1. Introduction
Bank-enterprise relationship refer to the economic interaction between banks and enterprises during the course of economic operations. This is primarily evidenced by a closer relationship between banks and enterprises that is based on factors such as business background, shareholding affiliation, and cooperation experience. This is frequently accompanied by the exchange of "inside information." A beneficial bank-enterprise relationship is conducive to overcoming the imbalance in information, promoting the mutual benefit of banks and enterprises, and assisting enterprises in starting up and innovating.
The flaws of the old productivity model, which is based on labor, capital, and resource-driven production, are becoming more and more apparent as development levels grow. This paradigm has been overly rude. When this model is juxtaposed with the new productivity quality, it clearly shows a production efficiency disadvantage. Additionally, it is characterized by issues such as high energy consumption, overcapacity, and vulnerability to bottlenecks. [1] This necessitates a transformation of the domestic economic structure from an "old" to a "new" state. This shift would facilitate the comprehensive advancement of new quality productivity within enterprises, thereby propelling innovative growth and accelerating the pace of Chinese modernization.
The new quality productivity of enterprises is predicated on science and technology innovation, which is distinct from the traditional productivity and economic growth model. [2] However, the potential for innovation introduces a heightened need for banks to possess robust risk screening and pricing capabilities. A positive relationship between banks and firms allows for the understanding of internal information, which is beneficial for the assessment of risk and the alleviation of financial constraints. Additionally, banks can monitor or participate in the decision-making processes of enterprises, which is also advantageous for the reduction of business risks and the enhancement of the new quality productivity level of enterprises in a high-quality and sustainable manner.
When compared to the body of current research, this paper's potential innovations and contributions mostly lie in the following areas: Firstly, current research on the NQP focuses mainly on analyzing how it is measured; or exploring the role of the NQP in contributing to the level of economic development, but the NQP of firms is underexplored. This paper examines the driving factors of the new quality productivity of enterprises, which is of greater significance for micro reference. Secondly, the current body of research on the interaction between banks and enterprises focuses mostly on the "impact of enterprise behavior on the bank-enterprise relationship" rather than the relationship's influence on enterprises' development, which is addressed in this paper. Thirdly, the current articles on the behavior of financial institutions in serving the development of new quality productivity are more inclined to qualitative inference, while this paper better explains the important mechanism of financial influence on the development of new quality productivity through quantitative analysis, which is an effective expansion of the research literature on the relationship between finance and new quality productivity.
2. Literature review
New quality productivity is the advanced result of productivity change. Pu Qingping and Xiang Wang [3] believe that the new quality productivity takes science and technology innovation as its core, and consists of "high-quality" laborers, "new medium" labor materials, and "new material quality" labor objects. In terms of measurement, Wang Ke and Guo Xiaoxi [4] constructed the index system from the three dimensions of workers, labor objects, and labor materials. Han Wenlong and Zhang Ruisheng [5] added indicators of new technology, production organization, and data elements on the basis of the above physical elements. These factors also facilitate the development of new quality productivity in enterprises.
Bank-enterprise relationship refers to a series of explicit and implicit contractual arrangements between firms and banks, and Fame posits that the "ongoing relationship" of bank-enterprise interactions can generate part of the "inside information" [6]. In regard to the influence of the bank-enterprise relationship, Huang Anzhong et al. posit that the relationship can mitigate the financing constraints resulting from information asymmetry. [7]. He Xiaoyu et al. found that the bank-enterprise association has a positive impact on technology-based businesses' performance in innovation [8]. Qian Long, conversely, posits that bank-enterprise relationships may give rise to the "lassoing problem," thereby increasing the cost of financing.[9]. The common ways of measuring bank-enterprise relationship include the length of time, the number of banks, the shareholding relationship and the executive relationship.
It can be found through combing the literature at this stage that the current research on new quality productivity focuses more on qualitative research on its connotation, characteristics, formation mode, etc., or the evolution of its development level, and less on quantitative research on the factors influencing the new quality productivity of enterprises. The current literature on bank-enterprise relationship focuses more on the formation of bank-enterprise relationship and its effect of promoting enterprise financing and innovation. However, there is a lack of assessment of the degree of influence of bank-enterprise relationship on the effect of high-quality development on enterprises from a more comprehensive perspective. Thus, it is very valuable to do research on the behavior of the new quality productivity level of firms and the banking-enterprise relationship at the micro level, with a focus on the goal of the relationship being to promote higher quality of enterprise development.
