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

• Published in: Metron (Rome) Vol. 82; no. 1; pp. 71 - 91
• Main Authors: Salvatore, Camilla, Biffignandi, Silvia, Bianchi, Annamaria
• Format: Journal Article
• Language: English
• Published: Milan Springer Milan 2024; Springer Nature B.V
• Subjects: Data sources; Indicators; Mathematics and Statistics; Statistical Theory and Methods; Statistics; Unstructured data; Twitter; Mazziotta–Pareto index; Socio-economic indicators; Sustainable development; Social media
• ISSN: 0026-1424; 2281-695X
• DOI: 10.1007/s40300-023-00261-4

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.