the National Institute of Statistics, Economic, and Demographic Studies (INSEED). Survey estimates are based on the subsample of households with valid GPS coordinates.

Typically, small area estimation applications combine survey data with covariates from census (or other population) data. However, except for Burkina Faso, the last time a census was conducted in these countries was between 2009 and 2014. Using out-of-date census data to update small area estimates can lead to biased estimates for example, if the distribution of the census covariates used for prediction has changed over time. This is an issue that is often not discussed in applied poverty mapping work. Literature on approaches to update poverty estimates in the intercensal period includes Isidro et al. (2016), Koebe et al. (2022) and AriasSalazar (2023). In this paper we rely on using contemporaneous geospatial covariates, as first illustrated by Battese et al. (1988) (see also Nguyen, 2012), to produce small area poverty estimates in countries that lack recent census data.

Advances in processing of geospatial data and the richness of geospatial data sources make their use as auxiliary information in small area models appealing. Newhouse et al. (2023) summarize recent literature on the use of geospatial data for small area estimation of wealth and poverty. Jean et al. (2016), Yeh et al. (2020), and Chi et al. (2022) show that satellite data are predictive of wealth indices. The present paper utilizes a method commonly used in small area estimation based on the empirical best predictor (EBP) under a nested error regression model (also referred to as mixed model) (Molina and Rao, 2010). When applied to predicting headcount poverty rates using geospatial covariates, this method has yielded predictions that are highly correlated with up-to-date census-based estimates in Mexico, Sri Lanka, and Tanzania (Masaki et al., 2022; Newhouse et al., 2022). The methodology we use in this paper deviates from the official approach endorsed by the World Bank’s Poverty Global Practice, as described in Corral et al. (2022), which is based on the EBP under a unit (household) level mixed model, and census micro-data as covariates (referred to as census-EBP). The main difference, besides the use of geospatial (instead of population census) covariates, is that our modeling approach utilizes only grid cell covariates, but the outcome is still modeled at the unit (household) level. This is why sometimes this latter model is referred to as the unit context model.

We explore the use of the unit context model in Chad, Guinea, Mali and Niger, which lack recent census data. We further leverage the availability of recent census data for a fifth country in West Africa, Burkina Faso, to conduct an evaluation exercise. The evaluation exercise compares estimates of headcount poverty rates obtained with a unit-level model and the empirical best predictor using census covariates, with poverty rates obtained using the empirical best predictor under the unit context model with geospatial covariates.

As noted above, an alternative approach to small area estimation using geospatial covariates is to use an area-level model (Fay-Herriot, 1979), case in which both the direct estimates of poverty rates and the geospatial covariates are aggregated at the target area level. Hence, in the evaluation exercise presented in Section 4 we also produce estimates under a Fay-Herriot model as a way of providing additional evidence about the validity of the estimates produced under the unit context model.

Using geospatial data instead of census data in SAE and the unit context model has been criticized in recent literature (for example, Corral et al., 2021). This is due to the possible introduction of omitted variable bias (relative to the unit-level model) resulting from the aggregation of the geospatial covariates. Although a detailed discussion of this issue is beyond