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 ()

No 1605, CEPREMAP Working Papers (Docweb) from CEPREMAP

Abstract: We build an indicator of individual 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 weekly time-series of subjective wellbeing measures collected 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 wellbeing. The model successfully predicts the out-of-sample evolution of most subjective wellbeing measures at a one-year horizon.

Keywords: Subjective Well-Being; Big Data; Bayesian Statistics (search for similar items in EconPapers)
Pages: 36 pages
Date: 2016-06
New Economics Papers: this item is included in nep-big, nep-edu, nep-hap and nep-ltv
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Citations: View citations in EconPapers (4)

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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:cpm:docweb:1605

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