What’s Going On? Digitization and Global Music Trade Patterns since 2006
Bibliographic record
Abstract
The objective of this paper is to document the evolution of cross-border music trade patterns in this transition period and to explain what drives digital music trade patterns. The shift from analogue to digital music distribution has substantially reduced trade costs and has enlarged the choice sets of music consumers around the world. Using comprehensive data on digital track sales in the US, Canada, and 16 European countries, 2006-2011, we document patterns of music trade in the digital era and contrast it with what’s known from elsewhere about trade in popular music for the past half century. While home bias in music consumption among the top 100 songs had grown in the pre-digital distribution period prior to 2006, home bias has declined since then. We find that the share of imported songs in music consumption has grown in all countries except in the US. Moreover, although the number of European songs available has risen faster than the number of US songs, the market share of the US in digital music sales has increased while the market shares of European repertoires have fallen. US repertoire holds the largest market share in almost every country. Home bias is lower in the long tail than at the top end of the distribution. We consider four candidate explanations for the shift away from domestic music: a) that growth in availability of particular repertoires explains their growth in total sales and market shares, b) that changes in the effect of distance-related trade costs on trade made possible by digitization explain changed patterns of trade, c) that changed preferences toward particular origin repertoires explains changed patterns, and d) that recent vintages of particular repertoires have grown more appealing to world consumers. We conclude that a combination of c) and d) offers the most credible explanation for the observed patterns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".