The probability of multidimensional poverty: A new approach and an empirical application toEU‐SILCdata
提出一种使用大量可行权重分析多维贫困的新方法，计算个体多维贫困概率，并结合广义洛伦兹优势技术，在最小价值判断下达成社会福利函数的一致同意，实证基于欧盟四波面板数据。
Abstract This paper proposes a novel method to analyze multidimensional poverty by using a large set of feasible weights to summarize the information about the poor, which enables remaining agnostic about the relative importance given to different poverty dimensions. This method allows for the calculation of the individual probability of being poor in a multidimensional perspective. The distribution of individual probabilities can then be combined with Generalized Lorenz dominance techniques to derive unanimous consent for a wide class of social welfare functions with a minimum load of value judgments. The innovations proposed here allow to move from a dual definition of poverty, where poor and non‐poor individuals are classified in a mutually exclusive context, to a continuous measure of deprivation capturing both the extensive and intensive margin of multidimensional poverty. The empirical application of the method consists of measuring multidimensional poverty in ten selected countries using four waves of EU‐SILC data (2008–2014).