Abstract
High-advanced industries upgraded by digital empowerment have gradually become an important support industry. Therefore, various provinces in China have issued relevant policies to support the prosperous of the digital economy and high-advanced industries. The collection and analysis of high-advanced industrial policy help to scientifically evaluate industrial policies and formulate scientific policy optimization paths. Based on a total of 168 high-advanced industrial policy documents from 26 cities in the Yangtze River Delta region from 2009–2021, this study adopts the PMC-Index model to evaluate the high-advanced industry policies in the digitalization context quantitatively. 12 representative high-advanced industry policy texts were selected for specific analysis. In addition, this study visualizes the measurement results of the internal structure and policy effectiveness of policies by PMC-Surface diagrams and then concludes that the design of high-advanced industry policies was relatively reasonable overall, with 11 policies rated as “Good Consistency” and only one “Acceptable Consistency.” The sample policies lack reasonable arrangements for different period plans, lack incentives, or have relatively single incentives. The policy influence among cities in the Yangtze River Delta urban agglomerations is small, and the integration trend is not apparent. There is a particular gap in the scores between Shanghai, Zhejiang, Jiangsu, and Anhui province. This study provides references and suggestions for formulating and revising high-advanced industrial policies.
1. Introduction
Digital technology is dramatically changing industrial production and organizational activities [1, 2]. The digitization of industries is providing the impetus for economic development in various countries. Each country has different directions and motivations for using digital technology to promote economic development, so the impact of digitization on national economies is also different [3]. Digitization refers to using digital technology to create new business models, new business formats, and industrial production models, thereby improving product quality, production quantity, and production efficiency [4]. Digitization empowers and embeds industries to help industrial transformation and technological innovation [5, 6].
China is gradually shifting its economic focus from rapid GDP growth to high-quality economic development, considering transforming from the world’s factory to an innovative powerhouse with leading-edge technologies [7]. China needs to transform and upgrade its industry with the help of the digital empowerment of industry [8]. The high-advanced industry, closely linked to digitalization, has become the main target of industrial transformation and upgrading [9]. High-advanced industries characterized by high technological knowledge density, high intensity of R&D investment, and high added value have gradually become essential support industries for national economic development and are an important symbol of a country’s core competitiveness [10]. This study takes the advanced industry proposed by the American Brookings Institution as the core definition of a high-advanced industry. The study “America’s Advanced Industries,” released by the Brookings Institution in February 2015, states that industries that meet the following two criteria are defined as the high-advanced industry: First, the industry must use at least 80% of the expenditure for research and development each year. Each worker must spend more than $ 450 on research and development. Secondly, the proportion of workers in the industry that requires high-level STEM (science, technology, engineering, and mathematics education) knowledge should also be higher than the national average, or 21% of all workers [11].
For the sake of meeting the trend of digital economy and realizing the transformation from low advanced industries to high advanced industries, the governments of various regions in China have adopted relevant industrial policies. However, in the policy playing a role, there are also some problems: the security measures and incentives of the policy are ineffective, there is a lack of mechanism for policy implementation and supervision, the content of the policy is incomplete, and the effect of policy implementation is not obvious. Therefore, it is necessary to scientifically evaluate and judge the high-advanced industrial policy, test the effectiveness of the policy, reasonably allocate the policy resource base, and provide a scientific governance basis [12].
The PMC-Index model can evaluate policies based on establishing a system of relevant indicators and calculate each policy’s score composition to effectively evaluate policies [13]. This study creatively puts forward the concept of China’s high-advanced industry, evaluates the effectiveness of policies by the PMC-Index model, taking the Yangtze River Delta region of China as an example.
The remaining structure of this study is as follows. Section 2 reviews the relevant literature. Section 3 introduces the research samples and research methods. Section 4 demonstrates the empirical results and analysis. Section 5 puts forward the conclusions and limitations.
2. Literature Review
Existing research on high-advanced industry mainly focuses on three categories. One is the research conducted with high-advanced industry as the subject term. Since the concept of the high-advanced industry mentioned above is not clearly defined, fewer studies directly use the high-advanced industry as a subject term [14]. The second is the study of a specific industry in the high-advanced industry [15]. Wu et al. processed and modeled the data through spatial econometric models to derive the impact of regional financial resources on the cluster of high-advanced horizontal service industries [16]. The third is the study of strategic emerging industries [17]. Prud’homme examined technological specialization in strategic emerging industries and found that China’s economic decentralization system ensures, to some extent, the effectiveness of provincial industrial policy making [18].
