model was trained on night-time lights, accuracy declined precipitously when the model attempted to predict within the lower portion of the per capita consumption distribution. In the African countries considered, most poor households live in dark rural areas, and a model trained to predict only nighttime lights cannot distinguish welfare levels among them.

Babenko et al. (2017) improved upon this method by using daytime imagery to train a CNN model directly using survey data on per capita income. They trained the CNN model to predict the share of the population in extreme and moderate poverty in different Area Geo Estadistica Basicas (AGEBs) – small areas analogous to a census block -- based on per capita income data collected in the 2014 MCS-ENIGH household survey. The prediction of AGEB-level poverty rates achieved an R<sup>2</sup> of 0.47 when compared with survey estimates from withheld AGEBs. Interestingly, this is within the range reported Jean et al. (2016) for per capita consumption, despite the difference in welfare measure (income vs. consumption) and country context. A prediction model based solely on land-cover classification predicted equally well, however, and models that used both predicted poverty (from the CNN) and the land-cover classification achieved an R<sup>2</sup> of 0.57. This suggests that the CNN-based estimates of poverty in this case did not capture all of the imagery features correlated with average household per capita income. We further touch on the differences between interpretable and CNN-derived features below.

Newhouse et al. (2022) use the CNN poverty estimates and land cover classification from Babenko et al. (2017) as inputs into Empirical Best Predictor (EBP) models to predict poverty at the municipality level. The EBP model provides a simple framework for combining the two features in a linear mixed model, in addition to offering a well-established parametric bootstrap method for estimating uncertainty (Gonzalez-Manteiga et al, 2008). Official estimates for municipalities developed by the government based on the household-level intercensus were used as the reference for comparison. The R<sup>2</sup> of the estimates was 0.74 for sampled municipalities, but only 0.49 for non-sampled municipalities. Because this is only based on one sample, the paper also performed design-based simulations using a measure of per capita labor income taken from the intercensus. In the simulations, the R<sup>2</sup> was 0.86 for sampled areas and 0.64 for out-of-sample areas. I further discuss the difference in accuracy between sampled and non-sampled areas below.

Steele et al. (2017) predict a wealth index and per capita expenditure in Bangladesh using a Hierarchical Bayes Structural Equation Model, allowing for spatial covariance. The paper differs from many others by also incorporating call detail record (CDR) features from mobile phones in addition to satellite features. Results were validated both using cross-validation and using poverty estimates derived from the 2012 census. The results showed that it is much easier to predict wealth than per capita consumption, a finding consistent with Jean et al. (2016). When using cross-validation for evaluation, out-of-sample R<sup>2</sup> for village-level estimates was 0.76 for wealth as opposed to 0.36 for consumption. However, when comparing Upazilla-level (sub-district level) estimates with previous estimates derived using traditional small area estimates from the 2010 census, R<sup>2</sup> was a much higher 0.95.

Similarly, Pokhriyal and Jacques (2017) combine CDR and satellite data from Senegal with census data to predict non-monetary poverty across communes in Senegal. They use Gaussian process regression, a non-parametric machine learning method. They evaluate their estimates using 10-fold cross-validation. The estimates for non-monetary poverty across Communes achieved an out-of-sample R<sup>2</sup> of 0.83 and a rank correlation of 0.87.

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