particular year available in PovcalNet to project future poverty rates. To be precise, this approach should be called an inequality-neutral approach because authors typically use the World Bank’s global poverty database, PovcalNet, to conduct this analysis where the distribution is allowed to change as long as its Lorenz curve remains the same.

An advantage of this approach is that it can predict the true poverty rate if income or consumption grows as expected without changing inequality. This approach uses the actual income or consumption distribution, so poverty rates fully reflect the curvature of the income or consumption distribution. Therefore, unlike the constant growth or semi-growth elasticity approach, this approach can predict poverty rates well _as long as inequality does not change_ .

Needless to say, the biggest disadvantage of this approach is the fact that it assumes no changes in inequality. And as Ravallion (2013) and Ravallion and Chen (2012) show, inequality has also been changing over time. Inequality was slowly declining in the 1980s and the 1990s, but, as the pace of poverty reduction started to accelerate in the 2000s, global inequality also started to increase. At the country level, the changes are even more complex. Evidently, assuming no changes in inequality is a strong assumption.

_Use of growth projections based on national accounts or household survey mean_ All recent studies project global poverty rates against income or consumption growth rates from either national accounts or household survey data (see Ravallion 2013, Lanjouw et al. 2013, Karver et al. 2012, Edward and Sumner 2013, and Chandy et al. 2013). It is important to decide which should be used to estimate growth rates since growth rates often vary largely depending on this choice. When national accounts are chosen, the resulting growth rates are often discounted when they are converted to growth rates from household survey mean (e.g., Chandy et al. 2013 and Lanjouw et al. 2013,).

The challenge with using national accounts is two-fold. First, the ratio of growth rates from the household survey mean to those from the national accounts varies across countries and over time. After extensive investigation of the statistical differences between the household surveys and national accounts, Dhongde and Minoiu (2013) conclude that: _Estimates of global poverty vary significantly when they are based alternately on data from household surveys versus national accounts (Dhongde and Minoiu, 2013, p.1)._ Nevertheless, several authors use growth rates from national accounts to project those from the household survey mean. For example, Karver et al. (2012) simply assume that both sets of growth rates will be the same, and growth rates from national accounts are used to project future poverty rates. Chandy et al. (2013) convert growth rates calculated from national accounts into growth rates from the household survey mean by adopting uniform conversion rates between the two. The ratio is estimated at 0.91 for income surveys and 0.81 for consumption surveys. However, the use of uniform conversion ratios masks large variations in conversion rates across countries and over time and likely causes non-negligible biases in global projections.

The second challenge is that global poverty rates are estimated based mainly on household survey data, although national accounts are used in a circuitous way. Ravallion (2013) explains how data in PovcalNet are created and used to estimate global and regional poverty rates. PovcalNet includes some 900 household surveys for 125 countries.<sup>5</sup> It provides estimates of poverty measures for “reference years” spanning 1981–2008 at three-year intervals and for 2010. Given the irregular and unsynchronized spacing of surveys, PovcalNet uses an interpolation method that employs growth rates in private consumption

> 5 This does not include countries for which no valid PPP is available (Afghanistan, Marshall Islands, Mongolia, and Uzbekistan).

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