Measuring Vulnerability to Multidimensional Poverty in Latin America
使用多维贝叶斯网络分类器模型，估计17个拉美国家2005/2006、2012和2017年的多维贫困脆弱性，发现贫困减少但脆弱性下降更慢，SDG1成果脆弱。
Abstract In this paper, we perform estimates of vulnerability to multidimensional poverty for 17 Latin American countries at three points of time: 2005/2006, 2012, and 2017. We use a Multidimensional Bayesian Network Classifier model to estimate the conditional probability of being multidimensionally poor and then we use these probabilities and the standard downside semi‐deviation as the risk parameter to identify the vulnerable households. Despite significant reductions over the study period, in 2017 approximately 150 million people—excluding Guatemala, Nicaragua and Venezuela for which we do not have recent data—remained vulnerable to multidimensional poverty. We also observe that vulnerability to poverty is reduced at a much slower rate than poverty itself, revealing that achievements in SDG1 can be quite fragile. We perform a decomposition and find that as poverty decreases, risk‐induced vulnerability becomes relatively more important than poverty‐induced vulnerability. However, the poor‐vulnerable still constitute the core vulnerability group.