Big Data Measures of Well-Being: Evidence From a Google Well-Being Index in the United States

Yann Algan, Elizabeth Beasley, Florian Guyot, Kazuhito Higa, Fabrice Murtin and Claudia Senik
Additional contact information
Elizabeth Beasley: CEPREMAP
Florian Guyot: Sciences Po, Paris
Kazuhito Higa: Kyushu University
Claudia Senik: Paris School of Economics

No 2016/3, OECD Statistics Working Papers from OECD Publishing

Abstract: We build an indicator of individual subjective well-being in the United States based on Google Trends. The indicator is a combination of keyword groups that are endogenously identified to fit with the weekly time-series of subjective well-being measures disseminated by Gallup Analytics. We find that keywords associated with job search, financial security, family life and leisure are the strongest predictors of the variations in subjective well-being. The model successfully predicts the out-of-sample evolution of most subjective well-being measures at a one-year horizon. Un indicateur de bien-être subjectif est construit pour les États-Unis sur la base des données de Google Trends. L’indicateur est une combinaison de mots-clés qui sont identifiés pour reproduire les séries hebdomadaires de bien-être subjectif de Gallup Analytics. Nous trouvons que les mots-clés associés à la recherche d’emploi, à la sécurité financière, à la vie de famille et aux loisirs sont les plus forts prédicteurs des variations du bien-être subjectif. Le modèle prévoit l’évolution hors échantillon de la plupart des mesures de bien-être à l’horizon d’un an.

Date: 2016-05-13
New Economics Papers: this item is included in nep-ltv
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Working Paper: Big Data Measures of Well-Being: Evidence from a Google Well-Being Index in the United States (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:oec:stdaaa:2016/3-en

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