Gini.<sup>25</sup> The average within-country Gini is slightly lower when countries are weighted by their population, at 44% in 2008, suggesting that the large African countries tend to have higher levels of within-country inequality.

Compared with other regions in the world, Africa has the highest regional inequality.<sup>26</sup> For example in 2008, the regional Gini indices for the mature economies is 41.1%, for Russia, Central Asia and South-East Europe 42.7%, and Latin America and Caribbean 52.2% (Lakner and Milanovic, 2015). For East Asia and Pacific the regional Gini index is 45.9% in 2007 and 44.4% in 2012, so the African Gini is about 10pp higher across the benchmark years.<sup>27</sup> At 67.0% in 2008, the global Gini index is higher than all these regional figures. This is not surprising given that between-country differences tend to be smaller at the regional than the global level.

The African Gini index increased by 9% between 1993 and 2008. By contrast, the average withincountry Gini has fallen by 5%, although this effect nearly disappears when population weights are used suggesting a slightly increasing Gini in larger countries. In other words, average withincountry inequality for an African hardly changed, while average inequality among this set of countries fell. The global Gini index has fallen somewhat from 69.1% in 1993 to 67.0% in 2008 (Lakner and Milanovic, 2015). For the EAP region, the Gini index increased by 8.6% between 1988 and 2012, and by 5.8% between 1992 and 2007, so slightly slower than Africa over a comparable time period. The regional Gini indices increased in most other regions, with the major exception of LAC where it fell from 56.4% in 1998 to 52.2% in 2008. Therefore, in terms of the regional Gini index Africa has overtaken LAC.

The finding of increasing African inequality is robust to choosing alternative measures of inequality. Figure 2 shows the Lorenz curve for the African distribution of consumption expenditure. The Lorenz curve for 2008 is uniformly to the right of the 1993 curve without crossing.<sup>28</sup> Hence, inequality has increased between 1993 and 2008 irrespective of the choice of inequality measure (Atkinson, 1970).<sup>29</sup> Consistent with this, all the alternative GE inequality measures increase over this period (Table 1, Panel A). The GE(2) measure is most sensitive at the very top of the distribution (Cowell, 2009), so it appears that inequality increased most strongly in this part of the distribution. As Anand and Segal (2015) point out, the estimation of standard errors for the regional Gini index (and other inequality measures) is not straightforward. This is because the Gini index is estimated from combining a set of country-distributions, not from a single regional household survey. However, while changes between some of the intermediate benchmark years are small, the change between 1993 and 2008 of four Gini points appears substantive.

> 25 These countries are Botswana, Central African Republic, Namibia and South Africa.

> 26 All global and regional inequality estimates in this paragraph use the 2011 PPPs, although Lakner and Milanovic (2015, Table A.3) use decile groups, and a slightly different spatial price adjustment for Indonesia and China (compared with Jirasavetakul and Lakner, forthcoming).

> 27 Furthermore and in contrast to the results for Africa, the EAP Gini exceeds all of the within-country Gini indices in the region.

> 28 The right-hand panel zooms in at the very bottom of the Lorenz curve. From this graph it appears that there might be some crossing at the very bottom which we ignore here.

> 29 To be precise, the Lorenz dominance result applies to inequality measures that satisfy the Pigou-Dalton transfer axiom. An inequality measure satisfying this axiom would increase after a transfer from a poor to a rich person.

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