Abstract
The performance of the Simple Poverty Scorecard is compared against the performance of established regression-based estimators. All estimates are benchmarked against observed poverty status based on household expenditure (or income) data from household socioeconomic surveys that span nearly a decade and are representative of subnational populations. When the models all adopt the same"one-size-fits-all"training approach, there is no meaningful difference in performance and the Simple Poverty Scorecard is as good as any of the regression-based estimators. The findings change, however, when the regression-based estimators are"trained"on"training sets"that more closely resemble potential subpopulation test sets. In this case, regression-based models outperform the nationally calculated Simple Poverty Scorecard in terms of bias and variance. These findings highlight the fundamental trade-off between simplicity of use and accuracy.