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Augmenting business statistics information by combining traditional data with textual data: a composite indicator approach

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  • Camilla Salvatore

    (Utrecht University)

  • Silvia Biffignandi

    (Consultant in Economic Statistics Studies)

  • Annamaria Bianchi

    (University of Bergamo)

Abstract

Combining traditional and digital trace data is an emerging trend in statistics. In this respect, new data sources represent the basis for multi-purpose extraction of different statistical indicators, which contribute to augmenting the statistical information, for feeding smart statistics. The production of business statistics can benefit from the use of unstructured data, especially to study novel aspects which are not covered by traditional data sources. This paper proposes a methodological general framework for augmenting information by combining data, both structured and non structured. The statistical challenges of using unstructured data and their integration with traditional data are discussed. The methodological general framework is applied to the construction of smart composite indicators using social media data and their metadata. An empirical exercise illustrates how to apply the methodology in practice.

Suggested Citation

  • Camilla Salvatore & Silvia Biffignandi & Annamaria Bianchi, 2024. "Augmenting business statistics information by combining traditional data with textual data: a composite indicator approach," METRON, Springer;Sapienza Università di Roma, vol. 82(1), pages 71-91, April.
    • Handle: RePEc:spr:metron:v:82:y:2024:i:1:d:10.1007_s40300-023-00261-4
    DOI: 10.1007/s40300-023-00261-4
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    1. M. Giovanna Ranalli & Jean-François Beaumont & Gaia Bertarelli & Natalie Shlomo, 2024. "Foreword to the special issue on “Survey Methods for Statistical Data Integration and New Data Sources: tools and real data applications for official statistics”," METRON, Springer;Sapienza Università di Roma, vol. 82(1), pages 1-3, April.

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