Assessing the Performance of ELL and EBP Models in Estimating District Level Poverty Indices in The Presence of Outliers in the Northern Region of Ghana.

Abstract

The objective of this study was to assess the performance of the Elbers, Lanjouw and Lanjouw (ELL) and the Empirical Best Predictor (EBP) Small Area Estimation (SAE) models in estimating the Foster-Greer-Thobecke (FGT) poverty indices for the Northern Region of Ghana in the presence of outliers. The sixth round of the Ghana Living Standard Survey (GLSS) data and the  Population and Housing Census ( PHC) data were used for the study. The performances of these SAE models under normality and non-normality assumptions were evaluated by computing and comparing their Absolute Relative Biases and Relative Root Mean Squared Errors values under both conditions by conducting a model-based simulation study in the absence and presence of outlier contaminated data. Results from the study showed that no matter the level of contamination, the EBP model is a better performer and more stable than the ELL model in estimating all the FGT poverty indicators for the Region. Therefore, it was recommended that in future poverty estimating exercises, the EBP model be used to estimate the FGT poverty indicators for the Northern Region of Ghana.