We implement our preferred methods for all countries to estimate global poverty, benchmarking the results against poverty estimates published by the World Bank. We find that the models track poverty rates relatively well in general but with notable exceptions. We show that these errors are in part due to poverty estimates not being comparable across countries and within countries over time but surely also due to modeling errors.

On average, our preferred models predict income or consumption off by around 30%. While this is a large error, it is important to assess it against an appropriate benchmark. A random forest on all 1,000+ possible predictors does not lead to any gains in the out-of-sample error, suggesting that much of the remaining error is likely irreducible. Furthermore, at a global scale, a 30% error is small in comparison with the large observed income differences: The median welfare of the richest country in our sample is more than 100 times that of the poorest country, and the 75<sup>th</sup> percentile of medians is 5 times greater than the 25<sup>th</sup> percentile.

A long-standing literature has tried to overcome data gaps and predict distributions when limited information was available. Survey-to-survey imputations can be used when survey data on welfare is unavailable at a desired point in time, but data on correlates of households’ wellbeing is available together with welfare data from a prior household survey (Stifel and Christiansen 2007, Roy and Van Der Weide 2024). Alternatively, national accounts data can be used to extrapolate older welfare vectors forward in time (Mahler et al. 2022; Angrist et al. 2021). Others have estimated full distributions when grouped data or summary statistics are available (Chen 2018; Chotikapanich et al. 2012, Eckernkemper and Gribisch 2021, Jorda and Niño-Zarazúa 2019; Hajargasht et al. 2012). Wealth indices have been used to predict full distributions for individual countries that lack consumption or income data but have a Demographic and Health Survey (Filmer and Prichett 2001, Dang et al. 2019). However, all these methods require at least one survey-based welfare vector, thus making them inapplicable for countries that do not have any survey data.

Remote sensing data and mobile phone data have been used to predict mean welfare, the poverty rate, or another distributional statistic in a country in the absence of survey data (Pinkovskiy and Sala-i-Martin 2016; Blumenstock et al. 2015, Steele et al. 2017; Pokhriyala and Jacques 2017; Lee & Braithwaite 2022; Engstrom et al. 2022). However, these approaches do not predict full distributions. Given the multiple poverty lines and welfare measures used in practice (see for example Jorda et al. 2023, Decerf and Ferrando 2022, Kanbur et al. 2022, Jolliffe & Prydz 2021), using such a model for each relevant welfare metric would jeopardize the simplicity of our approach. Moreover, the remote sensing data needed for these approaches do not stretch back far in time for long-term time trends and are not always publicly available, making these methods difficult to implement for practitioners.

The remainder of the paper is structured as follows. The data and method are described in sections 2 and 3. Sections 4 and 5 present results and robustness checks. Section 6 applies the models to global poverty measurement, and section 7 concludes.

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