Presentation of study data
As part of this project of memory project, we are particularly interested in economic growth in Africa. Variables dictated by literary and empirical journals will be retrieved from the World Bank database. In this case, GDP per capita, fossil energy consumption, physical capital ... etc. Other unobservable variables will be able to be calculated. In this sense, it is the savings rate, the net capital inflow, the rates of commercial opening. Our values are in current dollars. The study period or the interval by which we analyze the theme is between 1960 and 2016. In truth, more data remains unavailable for the year 2017. We will then solicit methods of clearing and correction of the missing values before to be able to carry out studies whose prospects seem already purified in the general introduction.
Analyse des résultats économétriques
(4 Significativité des variables exogènes au seuil conventionnels 1%, 5% et 10% : 0.000 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ )
In the results of Table C3.2, the estimates of the basic Solow model and the increase in human capital are shown. The regressions carried out are valid on the assumption that the countries are at their stationary state. The coefficient of the savings rate is not only positive but also very significant. It is perfectly suited to the expected result. This means that the rise in the savings or investment rate has a positive impact on per capita GDP growth.
For comparative purposes, the model estimated by dummy or dummy variable scrutinized by the country variable provides a reality opposite to what was expected on the sign of the coefficients compared to that of ordinary least squares. In both models, only the human capital variable of the augmented Solow model is significant with a positive coefficient. We can note that the country variable that has more than 50 modalities is not a better stratification of the studied sample. For this purpose, what about the year variable? This is what we will try to answer with the following estimate.
The least squares estimate by fictitious variable, here taking into account the years show another reality opposite to that by fictitious variable country (country). Rather, the results presented in Table C3.4 are rather close to the ordinary least squares in terms of the signs of the expected coefficients. Again, it is not surprising to see in this case the significance of the variables, because indeed there is a link between production, physical capital and human capital. The definition of the year variable as a dummy variable to measure the specific effect of our sample gives better results than that of grouping by country.
The grouped estimation method (Pooled) is the most appropriate when each observation is independent of the other. By reducing the observations by a panel observation through the calculation of the average. We have the same results from the least squares method and the fictitious year estimate. Physical capital (savings rate) puts upward pressure on GDP per capita as opposed to human capital. Apart from the comparisons made between the models, it is all the more interesting to determine which model is the most efficient and well suited to test the prediction of the convergence of the Solow model and the increased Solow model.
We remind you that the whole of this document is accessible on the Amazon site:
ADAN, A.-B. (2018). Croissance Economique et Développement Durable en Afrique: Analyses Statistiques et Econométriques [Kindle]. Amazon Media EU S.à r.l.
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