Economic Policy Uncertainty and Enterprise Financing Efficiency: Evidence from China

Abstract

This study investigates the effect of economic policy uncertainty on financing efficiency in China’s high-tech manufacturing industry from static and dynamic perspectives. Using data envelopment analysis (DEA) and the Malmquist index, we measure financing efficiency and its changes over time. The results show that China’s high-tech manufacturing firms exhibit low static financing efficiency, yet they exhibit relatively high dynamic efficiency. A significant positive relationship is found between economic policy uncertainty and both static and dynamic financing efficiency. The uncertainty motivates firms to improve their financing efficiency, mainly by enhancing technical and scale efficiency and by increasing comprehensive efficiency. Moreover, different financing channels, such as commercial credit, equity financing, bank credit, and internal financing, have varied effects on the relationship between economic policy uncertainty and static financing efficiency. This study demonstrates that high-tech manufacturing enterprises can respond to economic policy uncertainty by improving their financing efficiency.

1. Introduction

Since the outbreak of the COVID-19 pandemic, the global economy has been severely impacted, leading to instability in the world economic pattern and uncertainty for countries worldwide. Currently, China is in the important transition period of “unprecedented changes in a century”. On the one hand, the rise of economic deglobalization and various political and economic events have occurred, leading to an increase in protectionism and unilateralism, as well as resulting in significant adjustments to international economic, technological, cultural, security, and political patterns. On the other hand, China’s domestic economy is undergoing a “triple overlay” phase, characterized by the transition from high-speed growth to medium-high-speed growth, structural adjustment, and the digestion of previous stimulus policies [1]. At the same time, China’s economy is facing triple pressures of demand contraction, supply shocks, and weakening expectations. Macroeconomic objectives are facing multiple choices, leading to increased uncertainty in economic policies. Economic policy uncertainty refers to the inability of economic entities to accurately predict whether existing policies will change, when they will change, and how they will change [2].

Over the past decade, China’s manufacturing industry has experienced rapid growth, with value-added increasing from around RMB 16.98 trillion in 2012 to RMB 31.4 trillion in 2021, and the proportion of manufacturing added value in the world increased from about 20% to nearly 30%, maintaining China’s position as the world’s largest manufacturing country. In 2013, high-tech manufacturing was confirmed as a pillar industry of China’s national economy in the government’s first work report of the first session of the 12th National People’s Congress. According to the fourth national economic census report, by the end of 2018, there were 33,573 high-tech manufacturing enterprises above the designated size (Enterprise above the designated size refers to an enterprise with an annual main business income of RMB 20 million or more), accounting for 9.5% of the above designated size manufacturing enterprises. According to the 2020 National Economic and Social Development Statistics Bulletin, the value-added of high-tech manufacturing increased by 7.1% compared to 2019, accounting for 15.1% of the value-added of industries above the designated size. This highlights the importance of studying the financing efficiency of the high-tech manufacturing industry for China’s economic development.

The importance of the high-tech manufacturing industry continues to grow, and financial support for it has also increased. In August 2020, during the “Promoting financial support to stabilize enterprises and ensure employment” conference hosted by the People’s Bank of China, it was proposed to increase medium and long-term financing support for the manufacturing industry, especially the high-tech manufacturing industry, and to innovate financial products and services. According to the People’s Bank of China website, the China Banking and Insurance Regulatory Commission, new loans to the high-tech manufacturing industry in 2021 amounted to RMB 580.7 billion, accounting for 20.74% of the total new loans to the manufacturing industry. In terms of capital utilization, in 2021, the fixed investment in China’s manufacturing industry and high-tech manufacturing industry increased by 13.5% and 17.1%, respectively, compared to 2019, which is 8.6 and 12.2 percentage points higher than the national fixed assets investment growth rate, respectively. The high-tech manufacturing industry is a pillar industry of China’s national economy, and it is responsible for promoting industrial structural adjustment, transforming development momentum, and driving China’s manufacturing industry towards the high end of the global value chain. The optimal utilization of financing advantages and the stimulation of investment-driven effects are crucial issues for both the industry and individual enterprises. In the current context of high uncertainty in the world economy and the medium–low speed development of the Chinese economy, understanding the impact of economic policy uncertainty on the financing efficiency of high-tech manufacturing enterprises is of great practical significance for enhancing the resource allocation ability of enterprises and promoting industrial development. This is the main research motivation of this paper.

