Quantifying the Competitiveness of Cultural Industry and Its Impacts on Chinese Economic Growth

Abstract

One potential way to promote China’s economic growth is to develop a cultural industry and enhance its competitiveness. To confirm this hypothesis, this study first utilizes the five-element diamond model, principal component analysis, and factor analysis to evaluate the competitiveness of the cultural industry in the 31 Chinese provinces during the period 2013–2019. The results reveal that the competitiveness of cultural industry in the eastern region is the strongest, followed in descending order by the central, northeastern, and western regions of China. Then, the panel regression is employed to explore the impact of the cultural industry’s competitiveness index on economic growth. The results indicate that the cultural industry’s competitiveness is positively associated with China’s economic growth. We also conduct another panel regression analysis by examining the impact of cultural industry factors on China’s economic growth to gain insight into the influence of the cultural industry components on growth. In this analysis, our results indicate that cultural industry factors, including fixed asset investment and labor, significantly play an important role in Chinese growth. This study also finds that total patent applications, the total profit of cultural enterprises, and government expenditure positively impact economic growth, but the evidence is weak. Thus, these three variables could be considered potential future driver factors. The empirical findings offer insights into strategies that the national government could implement to strengthen the cultural industry’s competitiveness as China’s new powerful driver of economic development. Compared with previous empirical studies, this research deepens the competitive cultural analysis, increases the number of observations, and lengthens the period studied.

1. Introduction

Under the integration of culture and science and technology backgrounds in the digital era, the cultural and creative industries foster income generation, exports, and employment, and have become major drivers of the economic prosperity of many countries worldwide [1]. The United Nations [2] considers the creative economy as “a significant sector and a meaningful contributor to national gross domestic product.” In 2019, China’s culture and related industries generated an added value of 4501.6 billion-yuan, accounting for 4.54% of GDP, which indicates that the cultural industry, as an emerging industry with broad prospects, is a new driving force for economic growth. The fifth plenary session of the 17th Central Committee of the Communist Party of China proposed promoting the cultural industry to become a pillar industry of the national economy. Furthermore, in the report of the Nineteenth National Congress of the Communist Party of China, General Secretary Xi Jinping emphasized that it is necessary to advance the development of the cultural industry, improve the cultural management system, and build a mechanism that integrates social and economic benefits. The cultural industry creates the possibility for optimizing industrial structure, reconstructing regional economic patterns, and accelerating the transformation of the economic development mode.

The achievements of China’s cultural industry in recent years are mainly reflected in the diverse cultural goods, cultural serviceability, reformation of the cultural system, internal cultural exploration, and external cultural communication. Li [3] pointed out that the cultural and creative industries will provide a new path for the sustainable and healthy development of the economy and society, and get the economic transformation from “Made in China” to “Created in China” realized. The latest 14th five-year plan expounded the tasks of developing advanced socialist culture and enhancing the country’s cultural soft power. The strengthening of China’s cultural soft power will dedicate to the Two Centenary Goals and the Chinese Dream of the great rejuvenation of the Chinese nation [4]. The core issue of the development of the cultural industry is to improve its competitiveness [5]. The competitiveness of the cultural industry is an essential part of a country’s international competitiveness, which refers to the ability of a country or a regional cultural enterprise’s products and services to develop and occupy the market in world and obtain profits. The cultural and creative industries demonstrate a country’s economic growth and have become a critical strategy for enhancing the core competitiveness of the national economies [6]. Therefore, a comprehensive understanding of the interaction between the cultural industry’s competitiveness and economic growth is crucial. The present study aims to identify the competitiveness of the cultural industry and its impact on economic growth by using a combination of quantitative and qualitative methods. Based on Porter’s five-factor diamond model, this paper constructs an evaluation index system of the cultural industry and uses principal component analysis and factor analysis to evaluate the competitiveness of the cultural industry in 31 provinces of China from 2013 to 2019. Subsequently, the panel regression models are adopted to analyze the influence of the competitiveness index and relevant factors on economic growth.

Domestic and international experts and scholars have obtained some good research outcomes on the issues of the cultural industry’s competitiveness and the relationship between the cultural industry and economic growth [7,8,9,10]. They advocated some indexes to evaluate the competitiveness of the cultural industry and analyze its characteristics. However, there is a certain subjectivity, one-sidedness, and superficiality in the selection of indicators for evaluating the competitiveness of the cultural industry. Moreover, there is little data to support the conclusion that the cultural industry’s competitiveness positively affects economic growth. This paper selects the evaluation indicators of the cultural industry’s competitiveness with a systematic, dynamic, and operable principle. By quantifying the qualitative data to obtain the cultural industry’s competitiveness index and putting it into regression analysis, the relationship between the cultural industry’s competitiveness and economic growth can be fully explained and understood. This analysis can be used by decision-makers in crafting policies around the cultural industry to give full play to its leading role in the overall economic environment and coordinate the development of various regions in China.

2. Literature Review

In recent years, research on the development of the cultural industry has mainly focused on policy and management, the influence of new technologies, workforce size, and the competitiveness of the cultural industry from the perspective of countries, regions, and enterprises. Many studies have shown that in Asian countries, such as Singapore, Japan, the Republic of Korea, and China, the policies on the cultural industry and creative industry are key to their national development and attach importance to the role of national cultural characteristics and the creativity of cultural production [11,12,13,14]. The specialization of the cultural industry is inseparable from the support of technology and talents. For example, Bujor and Avsilcai [15] explored the contribution of technology to the creative industries at the European Union level and pointed out that creative tech (IT and Software and Computer Services) can continually help them to develop and to cope with market demands. Nathan et al. [16] compared creative economies in the United States of America and the United Kingdom by using the creative trident framework and found that the UK creative economy is larger in workforce shares, while the US creative workers are more evenly dispersed across all industries. Moreover, the cultural industry’s competitiveness can affect various sectors of the economy and create shared-value benefits is no longer debatable. Michael Porter’s diamond model is the most widely used among the evaluation models of the cultural industry’s competitiveness. Harabi [17] used Porter’s diamond model to explain the economic performance of four creative industries (book publishing, music sound recording, film production, and software industries) in five Arab countries (Morocco, Tunisia, Egypt, Jordan, and Lebanon). Chen et al. [18] applied the six-element diamond model to study the competitiveness of the cultural and creative industries in China and put forward suggestive thinking on improving the competitiveness of the 18 provinces and cities along the “Belt and Road”. The competitiveness of the sub-industries of the cultural industry also attracts the attention of scholars. For instance, Einarsson [19] described the competitive position of the music industry as music being a part of the cultural and creative industries in Iceland by adopting Porter’s five forces model.

