Non-Normal Empirical Bayes Prediction of Local Welfare

Estimates of the area- and household idiosyncratic error distributions from household income and consumption regression models across 142 household surveys from 16 different countries, the type of models that underpin poverty maps, points to significant deviations from normality. Accounting for non-normality in Empirical Best estimation of local welfare is found to increase precision relative to normal-Empirical Best estimation. Although the gains in precision range between meaningful and marginal, it is always positive. Given that non-normal-Empirical Best estimation is furthermore easy to implement, there is no downside to using it.