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

Author

Listed:
  • Yann Algan

    (Département d'économie)

  • Elizabeth Beasley
  • Florian Guyot

    (Département d'économie)

  • Kazuhito Higad

    (Organisation de Coopération et de Développement Économiques (OCDE))

  • Fabrice Murtin

    (Economics department)

  • Claudia Senik

    (Paris-Jourdan Sciences Economiques)

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," Sciences Po publications info:hdl:2441/5k53daedc28, Sciences Po.
  • Handle: RePEc:spo:wpmain:info:hdl:2441/5k53daedc2827oa91tfpuscvbn
    as

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    File URL: https://spire.sciencespo.fr/hdl:/2441/5k53daedc2827oa91tfpuscvbn/resources/2016-algan-big-data-measures-of-well-being.pdf
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    References listed on IDEAS

    as
    1. Nikolaos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 55(2), pages 107-120.
    2. Angus Deaton, 2008. "Income, Health, and Well-Being around the World: Evidence from the Gallup World Poll," Journal of Economic Perspectives, American Economic Association, vol. 22(2), pages 53-72, Spring.
    3. repec:tpr:restat:v:99:y:2017:i:5:p:756-768 is not listed on IDEAS
    4. D'Amuri, Francesco & Marcucci, Juri, 2009. "'Google it!' Forecasting the US unemployment rate with a Google job search index," ISER Working Paper Series 2009-32, Institute for Social and Economic Research.
    5. Andrew E. Clark & Ed Diener & Yannis Georgellis & Richard E. Lucas, 2008. "Lags And Leads in Life Satisfaction: a Test of the Baseline Hypothesis," Economic Journal, Royal Economic Society, vol. 118(529), pages 222-243, June.
    6. John F. Helliwell & Christopher P. Barrington-Leigh & Anthony Harris & Haifang Huang, 2009. "International Evidence on the Social Context of Well-Being," NBER Working Papers 14720, National Bureau of Economic Research, Inc.
    7. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    8. Daniel J. Benjamin & Ori Heffetz & Miles S. Kimball & Nichole Szembrot, 2014. "Beyond Happiness and Satisfaction: Toward Well-Being Indices Based on Stated Preference," American Economic Review, American Economic Association, vol. 104(9), pages 2698-2735, September.
    9. repec:pri:cheawb:deaton_income_health_and_wellbeing_around_the_world_evidence_%20from_gall is not listed on IDEAS
    10. Theo S. Eicher & Chris Papageorgiou & Adrian E. Raftery, 2011. "Default priors and predictive performance in Bayesian model averaging, with application to growth determinants," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(1), pages 30-55, January/F.
    11. Dimitris Mavridis, 2015. "The unhappily unemployed return to work faster," IZA Journal of Labor Economics, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 4(1), pages 1-22, December.
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