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Big Data Measures of Well-Being: Evidence from a Google Well-Being Index in the US

Author

Listed:
  • Yann Algan

    (ECON - Département d'économie (Sciences Po) - Sciences Po - Sciences Po - CNRS - Centre National de la Recherche Scientifique, CEPREMAP - Centre pour la recherche économique et ses applications - ECO ENS-PSL - Département d'économie de l'ENS-PSL - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres)

  • Elizabeth Beasley

    (CEPREMAP - Centre pour la recherche économique et ses applications - ECO ENS-PSL - Département d'économie de l'ENS-PSL - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres)

  • Florian Guyot

    (ECON - Département d'économie (Sciences Po) - Sciences Po - Sciences Po - CNRS - Centre National de la Recherche Scientifique)

  • Kazuhito Higad

    (OCDE - Organisation de Coopération et de Développement Economiques = Organisation for Economic Co-operation and Development)

  • Fabrice Murtin

    (Economics department - MIT - Massachusetts Institute of Technology)

  • Claudia Senik

    (PSE - Paris-Jourdan Sciences Economiques - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique, UP4 - Université Paris-Sorbonne, PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

Abstract
We build an indicator of individual wellbeing in the United States based on Google Trends. The indicator is a combination of keyword groups that are endogenously identified to fit with weekly timeseries of subjective wellbeing measures collected by Gallup Analytics surveys. We show that such information from Big Data can be used to build a model that accurately forecasts survey-based measures of subjective well-being. The model successfully predicts the out-of-sample evolution of most subjective well-being measures at a one-year horizon. This opens up the possibility to use Big Data as a complement to traditional survey data to measure and analyze the well-being of population at high frequency and very local geographic level. We show that we can also exploit the internet search volume to elicit the main life dimensions related to well-being. 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 in the United States. This paper contributes to the new research agenda on data sciences by showing how Big Data can improve our understanding of the foundations of human well-being.

Suggested Citation

  • Yann Algan & Elizabeth Beasley & Florian Guyot & Kazuhito Higad & Fabrice Murtin & Claudia Senik, 2015. "Big Data Measures of Well-Being: Evidence from a Google Well-Being Index in the US," Working Papers hal-03429943, HAL.
    • Handle: RePEc:hal:wpaper:hal-03429943
    Note: View the original document on HAL open archive server: https://sciencespo.hal.science/hal-03429943
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    References listed on IDEAS

    as
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    1. Rossouw, Stephanie & Greyling, Talita, 2020. "Big Data and Happiness," GLO Discussion Paper Series 634, Global Labor Organization (GLO).

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