Policy evaluation is the development of appropriate evaluation criteria through scientific methods to examine policies in multiple dimensions and provide references for policy improvement and new policy development. It can assess the usefulness and value of the policy itself and check the results and effectiveness of its implementation [19]. The first policy evaluation studies were the five-category assessment model proposed by Suchman [20] and the “Three E” Evaluation Model Architecture proposed by Poland [21]. Jun proposed a classical policy evaluation approach to policy analysis through causality [22]. At present, the commonly used policy evaluation methods include the hierarchical analysis process (HAP), BP neural network, and fuzzy comprehensive evaluation method [23, 24].
The policy modeling consistency index (PMC-Index) model was proposed by Estrada, which can evaluate any social policy to analyze the results and impacts of implementation and the reasons for the results or impacts [25]. The model provides policy researchers with a new tool for policy analysis that can detect policy strengths and weaknesses. The model has been applied to the evaluation of the arable land protection policy [26], pork price policy evaluation [13], and the new energy vehicle policy evaluation [27].
3. Materials and Method
3.1. Data Source and Sample Selection
To obtain high-advanced industry policy texts in a digital context systematically and comprehensively, the following search strategy is used in this study. Taking 2009 as the policy starting point, the policy and regulation databases such as “Beida Fabao” (http://www.pkulaw.cn/) and “Beida Fayi” (http://www.lawyee.net/) were used as data sources, supplemented by the official websites of cities in the Yangtze River Delta. We searched for “high-advanced industry” and “digitalization” as keywords to filter the policy texts of high-advanced industry in the context of digitalization in each city of the Yangtze River Delta. After the above screening, 168 policy texts were retrieved from 26 cities in the Yangtze River Delta region from January 1, 2009, to April 19, 2021. Based on the retrieved policies, these policy texts were sorted out to eliminate those that were irrelevant and repetitive to high-advanced industries, and finally, 44 policy texts with strong relevance were screened out.
3.2. Establishment of PMC-Index Model
As shown in Figure 1, there are five steps to construct the PMC-Index model: Policy text mining is used to classify variables, identify parameters, and then determine this evaluation system’s main variables and subvariables. Multi-input-output tables are then constructed based on variable classification and parameter identification to form the framework for data analysis. The results are further analyzed by calculating the PMC-Index to quantify the analysis and visualize the PMC-Surface diagram.
3.2.1. Word Division and Word Frequency Statistics
In this study, the policy text is processed with the help of the text mining tool ROST software. First, the core keywords of the policy text are obtained by reading the policy text, and then the keywords are imported into the word separation table of ROST for the next word separation and word frequency statistics. Next, 44 high-advanced industry policy texts were input into the ROST, and the obtained text sets are word-sorted and word frequencies are counted in the order from highest to lowest. Since ROST software automatically identifies keywords with no practical meaning, it is necessary to filter out high-frequency words such as “provide” and “above” that have no practical meaning for policy analysis. After eliminating the redundant words, the valid keywords are screened out and a list of the top 50 keywords in terms of word frequency is compiled as shown in Table 1.
3.2.2. Analysis of Social Networks
Reimport statistical keywords above into the Rost software and extract the high-frequency words and row features from the cooccurrence matrix. Based on the cooccurrence matrix, the visualization network diagram of China’s high-advanced industrial policy is drawn by Ucinet software.
As shown in Figure 2, each node represents a keyword, and the line between nodes represents the existence of a correlation between two nodes. In the social network, the node at the center is more important, can be interconnected with more nodes, and has more influence on the whole social network. In this social network, technology, innovation, R&D, etc., are at the center position, indicating that these keywords have a vital influence on the policy guidance in high-advanced industries. The digitization of high-advanced industry and technological innovation are inseparable. Relying on R&D to increase the added value of technology can realize the transformation and digitization of technology from low-advanced to high-advanced and realize industrial layout and growth. Keywords around the center are talent, market, entrepreneurship, industry chain, etc. That is, the transformation and digitalization of high-advanced industry cannot be separated from the support of talents, the improvement of the market, the vitality of entrepreneurship, and the construction of the industrial chain. Talents bring intellectual capital to the high-advanced industries. The perfection and development of the market realize the supply and demand balance. Entrepreneurship brings new growth points, creates a new pattern of high-advanced industrial chain development through industrial complementary cooperation, and lays out the industrial chain around the innovation chain, so as to enhance international competitiveness. These keywords are in between the central and marginal positions and have a relatively strong influence. They are both influenced by main keywords and can influence marginal keywords. The keywords in marginal positions are less influential and have even weaker interconnections with other keywords.
