by WIDER and UNDP. It built on, incorporated and expanded the original secondary-source inequality database, due to Deininger and Squire (1996). See Badgayan et al. (2015).

Branko Milanovic’s “All the Ginis” database also collects Ginis from a variety of sources, including primary databases and published research. Unlike WIID, however, it includes a series in which the “best’’ Gini coefficient is chosen for each country/year cell for which more than one figure is available.

# _Group 3: Imputation-based Data Sets_

The only member of this class, SWIID is a data set of Gini Coefficients calculated using a multiple imputations method, from a group of both primary sources, such as SEDLAC and LIS, and secondary sources, such as “All the Ginis.” Imputation of missing country/year cells gives SWIID the largest coverage of any of the data sets included in the special issue. SWIID version 5.0 provides users with 100 imputed estimates of the Gini coefficient for net and market income inequality, for each countryyear cell. The SWIID imputation procedure uses LIS definitions of income as the standard. SWIID net market income is post-tax, post-transfer, and benchmarked to LIS Key Figures. SWIID market income is pre-tax, pre-transfer, and benchmarked to an income inequality series generated from LIS microdata. Stata code on the SWIID website allows users to either calculate means, standard deviations and confidence intervals, or to use the 100 values produced by multiple imputations directly. Prior versions provided the users with the mean and standard error of the multiple imputations.

# _Data Sets Not Included in Special Issue_

As with most undertakings of this kind, our set of reviews is not exhaustive. Three sources of crossnational inequality statistics that are different in focus from those reviewed in this issue are nevertheless worth mentioning. The first is the _Commitment to Equity Database_ (CEQ), a project of the Center for Inter-American Policy and Research and the Department of Economics at Tulane University, the Center for Global Development and the Inter-American Dialogue. The CEQ is unique because it is designed to analyze the impact of taxation and social spending on inequality and poverty in individual countries applying a common methodology.<sup>6</sup> In that spirit, CEQ indicators include estimates of inequality before taxes or transfers; after direct taxes but before transfers or indirect taxes; after direct taxes and transfers and before indirect taxes and transfers; after direct and indirect taxes and transfers; and after all taxes and transfers and after receipt of government services. Although the goal of CEQ is to produce estimates of fiscal redistribution over time, thus far the CEQ database includes estimates for one particular (similar) year. This focus makes the CEQ sufficiently distinct from the other data sets reviewed here so as to warrant exclusion. Nevertheless, in what follows we will compare its in-country estimates of various pre- and post-fisc inequality measures t ~~o~~ analogous estimates obtained through multiple-imputation methods in SWIID.

> 6 The CEQ is directed by Nora Lustig. For more information on the project, visit <u>http://www.commitmentoequity.org.</u>

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