3. Mechanism analysis and research hypothesis
A good bank-enterprise relationship can play a role in influencing the productivity change of enterprises in a variety of ways. Firstly, the bank-enterprise relationship enhances the new quality productivity level of enterprises by alleviating their financing constraints. A good bank-enterprise relationship reduces the information imbalance, enables banks to evaluate the growth of firms more precisely, reduces non-interest rate financing constraints, eases the financing constraints of enterprises, and helps enterprises obtain flexible and applicable capital flow for technological innovation, equipment upgrading, talent recruitment, and other activities, thus enhancing the level of new quality productivity of enterprises. Secondly, the bank-enterprise relationship improves the new quality productivity level by raising the caliber of company information disclosure. The establishment of bank-enterprise relationships further strengthens cooperation between banks and enterprises. Enterprises can make use of the resources and platforms of banks to improve the transparency and quality of their information disclosure, which in turn improves their image and the confidence of investors, so as to broaden the opportunities for innovative cooperation and promote the growth of firms’ new quality productivity. Thirdly, the bank-enterprise relationship enhances the level of new quality productivity by reducing the agency costs of enterprises. The bank-enterprise relationship makes the bank become the external supervisory body of the enterprise, and by participating in or supervising the business behavior of the enterprise, the bank urges the enterprise to avoid short-term behaviors, reduce the agency cost, and seek the long-term goals and growth, which may enhance the effectiveness of the development of the new quality productivity level.
The above analysis leads to Hypothesis H1: The degree of new quality productivity of firms is positively impacted by the bank-enterprise relationship.
4. Research Design
4.1. Selection of Variables
The explained variable is the new quality productivity of enterprises (NQPE). Referring to the new quality productivity variable of enterprises calculated by Song Jia using the entropy method by weighting the breakdown indicators of live labor, materialized labor, hard technology, and soft technology [10].
The core explanatory variable is the bank-enterprise relationship (BC), which reflects the closeness between banks and enterprises in terms of economic behavior. Dummy variables are constructed with reference to Zhai Shengbao et al [11]. BC takes the value of 1 if there is a bank holding shares in the enterprise, an enterprise holding shares in the bank or an enterprise executive with a banking background.
With reference to the relevant literature, the following control variables are selected: firm size (SIZE): the natural logarithm of the book value of the firm's total assets; return on assets (ROA): EBITDA/total assets; intangible assets ratio (INT): net intangible assets/total assets; cash holdings (CASH): money funds holdings/total assets; financial leverage (LEV): Total liabilities/total assets (i.e., gearing ratio); corporate growth capacity (GMP): (total assets at the end of the current period - total assets at the end of the previous period)/total assets at the end of the previous period; corporate concentration of shareholdings (CR1): number of shares held by the first largest shareholder/total number of shares.
4.2. Modeling
Due to its individual-fixed effect and a time-fixed effect, the Two-way fixed-effects model is frequently employed in empirical research [12]. This study builds the following two-way fixed effect model for hypothesis H1 based on the preceding analysis:
\( {NQPE_{it}}={β_{0}}+{β_{1}}{BC_{it}}+{β_{n}}{control_{it}}++{μ_{i}}+{λ_{t}}+{ε_{it}}\ \ \ (1) \)
In model (1), the \( {NQPE_{it}} \) is the new quality productivity level in year t for the ith enterprise; \( {BC_{it}} \) is the status of bank-enterprise relationship of the ith enterprise in year t; \( {control_{it}} \) is the set of control variables; \( {μ_{i}} \) is the individual-fixed effect; \( {λ_{t}} \) is the time-fixed effect; \( {ε_{it}} \) is the randomized perturbation term; \( {β_{0}} \) is the intercept term; \( {β_{1}} \) is the coefficient of the core explanatory variable BC; \( {β_{n}} \) is the coefficient of the n control variables.
4.3. Sample Selection and Data Description
In this study, the sample period is chosen to be 2012–2022, and the data of listed businesses is taken from the CSMAR database to investigate the effect of the bank–enterprise relationship on the new quality productivity of enterprise. According to the research convention, the following data screening is carried out:
Excluding financial listed companies from the sample. Their asset structure is quite different from that of other companies, and their financial data and corporate liabilities also have special characteristics. Excluding ST, *ST, and delisted companies during the research period of this paper, these companies usually have delisting risks that need to be noted, and usually cannot represent normal company operating conditions. Excluding samples of listed companies with missing indicators of key variables. Winsorize the continuous variables involved in this paper by 1% above and below to prevent the effects of severe outliers on the findings of the study. Interpolation is applied to supplement the missing values of control variables.
