Big Data Measures of Well-Being: Evidence from a Google Well-Being Index in the United States
Yann Algan (),
Fabrice Murtin () and
Claudia Senik ()
No 1605, CEPREMAP Working Papers (Docweb) from CEPREMAP
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
New Economics Papers: this item is included in nep-big, nep-edu, nep-hap and nep-ltv
References: Add references at CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
Working Paper: Big Data Measures of Well-Being: Evidence From a Google Well-Being Index in the United States (2016)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:cpm:docweb:1605
Access Statistics for this paper
More papers in CEPREMAP Working Papers (Docweb) from CEPREMAP Contact information at EDIRC.
Bibliographic data for series maintained by Mathieu Perona ().