Playing to the Algorithm: How Spotify’s Recommendations Shape Music Production
I examine how recommender systems have influenced the music industry and shaped music production over the last decade. 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 $80,000 for songs entering Spotify’s Top 200, with a 26% gross profit margin. 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 8 seconds longer on average and more heterogeneous in length. 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 48%, but reducing consumer welfare by 16%. While recommender systems have reduced overall variety in music, they have enabled additional entry and increased consumer welfare.