Measuring National Happiness with Music

Emmanouil Benetos, Alessandro Ragano, Daniel Sgroi and Anthony Tuckwell
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Emmanouil Benetos: Queen Mary University of London and The Alan Turing Institute
Alessandro Ragano: University College Dublin
Anthony Tuckwell: University of Warwick and ESRC CAGE Centre.

CAGE Online Working Paper Series from Competitive Advantage in the Global Economy (CAGE)

Abstract: We propose a new measure for national happiness based on the emotional content of a country’s most popular songs. Using machine learning to detect the valence of the UK’s chart-topping song of each year since the 1970s, we find that it reliably predicts the leading survey-based measure of life satisfaction. Moreover, we find that music valence is better able to predict life satisfaction than a recently-proposed measure of happiness based on the valence of words in books (Hills et al., 2019). Our results have implications for the role of music in society, and at the same time validate a new use of music as a measure of public sentiment.

Keywords: subjective wellbeing; life satisfaction; national happiness; music information retrieval; machine learning. JEL Classification: N30; Z11; Z13 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-big, nep-cul, nep-hap and nep-his
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Working Paper: Measuring national happiness with music (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:cge:wacage:537

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