the area level. This is consistent with previous research showing that these texture algorithms or contextual features reflect spatial variability in building and road patterns, as well as poverty.<sup>26</sup> A Shapley decomposition indicates that the higher R<sup>2</sup> in Tanzania, compared with Sri Lanka, can be attributed to more predictive sub-area variables, which predict 13 percent of the variation in Tanzania as opposed to 6 percent in Sri Lanka. This is likely due to two main factors. The first is that the Tanzanian indicators include measures of building footprints at the subarea level. The second is that Tanzania is a less developed setting in which building density and climactic variables, which most of the satellite variables are capturing, are more strongly correlated with non-monetary welfare.<sup>27</sup>

# C. Evaluation Results

The previous section demonstrated that remote sensing indicators are predictive of both area-level poverty and household non-monetary welfare. This section turns to evaluating the small area estimates generated by different methods. Before considering the evaluation results, Table 7 reports model diagnostics for the unit-level model. The geospatial indicators in the model predict about 37 and 30 percent of the variation in household non-monetary welfare in Sri Lanka and Tanzania respectively.<sup>28</sup> The conditional R<sup>2</sup> , which reflects the gain in model fit from conditioning the mixed effect on the sample, is about 3 percentage points higher than the marginal R<sup>2</sup> in both countries. The distribution of both the area effect and the household error term is roughly normal, reflecting the success of the normalizing transformation. The Wilks-Shapiro test for normality of the area effect fails to reject the null hypothesis of a normally distributed area random effect in both countries. Figure 1, which plot the quantiles of both the area effects and the household residuals against the quantiles of the normal distribution for each country, confirms that the distribution of both components of the error term is approximately normal, although there are negative outliers in the left tail of the area random effect in Tanzania.

Table 8 begins by presenting the (unweighted) mean poverty rates and uncertainty of the small area estimates, as measured by the mean squared error, multiplied by ten thousand, and the mean coefficient of variation. The table presents the results for direct survey estimates for both the standard method and the Horvitz-Thompson estimates, as well as the Fay-Herriot area model and the unit-level model estimated with the modified EMDI package. All are simple unweighted averages across areas.

> 26 Engstrom et al. 2019b, Engstrom et al. 2019c, Sandborn and Engstrom 2016.

> 27 In the simulated surveys drawn from the census, the rural dummy explains 24 percent of the variation in household non-monetary welfare in Tanzania, while the urban and estate sectors only explain 6 percent in Sri Lanka.

> 28 This corresponds to the marginal R2  in Table 7. The R2 results differ slightly from the post-lasso model because the small area estimation model does not use weights.

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