For the best experience, view the interactive visualization on a desktop rather than a mobile device.
Authors: Jūra Liaukonytė (Cornell), Daniel Winkler (WU), Nils Wlömert (WU)
The interactive visualization displays the following two metrics for the top 1,000 musical artists in the U.S.:
- X-axis: Political Leaning Index measures the relationship between listening patterns across geographic regions and the voting patterns of those regions in the 2020 Presidential election. A larger absolute value of the index (further from zero) indicates a more uneven distribution of the artist’s popularity across politically divergent regions. See below for details on the methodology.
- Y-axis: Artist’s popularity in 2024, shown on a logarithmic scale, with a higher position indicating greater popularity. See below for details on the methodology.
The interactive graph allows you to:
- Hover over each dot to identify the artist.
- Click on any artist or search for an artist’s name to view how their position has changed over the last 7 years (2018-2024), with the other artists’ 2024 positions grayed out.
- Select a music genre to see the distribution of artists within that genre for the year 2024.
Disclaimer and Limitations: This index reflects the correlation between consumers’ listening patterns across geographic regions and the political voting trends in those regions, not the political affiliations of the listeners. For example, if an artist is especially popular in the South, their popularity will appear in the Republican-leaning section of the graph, even if many of the listeners in those areas may be Democrats. Additionally, the index does not represent the political views of the musical artists themselves.
Acknowledgements: Data comes from Luminate, the industry-standard platform for tracking music consumption. The authors thank Tomas Keršulis for excellent research assistance in developing the interactive tool.
Methodology:
The most granular geographic regions in our data are Designated Market Areas (DMAs). DMAs are an industry-standard for defining regions based on local TV and radio coverage, grouping together areas that typically share the same media markets.
The political leaning index is based on methodology adapted from Liaukonyte, Tuchman and Zhu (2023) and the accompanying research note.
We first selected the top 1000 artists by streaming volume in the U.S. from 2018 to 2024. For each year, we calculated the “market share” for each artist, defined as their number of streams divided by the total streams in the observed DMAs for that year. This provides a measure of artists’ popularity, which is plotted on the Y-axis using a logarithmic scale.
To determine the political leaning of each geographic region (DMA), we used 2020 election data. For each DMA, we mapped the corresponding counties using a crosswalk file and calculated the total votes for Trump and Biden in the 2020 presidential election. We then computed the vote share difference for each DMA: the percentage voting for Trump minus the percentage voting for Biden. For instance, the Seattle-Tacoma, WA DMA has a vote share difference of -0.30, indicating a strongly Democratic-leaning region, while the Oklahoma City, OK DMA shows a vote share difference of 0.19, reflecting a strong Republican leaning.
The Political Leaning Index of Listener Regions (plotted on the X-axis) links each artist’s “market share” in a given DMA with the political leaning of that DMA. An index value near zero suggests the artist is equally popular in both Democratic and Republican regions. A negative value indicates higher popularity in Democratic-leaning areas, while a positive value reflects greater popularity in Republican-leaning areas. The larger the absolute value of the index (the further it is from zero), the more unevenly distributed the artist’s popularity is across politically distinct regions.
Relevant research:
- Winkler, D., Wlömert, N., and Liaukonytė, J. 2024. Separating the Artist from the Art: Social Media Boycotts, Platform Sanctions, and Music Consumption. Working paper. Available at SSRN 4929261.
- Liaukonytė, J., Tuchman, A. and Zhu, X., 2023. Frontiers: Spilling the Beans on Political Consumerism: Do Social Media Boycotts and Buycotts Translate to Real Sales Impact?. Marketing Science, 42(1), pp.11-25.
- Liaukonytė, J., Tuchman, A. and Zhu, X., 2024. Political Polarization Indices of the Top CPG Brands: Research Note. Available at SSRN 4766899.
- Liaukonytė, J., Tuchman, A. and Zhu, X., 2024. Lessons from the Bud Light Boycott, One Year Later. Harvard Business Review.