Going beyond GDP to nowcast Well-Being using retail market data

Riccardo Guidotti, Michele Coscia, Dino Pedreschi, Diego Pennacchioli

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Résumé

One of the most used measures of the economic health of a nation is the Gross Domestic Product (GDP): the market value of all officially recognized final goods and services produced within a country in a given period of time. GDP, prosperity and well-being of the citizens of a country have been shown to be highly correlated. However, GDP is an imperfect measure in many respects. GDP usually takes a lot of time to be estimated and arguably the well-being of the people is not quantifiable simply by the market value of the products available to them. In this paper we use a quantification of the average sophistication of satisfied needs of a population as an alternative to GDP. We show that this quantification can be calculated more easily than GDP and it is a very promising predictor of the GDP value, anticipating its estimation by six months. The measure is arguably a more multifaceted evaluation of the well-being of the population, as it tells us more about how people are satisfying their needs. Our study is based on a large dataset of retail micro transactions happening across the Italian territory.

langue originaleAnglais
titreAdvances in Network Science - 12th International Conference and School, NetSci-X 2016, Proceedings
EditeurSpringer Verlag
Pages29-42
Nombre de pages14
Volume9564
ISBN (imprimé)9783319283609
Les DOIs
Etat de la publicationPublié - 2016
Evénement12th International Conference and School on Advances in Network Science, NetSci-X 2016 - Wroclaw, Pologne
Durée: 11 janv. 201613 janv. 2016

Série de publications

NomLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9564
ISSN (imprimé)03029743
ISSN (Electronique)16113349

Une conférence

Une conférence12th International Conference and School on Advances in Network Science, NetSci-X 2016
Pays/TerritoirePologne
La villeWroclaw
période11/01/1613/01/16

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