Researcher ORCID Identifier


Graduation Year


Date of Submission


Document Type

Open Access Senior Thesis

Degree Name

Bachelor of Arts



Reader 1

Yong Kim

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This paper examines data from Spotify’s Web API for all songs that have been on the Billboard Hot 100 chart from the first chart’s release in August 1958 to May 2021 to determine the relationship between music misery and economic misery. Twelve dependent variables– duration, danceability, energy, key, acousticness, speechiness, mode, loudness, instrumentalness, liveness, valence, and tempo– are used to measure the impact of Arthur Okun’s U.S. economic misery index on each characteristic. Using 12 individual linear regressions– one for each dependent variable– I find that during times of increased economic hardship, consumers are likely to choose to listen to longer, quieter, slower, happier songs that have a minor modality, higher levels of danceability, and lower levels of speechiness, liveness, and acousticness. Consistent with previous research, these results demonstrate how people seek comfort and an escape from a stressful reality when listening to music during uncertain economic times. Additionally, I propose a music misery index that puts a value to the regression results by dividing the statistically significant variables by their regression coefficients. The resulting music misery index has a positive correlation of 0.606 with economic misery, thus demonstrating a strong relationship between consumer preference in popular music and the state of the U.S. economy. Finally, given that 90% of the U.S. population listens to music regularly and that people regulate their emotions by listening to music, this paper argues that music misery can be utilized to estimate a real-time pulse of consumer confidence in the U.S. economy.