Measuring National Life Satisfaction with Music

Emmanouil Benetos, Alessandro Ragano, Daniel Sgroi and Anthony Tuckwell ()
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Emmanouil Benetos: Queen Mary, University of London
Alessandro Ragano: The Alan Turing Institute
Anthony Tuckwell: University of Warwick

No 14258, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: National life satisfaction is an important way to measure societal well-being and since 2011 has been used to judge the effectiveness of government policy across the world. However, there is a paucity of historical data making limiting long-run comparisons with other data. We construct a new measure based on the emotional content of music. We first trained a machine learning model using 191 different audio features embedded within music and use this model to construct a long-run Music Valence Index derived from chart-topping songs. This index correlates strongly and significantly with survey-based life satisfaction and outperforms an equivalent text-based measure. Our results have implications for the role of music in society, and validate a new use of music as a long-run measure of public sentiment.

Keywords: historical subjective wellbeing; life satisfaction; music; sound data; language; big data (search for similar items in EconPapers)
JEL-codes: C8 D6 N3 N4 O1 (search for similar items in EconPapers)
Pages: 16 pages
Date: 2021-04
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cul and nep-hap
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Published - published as 'Measuring national mood with music: using machine learning to construct a measure of national valence from audio data' in: Behavior Research Methods, 2022, 54, 3085–3092

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