Another important area for further work is to continue to experiment with additional forms of geographically comprehensive auxiliary data to better predict labor market outcomes. This could include, for example, information on building footprints or the presence of different types of businesses. New types of geospatial data are being released each year that can be incorporated into this type of approach.

There is room for further work on model diagnostics. The results of this study demonstrate that R<sup>2</sup> can be a misleading metric for assessing the usefulness of the model, since even models with relatively low R<sup>2</sup> s can contribute important information to reduce bias in the predictions (Marhuenda et al., 2017). At the same time, model R<sup>2</sup> s are much higher in the full enumeration sample, but the small area estimates contribute little additional information. An important research agenda for further work is to better understand which diagnostic indicators, in cases where census data is not available, can provide a rough assessment of the gains from incorporating geospatial data.

Another open question is how optimal sample design changes in the presence of free, predictive geospatial data that can be linked to surveys. The evidence presented above indicates that estimates for sampled municipalities are substantially more accurate than non-sampled municipalities, suggesting that surveys should try to cover all target areas if possible. It is not clear, however, how the benefit of small area estimation relates to the sampling structure, and what this implies for optimally structuring household surveys.

Importantly, it remains to be seen how well the results here will generalize to other contexts. As such, it will be important to conduct studies to evaluate the methodology in other settings before applying it generally. This is easier said than done, however. In order to validate the methodology, it is best to use a census – or an unusually large survey –as a measure of ground truth. Furthermore, either the survey or ground truth must have either geolocated enumeration areas or, as in this case, highly disaggregated geographic identifiers that can be matched with a shapefile.

A final important issue pertains to the appropriate weighting strategy for the conditional random effect models relied upon here and in other small area estimation applications. We make three points regarding weights. The first relates to the choice of whether to give equal weight to each municipality or to weight by population when evaluating the estimates. We consider only the former, as is common in the literature on small area estimation. However, in some policy contexts, there is a strong argument that it is more important to generate accurate estimates for more populous areas than less populous areas. This is a decision best made based on political rather than technical grounds.

Second, properly weighing the estimates of a sub-area model such as this one is not trivial. In this case, the weights for each AGEB in the sample were normalized by dividing each weight by its municipal average. This ensured that each municipality was given weight proportional to its sample size, while appropriately giving more weight to more populous AGEBs within municipalities. The latter is important because population weights are used to aggregate AGEBlevel estimates to municipalities. Furthermore, in most sub-area models, it would be beneficial to adjust the weights further to correct for heteroscedasticity, to the extent that the number of 33