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Record W2801918912 · doi:10.5430/rwe.v9n1p46

Impact of Cultural and Creative Industries on Regional Economic Development in China — A Spatial Econometric Approach

2018· article· en· W2801918912 on OpenAlex
Maoguo Wu, Qingshu Li

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueResearch in World Economy · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsnot available
Fundersnot available
KeywordsChinaCreative industriesEconomic geographyInvestment (military)Scale (ratio)EconomicsBusinessEconomyEconomic systemEconomic growthRegional sciencePolitical scienceGeography

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.220
GPT teacher head0.412
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it