poverty. It shows the prevalence of non-positive incomes in high- and middle-income countries, provides an analysis of the … standard poverty indexes. It is argued that negative and zero incomes cannot be treated equally in terms of household well …-being and that standard methods used by practitioners fail to recognize this fact likely resulting in overestimations of poverty. …

the prevalence of negative and zero incomes, and their implications for inequality and poverty measurement relying on 57 … and zero incomes and assesses the distributional impacts of alternative correction methods on poverty and inequality … materially deprived. Adjusting poverty and inequality measures for these findings can alter these measures significantly. …

Poverty measurement among forcibly displaced populations, including refugees and internally displaced persons, has been …, for long, neglected by the economics profession and by poverty specialists working across the social sciences. This has … reviews the evolution, current status, and future prospects of the poverty measurement literature on forcibly displaced …

measuring poverty among refugee populations. However, refugee data remain scarce, particularly in relation to the measurement of … income, consumption, or expenditure. This paper offers a first attempt to measure poverty among refugees using cross … system, the proposed methodology offers out-of-sample predicted poverty rates. These estimates are not statistically …

Household consumption surveys do not typically cover refugee populations, and poverty estimates for refugees are rare …. This paper tests the performance of cross-survey imputation methods to estimate poverty for a sample of refugees in Chad … poverty estimates based on administrative data that fall within a 95 percent margin of poverty estimates based on survey …

OLS models are the predominant choice for poverty predictions in a variety of contexts such as proxy-means tests …, poverty mapping or cross-survey imputations. This paper compares the performance of econometric and machine learning models in … predicting poverty using alternative objective functions and stochastic dominance analysis based on coverage curves. It finds …

Poverty prediction models are used by economists to address missing data issues in a variety of contexts such as … poverty profiling, targeting with proxy-means tests, cross-survey imputations such as poverty mapping, or vulnerability … models to predict poverty under these different scenarios. It finds that the quality of predictions and the choice of the …