Estimating poverty rates in subnational populations of interest: An assessment of the Simple Poverty Scorecard

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 based on the national sample, there is no meaningful difference in performance and the Simple Poverty Scorecard is as good as any of the regression-based estimators. The “one-size-fits-all” training approach based on the national sample results in overestimating poverty in the regions with lower poverty rates (wealthier regions) and underestimating poverty in the regions with higher poverty rates (poorer regions). In the poorest strata (regions/districts), average SPS discrepancies are as high as 15–25 percentage-points. 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.