's importance in Africa's national production, the results suggest increasing investments in Research and Development, popularizing services, and a policy of larger expansion of the technologies applied by the regions close to the optimal technology in order to facilitate new agricultural production techniques’ adoption and development. Agriculture plays an important role in the growth of the African continent. However, regions diversity of characteristics explains the use of different types of production technologies, resulting in a technology gap that delays the economic convergence of these regions.
Highlights
Adopting technologies and using improved agricultural practices are considered as the main factors responsible of agricultural productivity differences between countries and regions [1, 2]
As a matter of fact, this research’s objective is to analyse technological differences between the five African regions composed of 33 countries on the period 1980-2012
Differences in technologies between regions are estimated from frontiers specific to each region and the production metafrontier using the stochastic metafrontier approach (SMF) of Huang and al. [12]
Summary
Adopting technologies and using improved agricultural practices are considered as the main factors responsible of agricultural productivity differences between countries and regions [1, 2]. The traditional production theory points out that producers from different regions or countries face variable production possibilities for they have to choose among different types of input-output combinations considered as different set of technologies [4, 5]. Not taking into account the existing technological heterogeneity between regions leads to a wrong technical efficiency’s estimation and awards technological gaps to technical inefficiency. To correct this bias, Hayami and Ruttan [7] have introduced the metafrontier
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