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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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    1. Dubois, Corinne & Lambertini, Luisa & Wu, Yu, 2022. "Gender Effects of the Covid-19 Pandemic in the Swiss Labor Market," FSES Working Papers 525, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    2. 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.
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    6. 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.
    7. Herz, Holger & Kistler, Deborah & Zehnder, Christian & Zihlmann, Christian, 2022. "Hindsight Bias and Trust in Government: Evidence from the United States," FSES Working Papers 526, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
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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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    This paper has been announced in the following NEP Reports:

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