The cultural industry is expanding and reforming alongside the digital economy, which is more flexible with the downward economic pressures. Based on the understanding of the cultural industry’s commercial and ideological value, scholars and researchers have achieved certain results on the relationship between it and economic growth. Montalto, et al. [20] presented an econometric model that revealed the positive impact of the cultural and creative industries on the GDP per inhabitant, and the share of cultural employment in total employment on cultural consumption expenditure. Piergiovanni et al. [21] explored the impact of a series of factors, including creativity, intellectual property rights activities, new business formation, and the provision of amenities, on economic growth for 103 Italian provinces. Findings showed an increase in the number of firms active in the creative industries, net entry, a greater provision of leisure amenities, and the share of legal immigrants have a positive effect on regional economic growth. Daubaraitė and Startienė [22] discussed the impact of creative industries on the Lithuanian national economy by creating jobs, contributing to GDP, and enlarging exports. Correa-Quezada et al. [23] evaluated the impact of employment in creative industries on the regional and national economic growth of Ecuador. The main findings showed a significant influence of creative employment on regional production and development. In addition, domestic scholars not only further verified the promotion of economic growth by the cultural industry but also underlined the importance of investment scale, human capital, and innovation. Lu [24] found a significant positive correlation between China’s cultural industry investment and economic growth by using cointegration analysis, error correction model, Granger causality test, and vector autoregression (VAR) model. Wang and Gu [25] stated that the development of the cultural industry has significantly promoted the economic growth of the Yangtze River Delta region, and the stock of human capital and the scale of capital inflow will affect the growth effect of the cultural industry. Li and Liu [26] noticed that the cultural industry has a relatively significant impact on economic growth, especially the driving role of the tertiary industry, and emphasized the significance of cultural industry innovation to economic growth. The research on the relationship between cultural industry and economic growth has gradually attracted the attention of scholars. However, the competitiveness of a country or an industry is a complex concept that cannot be measured directly and requires extensive efforts for data collection [27,28]. As far as we are aware, there has not been a study that relates competitiveness of cultural industry and economic growth for China. Furthermore, the competitiveness of cultural industry has not been established yet, thus, this study aims to fill in this gap by building cultural industry competitiveness index (CI) using principal component analysis and factor analysis. Then, CI and other control variables are included in the panel regression analysis, which provides a more comprehensive and accurate method than using only a few indicators representing the cultural industry for investigating the impact of the cultural industry as a whole on economic growth. Moreover, the relevant factors of cultural industry are included in the second round of the panel regression analysis, which will benefit grasping the role of various factors in the economies and point out the further direction of cultural industry development and industrial structure adjustment.

3. Data

The data are retrieved from the National Bureau of Statistics of China [29], CNKI database [30], China statistical yearbooks of culture and related industries [31], China statistical yearbooks of cultural relics and tourism [32], China statistical yearbooks of the tertiary industry [33], China statistical yearbooks of science and technology [34], and provincial statistical yearbooks [35].

The research sample consists of China’s 31 provinces: Beijing, Tianjin, Hebei, Shanxi, Inner Mongolia, Liaoning, Jilin, Heilongjiang, Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, Shandong, Henan, Hubei, Hunan, Guangdong, Guangxi, Hainan, Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang. The period of the analysis covers 7 years, from 2013 to 2019. According to Porter’s five-element diamond model, the evaluation framework of China’s cultural industry’s competitiveness is constructed.

Table 1. China’s cultural and related industry competitiveness evaluation index.

First Level Indicator	Second Level Indicator	Third Level Indicator	Unit	Source
Factor Conditions	Capital element	X1: Fixed Asset Investment in Culture, Sports and Entertainment Industry	100 million yuan	The tertiary industry
	Labor element	X2: Labor in Cultural Enterprises	person	Culture and related industries
	Cultural elements	X3: Number of Collections in Museums	piece/set	Culture and related industries
		X4: Total Collections in Public Libraries	10,000 copies	Culture and related industries
Demand Conditions	Industrial structure	X5: Proportion of Tertiary Industry in Gross Regional Product	%	Culture and related industries
	Consumer behavior	X6: Per Capita Consumption and Expenditure on Culture and Recreation of Nationwide Households	yuan	Culture and related industries
Firm Strategy, Structure and Rivalry	Operations and management	X7: Number of Cultural Enterprises	number	Culture and related industries
		X8: Users of Cable Radios and TVs	10,000 households	Culture and related industries
		X9: Total Assets of Cultural Market Operating Institutions	1000 yuan	Cultural relics and tourism
	Innovation capacity	X10: Total Patent Applications Granted on Culture and Related Industries	piece	Culture and related industries
	Profitability	X11: Total Revenue of Cultural Enterprises	10,000 yuan	Culture and related industries
		X12: Total Profit of Cultural Enterprises	10,000 yuan	Culture and related industries
Government Action, Opportunity and Advantage	Fiscal expenditure	X13: Expenditure for Culture, Sport and Media of Regional Government Revenue	100 million yuan	Culture and related industries
Related and Supporting Industries	Tourism	X14: Earnings from International Tourism	USD Million	Culture and related industries
	Information service industry	X15: Number of Regional Internet Broadband Users	10,000 households	The tertiary industry

Notes: X₂, X₁₁, and X₁₂ represent the sum of data related to cultural industrial enterprises, cultural wholesale and retail trades enterprises, and cultural services enterprises above the designated size, respectively. The data sources are the China statistical yearbooks, and the names are listed in the table.

shows the index system, which contains 5 first-level indicators, 11 second-level indicators, and 15 third-level indicators.

Furthermore, the data in panel regression analysis consist of gross domestic product (GDP) growth rate, competitiveness index of cultural industry, total investment in fixed assets, number of employed persons, and the R&D expenditure input intensity. The list of variables used in this study is displayed in

Table 2. The list of panel regression variables.

Variables	Description	Unit	Source
GDP	The year-over-year change in a region’s economic output	%	CNKI
CI	The competitiveness level of cultural industry in various regions	number	own calculation
Inv	The purchase of newly produced fixed capital	100 million yuan	National Bureau of Statistics of China
EP	The number of people engaged in productive activities in an economy	10,000 persons	China provincial statistical yearbooks
RDE	R&D expenditure as a percentage of gross domestic product	%	China statistical yearbooks of science and technology

.

