Playing to the Algorithm: How Spotify’s Recommendations Shape Music Production
Published:
Abstract
I examine how recommender systems have influenced the music industry and shaped music production. Using a structural model of the recorded music industry, I analyze consumer behavior, platform recommendations, and rightsholder release decisions. I estimate a fixed cost of $170,000 for songs that enter Spotify’s Top 200. Counterfactual analysis shows that with randomized recommendations, fewer songs would enter the market, reducing consumer welfare by 4%. The songs that do enter would be 33 seconds longer on average and more heterogeneously long. Popularity-based recommendations that do not account for individual taste would generate a superstar effect—increasing gross profit margins for songs that enter the market to 40%—but reducing consumer welfare by 13%. Although recommender systems have reduced overall variety in music, they have also enabled additional entry and increased consumer welfare.
Presentations
- Asia-Pacific Industrial Organization Conference, December 2025
- Hamilton College, October 2025
- Cornerstone Research, May 2025 (scheduled)
- Indiana University Bloomington, December 2024 (scheduled)
- Southern Economic Association, November 2024
- Microsoft Office of the Chief Economist, November 2024
- DePauw University, November 2024
- Cornerstone Research, May 2024
- University of Virginia Economics Research Colloquium, May 2024
- University of Virginia Quantitative Collaborative, May 2024
- University of Virginia undergraduate lecture on Music and Economics, November 2022 (cancelled)