Transformation of Economic Growth Momentum and Policy Choice: A Case Study of Guangdong Province in China

Luo, Feng; Chen, Feng; Yang, Dandan; Yang, Simin

doi:https://doi.org/10.1155/2022/3820727

Discrete Dynamics in Nature and Society

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Abstract Introduction Literature Review Conclusion Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2022 | Article ID 3820727 | https://doi.org/10.1155/2022/3820727

Transformation of Economic Growth Momentum and Policy Choice: A Case Study of Guangdong Province in China

Feng Luo,^1,2Feng Chen ,^1,2Dandan Yang,^1,2and Simin Yang^1,2

Academic Editor: Ya Jia

Received04 Dec 2021

Revised27 Apr 2022

Accepted12 May 2022

Published13 Jul 2022

Abstract

The mystery of China’s economic growth has been widely concerned by the world. However, little attention has been paid to the transformation of the economic growth momentum of Guangdong Province, which has long occupied the top in China’s economic growth. This paper analyzes the evolution and the transformation of the momentum source of economic growth in Guangdong Province from 1979 to 2019, using Total Factor Productivity Growth Decomposition method. The results show that capital investment contributes the most to economic growth, the contributions of total factor productivity, and labor input rank second and third, respectively; the change of the contribution of each momentum source to GDP growth mainly comes from the impact of the secondary industry. Both industrial capital productivity and capital allocation structure play a negative role in driving the overall capital productivity, and both industrial labor productivity and labor allocation structure play a positive role in driving the overall labor productivity. Industrial factor productivity and industrial factor allocation structure play a major positive role and a weak negative role in promoting economic growth, respectively. Our findings provide some ideas on how to maintain economic growth in the new era especially for the developing countries.

1. Introduction

In recent years, the problems of declining traditional growth momentum and insufficient agglomeration of emerging growth momentum are common in countries all over the world. Excavating the growth potential and realizing the successful transformation of economic growth momentum has become an urgent problem all over the world. After nearly 40 years of rapid growth, China’s economy has also entered the “new normal” stage. With the diminishing marginal return of the old momentum, how to promote the momentum reform to cultivate new momentum and realize the conversion of new and old driving force has become one of the most important problems in the world. The source of economic growth momentum is mainly divided into two systems: demand pull and supply push. An economy faces different growth constraints at different development stages. The traditional framework of economic growth analysis is mainly based on the demand analysis of consumption, investment, and net export. In recent years, the momentum source on the demand side has been declining due to the decline in the growth rate of consumption demand, the overcapacity caused by the continuous expansion of investment, and the negative impact on exports caused by the intensification of trade friction. Therefore, since 2014, China has put forward the economic development idea of supply-side structural reform in order to tap the new driving force of economic growth. In recent years, some studies have proved that the new kinetic energy has obvious spatial spillover effect on economic growth and promoted the high-quality development of China’s economy. At present, the supply-oriented reform has achieved some results [1, 2]. However, Hong [3] found that there is a large gap in the level of economic growth kinetic energy conversion in all regions of China Guangdong Province is a leading industrialized region in China. After taking the lead in becoming the first province in China with GDP exceeding 10 trillion in 2019, it exceeded 12 trillion yuan in 2021, and the total GDP ranks first in China for 34 consecutive years. As a major province of consumption, foreign trade, and manufacturing in China, as well as the province with the largest gap between urban and rural areas in China, the evolution and transformation of economic growth momentum in Guangdong Province is not only typical significance but also an important support for China’s sustained economic development. Hence, the experience and lessons of momentum transformation have important significance.

2. Literature Review

The momentum of economic growth and its transformation have always been the core issues of economists. The early theory of economic growth limited the research scope to the field of production and believed that increasing factor investment and improving labor productivity were the main ways of economic growth. For example, Smith [4] pointed out that the source of economic growth is specialized production caused by division of labor. Ricardo [5] pointed out that capital accumulation is a favorable condition for economic growth. Improving labor productivity, shortening necessary labor hours, and reducing workers’ wages are the main means of economic growth. Keynes [6] created a new macroeconomics and pointed out that economic growth was mainly affected by effective demand. This expanded economic growth from micro to macro and from supply to demand, which provided a new logical perspective for the analysis of economic growth. Based on this, modern economic growth theory holds that in addition to traditional production factors such as labor and capital, technological progress is the most important driving factor of economic growth [7, 8]. Furthermore, the new economic growth theory holds that technological progress and the spillover effect of human capital are the basic conditions of economic growth [9–11]. Since then, the new institutional school believes that the momentum source of economic growth mainly comes from clear property rights and clear organizational system [12–14].

