magnitude in a two-year period is highly unlikely, certainly in the absence of war, revolution, or a major disaster. More likely than not, it reflects methodological differences in the primary data collection on the ground in Kenya between two surveys. Gibson et al. (2015) discuss the severity of comparability problems between different household surveys over time in a number of African countries. While these problems due to differences in primary data collection lie beyond the scope of this special issue, they obviously carry through to the compilations we review here (as an illustration of the old adage: garbage in, garbage out). Sadly, they suggest that even where there is agreement between multiple cross-national databases, one cannot be entirely confident that one is on safe ground. While SWIID smooths this decline, the overall trend is similar.

After 1992 the trajectory of inequality in Kenya is far less clear. Based on the World Development Indicators and All the Ginis, inequality rose steadily between 1994 and 2005. In both cases, however, there is no data for the years in between 1997 and 2005. SWIID estimates suggest that inequality fell over that period while WIID includes data from the Society for International Development that estimates inequality increased sharply by 1999. For a researcher studying inequality trends in Kenya in the 1990s and 2000s, this would be of great concern. If she began her analysis in 1992, every data set would show a decline in inequality. If, however, she chose to begin the analysis in 1994, WDI would show increasing inequality, ATG would show an increase followed by a sharp decline, and SWIID would show a steady decline. In sum, inequality trends in Kenya – and a number of other countries – appear to be very sensitive to the data set selected.<sup>11</sup> Moving now to Asia, Figure 3 displays the Gini coefficients reported by WDI, ATG, SWIID, and WIID for Indonesia.<sup>12</sup> All four data sets show inequality rising between 1993 and 1996, falling between 1996 and 1999, and then rising again. The extent to which inequality spiked in 1996, however, varies from a 20 percent increase in the Gini in the case of ATG, to only a 5 percent increase in the Gini in SWIID. The data sets can differ even in the direction in which inequality changed. A researcher using WIID or SWIID would come to the conclusion that inequality in Indonesia increased between 2005 and 2010, while someone using ATG would conclude that inequality fell.

Let us return to the Caribbean for a moment, to illustrate the consequences of a methodological choice by secondary source databases, namely whether or not to choose certain sources of input by precedence. Consider the erratic inequality time series for Jamaica, pictured in Figure 4. SWIID shows a fairly smooth decline in inequality from 1980 through to 1993, followed by a sharp increase in inequality over the next three years. The numbers for ATG, on the other hand, oscillate sharply between 1989 and 1999. Upon closer examination, the reason for this appears to be that SEDLAC – to which ATG gives precedence as a source – produced higher inequality estimates than the other sources, but published estimates for only 1990, 1996, and 1999. Jamaica is therefore an extreme example of the volatility introduced to data series created with ATG’s Choice by Precedence method.

> 11 For an additional example, see Wittenberg (2015).

> 12 The value for WIID is average of Gini Coefficients for that year, if more than one is included in the database.

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