The impact of macroeconomic factors on the creative economy: a comparative analysis of European countries and Asian countries
Bibliographic record
Abstract
Purpose This study investigates the impact of key macroeconomic indicators – innovation, GDP per capita, trade openness, R&D expenditure, IP law and labor force education – on the development of the creative exports in Asian and European countries from 2010 to 2022 and compares how these factors influence each region differently. Design/methodology/approach Using fixed effects model (FEM) and two-step difference generalized method of moments applied to 650 observations across selected Asian and European countries; the study evaluates the relationship between macroeconomic indicators and creative exports. Findings GDP per capita consistently drives creative exports in both regions, while labor skills and trade openness matter mainly in Europe. R&D shows no robust effect in either regions, and intellectual property protection is significant only in Asia. Originality/value The research offers comparative insights into the macroeconomic dynamics shaping creative industries in different regions, contributing to academic understanding and providing targeted policy recommendations for sustainable development in the creative economy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".