In recent years, scholars have conducted multidimensional research on the driving force and transformation of China’s economic growth and achieved rich results. The relevant research is mainly carried out from four aspects: first, from the perspective of demand. This paper mainly discusses the troika of economic growth and the impact of consumption, investment, and export on economic growth [15–21]; second, the perspective of supply. It mainly analyzes the impact of different factors on economic growth based on the contribution of factor input, technological innovation, and institutional reform to economic growth. According to different methods and data in different periods, the conclusions are different, but the general view is that the supply of innovative technologies is insufficient, factor prices rise, and the oversupply of low-end products and other problems reflect the supply constraints at this stage of economic growth in China [22–31]. In the supply research literature, measuring the contribution of total factor productivity to economic growth is the focus of academic attention, but the results are different due to different methods [32–39]; third, the perspective of structural adjustment. It mainly studies the impact of structural adjustment factors such as urbanization and economic agglomeration on economic growth. A more consistent conclusion is that the lag of China’s urbanization level is an important factor restricting China’s economic growth [40–45]. At the same time, [46] Wei and Zhang [1] pointed out that industrial upgrading and financial structure adjustment can improve the effect of local resource allocation and promote economic growth; fourth, from the perspective of system. The main view is that economic growth is a complex system process centered on human economic activities. The three momentum systems of capital, technology, and institution will jointly promote the process of economic growth [47, 48], and Qiang and Jian [49] pointed out that institutional quality will regulate the impact of natural resources on economic growth and help alleviate the “resource curse” effect.

To sum up, scholars have achieved fruitful results on economic growth and its momentum transformation, but there are still great limitations, which are mainly reflected in: firstly, the existed literature from the supply perspective mainly based on the factor perspective, while there are few types of literature on the contribution of different momentum sources to economic growth based on the industrial dimension. In fact, an important dimension to achieve high-quality economic growth is promoting the continuous optimization and upgrading of industrial structure. By investigating the contribution of different industries to economic growth, it is conducive to tap the space for the promotion of different industries to promote economic growth; secondly, the existed literature often examines TFP as an overall factor, but there are few research results on the contribution of different factors to economic growth and their change trend in the black box. How to open the black box of TFP and reveal the change law of the role of different factors on economic growth are great significance for policy-making. Based on this, this paper makes an in-depth analysis on the evolution trend and contribution of the momentum source of supply-side economic growth in Guangdong Province, a leading industrialization region in China, from 1979 to 2019 by using the three progressive analytical frameworks of surface decomposition of economic growth momentum, contribution decomposition of total factor productivity, and in-depth decomposition of growth momentum. The structure of the article is as follows: firstly, the research methods of three progressive levels are described; secondly, it explains the data source; thirdly, we use the three-level analysis framework to analyze the transformation trend of economic growth momentum in Guangdong Province; and finally, it puts forward policy suggestions according to the transformation of economic growth momentum in Guangdong Province.

3. Research Method

The key to analyzing the driving force of economic growth is to accurately measure total factor productivity. The mainstream methods to measure total factor productivity mainly include growth accounting method, nonparametric method, and production function method [40], and growth accounting method is generally accepted internationally [41]. However, there is a disadvantage of the above methods, total factor productivity cannot be measured directly, and the black box of total factor productivity is unknown. Therefore, this paper uses Zhu Ziyun’s [15] analysis framework based on growth accounting for reference to analyze the momentum transformation of economic growth in Guangdong Province. The advantages of this method are as follows: firstly, total factor productivity can be measured directly; and secondly, we decompose the total factor productivity, so as to provide a more sufficient basis for the formulation of relevant economic policies.

3.1. Surface Decomposition of Economic Growth Momentum

Firstly, the momentum sources of economic growth can be divided into capital factors, labor factors, and total factor productivity (TFP).

