The evolution of ticket pricing strategies in the North American concert industry: evidence from two decades of data
Bibliographic record
Abstract
This article examines changes in the ticket pricing strategies employed by popular musicians in the concert industry, with a particular focus on the use of second-degree price discrimination and its relationship with key concert outcomes; ticket revenue and capacity utilization. By analysing a large, longitudinal dataset of concerts performed by popular musicians between 1999 and 2019 in the United States and Canada, this article documents how the use of price discrimination, as well as the relationship between price discrimination and concert outcomes, has evolved over time. Additionally, this article reveals how the relationship between discriminatory pricing and concert outcomes depends on musician popularity and the intensity of price discrimination as measured by the difference between the highest and lowest ticket prices. In turn, this article provides some suggestive evidence as to how musicians may be able to improve their pricing strategies, and reveals the potential impacts that the use of dynamic pricing has had on equilibrium in the concert sector.
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How this classification was reachedexpand
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".