approach for producing interim model-based estimates because the alternative of using old census data carries risks especially if the distribution of census variables has changed over the intercensal period.

The presence of a recent census in Burkina Faso provided a valuable opportunity to evaluate the results of different models against ‘gold standard’ census-based estimates. In sampled areas, the estimates produced by the unit context model track the census-based estimates well and have lower MSEs than direct estimates. Across all areas, the correlation between the geospatial-based estimates and the census-based estimates is high, but this correlation was much higher in sampled areas than non-sampled areas. Models specified at the household level generated estimates that were moderately more accurate than those specified at the grid cell level, because the greater variation in per capita consumption allowed for the automated selection of a richer model. Both sets of estimates had lower MSEs than estimates under a model specified at the area level, which we think is due to the use of more granular auxiliary data.

Overall, the estimates for the countries without census data show large improvements in MSE reduction compared to direct estimates. In particular, the median cve in sampled areas is reduced between approximately 59% and 68%. The five countries that are the focus of this paper are neighbors and share many economic and social characteristics. Furthermore, all of them implemented highly harmonized surveys concurrently, and the set of geospatial variables available for model selection is identical. However, one cannot be certain that the results for Burkina Faso generalize to the other four West African countries as there are important differences to take into account. Burkina Faso is facing a significant internally displaced people crisis, affecting about 10% of the population, but hosts far fewer refugees than Niger or Chad. Burkina Faso and Guinea lack the large areas of mainly uninhabited desert that characterize Mali, Niger, and Chad. Nonetheless, the relatively low correlation between the geospatial estimates and the census estimates in non-sampled areas observed in Burkina Faso raises the prospect that the estimates for these areas could significantly change when upcoming censuses are collected and combined with survey data to produce updated poverty maps. Given the scarcity of evidence on out-of-sample prediction accuracy in the literature, we recommend treating the out-of-sample estimates in the remaining four countries with a high degree of caution.

There are several additional avenues for further research to inform these types of data integration efforts. These include additional empirical work to validate both point and uncertainty estimates against estimates using recent census data. Zonal statistics derived from geospatial data can be highly correlated. Initial results from current research indicate that the approach used for geospatial data processing and model building with geospatial data can impact the quality of the produced estimates. Model estimation also matters, it should be possible to improve upon existing methods for estimating mixed models with sampling weights. In addition, exploring the use of machine learning methods to capture complex relationships could improve estimation especially for out-of-sample areas. Despite room for further improvement, the modelbased estimates of the type calculated in this paper can provide interim estimates of the spatial distribution of poverty with acceptable uncertainty measures that cannot be obtained with survey data alone.

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