data can improve the targeting of social programs.<sup>8</sup> Less attention, however, has been paid to how predictive performance relates to the nature of the training data.<sup>9</sup> There are a few caveats to consider. The first is that we focus on one context: 10 districts in Malawi. While there can be some common features to other developing countries, additional research would be useful to confirm that the results apply in other contexts. Second, with respect to data availability, imputing per capita consumption into the 20 percent census extract was the best available measure of village-level welfare we could obtain. Nonetheless, because the welfare measure is imputed on the basis of slow-changing characteristics such as household size, head’s education, child dependency ratio, and household assets, the benchmark welfare measure is a longer-term measure of welfare that will only partially include transitory welfare shocks. A third caveat is that the sample of villages is constructed by matching village names by hand, which raises the possibility that the sample of villages used in the study is not fully representative. However, as demonstrated in table 3, most of key observed characteristics in the census are similar on average between matched and unmatched villages.

A final methodological issue is that, in villages included in the simulated partial registry, we utilize the same census model and data to construct both the benchmark measure and the partial registry predictions. As a result, the partial registry by construction perfectly predicts benchmark welfare in the 450 villages randomly selected for the registry. To address this issue, we show that replacing the perfectly accurate predictions from the partial registry with the imperfect predictions generated by the geospatial model leads to only a modest fall in predictive performance. This indicates that the vast majority of the improvement from utilizing the partial registry, relative to the other three methods considered, derives from its ability to training a much richer and more accurate predictive geospatial model, rather than the increased predictive accuracy in the villages it covers.

In this context, our findings provide convincing evidence that partial registries that collect a limited set of indicators for all households in a sample of villages, if collected properly, can greatly enhance the accuracy of geospatial predictions of village welfare. Many surveys routinely undertake full listing exercises in sampled enumeration areas, which could be extended to collect information on welfare proxies. Combining household surveys, partial registries, and geospatial data has to our knowledge yet to be implemented. Yet the cost would be relatively modest; a rough estimate is that the marginal cost of interviewing all households in 450 villages would be between $24,300 and $72,900.<sup>10</sup> Moreover, this strategy appears to offer a large improvement over existing feasible methods when targeting social assistance programs to poor villages in contexts where conventional data sources are incomplete or outdated. These partial registries could be integrated into current systems of data collection, to help existing surveys benefit more from the wealth of publicly available geospatial data.

> 8 Aiken et al, 2022 and Van der Weide et al. (2022).

> 9 Although Engstrom et al (2022) finds that the model performance is very sensitive to the size of the sample training data.

> 10 These are based on an estimated marginal cost of $3 to $9 per household to conduct face-to-face surveys in Malawi, and the average of 18 households per village in the census extract.

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