In formula (1), and represent the growth rate of capital investment and labor input, respectively, and and represent the growth rate of capital productivity and labor productivity, respectively. The third item 1/2 ∗ ln(1 + )/ln(1 + ) measures the contribution of capital investment in economic growth, and the fourth item 1/2 ∗ ln(1 + )/ln(1 + ) measures the contribution of labor investment in GDP growth, so 1/2 ∗ ln(1 + )/ln(1 + )+1/2 ∗ ln(1 + )/ln(1 + ) is the contribution of total factor productivity (TFP) to GDP growth. The calculation formula of capital productivity is GDP of Guangdong Province in year t/capital investment in year t. Similarly, the calculation formula of labor productivity is GDP of Guangdong Province in year t/number of social employed population in Guangdong Province at the end of year t.

3.2. Decomposition of Total Factor Productivity Components

Furthermore, the total factor productivity is decomposed and regarded as two parts: industrial capital productivity and industrial factor allocation structure. The specific calculation steps are as follows:where TP represents the overall capital (labor) productivity, which is obtained by dividing the total GDP by the amount of all capital (labor) inputs in Guangdong Province; TP is further decomposed into factor (capital and labor) productivity and factor (capital and labor) allocation structure. Then, P_i represents the capital or labor productivity of the i industry. The specific calculation method is obtained by dividing the total value of economic growth of the i industry by the total amount of capital (labor) input of the i industry. R_i represents the capital (labor) allocation structure of the i industry. The specific calculation method is obtained by dividing the total amount of capital (labor) input of the i industry by the total amount of capital (labor) input of the whole province, where t and t − 1 represent the year. Therefore, equation (2) can calculate the increment of overall (labor) capital productivity in adjacent years. (P_itR_it − P_it−1R_it−1) can be further decomposed into the respective increments of P_i and R_i in △TP through following formulas.

In equations (3) and (4), σ_pi represents the contribution of capital (labor) productivity of the i industry to (P_itR_it − P_it−1R_it−1), and σ_si represents the contribution of capital (labor) of the i industry to (P_itR_it − P_it−1R_it−1), where p_i represents the growth rate of the productivity of the i industrial factors (capital or labor), and s_i represents the growth rate of the i industrial factors (capital or labor). Combining equations (2)–(4), we can get equation (5) about the capital (labor) productivity of the three industries and the increment of the capital (labor) allocation structure of the three industries in △TP.

In equation (5), represents the increment of capital productivity (or labor productivity) of the three industries in △TP. represents the increment of the capital allocation structure (labor allocation structure) of the three industries in △TP. The contribution of factor productivity and factor allocation structure in total factor productivity can be further calculated through these two parts of increment.

3.3. Deep Decomposition of Economic Growth Momentum

On the basis of equation (5), the calculation formula of the contribution of capital productivity and labor productivity to economic growth is added to further obtain the contribution of factor productivity and factor allocation structure of the three industries to economic growth. The formula is as follows:

In equations (6) and (7), σ_P and σ_S represent the contribution of industrial factor productivity and factor allocation structure to economic growth, respectively, where △TP₁ and △TP₂ represent the increment of capital productivity and labor productivity, respectively.

4. Data Processing and Correlation Analysis

4.1. GDP Data

The total GDP at constant price is used as the output data in Guangdong Province. Specifically, it comes from the regional GDP over the years (calculated according to the current year price) in the Guangdong statistical yearbook and the regional GDP index over the years compared with the previous year. Based on this, the regional GDP (constant price) of Guangdong Province from 1979 to 2019 with 1978 as the base period can be obtained. In Table 1, the data are tabulated and presented.

4.2. Labor Input Data

The labor input data are replaced by the natural number of labor force. The data come from the total number of social employed population at the end of the calendar year since 1978 in the Guangdong statistical yearbook. In Table 1, the data are tabulated and presented.

4.3. Capital Investment Data

There are many specific methods to measure the physical capital stock, but the most widely used is the perpetual inventory method. Therefore, the perpetual inventory method is also used to calculate the physical capital stock of Guangdong Province and the three industries with constant price based on 1978. The formula is as follows:

