models provide a large gain in efficiency compared with traditional ELL methods that do not incorporate Empirical Best methods.

We compare the estimates generated by a household EBP model with direct estimates obtained solely from the survey, as well as the well-known Fay-Herriot area-level model.<sup>7</sup> The latter sacrifices precision by discarding the variation in the geospatial indicators across subareas within target areas. Once the area estimates are obtained from the household EBP and Fay-Herriot models, we compare them to nonmonetary poverty rates calculated directly from the full census. The availability of census data provides a credible benchmark for assessing how different methods and their variants perform, both in terms of the accuracy of both the small area point estimates and their confidence intervals. We compare the predictions from different methods in terms of their precision, their accuracy, and the extent to which the estimated confidence intervals contain the true value, which is known as the coverage rate.

The main result is that incorporating remote sensing data in an EBP framework substantially improves the accuracy and precision of small area estimates of non-monetary poverty, largely by incorporating information from non-sampled subareas.<sup>8</sup> This comes at no cost to coverage rates in Sri Lanka and a moderate cost in Tanzania, compared with standard direct estimates. The corresponding efficiency improvement is comparable to roughly tripling the size of the survey in Sri Lanka and quintupling it in Tanzania.<sup>9</sup> The small area estimates moderately underestimate mean squared error by, first, failing to account for uncertainty in estimated variance parameters from the model, second, by incorrectly assuming that sample observations are independent within areas, and third, by failing to incorporate sample weights in model estimation. Estimated coverage rates remain respectable, however, at 75 percent in Tanzania and 84 percent in Sri Lanka when benchmarked. This is comparable to the 76 percent coverage rate in both countries when using standard direct estimates. In Tanzania, the estimates from the unit level model are roughly as accurate and moderately more efficient than those from the area-level Fay-Herriot model. In Sri Lanka, where the poverty rate is low and the Fay-Herriot model is less predictive, the estimates from the unit-level model are substantially more accurate and efficient than the Fay-Herriot estimates.

These results are robust to a variety of robustness checks that explore alternative implementation options, including the absence of benchmarking and the use of a noisier welfare measure. When using the noisier welfare measure, the gain in efficiency is not as large in Sri Lanka, on the order of doubling the size of the sample, but the predictions remain accurate and coverage rates remain high. Finally, to further probe the robustness of the results, we implement both a model-based simulation and a designbased simulation of 250 randomly selected samples covering three regions in Northeast Tanzania. The small area estimates perform exceedingly well, while the design-based simulation also shows that the small area estimates substantially improve on the direct estimates in terms of efficiency and accuracy while maintaining high coverage rates.

> 7 See Fay and Herriot (1979).

> 8 While the main efficiency improvements occur by incorporating information from non-sampled sub-areas, there are also minor efficiency improvements from combining sample data with synthetic predictions in sampled subareas.

> 9 This is based on a comparison of the average coefficient of variation between the standard direct estimates and the household level model reported in Table 10.

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