Technological progress in enterprises can bring about improvements in financing efficiency. Enterprises can reduce production costs, improve production efficiency, and improve product quality through technological progress, thereby enhancing their competitiveness. To some extent, these can alleviate information asymmetry, attract more investors, expand financing channels, reduce financing costs, and thereby improve financing efficiency. From the perspective of the efficiency of fund utilization, technological progress in enterprises will bring about an increase in total factor productivity, increase the unit output rate of financing, and thus improve the financing efficiency of enterprises. We fully consider the characteristics of technology-driven innovation in high-tech manufacturing and further measure the improvement of financing efficiency brought by technological progress, which is defined as dynamic financing efficiency—the first moment of financing efficiency. In contrast, the traditional measure of financing efficiency, referred to as static financing efficiency, is calculated using DEA–BCC and DEA–CCR models. This study proposes using the DEA–Malmquist model to calculate the Malmquist index to evaluate dynamic financing efficiency. The DEA–Malmquist model is employed due to its ability to accurately capture dynamic changes in efficiency, making it a suitable choice for this research.

Efficiency can be evaluated using several methods, including Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA). The latter is primarily used for evaluating systems with multiple inputs per unit output. DEA does not need to specify a probability distribution function to establish production–function relationships, and it can directly measure efficiency in a multi-input and multi-output system without requiring any user intervention. Therefore, DEA is an objective analysis method. DEA is a method to deal with the multi-objective decision-making problem, so it can better reflect the information and characteristics of the evaluation object itself, and at the same time, it is unique for the multi-input and multi-output analyses of evaluating complex systems [3]. From the perspective of production efficiency analysis in economics, DEA is an ideal and effective model and method for evaluating decision-making units with multiple inputs, and especially multiple outputs, that are both “technically effective” and “scale effective” [4]. Therefore, this article chooses DEA for a financing efficiency measurement.

In this study, we present a pioneering approach by focusing on the impact of economic policy uncertainty on the financing efficiency of high-tech manufacturing industries, with a particular emphasis on the static–dynamic evolution of financing efficiency. Our research is the first to systematically analyze the influence of economic policy uncertainty on both static and dynamic financing efficiency, transcending the limitations of prior studies that solely examined static financing efficiency. We find a significant positive relationship between economic policy uncertainty and both static and dynamic financing efficiency, indicating that economic policy uncertainty can significantly enhance the financing efficiency of high-tech manufacturing industries. The mechanism effects of economic policy uncertainty are examined through pure technical and scale efficiency, which enhance static financing efficiency, while comprehensive efficiency change channels promote dynamic financing efficiency. Furthermore, the relationship between economic policy uncertainty and static financing efficiency is analyzed from the perspective of financing channels in heterogeneity tests. The findings indicate that equity financing strengthens the incentive effect of economic policy uncertainty on static financing efficiency, while internal financing weakens this effect. Commercial credit and bank credit do not have a significant impact.

The potential contributions of this study are multi-fold: Firstly, it considers the dynamic evolution of financing efficiency in high-tech manufacturing industries and examines the impact of external economic policy uncertainty shocks on both static and dynamic financing efficiency. Secondly, it empirically tests the mechanism of economic policy uncertainty on the financing efficiency of high-tech manufacturing industries, based on theoretical deduction and the decomposition of financing efficiency. Thirdly, it conducts empirical heterogeneity tests from different financing channel perspectives, including commercial credit, equity financing, bank credit, and internal financing. Given the continuously rising economic policy uncertainty, the findings provide valuable theoretical insights into selecting appropriate financing channels and relevant policies that can promote the development of China’s high-tech manufacturing industry. Finally, this study presents a novel approach to understanding the effects of economic policy uncertainty on financing efficiency in high-tech manufacturing industries, providing practical insights for policymakers and industry stakeholders.

The rest of this study is organized as follows: Section 2 provides a literature review and hypothesis development. Section 3 includes an indicator measurement and financing efficiency analysis. Section 4 presents materials and methods, including data processing, the model, and descriptive statistics. Section 5 presents the empirical results, endogenous test, and robustness check. Section 6 is an extension discussion, mainly focusing on mechanism effect tests and heterogeneity tests. Section 7 presents the empirical findings and a pertinent discussion. Section 8 presents the implications. Section 9 presents the conclusion.

