house characteristics, and household assets. The dependent variable is household per capita consumption.

W **e** train a machine learning model to predict household per capita consumption using an extreme gradient boosting model with optimal hyperparameters chosen via 5-fold crossvalidation. The parameter used in the models corresponds to the average of the selected parameters in each fold. Details on the range of parameters considered are shown in Table 4. Additional technical information regarding these parameters and other aspects of the extreme gradient boosting procedure can be found in Annex 3.

**Table 4.** Parameters for XGBoost models <u>to estimate benchmark welfare.</u>

|**Parameter**|**Range **|
|---|---|
|Maximum numberofboostingiterations|between50 and200|
|Maximumdepthofa tree|2or 4|
|Learningrate|0.1or0.3|
|Subsampleratio ofthe traininginstance|0.2,0.4, or0.6|
|Subsampleratio ofcolumns to construct eachtree|0.2,0.5, or0.7|

We estimate four models using different samples in the IHS: (1) The full sample (All districts-all households), (2) All households in the UBR districts (UBR districts-all households), (3) the poorest 50% of the households in all districts (All districts-poorest 50%), and (4) the poorest 50% of households in UBR districts (UBR districts-poorest 50%). Estimating four models allows us to assess the trade-offs along two dimensions: (1) using all districts in the sample rather than restricting to UBR districts, and (2) using all households rather than just the lower half. Annex 4 shows the R-squared for each model and the main explanatory variables in terms of the _gain_ measure.<sup>16</sup> Including all households (model 1) leads to higher R-squared, while limiting to the bottom half of UBR districts (model 4) leads to by far the lowest R-squared. All models assign high importance to similar variables, mostly household assets, household size, and the urban or rural location (for the specific gain measures of each variable, see Annex 5).

The model that best matches the UBR sample is model (4) since it uses only UBR districts and the poorest 50% of households; however, the sample size is small, and the R-squared is the lowest. Although Model (1) has the highest R-squared, it also has a wider range of variability in the dependent variable, making R-squared a potentially misleading metric. Models (2) and (3) have similar values of R-squared. We elect to use model (3) as the main census model because it resembles the sample structure of the UBR by using only the poorest 50% of households in each village. However, it also takes advantage of additional data on log per capita consumption by using the full set of sample survey enumeration areas nationwide. Table 5 shows the values for the R-squared and the importance of the top 15 features in the selected model. These features explain around 87% of the model being the “urban/rural” indicator the main contributor. We later present robustness analysis using the other three samples.

> 16 Gain reflects the improvement in accuracy brought by a feature to the branches it is on. This means that before adding a new split on a feature X to the branch there were some wrongly classified elements, once the split on this feature is added, there are two new branches, and each of them is more accurate. A higher value of gain indicates that the feature is more important for generating a prediction.

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