4. Research Methodology

4.1. The Competitiveness Index Calculation Method

4.1.1. Porter’s Diamond Model

Porter [36] proposed the diamond model as an analytical framework for building and enhancing the competitive advantage in particular industries. Porter suggested that factor conditions, demand conditions, related and supporting industries, and firm strategy, structure, and rivalry as a system constitute diamonds of national advantage. Moreover, the role of the government and chance also have exogenous influences on the playing field that each nation establishes and operates for its industries (Figure 1). Under the combined action of these determinants, the development of the industries’ competitiveness can be effectively promoted. According to Porter’s diamond model, the cultural industry competitiveness evaluation system is constructed, and principal component analysis and factor analysis are used to calculate the cultural industry competitiveness index (CI) in this study.

4.1.2. Principal Component Analysis

Principal component analysis is a common statistical method of dimensionality reduction, which transforms numerous variables into several new comprehensive variables and minimizes the loss of information at the same time. Generally, these new variables result from linear weighted combinations of the original variables, known as principal components. The principal components are not related to each other and account for much of the variance among the initial variables. The principal components are ordered according to the amount of variation in the original variables they explain. Therefore, the variance of the first principal component is the largest and contains the most information. The variance of the second principal component is less than that of the first, and the covariance between the second principal component and the first principal component is 0, which means that these two components are completely uncorrelated. Subsequent components can also be constructed in this way. This paper uses principal component analysis to reduce the dimension of the data space studied, and principal components are extracted for subsequent CI calculation.

4.1.3. Factor Analysis

Factor analysis is a statistical technique used to reduce the number of variables and to identify underlying representative factors, which can detect structure in relationships between relevant variables [37]. There are four basic steps for calculating the CI: assessing the suitability of the data used, determining factor extraction, rotational method, and constructing factor scores and the CI. Firstly, the Kaiser–Meyer–Olkin (KMO) Measure of Sampling Adequacy and Bartlett’s Test of Sphericity are common methods to detect whether the data are suitable for factor analysis. A KMO index close to 1.0 is considered ideal, while an index below 0.5 is not acceptable. The p-value of Bartlett’s Test of Sphericity that corresponds to the test statistic is less than some significance level (like = 0.05), which indicates that the data are suitable for factor analysis. Secondly, the method of principal component analysis is used to calculate the cumulative percentage of variance and factor load. According to the criteria of cumulative percentage of variance >85% and the characteristic root >1, factor extraction is determined. Thirdly, the factor rotation method is used to produce more interpretable and concise factors, which provides a solution for the naming and meaning of factors. The varimax rotation maximizes the sum of the variances of the factor-squared loadings and produces uncorrelated factor structures [38]. Finally, the factor score coefficient matrix is obtained by regression algorithm, which is used to calculate the score of each factor, and the CI is obtained by taking the variance contribution rate of each factor after the rotation as the weight [18]. The factor score equation can be presented as:

$${\mathrm{FS}}_{\mathit{kt}}={{\displaystyle \sum }}_{p=1}^n{\mathrm{a}}_{\mathit{pt}}{\mathrm{x}}_{\mathit{pt}}$$

(1)

where ${\mathrm{FS}}_{\mathit{kt}}$ is the kth factor score in year t, ${\mathrm{a}}_{\mathit{pt}}$ is the factor score coefficient in year t, ${\mathrm{x}}_{\mathit{pt}}$ is the pth initial variable in year t, and n is the number of initial variables.

The equation of the CI is presented as:

$${\mathrm{CI}}_{\mathit{it}}=\sum {\mathrm{b}}_{\mathit{kt}}{\mathrm{FS}}_{\mathit{kt}}$$

(2)

where ${\mathrm{CI}}_{\mathit{it}}$ is the competitiveness index of province i in year t, ${\mathrm{b}}_{\mathit{kt}}$ is the contribution rate of the variance of the kth factor in year t, and ${\mathrm{FS}}_{\mathit{kt}}$ is the score of the kth factor in year t.

4.2. Panel Regression Models

This paper investigates the impact of the cultural industry’s competitiveness and relevant factors on China’s economic growth. The following two panel regression models are employed:

Model 1:

$${\mathrm{lnGDP}}_{\mathit{it}}{=\mathsf{\theta }}_0{+\mathsf{\theta }}_1{\mathrm{lnCI}}_{\mathit{it}}{+\mathsf{\theta }}_2{\mathrm{lnInv}}_{\mathit{it}}{+\mathsf{\theta }}_3{\mathrm{lnEP}}_{\mathit{it}}{+\mathsf{\theta }}_4{\mathrm{lnRDE}}_{\mathit{it}}{+\mathsf{\epsilon }}_{\mathit{it}}$$

(3)

where ${\mathrm{GDP}}_{\mathit{it}}$ is the gross domestic product growth rate of province i in year t, ${\mathrm{CI}}_{\mathit{it}}$ is the competitiveness index of cultural industry of province i in year t, ${\mathrm{Inv}}_{\mathit{it}}$ is total investment in fixed assets of province i in year t, ${\mathrm{EP}}_{\mathit{it}}$ is the number of employed persons of province i in year t, ${\mathrm{RDE}}_{\mathit{it}}$ is R&D expenditure input intensity of province i in year t, ${\mathsf{\theta }}_0$ is the intercept, ${\mathsf{\theta }}_1$, ${\mathsf{\theta }}_2$, ${\mathsf{\theta }}_3$, ${\mathsf{\theta }}_4$ are the coefficients, and ${\mathsf{\epsilon }}_{\mathit{it}}$ is the error term.

Model 2:

$$\begin{gathered} {\mathrm{lnGDP}}_{\mathit{it}}{=\mathsf{\beta }}_0{+\mathsf{\beta }}_1{\mathrm{lnX}}_{1\mathit{it}}{+\mathsf{\beta }}_2{\mathrm{lnX}}_{2\mathit{it}}{+\mathsf{\beta }}_3{\mathrm{lnX}}_{3\mathit{it}}{+\mathsf{\beta }}_4{\mathrm{lnX}}_{4\mathit{it}}{+\mathsf{\beta }}_5{\mathrm{lnX}}_{5\mathit{it}}{+\mathsf{\beta }}_6{\mathrm{lnX}}_{6\mathit{it}}{+\mathsf{\beta }}_7{\mathrm{lnX}}_{7\mathit{it}} \\ {+\mathsf{\beta }}_8{\mathrm{lnX}}_{8\mathit{it}}{+\mathsf{\beta }}_9{\mathrm{lnX}}_{9\mathit{it}}{+\mathsf{\beta }}_{10}{\mathrm{lnX}}_{10\mathit{it}}{+\mathsf{\beta }}_{11}{\mathrm{lnX}}_{11\mathit{it}}{+\mathsf{\beta }}_{12}{\mathrm{lnX}}_{12\mathit{it}}{+\mathsf{\beta }}_{13}{\mathrm{lnX}}_{13\mathit{it}}{+\mathsf{\epsilon }}_{\mathit{it}} \end{gathered}$$

