The eight data sets reviewed in this special issue do not include any that could be classified in Atkinson et al.’s first two categories. All of the data sets reviewed in this special issue involve some level of expost standardization. LIS, CEPALSTAT and SEDLAC would belong to Atkinson et al.’s category 3 (ex post standardized microdata). Category 4 (ex-post customized results) would include IDD, and WDI/ PovcalNet. Category 5 (meta-analyses of results) include WIID, SWIID, and AlltheGinis.

# **3. How Do the Estimates Differ across Data Sets?**

The various methodological differences among databases discussed above are only of interest to the extent that they yield different results. Our inspection of estimates of inequality levels and trends across the databases suggests that, in general, assessments of long-term trends tend be similar across databases for most countries. Nevertheless, we have found a number of instances in which different sources would lead to different conclusions, as discussed below.

Table 4 displays within-country correlation coefficients between Gini indices reported by the data sets with the most similar geographic coverage. While the correlation coefficient is a crude instrument with which to compare data sets, it provides a minimum consistency requirement, since data sets that show similar trends in inequality should be highly correlated. Panel A presents results for Eastern Europe, Africa, Asia and the Pacific, whereas Panel B concerns Western Europe and the Americas. Overall, correlation coefficients across databases are fairly high. Eighty-three percent of the pairwise correlations are above an (arbitrary) threshold of 0.5. This result of course implies that 17% – or approximately one in six – of these pairwise correlations are lower than 0.5. As noted above, there are also negative correlations coefficients in every region of the world, and between most pairs of databases.

Among the primary-source data sets, SEDLAC and CEPALSTAT have the greatest overlap in coverage. Both report Gini coefficients calculated directly from household survey microdata for Latin America and the Caribbean. Bourguignon (2015) reviews both databases, with the noticeable takeaway that CEPALSTAT’s “more interventionist” methodology – correcting for underreporting and imputing missing data – leads to higher estimates of inequality.<sup>9</sup> Figure 1 graphs the evolution of this discrepancy over time. From 1990 to 2010, CEPALSTAT has estimated Gini Coefficients that have been, on average, 1.6 Gini points higher than SEDLAC – a nontrivial difference. In the case of the Dominic Republic, the discrepancy is greater than 8 Gini points in 2009, 2010, and 2011.

Despite this gap in levels, the second panel of Table 4 shows that inequality estimates from SEDLAC and CEPALSTAT are quite closely correlated when we examine within-country trends (the correlation coefficients range from 0.59 to 0.99). The average correlation between SEDLAC and CEPALSTAT across all country and year combinations is 0.86. Correlation coefficients for Latin American countries

> 9 CEPALSTAT is in the process of revising its methods for constructing the household income aggregates and it might stop doing the adjustments for underreporting (based on the comparison with National Accounts) altogether.

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