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Clustering the Swiss Pension Register

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Abstract

The anonymous data of the Swiss Pension Register (CCO/FSIO) (PR) are typically used to estimate (in the short, middle and long term) the future revenues and expenditures of the Old-Age and Survivors’ Insurance (OASI). In this perspective, it is essential to have a clear look at the register’s main statistical features. To better understand it and benefit more from its richness, we propose analysing the raw data by an appropriate clustering method.

Suggested Citation

  • Layal Christine Lettry, 2023. "Clustering the Swiss Pension Register," FSES Working Papers 529, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    • Handle: RePEc:fri:fribow:fribow00529
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    File URL: https://folia.unifr.ch/unifr/documents/324081
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    References listed on IDEAS

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    1. Corinne Dubois & Luisa Lambertini & Yu Wu, 2022. "Gender effects of the COVID-19 pandemic in the Swiss labor market," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 158(1), pages 1-29, December.
    2. Holger Herz & Deborah Kistler & Christian Zehnder & Christian Zihlmann, 2022. "Hindsight Bias and Trust in Government," CESifo Working Paper Series 9767, CESifo.
    3. Glenn Milligan, 1980. "An examination of the effect of six types of error perturbation on fifteen clustering algorithms," Psychometrika, Springer;The Psychometric Society, vol. 45(3), pages 325-342, September.
    4. W. Krzanowski, 1993. "The location model for mixtures of categorical and continuous variables," Journal of Classification, Springer;The Classification Society, vol. 10(1), pages 25-49, January.
    5. Christian Hennig & Tim F. Liao, 2013. "How to find an appropriate clustering for mixed-type variables with application to socio-economic stratification," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(3), pages 309-369, May.
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    More about this item

    Keywords

    Kamila; Clustering; R; AVS; AHV; OASI; Swiss Pension Register; FSIO; prediction strength criterion; classification; RAMD; AADR; UniFr;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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