The Luxembourg Income Study provides an invaluable source of income-surveys that are made compable across countries and over time. Not all the measurements of income are directly comparable, however. In some datasets the income variables were measured gross of income taxes and social contributions, whereas in other datasets they were measured net of income taxes and social contributions. Researchers seeking to do comparative analyses using the LIS will have to account for this difference between net and gross datasets.
In a new publication, we present netting down procedures, which are statistical tools that help improve the comparability of net and gross datasets in LIS. The paper discusses the issues involved with comparing net and gross income data, as well as the assumptions that are required when applying a netting down procedure. Two netting down procedures are discussed, and their performance in reducing bias is evaluated. The paper was co-authored by Rense Nieuwenhuis (that’s me – Institute for Innovation and Governance Studies (IGS), Universiy of Twente), Teresa Munzi (Data Team Manager and Research Associate of LIS) and Janet Gornick (Director of LIS).
The results indicate that directly comparing data on net and gross earnings (as a specific source of income) introduces bias to the analysis. This was not a surprising finding, because it is well known that progressive tax systems result in net earnings to be lower and distributed more equal than gross earnings. Nevertheless, it underlines the importance of carefully comparing net and gross earnings. Applying the netting down procedures allows users to approximate net earnings based on gross earnings and variables on income tax and social contributions. The paper provides the program code for use with SPSS, Stata, R, and SAS. The results of evaluating these netting down procedures suggest that the application of netting down improves comparative analyses across net and gross datasets in the Luxembourg Income Study.
Our new publication is titled “Netting Down Gross Earnings Data in the LIS Database: An Evaluation of Two Procedures”. The paper was published in the LIS Technical Paper Series, and is available online: http://www.lisdatacenter.org/wps/techwps/6.pdf