# 1 Introduction

Household surveys are needed to measure poverty and design distributional policies, yet in several countries they are not conducted due to low statistical capacity, conflict, or lack of resources. In other cases, household surveys are collected but not shared with researchers and policy makers (Dang et al. 2019, Ekhator-Mobayode and Hoogeveen 2022). Lack of data disproportionally affects poorer countries, meaning that ignoring or not properly accounting for such countries in global analyses biases results.

We address these gaps by developing a method that through a simple regression can predict annual income and consumption (henceforth welfare) distributions with a credible distributional shape for each country. The method uses widely available social and economic indicators at the country level and does not require any household survey data for the country of interest. We explicitly search for a simple model that can be easily taken up in applied work and can work for the most data deprived countries. To estimate such a model, we leverage rich information on welfare distributions from more than 2,000 household surveys available in the World Bank’s Poverty and Inequality Platform (PIP), which covers 168 countries, using data between 1991 and 2020. For each of these surveys, we have distributions of daily per-capita household income or consumption expressed in purchasing-power-parity-adjusted dollars.

We sequentially remove one of the 168 countries from the sample and predict the excluded distribution for this country using the remaining 167 countries and various predictors. As potential predictors, we search among more than 1,000 candidate variables spanning multiple databases and including remote sensing indicators. After repeating this leave-one-out cross-validation for each of the 168 countries with available data, we compare the predicted distributions to the survey-based distributions, favoring models that minimize the prediction error.

We find that a model that uses GDP per capita, under-5 mortality, life expectancy, rural population shares, and regional dummies predicts welfare well, and that adding more information does not lead to any relevant gains. National accounts data stand out as the single best predictor of welfare, even, but to a lesser extent, in data-deprived countries. This suggests that notwithstanding the gaps between welfare from surveys and national accounts (Deaton 2005; Pinkovskiy and Sala-iMartin 2016, Deaton and Schreyer 2022, Prydz et al. 2022) and measurement issues with GDP in autocratic countries (Martinez 2022), GDP provides valuable information on welfare in contexts where household surveys are unavailable. However, about half of the countries are so datadeprived that they not only lack an income or consumption distribution, but also GDP. For these countries, we use World Bank income groups as a proxy, resulting in two tiers of models depending on whether GDP data is available for the country of interest.

These two tiers outperform models based on remote sensing data (e.g., nighttime lights, vegetation), which do not meaningfully lower the out-of-sample error if added to the models. This suggests that, on average, remote sensing data are worse predictors of welfare at the national level than GDP, which is consistent with evidence suggesting that remote sensing data might produce household welfare estimates very different from survey-based ones (Van Der Weide et al. 2024). This suggests that a relatively simple model using readily available data outperforms more complicated models using costly data.

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