2. Literature Review and Hypothesis Development

2.1. Literature Review

The efficiency of corporate financing encompasses both the efficiency of fund-raising and fund-utilization. Specifically, it refers to a company’s ability to acquire necessary funds at a lower cost and maximize profits by using the obtained funds effectively. The classic theoretical foundation for studying financing efficiency is the MM theory proposed by Modigliani and Miller [5]. The MM theory laid the foundation for understanding how financing decisions impact a company’s market value, and it has since been extensively built upon and expanded by subsequent research. According to this theory, financing decisions, whether debt financing or equity financing, do not impact a company’s market value in the absence of taxes. However, in the presence of taxes, a company’s market value is influenced by taxes and marginal tax rates. Myers and Majluf [6] further develop the “financing priority theory”, taking into account the impact of information asymmetry on corporate financing decisions and capital structure.

In this study, we conduct a literature review from four perspectives—institutional factors, financial development, bank credit, and firm-specific factors—in order to examine the impact on financing efficiency.

Firstly, at the macro level of national governance, institution is a significant factor influencing corporate financing. Beck et al. [7] conducted a study on determinants of financing obstacles using survey data from 10,000 firms across 80 countries. They found that firms with a longer establishment time, larger size, and foreign ownership encounter fewer financing obstacles. Institutional development is the most crucial national-level characteristic that explains variations in financing obstacles across countries. Cam and Ozer [8] studied the effects of national governance, such as political stability, government effectiveness, regulatory quality, legal systems, and corruption control, on corporate capital structure and financing decisions. Using a sample of 31,749 firms from 65 countries, between 1996 and 2017, they found that firms in countries with stronger governance are more likely to issue long-term debt and equity, reduce short-term debt issuance, and engage in capital expenditure financing while lowering their leverage ratios.

Secondly, financial development significantly affects corporate investment and financing. Naeem and Li [9] conducted a study on the relationship between financial development and investment efficiency by using a sample of non-financial firms from 35 member countries of the Organization for Economic Cooperation and Development between 1990 and 2015. They found that financial development has a positive effect on firm investment, promoting investment efficiency improvement for firms with underinvestment or overinvestment issues. Furthermore, financial development significantly enhances resource allocation efficiency. Bena and Ondko [10] used micro-level data from Europe between 1996 and 2005, and they found that, in countries with more developed financial markets, social capital flows more into industries with growth opportunities, particularly for small firms with short establishment times and high financing constraints.

Thirdly, within the global economy, the bank credit market has consistently been a significant factor, influencing macro and microeconomics. Hasan et al. [11] studied the relationship between banking efficiency and regional economic growth in 147 regions across 11 European countries. The results showed that mature economies benefit significantly from higher banking efficiency, as banks can promote productivity growth through increased lending and more efficient operations. Biswas et al. [12] used an information asymmetry model to examine the relationship between corporate financing efficiency and bank market power. They found that both excessive competition and low competition among banks hinder the improvement of corporate financing efficiency, showing a reverse U-shaped relationship.

Finally, micro-level factors at the enterprise level can also affect their financing efficiency. Shen [13] analyzed the internal and external factors influencing corporate financing efficiency and concluded that internal factors, such as financing methods, enterprise size, quality, and equity structure, as well as external macroeconomic environmental factors, including inflation rate, macroeconomic growth, interest rate, money supply, and development of capital markets, all play a significant role. Liu et al. [3] measured the financing efficiency of 37 listed Chinese AI industry companies, from 2013 to 2016, using the DEA method, and they found that capital structure, operational capacity, and growth were all significantly related to financing efficiency, making them important factors affecting financing efficiency.

In summary, existing studies have analyzed the factors influencing financing efficiency from both internal and external perspectives, including the impact of national institutions, monetary markets, government support, and financial development on the external environment. The importance of investment and financing in enterprise development has been well established, and there is significant potential for further exploration into financing efficiency-related research.

Despite the extensive research on factors influencing financing efficiency, there is limited understanding of the impact of macroeconomic uncertainty on micro-enterprise financing efficiency, particularly in the high-tech manufacturing industry. Our study fills this gap by investigating the impact of economic policy uncertainty on financing efficiency in China’s high-tech manufacturing industry, incorporating both static and dynamic measures. Specifically, we decompose static efficiency into pure technical efficiency, and we decompose scale efficiency and dynamic efficiency into comprehensive efficiency change index, pure technical efficiency change index, scale efficiency change index, and technological progress efficiency index. Our study aims to provide a comprehensive and systematic analysis of the impact and mechanism of economic policy uncertainty on financing efficiency in China’s high-tech manufacturing industry.

