How much Confidence can we have in EU-SILC? Complex Sample Designs and the Standard Error of the Europe 2020 Poverty Indicators

If estimates are based on samples, they should be accompanied by appropriate standard errors and confidence intervals. This
is true for scientific research in general, and is even more important if estimates are used to inform and evaluate policy
measures such as those aimed at attaining the Europe 2020 poverty reduction target. In this article I pay explicit attention
to the calculation of standard errors and confidence intervals, with an application to the European Union Statistics on Income
and Living Conditions (EU-SILC). The estimation of accurate standard errors requires among others good documentation and proper
sample design variables in the dataset. However, this information is not always available. Therefore, I complement the existing
documentation on the sample design of EU-SILC and test the effect on estimated standard errors of various simplifying assumptions
with regard to the sample design. It is shown that accounting for clustering within households is of paramount importance.
Although this results in many cases in a good approximation of the standard error, taking as much as possible account of the
entire sample design generally leads to more accurate estimates, even if sample design variables are partially lacking. The
effect is illustrated for the official Europe 2020 indicators of poverty and social exclusion and for all European countries
included in the EU-SILC 2008 dataset. The findings are not only relevant for EU-SILC users, but also for users of other surveys
on income and living conditions which lack accurate sample design variables.