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, Claudia Senik
Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: OECD. OECD statistics working paper ; 2016, 03Publisher: Paris : OECD Publishing, 2016Description: 1 Online-Ressource (circa 38 Seiten) : graph. DarstSubject(s): Genre/Form: DOI: DOI: 10.1787/5jlz9hpg0rd1-enOnline resources: Summary: 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.PPN: PPN: 875236316Package identifier: Produktsigel: ZDB-13-SOC | ZDB-13-SOC-ebookNo physical items for this record
Zusammenfassung in französischer Sprache