5. Empirical analysis
5.1. Descriptive statistics
Table 1 displays the findings of the descriptive statistical analysis. The mean value of the BC is 31.6%, which indicates that during the period of 2012–2022, about 31.6% of Chinese listed firms have established bank-enterprise relationships, and about one-third of the firms realize that bank-enterprise relationships may have a significant impact on the development of innovation or the development of new quality productivity. The standard deviation of the new quality productivity of enterprise (NQPE) is 2.514, the minimum and maximum values are 0.78 and 14.972, respectively, indicating that there is a large gap in the new quality productivity indicator among Chinese enterprises and the overall level of new quality productivity of the enterprises needs to be improved.
Table 1: Descriptive statistics
Variables | Obs | Mean | Std. Dev. | Min | Max. |
Scode | 28889 | 309986.570 | 268065.040 | 2.000 | 873169.000 |
Year | 28889 | 2017.869 | 3.130 | 2012.000 | 2022.000 |
NQPE | 28889 | 5.210 | 2.514 | 0.780 | 14.972 |
BC | 28889 | 0.316 | 0.465 | 0.000 | 1.000 |
SIZE | 28889 | 22.292 | 1.297 | 20.089 | 26.389 |
ROA | 28889 | 0.039 | 0.057 | -0.206 | 0.197 |
INT | 28800 | 0.043 | 0.047 | 0.000 | 0.309 |
CASH | 28889 | 0.182 | 0.122 | 0.019 | 0.606 |
LEV | 28889 | 0.411 | 0.196 | 0.056 | 0.858 |
GMP | 27542 | 0.136 | 0.223 | -0.257 | 1.221 |
CR1 | 27451 | 0.346 | 0.147 | 0.091 | 0.747 |
5.2. Model analysis
Table 2 reports the baseline regression results of the model. From the results, it can be seen that the decidable coefficient R2 of the regression based on the two-way fixed effects model reaches 32.8%, which indicates that the model has a good degree of explanation. In the two-way fixed effects model, the coefficient of the bank-enterprise relationship is 0.087 at the 1% significance level. A one-unit change in the bank-enterprise relationship is associated with an average change of 0.087 units in the level of NQP. The hypothesis H1 is verified, which may be mainly due to the fact that the establishment of a bank-enterprise relationship brings the convenience of carrying out investment and financing activities of both banks and enterprises, as well as the fact that the bank enhances the information transparency of enterprises through external supervision, which creates favorable conditions for enterprises to carry out innovation and cooperation with the market, and to enhance the level of new-quality productivity.
Table 2: Regression results
(1) | (2) | (3) | |
VARIABLES | NQPE | NQPE | NQPE |
BC | 0.098*** | 0.077*** | 0.087*** |
(0.021) | (0.019) | (0.019) | |
SIZE | 0.104*** | 0.155*** | |
(0.017) | (0.020) | ||
ROA | -0.402** | -0.297* | |
(0.170) | (0.171) | ||
INT | 16.654*** | 16.939*** | |
(0.279) | (0.291) | ||
CASH | -2.122*** | -2.285*** | |
(0.087) | (0.089) | ||
LEV | -0.269*** | 0.009 | |
(0.082) | (0.085) | ||
GMP | -0.504*** | -0.556*** | |
(0.035) | (0.035) | ||
CR1 | -0.443*** | -0.262** | |
(0.113) | (0.125) | ||
Constant | 3.983*** | 2.084*** | 0.647 |
(0.030) | (0.365) | (0.437) | |
Observations | 28,889 | 27,367 | 27,367 |
R-squared | 0.182 | 0.328 | |
Year | Yes | Yes | Yes |
Fe | Yes | No | Yes |
Number of Scode | 4,132 | 3,953 | 3,953 |
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
5.3. Robustness Tests
First, this paper conducts robustness test on by narrowing the sample interval. After 2015, companies began to focus more and more on productivity transformation as well as scientific and technology innovation. The sample interval is therefore changed to 2015–2022. The robustness of the finding is confirmed by the results in Table 3's column (1), which show that the bank-enterprise relationship's coefficient is positive at the 10% significance level.
