Geography versus Institutions: New Perspectives on the Growth of Africa and the Middle East

Abstract

This article examines the growth performance of Africa and the Middle East for the period 1990-2005. It employs a Bayesian model-averaging method that constructs estimates as a weighted average of spatial autoregressive estimates for every possible combination of included variables. One of the results is that the inclusion of spatial dependences has a direct impact on the determinants of growth in Africa and the Middle East. A main contribution of this article is to consider geographical effects in a more flexible way, which allows an enriched comprehension of the role of institutional factors in explaining low economic development. (JEL: O11, O15, C11) Consequently, following the seminal works of Kormendi and Meguire (1985) and Barro (1991), recent researchers (Fernández, Ley, and Steel, 2001; Sala-i-Martin, Doppelhofer, and Miller, 2004) suggest using Bayesian methods to understand the determinants of growth. Assuming that we do not know which model is true, the Bayesian reasoning attaches probabilities to different possible models.Our contribution deals with two aspects. In the first place, our method includes a spatial dimension that has been recently explored in the empirical literature on economic growth. Indeed, the possibility that space could be a determinant of economic growth is suggested in several of the empirical contributions that allow for geographical effects. Quah (1996) formulates criticisms of the usual measures of convergence. He argues that most studies treat regions as "isolated islands" without taking into account effects of interaction due to spatial spillovers. Space influences the channels through which countries interact; however, these interactions are not modelled explicitly. In this paper, we introduce spatial modelling based on exogenously provided information about the spatial structure. As in LeSage and Parent (2007), we extend the literature on Bayesian model comparison for ordinary least-squares regression models to include a spatial autoregressive specification. We construct estimates as a weighted average of spatial autoregressive estimates for every possible combination of included variables. In the second place, in order to examine the importance of the spatial element in the determinants of growth, our study focuses on a specific territory, Africa and the Middle East. In much of the empirical literature on economic growth, Africa and the Middle East exist primarily as a regional dummy. However, recent papers (Block, 2001;Masanjala and Papageorgiou, 2008) suggest that the determinants of growth may be different in Africa from the rest of the world. In this paper we extend a BMA methodology used by Masanjala and Papageorgiou (2008) and others, taking into account spatial proximity to examine the sources of growth in Africa and in the Middle East.This last choice can be justified by economic as well as by historical considerations. From an economic point of view, Africa and the Middle East constitute an under-developed "block" whose spatial interrelations have not been stud...

Foreign aids and economic growth in Africa: Does third-country effect matter?

“…This led to the conclusion that a country's growth rate is influenced by the growth rates of its neighbours. Parent and Zouache (2009) investigated the growth of Africa and the Middle East from 1990 -2005. The Bayesian Model Averaging method, which generates estimates as a weighted average of Spatial Autoregressive estimates for all feasible variable combinations was employed.…”

“…Ramirez and Loboguerrero (2002) employed spatial econometrics to estimate a growth model that includes cross-country interdependence in a sample of 98 countries. Parent and Zouache (2009) investigated the growth of Africa and the Middle East from 1990 to 2005. According to one of the paper's results, the inclusion of spatial dependencies has a direct effect on the determinants of growth in Africa and the Middle East.…”

On the Political Economy of Conflicts in the Middle East and Africa

“…all possible combinations). The 𝑀𝐶 3 algorithm has been a popular strategy to explore the very large model space (see Masanjala andPapageorgiou, 2008, andParent andZouache, 2010, for empirical applications). Because of its specific conjugate priors, Clyde et al (2011) noticed poor mixing performance when covariates were highly correlated.…”

“…time-invariant effects, and spatial correlation with neighboring countries. In fact, as detailed inParent and Zouache (2012), the importance of geographic neighbors is detrimental when it comes to analyse the determinants of economic growth across Africa and the Middle East. More recently,Yesilyurt and Elhorst (2017) estimate the strong impact of country spillover effects on the level of military expenditures using spatial dynamic panel data models.…”

Application issues of spatial interaction marketing in the regional agribusiness sustainable development

“…According to the prevailing point of view, the implementation of the concept of sustainable development in the strategy of the positive evolution of the territory (for example, the agricultural sector) is possible when environmental well-being is included in the built brand of the territory, promoted in the interests of both external consumers (guests) and internal audiences who associate their future with this territory [11,12]. Coordination of the interests of investors (external and internal), local authorities, entrepreneurs involved in the agricultural sector of the region and the local community in terms of environmental priorities, allows in a prolonged perspective to focus also on achieving the planned economic effects [13][14][15][16][17].…”