registry improve the accuracy of prediction models using geospatial indicators? (3) How do predictions generated using extreme gradient boosting, which are more robust to outliers, compare to those generated using post-LASSO linear models?

The main result is that introducing the simulated partial registry yields predictions of village welfare that are vastly more accurate than the other methods considered. When using a partial registry, the rank correlation, Area Under the Curve (AUC) coefficients, and R<sup>2</sup> are 0.75, 0.89, and 0.57 respectively. In contrast, the rank correlation for the other three methods ranges from - 0.02 to 0.2, the AUC scores range from 0.5 to 0.6, and the R<sup>2</sup> of the predictions range from 0.001 to 0.04. These are huge differences in predictive accuracy.

These results are based on a simulated partial registry of approximately 450 villages, about 10 percent of the total number of villages with available data. However, the accuracy of the predictions does not substantially improve when the size of the simulated partial registry increases to 675 or 900 villages, which is 15% or 20% of the census extract. In other words, a partial registry of 450 villages is sufficient to train a high-performing predictive model in this context.

Finally, this exercise provides a useful opportunity to compare the predictive performance of extreme gradient boosting against a post-LASSO model, which has also been used in the literature but imposes a linear functional form on the model. We find that using post-LASSO for the geospatial model – the second step of the partial registry approach -- makes little difference to the accuracy of the predictions. However, using LASSO rather than gradient boosting for the census model used to construct the benchmark village welfare measure greatly reduces the explanatory power of the geospatial model, whether it is estimated using gradient boosting or LASSO. This is because extreme gradient boosting is a tree-based method that is more robust to outliers, and generates a benchmark measure of village welfare that is far easier to predict using geospatial indicators. This suggests that the census data in this case may be susceptible to outliers that introduce noise when using linear prediction models. Overall, the results demonstrate that investing in richer and context-specific training data, such as partial registries, can greatly improve the accuracy of predictions based on geospatial data.

This paper contributes to a growing literature on using satellite imagery to predict welfare. Initially, the most common remote sensing indicator used for this kind of analysis was nighttime lights, which measure the intensity of light in specific areas. Previous studies show strong correlations between night-time lights and GDP.<sup>5</sup> However, the association between night-time lights and other measures of household welfare is weak in most contexts, suggesting the limitation of this indicator for predictions of welfare in small areas.<sup>6</sup> More recent literature has demonstrated that indicators derived from daytime imagery is better suited for predicting welfare.<sup>7</sup> Recent literature has also demonstrated that advances in machine learning and available non-traditional

> 5 Henderson et al., 2009, Pinkovskiy and Sala-I-Martin, 2016.

> 6 Mellander et al., 2013.

> 7 Jean, et al., 2016; Babenko et al., 2017; Engstrom et al., 2015; Engstrom et al., 2017; Head, A., 2017; Yeh, et al.,2020;, Chi et al (2021); Masaki et al., 2022.

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