For the purposes of evaluation, a measure of benchmark welfare was constructed by predicting household per capita consumption as a function of household and demographic characteristics in the 2018 census extract using extreme gradient boosting, a popular machine learning algorithm. This model was then used to generate a prediction of per capita consumption for each household in the census extract, which we aggregate to construct a “ground truth” measure of village welfare. We refer to this model as the “census model”, because it is used to impute predicted per capita consumption into the census extract. This census model captures over half of the variation in log per capita consumption in the household survey, with an R<sup>2</sup> of 0.537. It is used both to generate “ground truth” using the full census extract and to impute welfare into the simulated partial registry, derived from a subsample of the census extract. To construct a “ground truth” measure of village welfare, the values of predicted household per capita consumption in the 2018 census extract are aggregated to the village level.

For the main set of results, the “ground truth” village welfare measure used is the average predicted per capita consumption of the poorest 50 percent of households in the census, when ranked according to their predicted per capita consumption. We select this as the main measure to generate a clean comparison with the UBR, which only collected information on the poorest 50% of households. Since the PMT scores in the UBR are based on the average predicted welfare of the bottom half of the distribution, evaluating it against a different benchmark could introduce an additional source of noise in the UBR comparison. However, since this is a non-standard measure of village welfare, we also show below that the key results are robust to using a more typical measure, namely the average predicted per capita expenditure across all households in each village.

The 20 percent census extract contains village, traditional authority (TA) and district names but not household or village geocoordinates. Obtaining information on the physical location of census villages is crucial for this exercise. Therefore, we match census village names with UBR administrative data, which contains the names of the administrative areas as well as the geocoordinates of interviewed households. This enabled us to calculate centroids based on the minimum and maximum latitude and longitude of households living in that village in the UBR administrative data as an approximation of the village centroid in the census for about 4,500 villages. These centroids were then matched to a set of grids constructed to cover the country, in order to link remote sensing information to the census.

The remote sensing indicators were obtained from Google Earth Engine, WorldPop, and Meta. For each village, we calculated the average of approximately 40 indicators: landcover indicators (e.g., percentage of vegetation, water, or build-up coverage), global precipitation measurement, soil moisture, nighttime data, and year of the transition from pervious to impervious areas. This was supplemented with gridded maps of building patterns (e.g., number, area, and length of buildings, among others) in 2017, population density indicators, build settlement growth, and distance to major roads taken from Worldpop. Grid-level averages of these satellite-derived features were then linked to the census data using the village centroids obtained from the matched UBR data.

This paper considers three main research questions: (1) How much does a hypothetical partial registry improve predictions of village-level welfare in this context, as compared with the existing UBR and two other feasible alternatives? (2) How much does increasing the size of the partial 3