Chi et al. (2021) used several Demographic and Health Surveys (DHS) and a mix of publicly available and proprietary geospatial data to predict an asset index for 2.4 km grids across 135 countries. The authors trained the model on the asset index available in the DHS, using data for 56 countries. Proprietary predictors include internet connectivity information obtained from Meta. In validation exercises, R<sup>2</sup> varied greatly, depending on the context, level of geographic disaggregation, and comparison indicator. When validated using cross-validation across enumeration areas in the survey data, the R<sup>2</sup> of the estimates was 0.56, similar to the 0.6 value reported by Yeh et al. (2020) for Africa. Meanwhile, when validated against independent wealth measures, R<sup>2</sup> was 0.60 across rural Kenyan villages, 0.70 across Nigerian Local Government Areas, and 0.84 across Togolese Cantons. But when validated against the predicted probability of being poor, or predicted per capita consumption or income, R<sup>2</sup> values are much lower: 0.04 across Malawian villages, 0.17 in rural Kenya, and approximately 0.3 across Mexican municipalities (Gualavisi and Newhouse, 2022, Chi et al, 2021, Newhouse et al, 2022). This is due to key differences between wealth and predicted per capita consumption, including the fact that the latter is expressed in per capita terms.

Masaki et al. (2022) also consider the prediction of non-monetary poverty in Tanzania and Sri Lanka. Their study used census data from both countries to construct a non-monetary welfare index, and classified households whose index fell below a percentile threshold roughly equal to the prevailing national poverty rate as non-monetarily poor. The analysis combines survey-based estimates with publicly available geospatial indicators using an Empirical Best Predictor model following Molina and Rao (2010). Relative to direct survey estimates, the correlation with the census rose from 0.72 to 0.88 in Sri Lanka and from 0.77 to 0.88 in Tanzania. Unlike previous papers integrating survey and geospatial data, this one estimates the efficiency gain as well as the gain in accuracy due to incorporating geospatial data and finds that it is roughly equivalent to expanding the size of the sample by a factor between three and five.

Van der Weide et al. (2022) generate small area estimates of monetary poverty in Malawi for Traditional Authorities by combining survey data with publicly available geospatial features. Like Steele et al. (2017), their study uses a Bayesian structural equation model that accounts for spatial correlation across areas and validates the prediction against census-based estimates. It finds a correlation between the geospatial features and census estimates above 0.9, although some individual target areas show substantial discrepancies between the census and geospatial-based estimates due to differences in the data used for prediction.

Krennmair and Schmid (2022) propose a mixed effect random forest model, which is tested in a designbased simulation using household-level covariates in Mexico. The results demonstrate the benefits of applying machine learning methods over traditional linear models. Design-based simulations from the Mexican state of Nuevo León indicate that this approach reduces median relative bias by about 20 percent relative to the more typical approach of applying an empirical best predictor (EBP) model with a transformation. The paper also evaluates a random effect block residual bootstrap approach to estimating uncertainty and finds that it performs well.

Merfeld and Newhouse (2023) evaluate small area estimates of an asset index for four countries: Madagascar, Malawi, Mozambique, and Sri Lanka. In addition, the paper evaluates small area estimates of poverty for Malawi obtained using publicly available geospatial auxiliary data. This study compares linear EBP models with three different types of machine learning models: Extreme Gradient Boosting, 7