Is Random Forest a Superior Methodology for Predicting Poverty? An Empirical Assessment

Random forest (RF) is in many fields of research a common method for data‐driven predictions. Within economics and prediction of poverty, RF is rarely used. Comparing out‐of‐sample predictions in surveys for the same year in six countries shows that RF is often more accurate than current common practice (multiple imputations with variables selected by Stepwise and Lasso), suggesting that this method could contribute to better poverty predictions. However, none of the methods consistently provides accurate predictions of poverty over time, highlighting that technical model fitting by any method within a single year is not always, by itself, sufficient for accurate predictions of poverty over time.