April 2021

IZA DP No. 14258: Measuring National Life Satisfaction with Music

Emmanouil Benetos, Alessandro Ragano, Daniel Sgroi, Anthony Tuckwell

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

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.