Measuring vulnerability to multidimensional poverty with Bayesian network classifiers

Bayesian network methods have recently gained great popularity in machine learning literature and applications to model uncertainty in complex phenomena that include relationships between multiple random variables. However, these models are not commonly applied in economics and development studies. Here, we introduce the Bayesian network classifier models to estimate the probability of a person to be welfare deprived in one and multiple dimensions. These probabilities are then used for measuring vulnerability to multidimensional poverty (VMP) in four alternative measurement frameworks. Currently, two of them can be found in the literature, but have been estimated with Probit and Logit models, which are unidimensional strategies. Instead of that, in this study, we follow a multidimensional strategy to solve an estimation problem that is multidimensional in nature. Two new VMP measurement procedures based on Bayesian network classifiers estimates are also introduced in this article. We illustrate the four estimation procedures using the household survey and the census data from Chile 2017. A 5-fold cross-validation exercise verifies a high predictive performance of these Bayesian network classifier models, with the highest accuracy being that of one of the new measurements that we put forward. Our findings reveal that the Bayesian network classifier models offer an adequate alternative to face the policy challenge of measuring vulnerability to multidimensional poverty.(This abstract was borrowed from another version of this item.)