Impact of Cultural and Creative Industries on Regional Economic Development in China — A Spatial Econometric Approach
Bibliographic record
Abstract
In the 1990s, the United States and some developed European countries began to focus on developing some emerging industries, such as the cultural and creative industries, which developed effectively with traditional industries and achieved great economic benefits. With the worldwide economic integration, as a developing country, China has gradually realized the importance of emerging industries in the 21st century. Cultural and creative industries have also attracted more attention and achieved rapid development in the past few years. However, cultural and creative industries in China are still in the early stage of development. Industrial investment and related facilities have not yet formed the scale. Besides, relevant industrial policies are constantly changing. Meanwhile, the speed of cultural and creative industries’ development and their impact on the economy vary greatly in different regions of China due to factors like the scale of industry-related talents and the level of scientific research, resulting in uncoordinated development of technical layout and unbalanced economic development. Therefore, it is imperative to study the relation between cultural and creative industries and regional economic development.This paper selects data of 31 provinces from 2003 to 2013 and forms spatial panel data set. Four types of spatial econometric models are utilized to assess the impact of cultural and creative industries on regional economic development in China. Empirical results show that there is a strong spatial autocorrelation among different regions’ economic development in China. The development of cultural and creative industries can effectively promote the development of the regional economy in many aspects. In particular, the economy in the Center and the East is affected more significantly by the development of cultural and creative industries. Cultivation and inflows of cultural and creative talents, expenditure of scientific research, support of government and construction of related facilities are important factors of improving regional economy. For the West, the development of cultural and creative industries has a certain hindrance to the regional economy and some more effective ways should be raised to improve the region’s economy. Finally, according to the empirical result, this paper puts forward corresponding policy implications for different cultural and creative industries and the economy in different regions.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".