An Endogenous Switching Model to Poverty Dynamic Assessment in Tunisia: A New Proposal

This paper focus on the determinants of poverty dynamics in Tunisia by performing a two-stage endogenous model using a Bayesian algorithm to avoid missing data. Based on this approach author show that panel data analysis can be performed through potential variables such presented by the work of Donald B. Rubin. This contribution is rather empirical; researcher propose a method based on the concept of causal inference that enable to execute a panel data model using independent surveys. As the author assert the dependence between poverty status over time, they choose to use an endogenous switching model take into consideration the form of endogeneity caused by initial condition.