household surveys are imputed in their entirety, so as to preserve statistical representativeness, as in the common practice of hot-decking. Rents not paid by home-owners are often imputed.

What is new about SWIID is the wholesale use of multiple imputation to populate country-year cells where no actual primary data collection took place at all, with values obtained from stability assumptions applied to other years for the same country, or even other (hopefully similar) countries. While this practice has obvious (apparent) benefits in terms of expanding coverage, there are serious questions as to the reliability of SWIID’s imputed estimates, in particular in data-poor regions (as indicated by Jenkins and Wittenberg in this issue). These concerns naturally extend to any downstream regression analysis based on these estimates.<sup>15</sup> In general, the tension between the competing requirements for a sufficient number of data inputs, versus a sufficient similarity of said inputs, is more likely to be manageable in data-rich environments, such as Western Europe and the Americas. It is likely to be extremely binding, on the other hand, precisely in the data-poor environments where its benefits would have been greatest. On this basis, we endorse Jenkins’s recommendation of WIID over SWIID, when no microdata-based data set provides sufficient coverage.

Fifth, it is clear from both the brief discussion in this Introduction and from the five articles that follow, that use of these databases places a considerable burden of responsibility on the user. The fact that the databases are readily downloadable does not excuse researchers from the need to acquire a sound understanding of the assumptions and methodological choices embodied in the data they are about to use. In some cases, we suspect, such an understanding may well result in a rethink of the research strategy.

Finally, this review also implies that database producers bear an important responsibility in documenting all their assumptions clearly and thoroughly, and making their data, programs and results available to allow for replicability (whenever it applies).<sup>16</sup> Both scholarly research and policy debate need to be based on the best evidence available. Until the micro data sets, programs, assumptions, and all the documentation become publicly available, it will be hard for users to be completely reassured that the reported inequality indicators obtained through a middle-man (i.e., the international data set producers) are truly reliable. Furthermore, data set producers would be wise to compare their methods and results with one another and, eventually, perhaps even agree on conventions of best-practice in the calculation of inequality indicators from primary, secondary or imputed sources.

> 15 Authors who insist on using SWIID inequality estimates in regression analysis should, at a minimum, treat them as a variable measured with error, and adjust estimation methods accordingly. Where such corrections rely on consistent estimators that may still be biased in small samples, this should evidently be noted.

> 16 Similar recommendations likely apply to many national statistical agencies as well, although that issue lies beyond the scope of this paper.

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