# 3 Data and Methodology

## 3.1 PovcalNet

To predict poverty in 2030, we rely on the surveys used in PovcalNet, which contains the World Bank’s official country-level, regional and global estimates of poverty. Most of the data in PovcalNet come from the Global Monitoring Database (GMD), which is the World Bank’s repository of multitopic income and expenditure household surveys used to monitor global poverty. PovcalNet contains more than 1,500 surveys from 164 countries covering 97% of the world’s population. The data available in PovcalNet are standardized as far as possible but differences exist with regards to the method of data collection, and whether the welfare aggregate is based on income or consumption. By relying on the PovcalNet database, we ensure consistency with the official numbers used by the World Bank and United Nations for monitoring poverty, inequality and related goals.

For 119 of the countries, housing 64% of the world’s population, micro data are available. For an additional 35 countries, or 14% of the world’s population, mostly comprising the high-income world, grouped data of 400 bins are available. For the purposes of these projections, we treat the bins as microdata. Finally, for China and nine other countries constituting about 20% of the world’s population, only decile or ventile shares and the overall mean are available. This concerns Algeria, China, Guyana, St. Lucia, Macedonia, Suriname, Turkmenistan, Trinidad & Tobago, Venezuela and Zimbabwe. For these countries, based on the decile shares and means reported in PovcalNet, we use a lognormal Lorenz curve to generate a distribution of 10,000 points for each country. The use of a parametric Lorenz curve is very similar to what is done in PovcalNet to calculate poverty when micro data are not directly available.<sup>5</sup>

### 3.2 Growth scenarios

Our starting point is the latest available survey in each country or, if the latest available survey was conducted prior to 2015, the welfare distribution the World Bank used to measure poverty for the country in 2015, which is the latest year with global poverty estimates at the time of writing.<sup>6</sup> The median year of the latest survey is 2015, but the range spans from 1992 to 2017. Before implementing various inequality scenarios, we bring the welfare aggregates from these surveys to 2018. To do so, we multiply each household’s welfare with (one plus) a fraction of the observed or forecasted growth rate in real GDP per capita. That is, we assume that the growth that occurred between 2015 (or the year of the latest survey) and 2018 in each country was distribution-neutral. We assume that only a fraction of the growth is passed through to the welfare vector, following the method described in the next subsection.<sup>7</sup>

> 5 From two parametric Lorenz curves – the General Quadratic and the Beta Lorenz – PovcalNet chooses the one with the best fit. Shorrocks and Wan (2008) suggest that a lognormal functional form fits better. Minoiu and Reddy (2014) show that for global poverty estimates a parametric Lorenz curve should be preferred to estimating kernel densities. We use the ungroup command included in the DASP Stata Package (Abdelkrim and Duclos, 2007) to fit a lognormal Lorenz curve. This command implements the Shorrocks and Wan (2008) approach which ensures that the fitted Lorenz curve matches the observed shares.

> 6 The method that PovcalNet uses to bring up the surveys to a common reference year is described in Appendix A of World Bank (2018a).

> 7 We use GDP throughout for consistency across countries, while PovcalNet chooses between growth in terms of GDP and household final consumption expenditure in national accounts.

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