some of the differences between the predicted poverty rates can be explained by particularities of how poverty is measured in a country.

Figure 8 shows the predicted and survey-based poverty rates for other populous, or poor and data deprived countries. In most cases, the two are fairly well aligned, though there are exceptions, such as the cases of China and India just discussed as well as the República Bolivariana de Venezuela, where the predicted poverty rates are much lower than the surveybased ones. Figures A.3-A.12 in the annex show similar plots for 218 economies.

PIP currently assigns countries without any poverty estimate (or without national accounts data to extrapolate/interpolate from an old poverty estimate) the regional poverty rate. This concerns about 3% of the global population. In Appendix B we show how PIP’s regional and global poverty rates would change if we used the estimates from our two tiers instead of applying the regional average, as PIP currently does. Global poverty is projected to be revised upwards in 2019 by 11 million at the $2.15 line and 16 million at the $6.85 line. For certain countries the implications are larger, notably Afghanistan, Somalia, and the Democratic People’s Republic of Korea.

# 7 Conclusion

This paper proposes a method to estimate welfare distributions in contexts where little data is available. Data deprivation is often a bigger concern in poorer and more fragile countries where data collection might be limited due to active conflict, lack of resources, or institutional fragility, but where efforts to monitor changes in living conditions are essential. While comprehensive surveys of household income and consumption remain the best way to measure household welfare, we offer a simple alternative to estimate welfare distributions when surveys are not available.

The method consists of leveraging income or consumption distributions from more than 2,000 household surveys available in the World Bank’s Poverty and Inequality Platform (PIP) for 168 countries covering the period between 1991 and 2000. We combine these distributions with more than 1,000 predictors at the country-year level, including remote sensing variables, across various databases. We develop a simple model which predicts distributions for all countries in the world from a regression on country-level data. Guided by variable-selection techniques, we try various versions of such regressions, each time sequentially excluding all surveys available for one of the 168 countries available in our dataset to predict the distribution for the excluded country using the remaining 167 ones. We then calculate the absolute deviation between the true welfare and predicted welfare at 99 points on each distribution. Once we have these deviations for all surveys in the 168 countries, we calculate the mean absolute deviation across all surveys and all countries.

We find that when predicting distributions with GDP per capita (or income groups if GDP/capita is unavailable), under-5-mortality, life expectancy, and rural population shares, no additional feature significantly reduces the error further. Our preferred model predicts log welfare off by around 30% on average. Though this may sound high, we show that a random forest using all 1,000+ predictors is unable to reduce the error, suggesting that much of the remaining error is likely to be noise.

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