Computational applications of poverty measurement through Markov model for income classes

Poverty measurements are of crucial importance for understanding people’s living conditions and the inequality of income distribution in a country. This perspective paper focuses on recent advancements in this field, considering dynamic measures of poverty. We discuss how a population dynamic model is the cornerstone over which poverty can be studied and understood dynamically, and we highlight the computational challenges this approach faces in the execution of a real application of the model. Furthermore, we update the results on the basis of recent data showing the foreseen behavior of different poverty indexes, their confidence sets, and perturbation bounds useful for investigating the sensitivity of the results to perturbations in the main parameters of the model.