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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.

The Warwick Economics Research Paper Series (TWERPS) from University of Warwick, Department of Economics

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. JEL codes: N30, Z11, Z13

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-cmp, nep-cul and nep-hap
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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