Music Scenes to Music Clusters: The Economic Geography of Music in the US, 1970–2000
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Bibliographic record
Abstract
Where do musicians locate, and why do creative industries such as music continue to cluster? This paper analyzes the economic geography of musicians and the recording industry in the US from 1970 to 2000, to shed light on the locational dynamics of music and creative industries more broadly. We examine the role of scale and scope economies in shaping the clustering and concentration of musicians and music industry firms. We argue that these two forces are bringing about a transformation in the geography of both musicians and music industry firms, evidenced in a shift away from regionally clustered, genre-specific music scenes, such as Memphis or Detroit, toward larger regional centers such as New York City and Los Angeles, which offer large markets for music employment and concentrations of other artistic and cultural endeavors that increase demand for musicians. We use population and income to probe for scale effects and look at concentrations of other creative and artistic industries to test for scope effects, while including a range of control variables in our analysis. We use lagged variables to determine whether certain places are consistently more successful at fostering concentrations of musicians and the music industry and to test for path dependency. We find some role for scale and scope effects and that both musicians and the music industry are concentrating in a relatively small number of large regional centers.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it