Second, we replacing the explained variables to perferm robustness test. To compute the proxy variable for businesses' new quality productivity to be regressed again, total factor productivity is calculated using the LP approach. The bank-enterprise relationship's coefficient is considerably positive at the 5% level, as indicated by the data in Table 3's column (2), further validating hypothesis H1.
Third, we conducts robustness tests by replacing explanatory variables. The Bank-enterprise relationship has a close relationship with the amount of new financing for enterprises. Therefore, this paper adopts the ratio of new liabilities to total assets of firms and the duration of bank-enterprise relationship as the proxy variables for the explanatory variables to measure the closeness and durability of the bank-enterprise relationship, respectively. In the results in columns (3) and (4) of Table 3, the effect of bank-enterprise relationship on the level of new quality productivity of enterprise is significantly positive at the 1% level, which further validates the robustness of the findings.
Table 3: Robustness test
(1) | (2) | (3) | (4) | |
VARIABLES | NQPE | TPF_LP | NQPE_r1 | NQPE_r2 |
BC | 0.036* | 0.012** | 0.344*** | 0.009*** |
(0.020) | (0.005) | (0.120) | (0.003) | |
Control | Yes | Yes | Yes | Yes |
Constant | 3.587*** | -4.469*** | 0.653** | 0.682 |
(0.495) | (0.120) | (0.281) | (0.437) | |
Observations | 22,049 | 27,046 | 27,367 | 27,367 |
R-squared | 0.203 | 0.577 | 0.113 | 0.328 |
Year | Yes | Yes | No | Yes |
Fe | Yes | Yes | No | Yes |
Number of Scode | 3,940 | 3,922 | 3,953 | 3,953 |
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
5.4. Heterogeneity analysis
There may be heterogeneity in the impact of the bank-enterprise relationships on the level of new quality productivity based on the nature of enterprise ownership. Compared with state-owned enterprises, which have more credit reliability and financing opportunities, private enterprises face relatively serious credit discrimination problems, and good bank-enterprise relationships have a more obvious effect on the new quality productivity level of private enterprises. Regression analysis is performed on a sample of private (non-state-owned) and state-owned firms; Table 4 displays the findings. BC and NQPE have a positive correlation in private enterprises at the 1% significance level, which is greater than the significance of the state-owned enterprises sample, verifying the previous speculation.
There may also be heterogeneity in the size of firms in terms of the impact of the establishment of banking relationships. Large-scale enterprises can rely on the scale effect to improve the utilization efficiency of the funds obtained based on the bank-enterprise relationship, so it is presumed that the positive impact of the bank-enterprise relationship of large-scale enterprises on the new quality productivity is more obvious. The average value of enterprise size within the study area is the boundary of the sample for regression. According to the results of columns (4) and (5) in Table 4, it can be seen that different sizes of enterprises to establish a banking relationship for the level of enterprise new quality productivity enhancement of the impact are positive and more significant, again verifying the paper's hypothesis H1. At the same time, the positive impact coefficient of the establishment of bank-enterprise relations for large enterprises on the new quality productivity level of enterprises is 0.070, which is significant at the 1% level; it is higher than the coefficient and significance level of small-scale enterprises, which is in line with the previous speculation.
Table 4: Heterogeneity analysis
(1) NQPE | (2) NQPE | (3) NQPE | (4) NQPE | (5) NQPE | |
VARIABLES | full sample | private business | nationalized business | Small-scale enterprises | Large-scale enterprises |
BC | 0.087*** | 0.075*** | 0.052 | 0.068** | 0.070*** |
(0.019) | (0.024) | (0.032) | (0.028) | (0.025) | |
Control | Yes | Yes | Yes | Yes | Yes |
Constant | 0.647 | 1.972*** | -1.624* | 2.935*** | 2.030*** |
(0.437) | (0.524) | (0.843) | (0.830) | (0.775) | |
Observations | 27,367 | 17,896 | 9,360 | 15,338 | 12,029 |
R-squared | 0.328 | 0.368 | 0.271 | 0.356 | 0.269 |
Number of Scode | 3,953 | 3,038 | 1,142 | 3,128 | 1,885 |
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
5.5. Mechanism testing
Based on the mechanism description in Section 3, the KZ, FC, and SA indices are used to measure financing constraints as the model mechanism variables, respectively; the KZ and FC indices are positive indicators, and the higher financial restriction is represented by the bigger absolute value of SA, which is negative. Table 5 displays the regression findings in columns (1) through (3). As the table shows, the regression findings of the KZ, FC, and SA indices all show that the bank-enterprise ralationship lessens the financing constraints of firms at the 1% significance level, which is consistent with the prior hypothesis.