3.2.3. Classification of Variables and Identification of Parameters
Based on Jin’s policy evaluation variables proposed by Jin [24] and the words combined with high-frequency of policy texts and their social networks analysis results, 10 main variables and 38 subvariables are set and shown in Table 2.
The 10 main variables are as follows: (X1) Policy type; (X2) Policy timeliness; (X3) Policy function; (X4) Incentives; (X5) Policy area; (X6) Policy evaluation; (X7) Policy focus; (X8) Policy object; (X9) Policy level and (X10) Public policy. Policy type (X1) is to evaluate whether the policy has a predictive, supervisory, advisory, descriptive, and guiding role for high-advanced industries. Policy timeliness (X2) is to judge whether the policy has the effective duration of long-term, short-term, medium-term, or less than 1 year. Policy function (X3) is to reveal the purpose of policy introduction and to judge whether the policy can improve the industry’s quality level, promote the industry’s restructuring, and promote innovation within the industry for the high-advanced industry. Incentives (X4) refers to incentive measures of the smooth implementation and the active cooperation of policy recipients, including talent introduction, tax subsidies, R&D subsidies, and other incentive measures. Policy area (X5) is a division based on economic, social, scientific, political, environmental, and other areas to judge the areas covered by the policy. Policy evaluation (X6) is to evaluate the reasonableness of the policy based on four aspects: adequate basis, a clear objective, a scientific program, and explicit content. Policy focus (X7) is to examine the focus involved in the policy content, including innovation, talent training, the transformation of results, market leadership, and other focus. Policy object (X8) is the object of policy action, including enterprises, financial institutions, regulatory authorities, and other objects. Policy level (X9) indicates the scope of policy implementation, which is divided into three levels: regional cluster, province, and industry. Public policy (X10) reflects whether the policy is publicly released.
If the policy text has the same content as the subvariables, the parameter of the subvariables is set to “1;” otherwise, it is set to “0.” After setting the main variable and subvariable, use the text mining method to score the variables. The variable scoring criteria are shown in Table 3.
3.2.4. Constructing Multi-Input-Output Table
This study stores, calculates, and analyzes data through multi-input-output tables and uses policy variables to evaluate the effect of the results.
3.2.5. Calculation of the PMC-Index
Step 1: place the main variables and subvariables into a multi-input-output table. Step 2: evaluate the subvariables based on the variable scoring criteria for text mining. Expression (1) is used to determine the subvariables to be evaluated. Step 3: integrate the values of each subvariable obtained in step 2 according to Expression (1) and calculate the value of variables. Step 4: measure the final PMC-Index according to Expression (2).a = main variables; b = subvariables:
3.2.6. Evaluation the Consistency of Policy Value
The PMC index value can reflect the strategic model’s consistency level. As shown in Table 4, when the PMC index value is 0–4.99, the consistency of policy is low. When the PMC index value is 5–6.99, the consistency of policy is acceptable. When the PMC index value is 7–8.99, the consistency of policy is good. When the PMC index value is 9-10, the consistency of policy is entirely perfect. The higher the PMC index value is, the more perfect the content of the policy text is. Then, the policy can have strong operability in practice.
To make the policy assessment more objective and reduce subjective errors, the corresponding score is increased only when the assessment indicators are clearly and explicitly described in the policy text; otherwise, no points are added. When there are indeterminable or highly subjective assessment indicators, discuss them with other researchers before deciding whether to add points. This will make the assessment results more objective and have higher credibility.
3.2.7. Visualization of the PMC-Surface
By visualizing the PMC index value, this study draws a PMC surface diagram, which can intuitively and clearly perspective the policy model, so as to judge the effectiveness of the policy. Draw PMC surface diagram according to PMC matrix in Expression (3). PMC matrix is a 3 × 3 matrix mainly composed of nine variables, namely (X1–X9). Because the number of rows and columns in the matrix are the same, these variables have a certain balance and symmetry. Among them, X1, X2, and X3 are series 1, X4, X5, and X6 are series 2, X7, X8, and X9 are series 3.
4. Results
Considering that the policy priorities issued by government agencies and the commonality and respective characteristics of policies in different regions are different, this study selects 12 representative policy texts according to the provinces or municipalities to which the Yangtze River Delta cities belong. As shown in Table 5, is the policy of Shanghai, is the policy of Anhui, is the policy of Jiangsu, and is the policy of Zhejiang.
