World Bank for its headline global poverty monitoring. For ease of reproducibility, the calculations reported here are performed on the “binned” version of the dataset created by Mahler `r` et. al. (2022), where the income distribution in each country-year is represented by data grouped into 1000 bins of equal population size per country and year, rather than on the underlying microdata itself.<sup>20</sup> Due to differences in the underlying survey instruments, the PIP combines surveys in which consumption is the main household-level measure of well-being (covering about threequarters of the world’s population) with surveys in which income is the measure of well-being (covering the remaining quarter of the world’s population). This presents challenges, since conceptually consumption and income are very different measures of well-being, and these differences are likely to be more pronounced in the tails of the distribution. For example, it is biologically impossible to survive with zero consumption, but it is possible to survive with zero income if households can draw down stocks of wealth. This conceptual difference is apparent in the data. For example, zero values for reported consumption are virtually nonexistent, while it is common for income surveys to have some mass of observations with zero or negative incomes.<sup>21</sup> Beyond these extremes in the lower tail of the distribution, household saving introduces conceptual and empirical differences between income and consumption throughout the income distribution. We follow the practice of the World Bank’s global poverty monitoring by abstracting from the difference between income- and consumption-based surveys when combining distributions across countries. For terminological convenience we will refer to the measure of well-being from the survey as “income” irrespective of whether it is drawn from an income or a consumption survey and note that it is measured in constant 2017 $PPP, per person per day.

An additional challenge is how to handle the effect of very low or zero measured income when constructing distribution-sensitive welfare measures. Specifically, our welfare measures are based on the ratio z/yi which is undefined when reported income is zero and can be very large when reported income is close to zero, to the point of dominating the index calculated as the average of these ratios across all individuals.<sup>22</sup> To address this problem, we bottom-code all binned observations at $0.50 per day. We arrive at this value by applying the consumption floor methodology outlined in Ravallion (2016) to estimate minimum consumption levels. Applying this method to consumption surveys in PIP, we find that essentially no country has a daily per

> <u>Methodology). For countries without any household survey data, it is assumed that their income distribution is equal</u> to the population-weighted average income distribution in their region.

> 20 The dataset is available at <u>https://datacatalog.worldbank.org/search/dataset/0064304/1000-Binned-GlobalDistribution.</u>

> 21 When zeros in consumption are reported in surveys, they typically are treated as unit non-response. Regarding incomes, Hlasny, Ceriani and Verme (2022) note that more than 75 percent of the 354 surveys from the Luxembourg Income Study that they examine, contain income values taking the value of zero.

> 22 The concern that distribution sensitive measures are highly sensitive to low incomes is not new. Cowell and VictoriaFeser (1996) discuss this issue in the context of data contamination (e.g., measurement error), using influence functions to demonstrate the extent to which a poverty or inequality measure is changed by small errors in the data.

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