(4)

where X_1it, X_2it,…, X_13it are the relevant factors of the cultural industry of province i in year t. β₁, β₂,…, β₁₃ are the coefficients corresponding to 13 indicators X₁, X₂,…, X₁₃. β₀ is the intercept, and ${\mathsf{\epsilon }}_{\mathit{it}}$ is the error term. The observations of panel data analysis involve two dimensions: a cross-sectional dimension and a time series dimension [39]. Consequently, panel data contain more information, more variability, and more efficiency to better analyze the relationship between the cultural industry’s competitiveness and economic growth. In Model 1, this paper’s most concerned independent variable is the cultural industry competitiveness index (CI), which is obtained through principal component analysis and factor analysis. The selection of other independent variables is based on the endogenous growth theory. After analyzing how CI affects China’s economic growth, there are still some deficiencies in investigating the impact of a certain indicator of cultural industry on economic growth. Therefore, the first 13 indicators in the cultural industry index system as independent variables are selected in Model 2 to discuss this issue. These 13 indicators represent the cultural industry’s relevant factors, including X₁, X₂, X₃, X₄, X₅, X₆, X₇, X₈, X₉, X₁₀, X₁₁, X₁₂, and X₁₃. As X₁₄ and X₁₅ represent related and supporting industrial factors, they are not considered here. The negative values in X₁₂ are replaced by 1, and their logarithm is taken.

Before setting and estimating the panel data regression model, evaluating the stationarity of the variables involved is necessary. Methods of Levin, Lin, and Chu [40] and the Augmented Dickey-Fuller [41] are used to test the null hypothesis that a unit root is present in the variables. In the panel regression analysis, F-test, Breusch-Pagan Lagrange Multiplier test, and Hausman test are used to make the selection among pooled regression model, fixed effects model, or random effects model [42,43,44].

5. Empirical Research

5.1. The Evaluation of the Competitiveness of China’s Cultural Industry

5.1.1. Data Pre-Processing

The Kaiser–Meyer–Olkin (KMO) and Bartlett’s tests are used to observe the correlation between selected variables from 2013 to 2019. The KMO values for the years from 2013 to 2019 are 0.807, 0.796, 0.833, 0.765, 0.792, 0.807 and 0.728, respectively. Moreover,

Table 3. KMO and Bartlett test results.

		2013	2014	2015	2016	2017	2018	2019
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.		0.807	0.796	0.833	0.765	0.792	0.807	0.728
Bartlett’s Test of Sphericity	Approx. Chi-Square	722.965	724.527	748.146	726.322	727.374	707.013	746.092
	p-Value	0.000	0.000	0.000	0.000	0.000	0.000	0.000

shows that all p-values of Bartlett’s sphericity test are less than 0.05 for 2013–2019, indicating that the null hypothesis is rejected. This means that the selected data are suitable for factor analysis.

5.1.2. Principal Component Analysis

We perform principal component analysis to calculate a composite competitiveness index (CI) based on the selected indicators related to China’s cultural and related industry (X₁–X₁₅). The results are reported in

Table 4. Total variance explained before and after rotation.

	Component	Initial Eigenvalues			Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
		Total	Variance%	Cumulative %	Total	Variance%	Cumulative %	Total	Variance%	Cumulative %
2013	1	9.883	65.884	65.884	9.883	65.884	65.884	6.880	45.868	45.868
	2	1.976	13.171	79.054	1.976	13.171	79.054	3.869	25.793	71.661
	3	1.261	8.409	87.463	1.261	8.409	87.463	2.370	15.801	87.463
2014	1	9.846	65.643	65.643	9.846	65.643	65.643	7.016	46.774	46.774
	2	2.062	13.749	79.391	2.062	13.749	79.391	3.522	23.478	70.253
	3	1.166	7.770	87.162	1.166	7.770	87.162	2.536	16.909	87.162
2015	1	9.746	64.973	64.973	9.746	64.973	64.973	7.484	49.896	49.896
	2	2.560	17.064	82.037	2.560	17.064	82.037	3.376	22.506	72.402
	3	1.104	7.361	89.398	1.104	7.361	89.398	2.549	16.996	89.398
2016	1	9.581	63.872	63.872	9.581	63.872	63.872	7.230	48.202	48.202
	2	2.514	16.759	80.631	2.514	16.759	80.631	3.043	20.289	68.492
	3	1.163	7.755	88.386	1.163	7.755	88.386	2.984	19.894	88.386
2017	1	9.592	63.949	63.949	9.592	63.949	63.949	7.529	50.191	50.191
	2	2.622	17.483	81.432	2.622	17.483	81.432	3.050	20.334	70.524
	3	1.179	7.861	89.293	1.179	7.861	89.293	2.815	18.769	89.293
2018	1	9.372	62.477	62.477	9.372	62.477	62.477	7.635	50.900	50.900
	2	2.563	17.089	79.566	2.563	17.089	79.566	2.800	18.665	69.566
	3	1.250	8.336	87.902	1.250	8.336	87.902	2.750	18.336	87.902
2019	1	9.158	61.056	61.056	9.158	61.056	61.056	7.555	50.368	50.368
	2	2.440	16.270	77.326	2.440	16.270	77.326	2.758	18.384	68.752
	3	1.255	8.368	85.694	1.255	8.368	85.694	2.541	16.942	85.694

, and we find that the first three components are the main components according to the standard of the characteristic root greater than 1. Moreover, the cumulative contribution rates from 2013 to 2019 have reached 87.463%, 87.162%, 89.398%, 88.386%, 89.293%, 87.902%, and 85.694%, respectively, indicating the three principal components can express most of the relevant information of the original 15 variables. This paper records the labels of the three principal components as F1, F2, and F3.

5.1.3. Factor Analysis

The three principal components, F1, F2, and F3, obtained from principal component analysis can be used as the main factors in factor analysis to comprehensively evaluate the competitiveness of China’s cultural industry. Moreover, the three principal components of a certain year correspond to the comprehensive evaluation score of that year. After factor analysis, the significance of the factor load of 15 variables in the component matrix is not obvious, and it cannot give a reasonable explanation for the relationship between the three factors and variables. Through the method of orthogonal rotation of the maximum variance,

Table 5. Rotated component matrix.

