Longitudinal approach to poverty analysis: the latent class markov model

Abstract

The aim of this work is the longitudinal analysis of the poverty phenomenon. In particular the attention is pointed out to the distinction between transitory and permanent poverty situations, which cannot be distinguished in a traditional cross-section analysis. The empirical analysis is performed by latent class Markov models: this approach interprets poverty as a latent variable and Markov chains are used to correct measurement error in panel data. These models are used in a specific territorial context and show a good performance in fitting observed data, due to the different possible definition of restrictions as characteristics of the process under examination.