A Monte Carlo Study of Some Empirical Methods to Find the Optimal Poverty Line in Multidimensional Poverty Measurement

A key problem in multidimensional poverty measurement is how to identify the optimal poverty line, or threshold value, in order to split the ‘poor’ and ‘not poor’ groups. Intersection approaches aim to set a cut-point k for deprivation distribution and a cut-point Z for income/expenditure distributions. Union approaches require a unique k threshold for the combined distribution of income and deprivation and measures based only on the deprivation score need a k cut-point exclusive to this domain. The selection of a cut-point is a contested issue, but empirical methods have the potential to advance these debates. For both bi-domain (intersection) and uni-domain approaches, there is a lack of clear guidance about the reliability of some existing statistical approaches (the Bristol optimal method (BOM), the Poisson-based and negative binomial frameworks and mixture univariate analysis) for choosing k. Monte Carlo simulation is used to evaluate the performance of these methods, with the findings suggesting that the BOM is the most reliable method when certain reasonable assumptions hold.