2.2. Hypothesis Development

Financing efficiency refers to an enterprise’s ability to obtain funds at the lowest possible cost and utilize those funds effectively to create value, which is a reflection of the enterprise’s ability to create value using the capital raised [14]. Financing efficiency is influenced by both the cost of financing and the efficiency of capital utilization, which is represented by the return on investment. Therefore, this paper mainly analyzes the impact of economic policy uncertainty on static financing efficiency from two aspects: the availability of corporate financing and the impact of investment.

Currently, bank credit remains the main source of financing for enterprises in China. The increase in economic policy uncertainty exacerbates the weakness in credit demand and leads to a growing reluctance by banks to lend, which suppresses the total amount of bank credit available [15], significantly reduces the debt financing scale of enterprises [16], increases the cost of credit, and subsequently lowers the financing efficiency of enterprises. On the other hand, when economic policy uncertainty rises, the bank credit market takes more cautious lending measures, implements more stringent screenings of lending projects, and credit resources are more likely to flow into projects with higher quality and better returns, improving the efficiency of capital utilization and, thus, the financing efficiency of enterprises.

From the perspective of the capital market, economic policy uncertainty has a series of impacts on the stock market. Economic policy uncertainty increases stock market volatility [17] and reduces stock market returns [18]. In terms of stock market risk, economic policy uncertainty can exacerbate adverse fluctuations in the stock market [19]. The increasing economic policy uncertainty positively impacts the relationship between non-financial companies’ shadow banking businesses and stock price crash risks [20]. At the same time, economic policy uncertainty significantly increases the risk of stock market crashes and is one of the inducing factors of such crashes [21,22]. In terms of financing costs, economic policy uncertainty increases the cost of raising equity capital, especially when the economy is weak [23].

Based on the investment perspective, this section provides a theoretical analysis of the impact of economic policy uncertainty on financing efficiency. At the micro level, firms’ investment decisions are directly related to the efficiency of fund utilization, thereby affecting their financing efficiency. There are three main reasons why the increase in economic policy uncertainty can inhibit corporate investment: the uncertainty aversion perspective [24], the investment irreversibility perspective [25], and the financial friction perspective [26,27]. Therefore, when the rise in economic policy uncertainty leads to a delay or reduction in corporate investment, it undoubtedly causes firms to miss investment opportunities, reduce their return on investment, affect their fund utilization efficiency, and thus lower their financing efficiency. On the other hand, according to the growth option theory, when economic policy uncertainty increases, firms can gain a competitive advantage through immediate investment and obtain excess returns, which can improve their investment efficiency.

Financing efficiency refers to the ability of a company to integrate funds at the lowest possible cost, as well as fully and effectively utilize the funds, reflecting its ability to create value through the use of the funds [14]. This article mainly analyzes the impact of economic policy uncertainty on financing efficiency from two aspects: the availability of corporate financing and the impact of investment. In terms of the impact of economic policy uncertainty on financing efficiency, there may be a threshold for economic policy uncertainty, and different relationships may arise on both sides of the threshold. Theoretically, when economic policy uncertainty is small or non-existent, its impact on corporate financing efficiency is relatively small or even has no impact. The main issue studied in this article is the relationship between economic policy uncertainty and corporate financing efficiency, which refers to the relationship between uncertainty and corporate financing efficiency when economic policy uncertainty continues to rise. In summary, this section analyzes the impact of economic policy uncertainty on financing efficiency, from the perspectives of financing and investment, based on the connotation of financing efficiency. Through the review and analysis of relevant literature, it has been found that there is an inconsistency in previous research conclusions which requires further exploration. Therefore, this study proposes a pair of competing hypotheses:

H_1a: 

The increase in economic policy uncertainty is expected to enhance the static financing efficiency of enterprises.

H_1b: 

The increase in economic policy uncertainty is expected to decrease the static financing efficiency of enterprises.

The high-tech manufacturing industry is characterized by its high level of technological content, strong innovation, high value-added, strong industrial spillover effects, and low energy consumption. In terms of investment decision-making, this industry tends to invest in research and development to gain a competitive advantage in the market and achieve excess profits. Drawing on the real options theory, when uncertainty increases, companies are more likely to invest in innovation [28,29]. Compared to general capital expenditures, innovation involves a recombination of production factors, which can improve enterprise productivity and produce a higher marginal contribution to enterprise value [30], ultimately leading to higher returns on investment. Therefore, in the context of rising economic policy uncertainty, the high-tech manufacturing industry, with high research and development investment, is more inclined to invest in innovation to promote technological progress and improve total factor productivity and dynamic financing efficiency.