The improvement of corporate disclosure quality is another important mechanism for the bank-enterprise relationship to influence the new quality productivity level of enterprises. The negative indicator KV index was selected to measure the quality of corporate disclosure. Table 5’s column (4) indicates that the establishment of a bank-enterprise relationship significantly enhances the quality of corporate disclosure at the 10% level.
The mechanism test is conducted by measuring corporate agency costs (AC) in terms of end-of-period administrative expenses/operating income. The regression findings in Table 5's column (5) show that, with a significance level of 5%, the development of a bank-firm connection considerably lowers the businesses' agency costs, passing the test.
Table 5: Mechanism test
(1) | (2) | (3) | (4) | (5) | |
VARIABLES | KZ | FC | SA | KV | AC |
BC | -0.073*** | -0.006*** | 0.035*** | -0.005* | -0.001** |
(0.019) | (0.002) | (0.002) | (0.003) | (0.001) | |
Control | Yes | Yes | Yes | Yes | Yes |
Constant | 7.774*** | 3.844*** | -0.880*** | -0.650*** | 0.596*** |
(0.371) | (0.032) | (0.030) | (0.027) | (0.013) | |
Observations | 26,987 | 26,987 | 27,359 | 27,321 | 27,367 |
R-squared | 0.504 | 0.493 | 0.492 | 0.145 | |
Year | No | No | No | Yes | No |
Fe | Yes | Yes | Yes | No | Yes |
Number of Scode | 3,953 | 3,953 | 3,953 | 3,953 | 3,953 |
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
5.6. Moderating effects test
The influence of bank-enterprise relationship on the new quality productivity of enterprises may be moderated by several variables. This article focuses on the degree of digital transformation of enterprises, the amount of competition in the banking industry, and the moderating impacts of the business environment.
The business environment in which a company operates is directly related to its operating costs, market access, exit mechanisms, etc. A better business environment tends to expose the enterprise to more diversified financing and cooperation opportunities, which weakens the marginal effect of improving the enterprise's new quality productivity level through the path of establishing a bank-enterprise relationship. The regional business environment (BE) is selected as the moderator, and the data are obtained from the China Sub-Provincial Business Environment Index 2023 Report. The moderator weakens the main effect, confirming the moderating effect of the business environment where enterprises are located, according to the regression results in column (1) of Table 6. The coefficient of the interaction term between the bank-enterprise relationship and regional business environment of enterprises is significantly negative at the 1% significance level.
The stability of the bank-enterprise relationship may be impacted by the degree of competition in the banking industry. As the banking industry becomes more competitive, banks may prioritize short-term gains over long-term gains, which might sour the connection between banks and enterprises and lessen their ability to support businesses' efforts to raise their level of new, high-quality production. The Herfindahl-Hirschman Index (HHI), a negative indicator, is used to gauge the degree of bank competitiveness within an area. Based on the findings in Table 6's column (2). As can be shown, the moderating effect amplifies the main impact, and the coefficient of the interaction term between the bank-enterprise connection and HHI is considerably positive at the 1% level. That is, with the increase in the HHI index (the intensity of bank competition is weakened), the positive impact of the bank-enterprise relationship on the new quality of productivity has strengthed.
There may also be a moderating impact of the degree of enterprises’ digital transformation. On the one hand, enterprises that have heavily transformed digitally can usually rely on more flexible information channels to obtain financial services, reducing their reliance on the banking relationship; on the other hand, as the main drivers of enterprises' new quality productivity,digital technology and digital factors have a tendency to diminish the marginal utility. The digital transformation (DIG) of the enterprise is used as the moderator, expressed as the proportion of intangible digital assets. Table 6 shows the regression findings in column (3). The moderating impact lessens the main effect, and the coefficient of the interaction term between the bank-enterprise connection and the amount of digital transformation of firms is considerably negative at the 1% level. The aforementioned analytical assumptions are supported by the fact that when the degree of enterprise digital transformation increases, the promotion impact of the bank-enterprise connection on the level of new quality productivity of firms is diminished.
