Despite these issues, failing to line up poverty and population estimates is also problematic. In particular, pooling estimates from different years would bias the global profile towards characteristics of the poor in older surveys, many of whom might have since escaped poverty. For example, the most recent survey from India dates from 2011/12. Survey-based estimates show a fall in rural poverty from 36.3% to 24.8% between 2009/10 and 2011/12, and the “lined up” estimate for 2013 is 19.9%. Even though this latter estimate may overestimate poverty reduction, failing to adjust for this dramatic decline would over-represent poor rural Indians in the global poverty profile. At the same time, failing to account for population growth between 2010/11 and 2013 would underrepresent India relative to other countries.

Although we know of no systematic evidence that using this line-up method improves the accuracy of the poverty profile, our main specification is based on the “lined up” figures available on the PovcalNet website. In other words, we take the 2013 poverty rates published by the World Bank and use the corresponding percentile value to set poverty lines in the GMD sample. This ensures consistency with the published estimates and eases interpretation of the results. It also maintains continuity with the approach adopted in Olinto et al. (2013), except that we implement an additional population adjustment. In particular, the sample weight is adjusted to match population projections for each gender and age group in each country as of 2013, taken from the United Nations Department of Economic and Social Affairs (UNDESA).<sup>19</sup> In cases where population estimates by age group and gender are not available from UNDESA, we simply re-scale the sample weight to match the total population reported in the World Development Indicators. Section 3 below reports estimates based on this method.

In section 4, we test the sensitivity of key results to the choice of line-up method. In particular, we compare key results to an alternative approach that simply takes the population weighted average of country-level profiles, without any attempt to line-up data from different survey years to a common year, and find minor changes. Even though the results are generally similar under this approach, they cannot be referenced to a specific year and thus must be interpreted with caution. Appendix 1 describes the line-up procedures in greater detail.

Once the household surveys have been lined up, it is straightforward to use the pooled household survey data to construct poverty profiles. The figures displayed below are based on the international poverty lines of $1.90 and $3.10 per person per day in 2011 PPP as currently reported by the World Bank. The $1.90 line is the average national poverty lines from the 15 poorest countries originally used by Chen and Ravallion (2010) to establish the $1.25 a day poverty line in 2005 PPP terms.<sup>20</sup> As mentioned earlier, statistics are reported separately for the extreme poor, the moderate poor, and the non-poor. The moderate poor are defined as individuals whose household per capita consumption (or income) lies between $1.90 and $3.10

> 19 This reweighting procedure multiplies household weights by a scale factor in order to maintain a constant weight within each household. However, in cases where that is not sufficient to match the age group and genderdisaggregated UNDESA population estimates, weights are rescaled according to individuals’ age and gender groupings to exactly match these totals, meaning that weights for different members of the household may vary.

> 20 See Ferreira et al (2015) for more details on how these lines are derived.

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