Abstract
This thesis contributes to the econometric approach to pro-poor growth. It presents theoretical and empirical contributions. First, it presents the different definitions, indices and the policies of pro-poor growth proposed in the theoretical literature. It also examines the theoretical and empirical models on the interactions between income distribution and growth. It shows that the traditional measures, in addition to their partial characters, can lead to contradictory results. To avoid these limits this thesis emphasizes the alternative approach by using econometric models. The latter approach, although it has the advantage of including all the dimensions of poverty, suffering from two types of bias: selection bias and bias of endogeneity. These are due to the limitations of the data: measurement error, outliers. In addition, the results obtained with this approach are sensitive to selected functional forms. So, There are good reasons to use the Gini regression. Unfortunately, the Gini regressions existed only cross sectional and time series. Thus, in a second time, this thesis proposes to extend the Gini regression on the panel. It introduces within and between estimators, the individual effect test and the Gini Aitken estimator. Finally, this thesis presents empirical applications that illustrate the robustness of our estimators. She is particularly interested in the consequences of the estimation method and the sample section. It concludes that the growth process promotes poverty reduction when income inequalities are overcome. But also, the impact of agricultural growth on poverty reduction varies depending on the country's level of development.