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 …

Poverty statistics are conventionally compiled using data from household income and expenditure survey or living … standards survey. This study examines an alternative approach in estimating poverty by investigating whether readily available … geospatial data can accurately predict the spatial distribution of poverty in Thailand. In particular, geospatial data examined …

Tracking poverty is predicated on the availability of comparable consumption data and reliable price deflators. However …, regular series of strictly comparable data are only rarely available. Poverty prediction methods that track consumption … tested directly. This study analyses the performance of poverty prediction models based on small area estimation (SAE …

Poverty in low-income countries is usually measured with large and infrequent household surveys. A challenge is to find … methods to measure poverty more frequently. The objective of this study is to test a method for predicting poverty, based upon … a statistical model utilizing consumption surveys and light annual surveys. A decade of poverty predictions and regular …