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

Last week, I was invited to present my dissertation research during the LIS Summer Workshop. My lecture was titled “Family Policies, Women’s Earnings, and Between-Household Inequalities: Using LIS for comparative analyses“. It consisted of three parts.

The first part of my lecture dealt with my argument to combine institutional and demographic explanations of women’s employment. I differentiate between two types of family policies, and how these affect women’s employment. These two types of family policies are reconciliation policies and financial support policies to families. Reconciliation policies were found to stimulates the employment of specifically mothers, thereby decreasing the employment gap between mothers and women without children. Financial support policies to fmailies with children, on the other hand, were found to increase this motherhood-employment gap.

Secondly, I have answered the question to what extent – and in what direction – women’s increased earnings have affected earnings inequality between households. Put simply: women’s earnings attenuate the earnings inequality between households. Moreover, reconciliation policies were found to stimulate women’s employment in such a way, that her attenuating contribution to household inequality was stronger, while financial support policies suppress this attenuating effect. This means that a family policy-context facilitating women’s employment contributes both to smaller inequalities within households, as well as between households.

The third part of my lecture was technical in nature, and specifically addressed comparative analyses using the LIS database. I presented a ‘netting down’ tool that may assist in comparing net and gross earnings data in the LIS database. Evaluations of the performance of this netting down tool suggest that netting down improves the quality of comparative analyses, but residual bias (between net and gross datasets) remained.

Presenting at the LIS Summer Workshop was an amazing experience, with attendants asking smart and constructive questions. I learned a lot from the experiernce, myself. The slides of my presentation are available upon request (r.nieuwenhuis@utwente.nl).