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dc.contributor.authorRossouw, Sen_NZ
dc.contributor.authorGreyling, Ten_NZ
dc.date.accessioned2020-09-08T04:08:07Z
dc.date.available2020-09-08T04:08:07Z
dc.date.copyright2020-09-02en_NZ
dc.identifier.citationGLO Discussion Paper, No. 634, Global Labor Organization (GLO), Essen. Retrieved from: http://hdl.handle.net/10419/223012
dc.identifier.urihttp://hdl.handle.net/10292/13644
dc.description.abstractThe pursuit of happiness. What does that mean? Perhaps a more prominent question to ask is, 'how does one know whether people have succeeded in their pursuit'? Survey data, thus far, has served us well in determining where people see themselves on their journey. However, in an everchanging world, one needs high-frequency data instead of data released with significant time-lags. High-frequency data, which stems from Big Data, allows policymakers access to virtually real-time information that can assist in effective decision-making to increase the quality of life for all. Additionally, Big Data collected from, for example, social media platforms give researchers unprecedented insight into human behaviour, allowing significant future predictive powers.
dc.publisherGlobal Labor Organizationen_NZ
dc.relation.urihttps://www.econstor.eu/handle/10419/223012en_NZ
dc.rightsEconStor supports Green Open Access. All papers on EconStor are made freely available without restrictions or embargo periods. Publication on EconStor is based on usage agreements with authors or the editors/publishers of a series or journal. Authors' copyrights are safeguarded. Publication on EconStor does not inhibit further publication of the documents in journals or on other document servers. Disseminating publications with EconStor is free for publishers and authors.
dc.subjectHappiness; Big Data; Sentiment analysis
dc.titleBig Data and Happinessen_NZ
dc.rights.accessrightsOpenAccessen_NZ
aut.relation.softwareversion634en_NZ
pubs.elements-id390733


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