According to the PMC index model of the above high-advanced industrial policy evaluation, this study uses the text mining method to construct multi-input-output tables for 12 policies and thus calculates scores for each policy. Finally, the PMC-Index and evaluation level of the policies is constructed and shown in Table 6.
As shown in Table 6, the mean value of the 12 policies is 8.09. Among the 12 policies, only the level of is acceptable, while the level of the PMC-Index for the rest of the policies is good. The overall quality of the 12 policies is excellent. No low consistency policies, with a certain degree of scientific and rationale, can provide guidance for the rapid growth of the high-advanced industry. Nevertheless, the lack of perfect, consistency policies also mean there is still a need and room for further improvement in the design of the policies in terms of content. According to the policies divided by different regions, the mean PMC-Index of Shanghai is 8.24, the mean PMC-Index of Anhui is 7.57, the mean PMC-Index of Jiangsu is 8.35, and the mean PMC-Index of Zhejiang is 8.20. The policy with the highest PMC-Index score is in Jiangsu province, Zhejiang and Shanghai, while Anhui’s policy performs poorly compared to other provinces.
To facilitate comparisons between policies, this study plots PMC-Surface for each of the 12 policies by using PMC-Index and PMC-Matrix and conducts a detailed analysis which are shown in Figures 3(a)–3(l). The different colors in the graph represent different segments, with a depression indicating that the variable has a lower score than the other variables and a bump indicating that the variable has a higher score than the other variables.
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Most of the 10 main variables scored high and achieved good performance. Among them, X10 (Public policy) has a score of 1 with perfect consistency, which is since the implementation of the country’s policies is based on the people, so an open-ended approach is taken to the policies. In comparison, X2 (Policy timeliness) and X4 (Incentives) have poorer scores, which differ significantly from the mean. Since most of these policy texts play a guiding role in the policy implementation, the specific arrangements for different periods are not described in great detail. It is also because these policy texts describe the policy in general terms that they do not describe in detail the incentives for the development of the high-advanced industry. Relevant institutions need to improve these, make more detailed and reasonable arrangements for the content of the policy texts, and add some incentives to make the guidance and role of the policy text clearer. To facilitate the comparison of the degree of depression between each principal variable, plot the average depression index of each principal variable in the PMC-Surface as a radar plot in Figure 4. In Figure 4, the mean values of concavity indices for X2 and X4 are 0.60 and 0.52, respectively, which are significantly more concave than the other main variables. The mean value of the depression index for X9 is similarly higher than the overall mean. The concavity indices of the remaining main variables are all smaller than the mean, indicating that these policy texts are more consistent and dominant in these areas.
When divided by region, there is convergence in the approach of policies introduced by local institutions, and there is some similarity in the values of the resulting PMC-Index. However, there is some variability in the PMC-Index of policies between regions. At a macro level, this is due to the economic, political, and cultural influences of different regions, which lead to differences in the level of policies introduced. At a micro level, the level of policies introduced is limited by the scope of authority of different institutions and their level of competence.
Most of the 12 policy texts have good consistency with a mean value of 1.91 for the degree of concavity of the PMC-Surface. Only the policy level of is acceptably consistent, and the degree of depression is much greater than the average degree of depression. This study compares the with the best-performing to compare where the gap exists between the two, and to arrive at the same advantages or disadvantages. As shown in Figure 5, has a particular gap with overall. The scores of the main variables X1, X9, and X10 of are the same as , but the scores of the remaining main variables of are lower than .
X2 (Policy timeliness). In , there is only a medium-term plan for the next 5 years, not a long-term plan or a more detailed plan for the short term. has not only a 5-year medium-term plan but also a long-term plan beyond 5 years, but the description of the short-term plan is still missing in .
X3 (Policy function). contains the content of promoting the quality of the industry and scientific and technological innovation, but there is no clear proposal for restructuring the industry. On this issue, does not keep pace with the times and makes strategies to improve the industrial structure. ’s policy features are much more comprehensive, providing more detailed descriptions of each of these directions.
X4 (Incentives). Regarding incentives, only mentions making corresponding financial subsidies, and there are no more incentives to encourage the development of the high-advanced industry. While in , not only financial subsidies and loan subsidies are mentioned, but also talent introduction and tax subsidies, which are more conducive to accelerating the development of the high-advanced industry. Nevertheless, still has no content about R&D subsidies, lacking emphasis and focus on science and technology R&D.
