What distinguishes creative industry exporters? And does engaging in innovation, R&D and design matter?
Bibliographic record
Abstract
Post-Brexit, the UK government is keen to accelerate UK exporting, including in creative goods and services. To best support and expand exporting, it is vital to better understand the characteristics that distinguish businesses that export from those that don’t. It is also valuable to recognise businesses which have a greater potential for exporting. Such knowledge can be used to better design policy, target support and benefit the economy. This paper identifies the characteristics that distinguish exporters from non-exporters among Creative Industries Organisations (CIOs). It also identifies the characteristics of high-intensity exporters, defined as those that earned at least a quarter – and typically half – of their sales income from exports. It does so by examining the responses from 625 CIOs, to the DCMS’s 2020 survey on new product and service development activities. The paper finds that even very small CIOs export, including at high intensity. Tradability of outputs is found to matter most, as does productivity and innovation. Importantly, the paper notes exporting need not involve the development of ‘new to the market innovations’, and while creative Industry exporters tend to invest in R&D and/or design, the amounts invested are usually modest. Engaging in both R&D and design is more strongly associated with exporting than engaging in R&D alone. In the conclusion the paper sets out how policymakers might further support creative industry exporting
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".