census data. The presence of a recent census in Burkina Faso provides a valuable opportunity for evaluating this method. As with every SAE application, the performance of different methods will depend on the country context and the characteristics of the available survey and auxiliary data they are applied to. Evaluations of the estimates therefore remains of paramount importance.

The paper is organized as follows. Section 2 describes the data sources and the process of integrating geospatial and survey data. Section 3 presents the core of the small area methodology, model selection and assessment, small area estimation, mean squared error estimation and measures to assess the small area estimates for all countries of focus in this paper. Section 4 presents an evaluation exercise using recent census and survey data in Burkina Faso. This allows us to compare small area estimates produced with geospatial covariates to small area estimates produced using covariate information from census microdata. The results of the evaluation exercise add new insights to the body of literature on the use of geospatial data in small area estimation and motivate the use of the unit context model with geospatial data in the four remaining countries that lack up-to-date census data. Section 5 presents experimental point and uncertainty estimates for all countries using the unit context model. The paper concludes with a summary of the main findings and areas for further research.

# **2. Data sources and geospatial data integration**

In this paper, we use geospatial covariates because, as shown in Table 1, the most recent censuses in the four focus countries were conducted in 2014 in Guinea, 2012 in Niger, and in 2009 in Chad and Mali. If more recent census data existed, using these data would be the preferred option. For example, several variables routinely collected in censuses such as household size, education, and sector of employment have been shown to be highly predictive of household welfare. Estimates based on recent census data are expected to be more accurate and precise than estimates based on geospatial data, which is often only available at an aggregated level (see for example Corral et al., 2021).

In this paper, however, we avoid using household-level predictors in the model because information for the same predictors from a recent census is not available. Using old census data can be problematic because it is not guaranteed to capture developments since the last census, especially in countries impacted by rapid changes. Interpreting the estimates as if these arise from the census year requires that the distribution of the census predictors, as well as their relationship to poverty, has not changed over time. This is a particular concern in countries such as these under study in this paper, which have among the highest fertility rates in the world and, in addition, have suffered from recent conflict and climate shocks which likely affected the geographic distribution of poverty and the geographic distribution of the population. Alternative sources of administrative data, such as health, land, or other administrative records, can also be useful sources of auxiliary data for small area estimation. However, these were not possible to obtain, and would not necessarily be commonly available for all four countries. We therefore decided to use publicly available, up-to-date geospatial data as covariates in small area models. The full list of candidate geospatial covariates, as well as a brief description of each of them are included in Table A1 in Appendix A.

To estimate the model, we use survey data from the 2018 EHCVM surveys in the focus countries. The process of integrating the geospatial covariates with the survey data in each