Small area estimation-based prediction methods to track poverty: validation and applications

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. Price deflators are also often missing or disputed. In response,
poverty prediction methods that track consumption correlates as opposed to consumption itself have been developed. These methods
typically assume that the estimated relation between consumption and its predictors is stable over time—assumptions that cannot
usually be tested directly. This study analyzes the performance of poverty prediction models based on small area estimation
(SAE) techniques. Predicted poverty estimates are compared with directly observed levels in two country settings where data
comparability over time is not a problem. Prediction models that employ either non-staple food or non-food expenditures or
a full set of assets as predictors are found to yield poverty estimates that match observed poverty well. This offers some
support to the use of such methods to approximate the evolution of poverty. Two further country examples illustrate how an
application of the method employing models based on household assets can help to adjudicate between alternative price deflators.