Linden McBride, assistant professor of economics, has been published in The World Bank Economic Review. The article, titled “Retooling Poverty Targeting Using Out-of-Sample Validation and Machine Learning” examines how proxy means test (PMT) poverty targeting tools have become common tools for beneficiary targeting and poverty assessment where full means tests are costly.
Currently popular estimation procedures for generating these tools prioritize minimization of in-sample prediction errors; however, the objective in generating such tools is out-of-sample prediction. McBride’s article presents evidence that prioritizing minimal out-of-sample error in PMT tool development can substantially improve the out-of-sample performance of these targeting tools.