beneficial effect on precision than accuracy. More research can shed more light on the pros and cons of “sub-area models” that predict poverty rates with the sub-area as the unit of observation, vis-à-vis household level models with sub-area mean predictors, where repeated simulations are used to generate poverty estimates.

Finally, there are new developments applying tree-based machine learning techniques. These generally offer greater predictive power than linear models at the cost of parsimony and transparency (Efron, 2020). Tree-based machine learning models are more robust to outliers, and less susceptible to bias arising from informative sampling when predicting out of sample. Two important obstacles to more widespread adoption of machine learning methods have recently been surmounted. The first was the lack of asymptotic theory. Asymptotic theory has, however, recently provided for generalized random forests, a large class of methods that includes boosted regression forests, which is a type of gradient boosting (Athey et al, 2018). The second obstacle was the lack of an accepted method for uncertainty estimation. Although Athey et al. (2018) developed methods to estimate uncertainty for boosted regression forests, the random effect residual bootstrap developed by Chambers and Chandra (2013) and first applied by Krennmair and Schmid (2022) is also an attractive and simple option that appears to work well for wealth prediction using extreme gradient boosting in multiple contexts (Merfeld and Newhouse, 2023).

While the potential of regularly pairing survey data with geospatial data is clear, more work on research and tools is needed to further instill confidence in the estimates and facilitate use. Research could benefit from more comparative work on methods, ideally utilizing design-based simulations using georeferenced census data. These can examine several outstanding research questions, including the relative benefit of convolutional neural networks versus simpler estimation approaches, quantifying the benefits of including conditional random effects when using machine learning models, probing the robustness of methods for estimating the uncertainty associated with tree-based machine learning estimates, experimenting with different geospatial features, and determining the age threshold at which census-based estimates become less accurate than geospatial estimates in different contexts. The question of which features best predict changes is also important. For example, changes in the rate of building construction, the characteristics of new buildings, or changes in crop types and forecasted yields have not yet to our knowledge been tested as correlates of welfare changes.

In addition to further research, further improvements to open-source tools are critical to make these techniques more accessible and educate users. This is particularly important to facilitate the adoption of more sophisticated methods in developing countries given the financial and technical constraints faced by national statistical offices and other practitioners. The R EMDI, SAE, SAEforest, and GRF packages available on CRAN are all examples of good practice, as the documentation for each is clear, comprehensive, and up to date. No such comparable user-friendly package exists for neural network models at this time. No matter how many new and improved methods are published by statisticians, they are unlikely to be used in practice without software that is accessible to non-specialists and thoroughly documented. User-friendly features automating common diagnostics and parallelizing across multiple cores to speed estimation, as implemented in the EMDI package, are very valuable. Finally, software that makes it simple to obtain and link publicly available geospatial indicators with survey data will also help facilitate data integration. As these tools are developed, small area estimates that combine survey data with publicly available geospatial data will inevitably become more popular worldwide, belatedly fulfilling the promise demonstrated thirty-five years ago by Battese, Harter, and Fuller (1988).