Monitoring Poverty in a Data-Deprived Environment: The Case of Lebanon

Abstract

This paper addresses the lack of data and limited statistical capacity in the Middle East and North Africa, particularly amid Lebanon's economic collapse. We apply a novel data augmentation technique to analyze poverty when traditional income data are limited or unavailable. By adapting existing methods, we recover continuous income distributions from interval data and derive dominance conditions for such data, accounting for non-response. The proposed approach enables robustness checks by estimating the bounds of admissible cumulative distribution functions. Our empirical analysis uses Lebanese data to perform first-order dominance tests on these bounds, highlighting the importance of the approach. We demonstrate how alternative data sources can be leveraged for essential poverty analysis.