account for sample design effects among the residuals, which would increase the estimated mean squared errors and improve coverage rates.<sup>43</sup> In addition, benchmarking could be incorporated directly into the bootstrap procedure, to obtain more accurate estimates of benchmarked mean squared errors. Third, it should be fairly straightforward to incorporate the ordered quantile normalization transformation directly to the R EMDI package, which already allows for Box-Cox and log shift transformations. This would enable the software to estimate poverty gaps and severity in addition to headcount rates when using the ordered quantile normalization transformation.

This study only considered a measure of non-monetary poverty, in order to evaluate the results of the area predictions against actual census data. This raises the important question of the extent to which the results generalize to predicting monetary poverty rates. The types of geospatial indicators considered in this study are moderately less predictive of monetary welfare than non-monetary welfare. For example, when predicting district-level monetary poverty in Tanzania using the same geospatial indicators considered above, the geospatial variables selected by a stepwise procedure explained 23.5 percent of the variation in household per capita consumption, versus 30 percent for a lasso-selected model of non-monetary welfare.<sup>44</sup> This reflects the difficulty of predicting both transient household welfare shocks and measurement error, both of which are present to a greater degree in measures of monetary poverty than non-monetary poverty. It is encouraging that the household-level EBP model continues to perform well when noise is added to non-monetary welfare. The efficiency gain from using the household model was notably smaller in Sri Lanka when using the noisier welfare measure, but even in that context incorporating geospatial data led to an increase in precision roughly equivalent to doubling the sample size. These results are only suggestive, however, and an important outstanding research agenda is to compare small area estimates of monetary poverty obtained using geospatial data to those obtained from standard census-based poverty maps directly from census data. This can confirm that the encouraging results presented above apply to monetary poverty.

A final caveat, which applies to small area estimation in general, is the challenge of measuring local transient shocks. In the context of geospatial data, key indicators used to generate model predictions, such as building counts, are not necessarily affected by local economic shocks. This also poses an issue for traditional census-based poverty maps, as the census predictions tend to rely heavily on indicators such as educational attainment and household size that may also fail to track local shocks. Empirical Best methods partly address this issue by incorporating welfare information from the sample. Nonetheless, future research is needed to explore which geospatial indicators better reflect local economic shocks.

Overall, the results from an evaluation exercise using data from two countries demonstrate major efficiency gains from combining survey data with geographically comprehensive geospatial data to generate small area estimates of non-monetary poverty. These efficiency gains came at no cost to coverage in Sri Lanka and a moderate cost in coverage rates in Tanzania, and the latter can be addressed through further refinements to the methodology. The financial cost of incorporating geospatial data is low, relative to the cost of achieving similar efficiency gains by surveying additional households. While there is room for further methodological improvement, these techniques can currently be applied at modest cost to generate more granular and informative estimates of non-monetary welfare.

> 43  Chambers and Das (2017) propose a similar strategy of adjusting traditional ELL estimates to correct for biased estimates of mean squared error.

> 44 See Belghith et al, 2020. This corresponds to the marginal R2 reported in Table 6.

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