	Variable	Component			Variable	Component			Variable	Component
		1	2	3		1	2	3		1	2	3
2013	X2	0.926	0.281	0.101	X11	0.825	0.409	0.319	X3	0.033	0.823	0.319
	X4	0.612	0.611	0.276	X12	0.789	0.454	0.337	X8	0.625	0.753	−0.115
	X7	0.832	0.427	0.260	X14	0.866	0.064	0.278	X13	0.456	0.745	0.253
	X9	0.848	0.172	0.189	X15	0.769	0.575	−0.173	X5	0.185	−0.067	0.903
	X10	0.870	0.291	0.086	X1	0.353	0.738	−0.309	X6	0.280	0.255	0.873
2014	X2	0.932	0.271	0.102	X11	0.830	0.379	0.337	X3	0.041	0.824	0.346
	X4	0.632	0.583	0.288	X12	0.810	0.393	0.359	X8	0.676	0.707	−0.111
	X7	0.861	0.408	0.209	X14	0.875	0.019	0.281	X13	0.589	0.652	0.251
	X9	0.596	0.330	0.383	X15	0.778	0.560	−0.167	X5	0.158	−0.076	0.903
	X10	0.945	0.171	0.117	X1	0.367	0.733	−0.332	X6	0.283	0.207	0.877
2015	X2	0.948	0.177	0.174	X11	0.836	0.402	0.302	X5	0.027	0.928	−0.035
	X4	0.740	0.287	0.438	X12	0.808	0.451	0.279	X6	0.260	0.900	0.174
	X7	0.895	0.225	0.325	X13	0.695	0.358	0.518	X9	0.357	0.864	0.064
	X8	0.778	−0.069	0.595	X14	0.838	0.331	−0.074	X1	0.519	−0.285	0.653
	X10	0.950	0.167	0.072	X15	0.847	−0.132	0.481	X3	0.086	0.244	0.853
2016	X2	0.943	0.146	0.239	X11	0.831	0.360	0.358	X5	0.022	0.944	−0.033
	X4	0.725	0.292	0.465	X12	0.824	0.354	0.318	X6	0.269	0.876	0.164
	X7	0.872	0.169	0.400	X13	0.704	0.376	0.487	X9	0.450	0.754	−0.041
	X8	0.751	−0.083	0.618	X14	0.838	0.314	−0.075	X1	0.424	−0.280	0.735
	X10	0.954	0.154	0.088	X15	0.745	−0.149	0.610	X3	0.053	0.238	0.864
2017	X2	0.945	0.126	0.246	X11	0.854	0.361	0.312	X5	0.083	0.939	−0.106
	X4	0.766	0.257	0.431	X12	0.850	0.364	0.250	X6	0.275	0.877	0.122
	X7	0.870	0.138	0.410	X13	0.743	0.456	0.388	X9	0.619	0.688	0.006
	X8	0.704	−0.094	0.658	X14	0.862	0.253	−0.067	X1	0.317	−0.371	0.771
	X10	0.961	0.110	0.066	X15	0.747	−0.182	0.587	X3	0.037	0.314	0.826
2018	X2	0.947	0.258	0.069	X10	0.971	0.030	0.048	X15	0.699	0.618	−0.207
	X4	0.779	0.422	0.237	X11	0.882	0.236	0.378	X1	0.210	0.805	−0.345
	X7	0.887	0.397	0.083	X12	0.818	0.202	0.421	X3	0.035	0.834	0.321
	X8	0.706	0.648	−0.097	X13	0.769	0.371	0.414	X5	0.134	−0.118	0.927
	X9	0.692	−0.025	0.469	X14	0.872	0.004	0.204	X6	0.274	0.062	0.890
2019	X2	0.938	0.259	0.076	X10	0.957	0.004	0.013	X15	0.677	0.625	−0.226
	X4	0.809	0.401	0.201	X11	0.864	0.228	0.410	X1	0.135	0.795	−0.268
	X7	0.896	0.330	0.162	X12	0.892	0.115	0.355	X3	0.061	0.835	0.271
	X8	0.701	0.635	−0.114	X13	0.754	0.458	0.321	X5	0.095	−0.128	0.925
	X9	0.617	−0.002	0.440	X14	0.854	0.044	0.184	X6	0.318	0.069	0.883

shows that the rotated component matrix from 2013 to 2019 is obtained, which can make each factor have a high load only corresponding to a few variables, and each variable has a high load only on a few factors. The main factor F1 has a larger load value on the core of the cultural industry’s competitiveness. The main factor F2 has a larger load value on the relevant resources of the cultural industry. The main factor F3 has a larger load value on the demand market of the cultural industry. Therefore, the naming of factors can be reasonably explained: F1 is the core competition factor, F2 is the resource competition factor and F3 is the demand competition factor.

Then, the factor score coefficients are substituted into Equation (1), and the scores of each main factor in 2013–2019 are obtained. Finally, taking the contribution rate of the variance of each factor as the weight, the comprehensive scores of the competitiveness of the cultural industry of 31 provinces in China from 2013 to 2019 are calculated by Equation (2), and the CI and rankings are shown in

Table 6. Competitiveness index and ranking of cultural industry of 31 provinces in China, 2013–2019.

