The Reliability of Small Area Estimation Prediction Methods to Track Poverty

Tracking poverty is predicated on the availability of comparable consumption data and reliable price deflators. However, regular series of strictly comparable data are only rarely available. Poverty prediction methods that track consumption correlates as opposed to consumption itself have been developed to overcome such data gaps. These methods typically assume that the estimated relation between consumption and its predictors is stable over time—assumptions that usually cannot be tested directly. This study analyses the performance of poverty prediction models based on small area estimation (SAE) techniques. Predicted poverty estimates are compared to directly observed levels in a series of country settings that are widely divergent, but where data comparability over time is not judged to be a problem. Prediction models that employ either nonfood expenditures or a full set of assets as predictors, yield poverty estimates that match observed poverty fairly closely. This offers some support for using SAE techniques especially those based on models employing household assets, to approximate the evolution of poverty in settings where comparable consumption data are absent or settings where price deflators are of dubious validity. However, the findings also call for further validation especially in settings with rapid, transitory poverty deterioration, as in Russia during the 1998 financial crisis.