perform less well when predicting income or consumption-based poverty measures. For example, the RWI only explains 32 percent of the variation in average per adult equivalent consumption across Cantons in Togo (Aiken et al, 2021). Furthermore, for a microcensus conducted in rural Kenya, the RWI explains 70 percent of the variation in wealth but only 17 percent of the variation in the predicted probability of being poor, defined using household consumption (Chi et al, 2022). Thus, the performance of the RWI varies greatly depending on the context, and particularly depends on whether it is evaluated against a wealth or consumptionbased measure of welfare. This study therefore contributes additional information on the performance of the RWI in distinguishing among very poor villages by comparing it to the benchmark measure of village welfare in Malawi.

# IHS plus geospatial indicators.

The third alternative method for targeting consists of using the IHS survey data to train a welfare model against satellite indicators. This model can then be used to generate out-of-sample predictions into villages for which matched census data is available, to compare against the benchmark. This method has the advantage of being free, but may suffer from limited training data. We estimate the model only for the poorest 50% of households in each village to resemble the structure of the UBR data set and train it using extreme gradient boosting techniques.

Partial registry As a final alternative method to target the poor, we consider a hypothetical collection of a partial registry data set from a sample of villages. This exercise would consist of collecting the subset of household welfare proxies used in the census model from all households in a random sample of villages, similar to an expanded sample listing procedure of the type typically carried out for household sample surveys. In practice, for this analysis we simulate a partial registry by drawing a random sample of villages from the census extract. This sample, consisting of all households in the sampled villages, is used as the hypothetical partial registry in the two-step procedure. The first step involves utilizing the parameters from the census model used to predict benchmark welfare (described above) to predict per capita consumption into the simulated partial registry. The predictions are based on 39 independent variables common to both the census and survey. The resulting predictions for households are then aggregated into a measure of village welfare.

The second step of the prediction process entails estimating a second model, the geospatial model. The dependent variable in this model is predicted village welfare from the simulated partial registry, and the independent variables are village geospatial indicators, also estimated using extreme gradient boosting. This “geospatial model” is used to predict out-of-sample welfare predictions for villages not included in the partial registry. The predictions from the two models are combined, by using the census model predictions from the simulated partial registry in the villages for which they are available, and the geospatial model predictions for the remaining villages not included in the registry. In other words, in villages covered by the simulated partial registry, we use predictions from the census model rather than those from the geospatial model. This is because the simulated partial registry provides more accurate predictions than geospatial data, which are in fact exactly equivalent to the benchmark welfare measure by construction. We show below, however, that predictions from this procedure become 14