Region		2013		2014		2015		2016		2017		2018		2019
		Score	Rank	Score	Rank	Score	Rank	Score	Rank	Score	Rank	Score	Rank	Score	Rank
Eastern China	Beijing	0.74	4	0.80	4	0.91	4	0.86	4	0.89	4	0.90	4	0.84	4
	Tianjin	−0.20	15	−0.17	15	−0.16	15	−0.16	15	−0.21	16	−0.23	16	−0.25	17
	Hebei	−0.16	14	−0.14	14	−0.15	14	−0.15	13	−0.15	13	−0.19	15	−0.14	13
	Shanghai	0.69	5	0.68	5	0.75	5	0.66	6	0.76	5	0.71	5	0.78	5
	Jiangsu	1.41	2	1.39	2	1.43	2	1.37	2	1.27	2	1.17	2	1.09	3
	Zhejiang	1.02	3	1.04	3	1.01	3	0.94	3	0.92	3	1.00	3	1.20	2
	Fujian	0.11	7	0.12	7	0.16	7	0.14	8	0.15	9	0.21	7	0.13	10
	Shandong	0.68	6	0.65	6	0.67	6	0.69	5	0.62	6	0.56	6	0.47	6
	Guangdong	2.10	1	2.10	1	2.07	1	2.11	1	2.22	1	2.31	1	2.32	1
	Hainan	−0.47	27	−0.54	28	−0.55	28	−0.56	28	−0.53	28	−0.53	28	−0.53	28
Central China	Shanxi	−0.38	23	−0.37	23	−0.36	21	−0.37	21	−0.40	23	−0.38	21	−0.38	21
	Anhui	−0.09	13	−0.06	12	−0.12	12	−0.10	12	−0.11	12	−0.10	13	−0.07	12
	Jiangxi	−0.27	17	−0.25	16	−0.22	16	−0.23	17	−0.24	17	−0.25	17	−0.23	16
	Henan	0.03	9	0.08	8	0.11	8	0.11	10	0.08	10	0.02	11	0.03	11
	Hubei	−0.03	12	−0.04	11	−0.06	11	−0.04	11	−0.02	11	0.12	10	0.19	8
	Hunan	0.03	9	0.07	10	0.11	8	0.19	7	0.18	7	0.16	9	0.14	9
Western China	Mongolia	−0.29	19	−0.34	21	−0.39	22	−0.39	22	−0.37	21	−0.41	23	−0.43	23
	Guangxi	−0.29	19	−0.28	19	−0.29	18	−0.30	19	−0.31	19	−0.33	20	−0.29	20
	Chongqing	−0.31	21	−0.31	20	−0.30	19	−0.31	20	−0.33	20	−0.26	18	−0.27	18
	Sichuan	0.10	8	0.08	8	0.03	10	0.12	9	0.17	8	0.21	7	0.21	7
	Guizhou	−0.43	25	−0.36	22	−0.42	25	−0.42	25	−0.42	24	−0.43	24	−0.42	22
	Yunnan	−0.27	17	−0.27	17	−0.32	20	−0.29	18	−0.30	18	−0.29	19	−0.28	19
	Tibet	−0.60	30	−0.60	30	−0.61	31	−0.63	31	−0.63	31	−0.67	31	−0.65	31
	Shaanxi	−0.26	16	−0.27	17	−0.27	17	−0.16	15	−0.16	14	−0.14	14	−0.14	13
	Gansu	−0.47	27	−0.49	27	−0.47	27	−0.45	26	−0.47	26	−0.48	26	−0.49	27
	Qinghai	−0.62	31	−0.61	31	−0.60	30	−0.61	30	−0.61	29	−0.64	30	−0.62	30
	Ningxia	−0.58	29	−0.59	29	−0.59	29	−0.59	29	−0.61	29	−0.61	29	−0.61	29
	Xinjiang	−0.45	26	−0.44	26	−0.45	26	−0.46	27	−0.47	26	−0.50	27	−0.48	26
North- eastern China	Liaoning	0.02	11	−0.07	13	−0.13	13	−0.15	13	−0.16	14	−0.08	12	−0.22	15
	Jilin	−0.41	24	−0.42	25	−0.41	24	−0.41	24	−0.42	24	−0.43	24	−0.44	25
	Heilongjiang	−0.36	22	−0.38	24	−0.39	22	−0.39	22	−0.37	21	−0.40	22	−0.43	23

. In Table 6, it is clear that the comprehensive score reflects the competitiveness level of the cultural industry in 31 provinces of China from 2013 to 2019. If the score of a certain province is greater than 0, it indicates that its competitiveness level is above the national average level for that year.

In China, there are great differences in the competitiveness level of the cultural industry in 31 provinces, which is basically in agreement with the conclusions of many studies on China’s cultural industry competitiveness evaluation [45,46,47]. The top provinces in 2013–2019 are mostly located in eastern China, which indicates that economic development has laid the foundation for competitiveness in the cultural industry. The eastern region has considerable advantages in talent, capital, technology, and information and has a large market consumer group [48]. The cultural industry competitiveness of central and northeastern China’s provinces is at a medium level and has great development potential. There are still quite a few problems in the development of the cultural industry in the central and northeastern regions, for example, an unreasonable input–output structure, weak cultural brand awareness, lack of regional development linkage, and lack of supporting competition and management mechanisms [49,50]. In recent years, the national policy has been inclined toward western China, and its economic strength has been greatly improved. However, compared with the eastern, central, and northeastern regions, the development potential of cultural industry in western China needs to be further developed. Hu and Ma [51] pointed out that cultivating characteristic culture, improving cultural industry infrastructure construction, and breaking ethnic and religious segregation are conducive to accelerating the development of cultural industry in the western region.

5.2. Analysis of the Impact of the Competitiveness Index of Cultural Industry on China’s Economic Growth

5.2.1. Results of Panel Unit Roots Test

Table 7. Results of the unit-roots test (first analysis).

Variable	Method
	Levin, Lin & Chu	ADF—Fisher Chi-Square
lnGDP	−99.8470 ***	95.3349 ***
lnCI	−23.8080 ***	129.591 ***
lnInv	−12.9577 ***	120.158 ***
lnEP	−13.3925 ***	74.4365 ***
lnRDE	−32.4257 ***	121.615 ***

Notes: H₀: Panels contain unit roots, Ha: Panels are stationary. *** denotes significance at 1% critical value.

reports the results of the panel unit roots test in the level variables. The null hypothesis of the Levin, Lin and Chu test and ADF tests are rejected at the 1% significance level for all variables. Therefore, all variables are stationary at the level. In addition, we also examine the correlation among the independent variables in the first analysis in order to multicollinearity problem. The result is reported in

Table 8. Results of the correlation (first analysis).

	lnCI	lnInv	lnEP	lnRDE
lnCI	1.0000
lnInv	0.6628	1.0000
lnEP	0.6993	0.6201	1.0000
lnRDE	0.6489	0.4943	0.4472	1.0000

, and we observe that the independent variables are low correlated (less than 0.70). Hence, we could conclude that there is no multicollinearity in our model.

5.2.2. Model Comparison and Results

Table 9. Estimates of the pooled regression, fixed effects model, and random effects model (first analysis).

	Pooled Regression	Fixed Effects	Random Effects
Constant	9.2499 *** (1.8809)	52.1326 ** (22.2890)	14.5776 *** (2.8251)
lnCI	0.9914 ** (0.4550)	8.1024 *** (2.2233)	1.2470 * (0.7095)
lnInv	−0.0029 (0.3851)	−2.7108 *** (0.5059)	−1.4232 *** (0.3900)
lnEP	−0.1141 (0.4154)	−2.3168 (3.1608)	0.9626 * (0.5006)
lnRDE	−1.2182 *** (0.3061)	1.2232 (1.0602)	−0.9593 ** (0.4643)
R-squared	0.0791	0.5649	0.0893
Adjusted R-squared	0.0617	0.4832	0.0721
Sum squared residuals	706.0317	333.5361	448.2363
Log-likelihood	−434.4039	−353.4138	−
Akaike info criterion	4.0685	3.5964	−

Notes: ***, **, * denote significance at 1%, 5%, 10% critical value, respectively. Standard errors are in parentheses. Time and country fixed effects are considered in this analysis.

shows the parameter estimates of the pooled regression, fixed effects model, and random effects model. In the current research, the F-test, BP-test, and Hausman test are used for selection between the pooled regression, fixed effects model, and random effects model. From

Table 10. Specification Tests (first analysis).

