Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/236289 
Year of Publication: 
2021
Series/Report no.: 
IZA Discussion Papers No. 14258
Publisher: 
Institute of Labor Economics (IZA), Bonn
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.
Subjects: 
historical subjective wellbeing
life satisfaction
music
sound data
language
big data
JEL: 
C8
N3
N4
O1
D6
Document Type: 
Working Paper

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