Table 6: Moderating effects test
(1) | (2) | (3) | |
VARIABLES | NQPE | NQPE | NQPE |
BC | 0.060*** | 0.086*** | 0.089*** |
(0.019) | (0.021) | (0.020) | |
BE | 0.022 | ||
(0.109) | |||
BC x BE | -0.489*** | ||
(0.066) | |||
HHI | 0.455 | ||
(0.689) | |||
BC x HHI | 1.100*** | ||
(0.355) | |||
DIG | 11.054*** | ||
(1.120) | |||
BC x DIG | -2.622** | ||
(1.160) | |||
Control | Yes | Yes | Yes |
Constant | 0.513 | 0.741 | 0.942** |
(0.444) | (0.477) | (0.479) | |
Observations | 27,367 | 24,583 | 22,582 |
Year | Yes | Yes | Yes |
Fe | Yes | Yes | Yes |
R-squared | 0.330 | 0.324 | 0.332 |
Number of Scode | 3,953 | 3,547 | 3,756 |
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
6. Conclusions and recommendations
This study builds a two-way fixed-effects model using data from listed businesses in China's non-financial industry from 2012 to 2022 in order to investigate the link between the bank-enterprise relationship and the new quality productivity of enterprises. The study's findings indicate that: ① The establishment of bank-enterprise relationships significantly contributes to the enhancement of the new quality productivity of firms, and the results are robust. ② Heterogeneity study results demonstrate that the establishment of bank-enterprise relationship encourages raising the new quality productivity level of private enterprises and large-scale enterprises more significantly, and also play a part in promoting the new quality productivity of small-scale enterprises. However, the impact on state-owned enterprises is not obvious. ③ The mechanism test demonstrates that the bank-enterprise relationship can help relieve the financing constraints of firms, enhance the standard of corporate information disclosure, and lower the cost of corporate principal-agent relationships. These avenues can help businesses reach higher levels of new quality productivity. ④ The moderating effect test shows that: a lower level of business environment, a weaker degree of competition in the banking industry, and a lower degree of digital transformation of enterprises strengthen the role of the bank-enterprise relationship in the promotion of the level of new quality productivity of enterprises. Drawing on the aforementioned results, this study proposes the subsequent recommendations:
First, enterprises should strengthen the construction and maintenance of relationships with banks in order to reduce information asymmetry and broaden enterprise financing channels. Through the introduction of talents and cross-shareholding, banks can be promoted to participate in or supervise the business process of enterprises, help enterprises optimize their governance and information disclosure, and empower enterprises to enhance their new quality productivity.
Second, banks need to increase their attention to enterprises and deepen their cooperation. They should take the initiative to innovate financial products to provide programs that are more suitable to meet the financing needs of enterprises. Strengthen the supervisory function of banks as external subjects.
Thirdly, the government has to foster a favorable business climate and streamline the bank-enterprise docking system. Encourage the banking industry's competitive climate and the business environment to grow healthily. Lower credit obstacles by means of policy guarantees; direct banks to provide more robust support for the emergence of new quality productivity in small and micro businesses; and encourage collaboration between banks and firms. Assist enterprises in undergoing a digital transition and in creating new quality productivity.
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Cite this article
Wang,H. (2024). Bank-Enterprise Relationship and New Quality Productivity of Enterprises. Advances in Economics, Management and Political Sciences,104,206-215.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Cai Jiming. Coordinating the development of new and traditional productive forces[J]. China Finance,2024,(07):18-20.
[2]. Bao-Ping Ren,Yuan-Bo Dou. New Quality Productivity:Literature Review and Research Outlook[J/OL]. Economic and Management Review,2024(03):1-12[2024-07-03].
[3]. Pu Qingping,Xiang Xiang. Connotative Characteristics, Internal Logic and Realization Path of New Quality Productivity--New Dynamic Energy for Promoting Chinese-Style Modernization[J]. Journal of Xinjiang Normal University (Philosophy and Social Science Edition),2024,45(01):77-85.
[4]. Wang Ke,Guo Xiaoxi. China's new quality productivity level, regional differences and spatio-temporal evolution characteristics[J/OL]. Statistics and Decision Making:1-7[2024-04-23].
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