Spec. Tests	Statistic	Tested	Selection
F-test	6.7753 ***	pooled regression/fixed effects	fixed effects
Breusch-Pagan	359.7893 ***	pooled regression/random effects	random effects
Hausman	36.1481 ***	random effects/fixed effects	fixed effects

Note: *** denotes significance at 1% critical value.

, it can be seen that the fixed effects model is more suitable for this study.

According to the fixed effects model in Table 9, the competitiveness index of the cultural industry (CI) contributed to economic growth significantly (p-Value < 0.001). If the competitiveness index of the cultural industry (CI) increases by 1%, GDP will increase by 8.1024%, on average. When the cultural industry is more competitive, it will have a considerable impact on economic growth. However, it is found that the purchase of newly produced fixed capital (Inv) has a significant and negative impact on the Chinese economy. This implies that the purchase of newly produced fixed capital did not play an essential role in increasing growth levels in China during 2013–2019. It could have decreased growth. The possible reason for explaining this result is due to the low technology absorption capacity and the low level of human capital in China. This conclusion is corroborated by Curwin and Mahutga [52], who suggested that the penetration of FDI reduces economic growth in the short and long term in socialist countries. In recent years, the steady growth of China’s economy is no longer driven mainly by investment but by the development of knowledge-intensive and high-tech industries to achieve sustainable economic growth. Zhang and Yao [53] discussed the reasons that fixed asset investment had not played an important role in explaining China’s economic growth since the beginning of the 21st century.

In terms of the control variables, we find that the number of the labor force cannot help to increase GDP in China under the current situation as the demographic dividend gradually disappears. Chen [54] found that the workforce size has no significant effect on China’s economic growth but suggested that the quality of the labor force is more important. Similarly, research and development is not found to have a significant influence on economic growth

5.3. Analysis of the Impact of Cultural Industry Factors on China’s Economic Growth

In addition, to gain more information on the cultural industry ‘s impact on growth, 13 sub-competitiveness indexes of the cultural industry, presented in Table 1, are used to estimate their impacts on Chinese growth. This enables us to explore the key driver of China’s economic growth.

5.3.1. Results of Panel Unit Roots Test (Second Analysis)

Again, the panel unit root tests of LLC and ADF and correlation tests are also used to examine the stationary and correlation among 13 sub-competitiveness indexes. The results of these tests are reported in

Table 11. Test results for panel unit roots (second analysis).

Variable	Method		Variable	Method
	Levin, Lin & Chu	ADF—Fisher Chi-Square		Levin, Lin & Chu	ADF—Fisher Chi-Square
lnX1	−54.3690 ***	93.8035 ***	lnX8	−608.241 ***	117.162 ***
lnX2	−13.1298 ***	100.978 ***	lnX9	−34.6636 ***	83.1386 **
lnX3	−36.5545 ***	98.4927 ***	lnX10	−108.700 ***	145.841 ***
lnX4	−30.8708 ***	128.742 ***	lnX11	−9.41097 ***	95.0712 ***
lnX5	−10.6520 ***	127.488 ***	lnX12	−26.3860 ***	133.158 ***
lnX6	−58.1632 ***	101.275 ***	lnX13	−13.7596 ***	100.708 ***
lnX7	−3.48069 ***	77.3883 *

Notes: H₀: Panels contain unit roots, Ha: Panels are stationary. ***, **, * denote significance at 1%, 5%, 10% critical value, respectively.

and

Table 12. Results of the correlation (second analysis).

	lnX1	lnX2	lnX3	lnX4	lnX5	lnX6	lnX7	lnX8	lnX9	lnX10	lnX11	lnX12	lnX13
lnX1	1.000
lnX2	0.684	1.000
lnX3	0.657	0.613	1.000
lnX4	0.577	0.705	0.678	1.000
lnX5	0.665	0.617	0.659	0.597	1.000
lnX6	0.707	0.624	0.578	0.659	0.558	1.000
lnX7	0.553	0.718	0.688	0.484	0.727	0.485	1.000
lnX8	0.665	0.591	0.556	0.647	0.507	0.578	0.565	1.000
lnX9	−0.132	0.218	0.202	0.143	0.266	0.118	0.162	0.155
lnX10	0.209	0.637	0.586	0.478	0.570	0.615	0.529	0.673	0.577	1.000
lnX11	0.650	0.645	0.463	0.674	0.501	0.683	0.713	0.693	0.166	0.626	1.000
lnX12	0.671	0.560	0.631	0.662	0.637	0.682	0.726	0.699	−0.077	0.467	0.693	1.000
lnX13	0.460	0.786	0.670	0.627	0.639	0.739	0.558	0.745	0.472	0.694	0.566	0.623	1.000

, respectively. The results show that all panel variables are stationary at the level, and there is weak correlation among the independent variables, as the correlations of all pairs are lower than 0.75.

5.3.2. Model Comparison and Results (Second Analysis)

The parameter estimates of the pooled regression, fixed effects model, and random effects model are shown in

Table 13. Results of the pooled regression, fixed effects model, and random effects model (second analysis).

	Pooled Regression	Fixed Effects	Random Effects
Constant	34.5857 *** (4.2046)	69.4778 *** (9.2848)	39.2223 *** (5.1680)
lnX1	−0.6355 *** (0.2022)	0.1633 *** (0.0452)	−0.4368 ** (0.1940)
lnX2	0.2817 (0.5378)	1.2162 * (0.7326)	1.8169 *** (0.5486)
lnX3	0.1448 (0.1512)	−0.1516 (0.2832)	0.0426 (0.2010)
lnX4	−1.0142 *** (0.3753)	−2.0475 ** (0.9835)	−0.9249 * (0.5214)
lnX5	−4.8332 *** (0.9052)	−6.9455 *** (1.4336)	−5.9944 *** (1.0606)
lnX6	−1.5476 *** (0.3657)	−1.8289 ** (0.7387)	−1.6780 *** (0.4801)
lnX7	0.5248 (0.5199)	−0.2798 (0.6546)	−0.3414 (0.5487)
lnX8	−1.0532 *** (0.3247)	−2.3706 *** (0.5443)	−1.3267 *** (0.3741)
lnX9	0.0358 (0.1471)	−0.0265 (0.1319)	−0.0317 (0.1213)
lnX10	0.1002 (0.1903)	0.0804 (0.2133)	0.0727 (0.1843)
lnX11	0.5227 (0.3291)	−0.3869 (0.4760)	−0.0558 (0.3665)
lnX12	0.0930 * (0.0526)	0.0426 (0.0429)	0.0902 * (0.0401)
lnX13	0.0028 (0.3367)	0.4323 (0.6071)	0.1449 (0.4421)
R-squared	0.4966	0.7950	0.5556
Adjusted R-squared	0.4644	0.7441	0.5272
Sum squared residuals	385.9677	157.1525	210.6021
Log-likelihood	−370.3901	−272.8988	−
Akaike info criterion	3.5427	2.9207	−

Notes: ***, **, * denote significance at 1%, 5%, 10% critical value, respectively. Standard errors are in parentheses. Time and country fixed effects are considered in this analysis.

