Poverty estimation

Chair: Prof. Timo Schmid

Poverty estimation is a field of great interest in small area estimation. This session contains three contributions that introduce some new methodologies and that give interesting comparisons of alternative methodologies under different scenarios.

A Comparison of Alternative Methods for Poverty Estimation in Developing Countries

Small area estimation (SAE) has been widely used as an indirect estimation technique for geographic profiling of poverty indicators. Three unit-level SAE techniques: the method of Elbers, Lanjouw, and Lanjouw (2003) also known as ELL or World Bank method, the Empirical Best Prediction (EBP) method of Molina and Rao (2010), and the M-Quantile (MQ) method […]

Assessing the quality of small area estimates for poverty rate in Poland using taxonomy analysis

Comprehensive and reliable assessment of the quality of estimates obtained using small area estimation methodology is one of the key challenges facing national statistical institutes. Indirect estimation theory provides many criteria for the statistical assessment of results and model diagnostics. They involve assessing relative estimation errors and relative bias, measures of the goodness of fit, […]

Small area estimation of poverty indicators using interval censored income data

Extreme poverty rates have been cut by more than half since 1990. While this is a remarkable achievement, it is still one of the main goals defined by the United Nations to eradicate extreme poverty by 2030. To fight poverty, it is essential to have knowledge about its spatial distribution. Small area methods enable the […]

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