and quintupling the effective sample size. The results are robust to alternative implementation options, adding additional noise to the welfare measure, and the implementation of both a model-based Monte Carlo simulation, and a design-based simulation from three regions in Northeast Tanzania. When rescaling mean squared errors to equalize coverage rates, the household-level model remained more efficient than a Fay-Herriot model, especially in Sri Lanka. The strong performance of the householdlevel EBP model, in comparison with the area-level Fay-Herriot model, is consistent with simulation results reported in Hidiroglou and You (2016). That study concluded, on the basis of a model-based simulation study with a linear model and unit-level auxiliary data, that unit-level models achieve lower bias and higher coverage rates than area-level models.

The household-level EBP model moderately underestimates uncertainty, leading to sizeable drops in coverage rates compared with estimates from the area-level Fay-Herriot model, especially in Tanzania. This occurs because the model does not incorporate sample weights, and assumes that survey data are independent within area when conditioning the random effect on the sample residuals. In addition, both the household and area-level models do not account for uncertainty in the estimated variance components in the model. Nonetheless, the coverage rate of the household model in both cases is comparable to standard survey estimates that cluster on enumeration areas. Furthermore, the household model performs significantly better than the Fay-Herriot model in terms of accuracy and efficiency when additional noise is added to the welfare measure, especially in Tanzania.

The financial cost of this type of small area estimation procedure is generally low and falling rapidly. Much of the auxiliary data used for the small area estimation, such as estimates of built-up area, nighttime lights, and vegetation, are freely available. There are two notable exceptions. First, the calculation of spatial features at a national scale requires constructing a cloud-free mosaic of Sentinel imagery, considerable computing power, and the expertise to implement software to calculate spatial features. Second, the data on Tanzanian building footprints are proprietary. However, data on building footprints are increasingly being released in the public domain and it is not difficult to envision information on building footprints becoming freely available in the coming years, potentially through Open Street Map as it continues to improve in accuracy and coverage. Finally, access to subarea survey identifiers and shapefiles, or access to EA geocoordinates, is necessary to link survey data to geospatial indicators. This is not feasible in all contexts, but the growing popularity of geospatial analysis and CAPI data collection is making geospatial survey analysis more common. A conservative estimate is that the time and expertise required to generate these types of estimates costs $50,000 to $150,000. This is minor compared to the value created by even doubling the effective sample size of nationally representative household surveys that often cost at least a million dollars to field.

The results could be improved by further refinements in software and methods. One avenue for further research is to explore the performance of a subarea-level model, an extension of the Fay-Herriot model specified at the subarea level with an area-level mixed effect (Torabi and Rao, 2015). Estimating such a model at the subarea level makes it easier to properly account for sample design effects. However, modeling poverty rates as a linear function of predictors may generate less accurate estimates, especially when poverty rates are low. It would be useful to better understand how the results of a properly specified subarea model compare with a household-level model using subarea predictors.

A second line of research could focus on improving the household-level model by estimating uncertainty more accurately. The estimated variance of the area random effect, for example, could be adjusted to 41