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Estimation of Social Exclusion Indicators from Complex Surveys: The R Package laeken

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  • Alfons, Andreas
  • Templ, Matthias

Abstract

Units sampled from finite populations typically come with different inclusion proba- bilities. Together with additional preprocessing steps of the raw data, this yields unequal sampling weights of the observations. Whenever indicators are estimated from such com- plex samples, the corresponding sampling weights have to be taken into account. In addition, many indicators suffer from a strong influence of outliers, which are a common problem in real-world data. The R package laeken is an object-oriented toolkit for the estimation of indicators from complex survey samples via standard or robust methods. In particular the most widely used social exclusion and poverty indicators are imple- mented in the package. A general calibrated bootstrap method to estimate the variance of indicators for common survey designs is included as well. Furthermore, the package contains synthetically generated close-to-reality data for the European Union Statistics on Income and Living Conditions and the Structure of Earnings Survey, which are used in the code examples throughout the paper. Even though the paper is focused on showing the functionality of package laeken, it also provides a brief mathematical description of the implemented indicator methodology.

Suggested Citation

  • Alfons, Andreas & Templ, Matthias, 2013. "Estimation of Social Exclusion Indicators from Complex Surveys: The R Package laeken," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 54(i15).
    • Handle: RePEc:jss:jstsof:v:054:i15
    DOI: http://hdl.handle.net/10.18637/jss.v054.i15
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    References listed on IDEAS

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    1. Lumley, Thomas, 2004. "Analysis of Complex Survey Samples," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 9(i08).
    2. Alfons, Andreas & Templ, Matthias & Filzmoser, Peter, 2010. "An Object-Oriented Framework for Statistical Simulation: The R Package simFrame," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 37(i03).
    3. Matthias Templ & Andreas Alfons & Peter Filzmoser, 2012. "Exploring incomplete data using visualization techniques," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(1), pages 29-47, April.
    4. Andreas Alfons & Stefan Kraft & Matthias Templ & Peter Filzmoser, 2011. "Simulation of close-to-reality population data for household surveys with application to EU-SILC," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(3), pages 383-407, August.
    5. Templ, Matthias & Kowarik, Alexander & Filzmoser, Peter, 2011. "Iterative stepwise regression imputation using standard and robust methods," Computational Statistics & Data Analysis, Elsevier, vol. 55(10), pages 2793-2806, October.
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