. The comparison method is the same as that of the previous panel regression. The results in

Table 14. Specification tests in the second-panel regression model (second analysis).

Spec. Test	Statistic	Tested	Selection
F-test	8.3963 ***	pooled regression/fixed effects	fixed effects
Breusch-Pagan	110.7982 ***	pooled regression/random effects	random effects
Hausman	41.8395 ***	random effects/fixed effects	fixed effects

Note: *** denotes significance at 1% critical value.

suggest that the fixed effects estimation is preferred and is used for the discussion. The robustness results of the fixed effects estimation is also confirmed in

Table A1. Robustness check of fixed effects models.

Robustness Check	First Analysis	Second Analysis
Fixed effects (time)	AIC = 3.6930	AIC = 3.1254
Fixed effects (country)	AIC = 3.8490	AIC = 3.0192
Joint test for Normality on error term	${\chi }^2$= 2.9324	${\chi }^2$= 1.6423
Modified Wald test for Heteroscedasticity	${\chi }^2$= 3.0024	${\chi }^2$= 3.3001
Wooldridge Autocorrelation test	$F$= 2.3849	$F$= 1.9384

.

According to Table 13, the fixed effects model shows evidence that the estimated coefficients of Fixed Asset Investment in Culture (X₁), labor in Cultural Enterprises (X₂), Total Collections in Public Libraries (X₄), Proportion of Tertiary Industry in Gross Regional Product (X₅), Per Capita Consumption and Expenditure on Culture and Recreation of Nationwide Households (X₆), and Users of Cable Radios and TVs (X₈) are significant (p-Value < 0.10).

The results show that the labor of cultural enterprises is a driver of economic growth Indicating that an increase in investment and labor enhance growth in the long run. This should not be surprising. Research in growth theories, such as Solow growth [55] and endogenous growth theories [56], provides evidence that investment and labor are the most important long-term source of economic growth [57]. However, it seems that total cultural collections, tertiary industry, consumption and expenditure, and the number of cable radios and TVs users are not likely to boost Chinese economic growth.

According to the above results, the evolution of an industry cannot rely on only some factors, so improving industrial competitiveness and optimizing industrial structure is key to the development of China’s cultural industry. Increasing fixed investment and labor in the cultural industry not only gives full play to the potential advantages of the cultural industry but also helps to promote China’s economic growth.

Finally, when we consider other insignificant variables, we observe that Total Patent Applications Granted on Culture and Related Industries (X₁₀), Total Profit of Cultural Enterprises (X₁₂), and Expenditure for Culture, Sport, and Media of Regional Government Revenue (X₁₃) show weak evidence of positive impact on the Chinese economy. Despite their insignificant effect on growth, we may view them as a potential factor in the future.

6. Conclusions

The evaluation of the competitiveness of cultural industries and its impact on Chinese economic growth remains neglected in the literature. Although Fu, Song, and He [8] and Wu, Xu, and Chen [9] have attempted to evaluate cultural industry competitiveness in Tianjin and Guifeng, there is a certain subjectivity, one-sidedness, and superficiality in the selection of indicators for evaluating the competitiveness of the cultural industry. Additionally, their evaluation is limited at the provincial level. Moreover, there is little data to support the conclusion that the cultural industry’s competitiveness positively affects economic growth. To fill this gap, this study selects the evaluation indicators of the cultural industry’s competitiveness with a systematic, dynamic, and operable principle. The present study conducts a comprehensive evaluation of the competitiveness of cultural industries in 31 provinces in China using the period 2013–2019 and also adds another research value by investigating the influence of cultural industry competitiveness, as well as its relevant factors, on economic growth in China.

The following conclusions can be drawn from the present study based on the current empirical findings. Firstly, the main outcome of this research is additional evidence that suggests a new cultural industry’s competitiveness index, which could be added to the cultural literature [58]. We find that provinces in eastern China, such as Guangdong, Jiangsu, Zhejiang, and Shanghai, are more competitive in the cultural industry, and 31 provinces have a large gap in the competitiveness level of the cultural industry. Secondly, the competitiveness of the cultural industry is found to be an important factor in China’s economic growth, according to the fixed effects model. The result is consistent with the research of Ager and Brückner [58] and Bălan and Vasile [59], who revealed a positive impact of culture on the economic growth of the US and Romania, respectively. The second analysis of panel regression estimation shows that investment and labor in cultural industries are the key drivers of Chinese GDP growth. This implies that the investment and labor of the cultural industry are not only the key to enhancing its competitiveness but also play an influential role in economic growth. This result is in line with Solow growth [55] and endogenous growth theories [56].

However, the development of the cultural industry in China continues to have problems, such as insufficient capital investment, lack of professional talents, weak innovation ability, and imperfect management mechanisms. It is particularly urgent to accelerate industrial restructuring and upgrading. To transform the development mode of the cultural industry and cultivate its core competitiveness, an open, competitive, and orderly cultural market system needs to be established. It is necessary to give full play to the government’s public service functions to promote the prosperity of the cultural market and improve the legalization of the management of the cultural market.

The recommendations for promoting the development of China’s cultural industry are put forward from four aspects: investment and trade, technology, creativity, and talent. Firstly, enhancing the effectiveness and guidance of financial and cultural investment can attract overseas capital and other social capital into the cultural industry. China should also actively explore the international cultural market and participate in the competition to adapt to the changes in cultural consumption in the market economy. Secondly, information technology promotes the diversification of cultural product dissemination methods and the intellectualization of cultural management. To achieve the goal of the high-quality development of the cultural industry, China should improve technological innovation capabilities and expand the supply of cultural products and services. Thirdly, enhancing the innovation and competitiveness of cultural products can be possible by developing cultural and creative products with Chinese style, promoting the creative transformation of Chinese traditional culture, and thus functioning the cultural industry’s social and economic benefits. Finally, talents are the leading force for the transformation of cultural resources into cultural productivity; that is, the construction of cultural industry is essentially the construction of knowledge and high-value-added human resources products. Therefore, China should recognize the importance and urgency of cultivating artistic and managerial talents in the cultural industry. Local governments and higher education sectors should formulate talent training layout plans for the cultural industry and actively connect industrial resources to integrate learning and research with further access to the level of production and applications.