muted effect on _global_ inequality, for which between-country differences matter greatly. One challenge with modeling the impact of changes in the Gini index on poverty is that there are infinitely many possible distributional changes resulting in the same change in the Gini index. If the change in the Gini index comes from redistributing resources from the wealthiest 1% to the middle class, poverty may remain unchanged in countries with moderate to low levels of poverty. If the change comes from instituting a basic income to all households, then a similar change in the Gini may completely eliminate poverty. Our baseline results are based on a linear growth incidence curve, but in a robustness check we use a convex growth incidence curve (GIC), which gives higher growth rates to the lowest percentiles compared to the linear version. With the convex functional form, a 1% annual decrease in the Gini in all countries has a larger impact on global poverty than a 2 pp higher annual growth in each country. In other words, the convex GIC further highlights the importance of reducing inequality for ending extreme poverty.

The literature has adopted several alternative approaches to model distributional changes in simulating global poverty trajectories. Some authors have simply imposed distribution-neutral growth, thus ignoring any future changes in within-country inequality (Birdsall et al., 2014; Karver et al., 2012; Hellebrandt and Mauro, 2015). Others have projected distribution-neutral growth but chosen initial distributions with different levels of inequality (Ravallion, 2013; Edward and Sumner, 2014). Other studies, which are most closely related to the approach taken by this paper, simulate additional distributional changes, by extrapolating the trend in the Q5/Q1 ratio (Edward and Sumner, 2014; Hillebrand, 2008; Higgins and Williamson,2002), the Palma ratio (Chandy et al., 2013), or the income share of the bottom 40% (Ncube et al., 2014). A previous version of this paper used differences in growth rates of the bottom 40% and the mean to project poverty towards 2030 (Lakner et al. 2014), similar to Hoy and Samman (2015).

While our focus is on the impact of the distributional nature of future growth, we also develop our own baseline distribution-neutral growth scenarios. Two main approaches are used in the literature, which can produce quite different results for global poverty (Dhongde and Minoiu, 2013; Edward and Sumner, 2014). First, scenarios based on historical survey growth rates (e.g. Yoshida et al., 2014). Second, scenarios derived from national accounts either through growth models (Birdsall et al., 2014; Hillebrand, 2008), or projecting historical or forecasted growth rates into the future (Karver et al., 2012). Similar to our approach (explained in more detail in Section 4), Chandy et al. (2013) use Economist Intelligence Unit (EIU) and IMF’s World Economic Outlook (WEO) growth rates adjusted to survey growth using factors from a cross-country regression. We base our projections on both country-specific historical growth rates and forecasted growth rates, adjusted for observed differences between household survey growth and national accounts growth. The distribution-neutral global poverty projections remain at around 6.5% in 2030 regardless of which growth scenario we use.

We model distributional changes and growth rates in GDP independently of each other. Although the famous Kuznets Hypothesis (Kuznets, 1955) would predict that higher growth in low-income countries would tend to increase inequality, the empirical support for this hypothesis is weak. Ferreira and Ravallion 3