X5 (Policy area). covers various areas such as economy, politics, ecology, etc., but the description of the social area is less clear, so it does not get the corresponding score. , on the other hand, clearly articulated each area and received a score of 1.
X6 (Policy evaluation). has sufficient basis, clear objectives, scientific arrangement, and detailed description. The content of seems to be general and cursory, and the short length does not describe clearly in detail, but other aspects of the performance are not bad.
X7 (Policy focus). The content of has the focus on policies such as innovation, the transformation of achievements, and market leadership. In addition to these elements, also mentions the introduction of human resources and the importance of being people-oriented.
X8 (Policy object). mentions enterprises, financial institutions, management agencies, and other policy objects, but the descriptions are cursory and there are no other policy objects. then clearly and explicitly states these policy objects, on top of which other policy objects such as service organizations are also mentioned.
5. Discussion and Conclusions
5.1. Conclusion and Implications
This study adopts the content analysis method and text mining method, combined with the PMC-Index model to evaluate high-advanced industry policies in Yangtze River Delta region quantitatively and selects 12 representative high-advanced industry policy texts for specific analysis. The study found that the design of high-advanced industry policies was relatively reasonable overall, with 11 policies rated as “Good Consistency” and only one policy rated as “Acceptable Consistency.” In general, the high-advanced industry policy has, to some extent, promoted the industrial restructuring and the development of high-advanced industry in China, but there are still some problems to be improved.
First, the policy lacks reasonable arrangements for different period plans. The mean value of X2 (policy duration) is only 0.4, and most of the policy samples have only planned arrangements for one or two periods. Although the formulation is detailed and precise, there is no mention of arrangements for other periods. The establishment of planning arrangements for different periods is beneficial for enterprises, local governments, and relevant departments to have clear objectives at different periods of high-advanced industrial development. It also needs to optimize “guidance” in X1 and “detailed content” in X6.
Second, the sample policies lack incentives or have relatively single incentives. Most of the sample policies contain incentives in the form of financial subsidies and loan subsidies, but almost all of them do not mention R&D subsidies, reflecting China’s insufficient incentive policy support for R&D in high-advanced industries. The level of R&D subsidies in China has increased in recent years, but most of them are provided directly to state-owned enterprises, with only a tiny percentage allocated to private and triple-funded enterprises. As a result, there is little mention of R&D subsidies in the sample policies. In the subsequent policy development and modification, attention should be paid to the related issues, and resources should be allocated rationally to improve the effectiveness of incentives.
Third, the policy influence among cities in the Yangtze River Delta urban agglomerations is small, and the integration trend is not apparent. The score for the subvariable “regional cluster” in X9 is almost 0. The synergy effect of high-advanced industry policies between different regions is relatively low, and the advantages of integrated development of the Yangtze River Delta urban agglomerations have not been shown. There is a certain gap in the scores of high-advanced industrial policies between Shanghai, Zhejiang, Jiangsu, and Anhui. The policy with the highest PMC-Index score is in Jiangsu, Zhejiang, and Shanghai, while Anhui’s policy performs poorly compared to other provinces’ policies, which means there is still a need and room for further improvement in the design of the policies in terms of content. Partners between urban agglomerations can benefit from the dynamic synergies of mutual growth through reciprocity, knowledge exchange, and realizing significant economies of scope.
5.2. Limitation
There are still some limitations in this study. First, further research is still needed for the dimensionality and extension of the variable selection [28–32]. On the one hand, some variables with universal applicability can be set according to the basic attributes of the policy [33]. On the other hand, some variables with targeted nature can be set out according to the special attributes of the studied policies, which can be analyzed for specific directions [34]. Second, although this study integrates the results of content analysis and text mining, the evaluation of policies and the set of variables are still somewhat subjective [35, 36]. Third, because the PMC-Index model needs to take into account all global variables, the model does not enable a detailed analysis for a particular direction.
Data Availability
The data supporting the conclusions of this research can be obtained from the corresponding author upon request.
Conflicts of Interest
The authors declare that there are no conflicts of interest in this study.
Acknowledgments
This work was supported by the Youth Project of Shanghai Philosophy and Social Sciences Planning (2020) (Grant no. 2020EJB001), the Project of Research Center for Cultural Industry Development of Sichuan Provincial Key Research Base of Social Sciences (2020) (Grant no. WHCY2020A01), the Teacher Development Research Project of the University of Shanghai for Science and Technology (2022) (Grant no. CFTD223013), and the Ministry of Education Industry-University Cooperation Collaborative Education Project (2022).