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Statistical framework for fully register based population counts

Author

Listed:
  • Fabrizio Solari

    (Istat)

  • Antonella Bernardini

    (Istat)

  • Nicoletta Cibella

    (Istat)

Abstract

The increasing availability of registers or administrative archives has been a strong push towards moving from traditional censuses to combined censuses or even completely register based censuses. In this context, a statistical framework needs to be designed in order to delineate all the statistical issues of the new estimation process. To this aim, a population frame needs to be defined for both surveying and estimation phases. Sampling surveys should be designed for quality assessment and for improving the quality of the register based estimation process. Drawing on similar experiences, a formalisation of the population size estimation process fully based on administrative data is presented. An application to Italian estimation process is reported.

Suggested Citation

  • Fabrizio Solari & Antonella Bernardini & Nicoletta Cibella, 2023. "Statistical framework for fully register based population counts," METRON, Springer;Sapienza Università di Roma, vol. 81(1), pages 109-129, April.
    • Handle: RePEc:spr:metron:v:81:y:2023:i:1:d:10.1007_s40300-023-00244-5
    DOI: 10.1007/s40300-023-00244-5
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    References listed on IDEAS

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    1. Li‐Chun Zhang, 2021. "Proxy expenditure weights for Consumer Price Index: Audit sampling inference for big‐data statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 571-588, April.
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    Cited by:

    1. Antonella Bernardini & Angela Chieppa & Tiziana Tamburrano, 2024. "Discovering individual profiles from administrative signs of life useful for the estimation of Census results," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 78(1), pages 15-24, January-M.
    2. M. Giovanna Ranalli & Jean-François Beaumont & Gaia Bertarelli & Natalie Shlomo, 2023. "Foreword to the special issue on “Survey Methods for Statistical Data Integration and New Data Sources”," METRON, Springer;Sapienza Università di Roma, vol. 81(1), pages 1-3, April.

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