In formula (8), i represents three industries, i = 1, 2, 3; and T represents the year, t = 1979, 1980, …, 2019. Therefore, the calculation of perpetual inventory method includes the following four parts:(1)Invest in I_it in the current year. In the study, the reasonable index to measure the investment amount of the current year is the total fixed capital formation of the current year. The specific data come from the Guangdong statistical yearbook over the years. The fixed capital formation data of the three industries are only published in 1993, 1994, and 1995, which are replaced by the fixed capital investment data. The specific data are also from the Guangdong statistical yearbook over the years.(2)Investment reduction index. Firstly, the investment reduction index of Guangdong’s total fixed capital formation is divided into two parts: first, the investment reduction index is constructed based on the ratio of total fixed capital formation (constant price) with 1978 as the base period to the total fixed capital formation of the current year in Guangdong Province. Secondly, since the development index of the province’s total fixed capital formation from 2004 to 2019 could not be obtained, the fixed asset investment price index was selected to replace it. Thirdly, the fixed asset investment price index of the three industries in Guangdong Province was not published, so refer to Xu et al. [50] The calculation method of the investment reduction index of the three industries is as follows: In the above formula, P_it is the corresponding GDP deflator of the three industries; IP_t is the fixed capital investment reduction index of Guangdong Province; and P_t is the GDP deflator of Guangdong Province.(3)Depreciation rate δ_t. Different types of literature have great differences on the selection of depreciation rate. This paper adopts the method of Zhang [51] and selects the depreciation rate of 9.6%.(4)Base period capital stock K_i1978. The capital stock in the base period is calculated by Xu [52] based on the material capital stock data of Guangdong Province and three industries in 1979.

In Table 1, the data are tabulated and presented.

4.4. Correlation Analysis

The correlation analysis results are shown in Table 2. The correlation coefficient among GDP, labor input, and capital investment is greater than 0.9 in Guangdong Province. The results show that there are close relationships among GDP, labor input, and capital investment.

5. Analysis of Empirical Results

5.1. Contribution of Capital, Labor, and Total Factor Productivity to GDP Growth in Guangdong Province

The evolution trend of the contribution of various factors to the economic growth of Guangdong Province is shown in Tables 3 and 4.

5.1.1. Capital Investment Plays a Primary Role in Promoting Economic Growth

From 1979 to 2019, the contribution of capital investment to GDP growth in Guangdong Province ranked first among the three factors, and the annual average contribution rate and pulling rate reached 58.8% and 6.9%, respectively. Its evolution characteristics are as follows: firstly, from the change trend, capital investment has maintained the first driving position for GDP growth for a long time. Among the 41 years, 34 are the years in which capital plays the primary role. Since 1992, capital investment has maintained a stable first contribution position with an average annual contribution rate of 70%. Secondly, from the perspective of industry, the change of the positive force of capital investment on GDP growth since 1979 mainly comes from the secondary and tertiary industries. The capital investment in the secondary and tertiary industries has driven 6.9% and 6.5% of the GDP of the secondary and tertiary industries, respectively.

5.1.2. Total Factor Productivity Plays a Second Role in Promoting Economic Growth, with Large Fluctuation in Change Trend and Low Contribution Level

From 1979 to 2019, the average annual contribution rate of total factor productivity to GDP growth in Guangdong Province was 25.8%, while the pulling rate was 3.8%. Its evolution characteristics are as follows: firstly, from the change trend, the contribution of total factor productivity to economic growth ranks second in most years, but fluctuates greatly. From 1979 to 1992, total factor productivity had a great positive impetus to economic growth and was the first driving force to promote economic growth in seven years, while from 1993 to 2019, the force of total factor productivity on economic growth showed a certain attenuation trend and decreased to the third driving force in 10 years. Secondly, from the industrial dimension, the change of the positive force of total factor productivity on GDP growth mainly comes from the secondary industry. The total factor productivity of the secondary industry drives the GDP growth of the secondary industry by 4.4% on an average annual basis.

5.1.3. The Contribution of Labor Input to Economic Growth Is Relatively Stable and Low

From 1979 to 2019, the annual average contribution rate of labor input to GDP growth in Guangdong Province was 10.9%, while the pulling rate was only 1.3%. The evolution characteristics of labor input contribution are as follows: firstly, from the change trend, the contribution of labor input to GDP growth is relatively stable, and it has been ranked third in most years. Secondly, from the industrial dimension, the contribution of labor input to GDP growth mainly comes from the secondary and tertiary industries. Among them, labor input drives 2.7% of the total economic growth value of the secondary industry, while the labor input of the tertiary industry plays a greater role in economic growth, with a pulling rate of 3.2%.

5.2. Decomposition of Total Factor Productivity Growth

The pulling rate of industrial capital (labor) productivity and industrial capital (labor) allocation structure on capital (labor) productivity growth in Guangdong Province from 1979 to 2019 is calculated, as shown in Tables 5 and 6.

