Big Data Measures of Well-Being : Evidence From a Google Well-Being Index in the United States

Big Data Measures of Well-Being Evidence From a Google Well-Being Index in the United States

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...

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Detalles Bibliográficos
Autor principal: Algan, Yann (-)
Otros Autores: Beasley, Elizabeth, Guyot, Florian, Higa, Kazuhito, Murtin, Fabrice, Senik, Claudia
Formato: Capítulo de libro electrónico
Idioma:Inglés
Publicado: Paris : OECD Publishing 2016.
Colección:OECD Statistics Working Papers, no.2016/03.
Materias:
Employment
Economics
United States
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009704917406719
Descripción
Sumario: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.
Descripción Física:1 online resource (37 p. )

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