across other databases (e.g. ATG and SWIID, or WDI and SWIID) are typically lower. Nevertheless, when zooming in on a particular country and a small timeframe, there can be important differences even between CEPALSTAT and SEDLAC. For example, CEPALSTAT data shows an increase inequality in Panama from 2008 to 2011, while SEDLAC’s metrics show a decline over the same period.

LIS and IDD also have a substantial overlap in coverage, with 78 common country-year combinations. The databases produce very similar results in both levels and trends. There is never a case of the Gini coefficient diverging more than 0.04 and the average discrepancy is only 0.001. Panel B of Table 4 shows that these data sets are also highly correlated at the country level (0.630 to 0.997). Looking at the individual data, both data sets show a jump in inequality in Italy in the early 1990s, and fairly steadily increasing inequality in Germany, Israel, and the United States. Interestingly, the correlations for the same set of (Western European and North American –WENA) countries is much lower for other pairs of data sets, notably ATG and SWIID. Between these two data sets, there are four negative correlation coefficients for WENA countries, in contrast to LIS and IDD, and despite the fact that LIS is an input into both!

The picture is even less reassuring among developing countries outside Latin America. The original motivation for this special issue was the case of Sub-Saharan Africa, and its inequality dynamics over the 1990s and 2000s. Figure A1.1 in the IMF Fiscal Monitor (October 2012, p.51) displays inequality estimates for a number of countries in two periods: 1985–1995 and 2000–2010. This comparison suggested that inequality had fallen in most countries (11 out of 16) in their Sub-Saharan African sample between those two periods. Table 5 displays the change in Gini coefficient in the IMF report (in column 1) for eight of those countries, and compares it to data from PovcalNet and SWIID version 5.0.<sup>10</sup> Of course, multiple comparisons are possible. For PovcalNet and SWIID, we compare inequality across these time periods in three ways: First by comparing the Gini in the first available year after 1985 with the Gini in the first available year after 2000; then by comparing the Gini in the last available year prior to 1995 with the Gini for the last available year prior to 2010; and finally by comparing the average Gini coefficient in each time period. As table 5 suggests, not only the levels but, more importantly, also the direction of the change is sensitive to both the choice of data set and the starting and end points used for the comparison. The case of Madagascar is illustrative: whereas the IMF Fiscal Monitor reported a 1% decline in inequality, a comparison of average inequality in the two periods suggests a 0.2% increase (in PovcalNet) or a 4.8% increase (in SWIID). The number of negative entries in column 1 (six instances of decline for the eight countries) is higher than for any of the other possible comparisons from PovcalNet and SWIID, reported in columns 2 to 7.

In Figure 2 we highlight the inequality estimates for Kenya. Jenkins (2015) also highlights Kenya to compare WIID and SWIID, and to discuss the variability in the range of SWIID’s imputed values. Here, we focus on differences in trends. WDI, ATG, and WIID all agree that there was a sharp decline in inequality between 1992 and 1994. The magnitude of the decline (of about 15 Gini points), however, points to another problem facing students of inequality in Africa. A decline of inequality of that

> 10 Those eight are the countries for which we have comparable data in PovcalNet.

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