5.2.1. Both Capital Productivity and Allocation Structure Have a Negative Effect on the Overall Capital Productivity

The specific evolution characteristics are as follows: from the perspective of the overall pulling effect, the capital productivity of the three industries plays a weak negative pulling role in the growth of the overall capital productivity, while the capital allocation structure of the three industries plays a major negative pulling role in the growth of the overall capital productivity. From the industrial dimension, only the capital productivity of the primary industry has played a positive role in driving the overall capital productivity, with an average annual driving rate of 0.9%. On the contrary, the negative pull effect of capital allocation structure on overall capital productivity in the three industries also mainly comes from the role of the primary industry, with an average annual pulling rate of −1.5%, much higher than the other two industries.

5.2.2. Industrial Labor Productivity and Labor Allocation Structure Play a Positive Role in Driving the Overall Labor Productivity

From 1979 to 2019, the average annual pulling rates of the labor productivity and labor allocation structure of the three industries on the overall labor productivity were 8% and 1.8%, respectively. The evolution characteristics are as follows: firstly, in the growth of the overall labor productivity, the labor productivity and allocation structure of the three industries have played a positive role. The pulling effect of labor productivity of the three industries is positive, the maximum value is 18.7% in 1993, and the minimum value is 0.3% in 2003. The pulling rate of the labor allocation structure of the three industries is positive in most years. Among the 41 years, only 11 years have a negative pulling rate, of which the largest contribution is 2003, with a pulling rate of 9.8%, and the smallest is 2000, with a pulling rate of −3.6%. Secondly, from the perspective of the contribution of the three industries, the pulling effect of the labor productivity and labor allocation structure of the three industries on the overall labor productivity is mainly the secondary industry, with the pulling effect reaching 5.4% and 1.2%, respectively.

5.3. Decomposition of GDP Growth Momentum

Using the deep decomposition formula of GDP growth momentum, this paper calculates the contribution rate and pulling rate of three industrial factor productivities and industrial factor allocation structure in Guangdong Province from 1979 to 2019. The calculation results are shown in Tables 7 and 8.

5.3.1. Industrial Factor Productivity Plays a Major Role in Promoting the Economic Growth of Guangdong Province

From 1979 to 2019, the average annual contribution rate of industrial factor productivity to GDP growth in Guangdong Province was 26.8% and the average annual pulling rate was 3.7%. Its evolution characteristics are as follows: firstly, the contribution of industrial factor productivity to GDP growth plays a positive contribution and pulling effect. Among the 41 years from 1979 to 2019, there were 38 years in which industrial factor productivity played a positive contribution and pulling effect, with the maximum contribution rate of 71.9% in 1980 and the minimum contribution rate of −5.4% in 2005; secondly, from the industrial dimension, the contribution of industrial factor productivity to GDP growth mainly comes from the secondary industry, with an average annual pulling rate of 2.2%, accounting for 60.6% of the total pulling rate; and thirdly, from the perspective of internal factor structure contribution, labor productivity plays a major role in GDP growth, with an average annual contribution rate of 32% and an average annual pulling rate of 3.9%, while capital productivity has a negative contribution to economic growth, with an average annual contribution rate of −6.2% and an average annual pulling rate of −0.2%.

5.3.2. The Industrial Factor Allocation Structure Plays a Weak Negative Role in Promoting the Economic Growth of Guangdong Province

From 1979 to 2019, the average annual contribution rate and pulling rate of industrial factor productivity to GDP growth in Guangdong Province were −3.4% and 0.1%, respectively. The change trend of the contribution rate and pulling rate of industrial factor allocation structure to GDP growth mainly shows the following characteristics: firstly, the force of industrial factor allocation structure on GDP growth fluctuates up and down between positive and negative promotion, and the years with positive and negative contribution rates are 21 and 20, respectively. Furthermore, from the perspective of the five-year plan period, during the ninth five-year plan period, the industrial factor allocation structure played the largest negative role in promoting GDP growth. At this time, the average annual contribution rate and pulling rate were −34.9% and −3.8%, respectively. Secondly, from the industrial dimension, the factor allocation structure of the tertiary industry is the most important factor affecting the positive contribution of the industrial factor allocation structure to GDP growth, with an average annual contribution rate and pulling rate of 4.1% and 0.5%, respectively. Thirdly, from the structural contribution of internal factors, the positive force of industrial factor allocation structure on GDP growth mainly comes from industrial labor allocation structure, which has an average annual contribution rate of 4.4% and an average annual pulling rate of 0.8% to economic growth, while the role of industrial capital allocation structure is negative, with an average annual contribution rate of −8% and a pulling rate of −0.7% to economic growth.

