convolutional neural network, are less transparent and interpretable than the geospatial features used in the main specification. Furthermore, since machine learning methods already have the flexibility to incorporate interactions, interacting features with a measure of urbanity has little systematic positive impacts on accuracy.

These findings primarily contribute to a newer literature using new types of data to estimate economic statistics of interest, especially welfare. In the past decade, there has been a proliferation in the use of satellite imagery to estimate poverty and welfare (Jean et al., 2016; Yeh et al., 2020; Engstrom et al., 2022; Newhouse et al., 2025; Chi et al., 2022). However, processing raw imagery is typically computationally intensive and requires specialized skills. In comparison, other types of data are easier to use, like mobile phone call data records (Aiken et al., 2023; Blumenstock et al., 2015), but these can be more difficult to access due to privacy concerns and also raise issues related to representativeness. The satellite indicators we use can be obtained from publicly available sources relatively easily and are much smaller in size.<sup>3</sup> We also contribute to a related literature on small area estimation, which grew out of the statistics literature in the 1970s (Efron and Morris, 1973; Carter and Rolph, 1974; Fay III and Herriot, 1979; Battese et al., 1988). Earlier work proposed the use of census data for prediction (Elbers et al., 2003) and the empirical best predictor (Jiang and Lahiri, 2006; Molina and Rao, 2010; Tzavidis et al., 2018) is now one of the most common implementations of small area estimation. One reason the EBP model is preferred in many applications is its transparency; a nested-error regression model allows for a straightforward estimation of linear coefficients with random effects specified at the target area level. A simple table of coefficients indicates exactly how each variable is related to the measure of household welfare. EBP estimates are also “design consistent,” in the sense that the estimates converge to the population value as the sample becomes large. On the other hand, machine learning methods, while possibly generating more accurate predictions, suffer from a lack of parsimony and transparency (Efron, 2020). While we employ Shapley decompositions to shed light on which predictors have large impacts on predicted outcomes, it is not straightforward to understand the relationship between the set of predictors and the prediction. In addition, much of the formal statistical theory related to measuring the uncertainty associated with predictions from tree-based machine learning is new (Athey et al., 2019). To the best of our knowledge, this is the first paper to show rigorously that a two-stage, residual bootstrap can estimate confidence intervals at least as accurate as those currently used for EBP predictions, when evaluated against unit-level census data as ground truth. In cases where economists and statisticians are willing to sacrifice parsimony and transparency to achieve more accurate predictions and the sample data are sufficiently rich to train accurate machine learning models, the availability of a simple and accurate bootstrap method for estimating uncertainty surmounts a crucial barrier to the use of tree-based machine

> 3For example, most of the predictors we use in this paper are (freely) available through Google Earth Engine.

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