in the sample. When this is not possible, however, XGBoost appears to offer a notable advantage when predicting into non-sampled areas. Further research could explore whether and how re-weighting the sample can improve the accuracy of out-of-sample predictions. Related to this is the difficulty of predicting accurately at the tails of the welfare distribution, suggesting potential benefits from oversampling tails. Further research can also explore how the relative accuracy of different methods depends on the size and structure of the sample, as this analysis only considers one type of sample.

A key contribution of the paper is to evaluate the accuracy of the uncertainty estimates generated by a two-stage residual block bootstrap. These estimates perform reasonably well on average, as coverage rates average approximately 89 percent for BRF, 87 percent for XGBoost, and 84 percent for Cubist, all of which exceed the 78 percent average for EBP. Overall, the results indicate that the combination of Extreme Gradient Boosting or Cubist Regression, the random effect block residual bootstrap, and publicly available geospatial data offer a practical way to improve on EBP estimates when geolocated survey data are available.

While our results offer useful input for poverty estimates at a more disaggregated level that can result in more cost-effective budget planning, particularly in contexts where recent census data are unavailable, it should be noted that these results may not extend to other welfare outcomes. In addition, while we include seven different countries, it is not possible to claim generalizability in all contexts. The relative performance of different machine learning methods may well depend on the specific outcomes and model features under consideration. For example, one method may work well for poverty but may work less well for food insecurity or children’s malnutrition status. The performance of different methods will also depend on the nature of the sample data. We encourage further research on comparing the performance of different methods, contexts, and outcomes. Further research could also shed light on whether models are sufficiently stable to use for intertemporal prediction. It is also useful to evaluate the performance of these methods in richer data contexts where we can compare models using satellite-based variables versus those using variables coming from household surveys and censuses.

Last but not least, a practical challenge with implementing machine learning methods involves limitations with analytical capacity of national statistical offices. We note that in these contexts, using existing tools to investigate modeling issues is good practice and should be done as part of the standard data checking and model exploration process before employing more advanced ML methods. For these methods to be employed more widely in a responsible way to better address data gaps, we call for more efforts by various stakeholders to improve local analytical capacity. For example, donors can support leading experts to jointly collaborate with national statistical staff to produce statistical training and better results. Providing ungated, public access to the latest research results and well-documented software packages (e.g., the _EMDI_ and _povmap_ 42