6. Conclusion and Policy Implication

6.1. Conclusion

Based on the analysis of the basic composition and momentum transformation law of economic growth momentum in Guangdong Province from 1979 to 2019, the conclusions are as follows: Firstly, the first driving force to promote economic growth is capital investment that has a stable position. Total factor productivity plays a second driving role in economic growth, with large fluctuation trend and low contribution level. Labor investment plays a role of stabilizing the third driving force for economic growth. Further, from the industrial dimension, the changes of capital investment, pull investment, and total factor productivity on economic growth mainly come from the changes of the secondary and tertiary industries. Secondly, both industrial capital productivity and capital allocation structure play a negative role in driving the overall capital productivity, and both industrial labor productivity and labor allocation structure play positive role in driving the overall labor productivity. Furthermore, from the industrial dimension, the change of the pull effect of industrial capital productivity and capital allocation structure mainly comes from the change of the pull effect of the primary industry, while the change of the pull effect of industrial labor productivity and labor allocation structure mainly comes from the change of the pull effect of the secondary industry. Thirdly, total factor productivity plays a major role in promoting economic growth, mainly relying on industrial factor productivity, and industrial factor allocation structure plays a weak negative role in economic growth. Furthermore, whether it is industrial factor productivity or industrial factor allocation structure, its internal main role is labor force.

6.2. Policy Implication

Our findings provide some policy implications on how to maintain economic growth in the new era, especially in the developing countries. Firstly, deepening market reform at home and opening up to the outside world at a higher level. By creating a more fair and effective market competition environment; optimizing the development environment of private enterprises; further reducing taxes and fees; reducing the production costs of private enterprises; strengthening financial support for small-, medium-sized, and microenterprises; and avoiding the rupture of capital chain, we can improve the international competitiveness of private enterprises in today’s complex international environment. On the other hand, we actively build a global high-level free-trade zone, promote the accelerated agglomeration of innovation factors, and guide the establishment of a higher-level open economic system marked by institutional openness. Secondly, increasing human capital investment and attracting high-level talents to create new growth momentum. The municipal governments should issue more advantageous and attractive policies according to their own conditions and needs to provide high-level innovative talents for major enterprises. Many small- and medium-sized enterprises are faced with big financial pressure, forced to pay cuts and layoffs, which has led to an increase in the unemployment rate under the COVID-19. The primary task of achieving stable economic operation at this stage is to achieve stable employment. The government and major universities should provide continuous employment services and expand the recruitment of grass-roots service projects in order to realize the drainage of labor force. Developing economies should pay particular attention to how to avoid a huge impact on employment in the era of artificial intelligence. Thirdly, promoting the rational allocation of resource investment among industries, and combining the revitalization of traditional industries with the vigorous development of emerging industries in the development strategy to promote the optimization and upgrading of industrial structure. Fourthly, optimizing the allocation of interindustry resource investment, and combining the revitalization of traditional industries with the vigorous development of emerging industries in the development strategy, which can promoting the optimization and upgrading of industrial structure. Fifthly, promoting industrial digitization and digital industrialization. The digital economy has become a new economic form, which will have a significant impact on all countries in the world and is also the main driving force of economic growth in the future. On the one hand, we should actively promote the digital transformation of industries, especially through the digital transformation of traditional industries, so as to achieve high-quality economic development. On the other hand, we should actively promote digital industrialization and build a larger digital economy industry.

Data Availability

The basic data of this paper are from Guangdong Statistical Yearbook. The statistical Yearbook is available at https://stats.gd.gov.cn/gdtjnj/. The research data can be found in https://kdocs.cn/l/crL1dWgWTwLu.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

This paper was supported by the Philosophy and Social Science Planning Project of Guangdong Province, “Implementation performance and Policy Optimization of Fiscal Subsidy Policies for Scientific and technological Innovation in Guangdong Province” (No: GD20CYJ33), and “Research on mechanism and policy of heterogeneous knowledge agents’ cooperative innovation in the context of ‘Internet +’ and manufacturing industry” (No: GD18XGL05).

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