The interest rate will affect the cost of enterprise investment and the opportunity cost of investment. The economic policy uncertainty can affect the fluctuation of interest rates, which, in turn, affects the investment of enterprises. In this paper, we assume that companies will make investments regardless of whether there is a change in interest rates. Interest rates can affect the overall investment of a company, but to a certain extent, they have a relatively small impact on the selection of investment types. The research logic of this paper is that, when economic policy uncertainty increases, China’s high-tech manufacturing industry will prioritize innovation investment [25], which, in turn, affects the dynamic financing efficiency of enterprises. Therefore, in this section of the analysis, it is assumed that interest rates are fixed. Based on this, the impact of economic policy uncertainty on the dynamic financing efficiency of enterprises is analyzed.

As economic policy uncertainty increases, the high-tech manufacturing industry’s investment scale grows faster than that of non-high-tech manufacturing industries, and the capital allocation efficiency is higher [31]. China’s high-tech manufacturing industry is more motivated than other industries to optimize its investment and financing structure and improve its dynamic financing efficiency. Influenced by economic policy uncertainty, decision-making departments tend to increase investment in innovation to achieve future value, and although there are no significant changes in senior management or employee scale, increased investment in technology research and development leads to capital deepening, significantly improving the proportion of capital to labor and enhancing an enterprise’s total factor productivity [32], ultimately leading to improved enterprise dynamic financing efficiency. Based on these, the second core hypothesis of this study is proposed.

H₂: 

The increase in economic policy uncertainty may enhance the dynamic financing efficiency of enterprises.

3. Index Calculation and Financing Efficiency Analysis

3.1. Sample Selection

According to the “Classification of High-tech Industries (Manufacturing) (2013)” formulated by the National Bureau of Statistics in 2013 (revised and issued in 2018), high-tech manufacturing industries refer to the manufacturing industries with relatively high R&D investment intensity in the national economy. It includes six major categories: pharmaceutical manufacturing, aerospace and aerospace equipment manufacturing, electronic and communication equipment manufacturing, computer and office equipment manufacturing, medical equipment and instrument manufacturing, and information chemical product manufacturing. Due to the inability to obtain data on all high-tech manufacturing enterprises in China, based on the availability of data, this paper takes listed high-tech manufacturing enterprises as the research sample. The specific steps are to match the high-tech manufacturing industry code in the “Classification of High-tech Industries (Manufacturing) (2017)” with the industry code in the “Guidelines for Industry Classification of Listed Companies (2012)” and select high-tech manufacturing enterprises among listed companies. Finally, this paper selects high-tech manufacturing enterprises listed on the Shanghai and Shenzhen A-share markets in China, from 2013 to 2020, as the research sample.

3.2. Index Calculation

For static financing efficiency, Data Envelopment Analysis (DEA) is a multi-objective decision analysis method for calculating the relative effectiveness of decision-making units. It is proposed by Charnes et al. [33] based on the concept of “relative efficiency”. DEA is a non-parametric systematic analysis method for relative efficiency. At present, DEA is widely used in the field of financing efficiency, and it has achieved many important research milestones. Pang and Gai [34] study the financing efficiency of small and medium-sized enterprises based on the DEA method. ** et al. [35] use the DEA method to measure the financing efficiency of listed companies in the energy conservation and environmental protection industry. Yin et al. [36] study the impact of social networks on corporate financing efficiency. DEA has the advantages of being easy to understand and easy to apply. Data Envelopment Analysis (DEA) is a method to deal with multi-objective decision-making problems with multiple inputs and outputs. Therefore, this article chooses the DEA method for calculating financing efficiency.

In this study, the DEA method is used to measure financing efficiency in high-tech manufacturing. Assuming there are n decision-making units (DMU), each evaluated object has m types of inputs and s types of outputs. The input and output vectors of each decision-making unit ($\mathrm{DMU}$) are represented by X and Y, respectively:

$$X_j={\left(x_{1j},x_{2j},\dots ,x_{mj}\right)}^T,j=1,2,\dots .,n$$

(1)

$$Y_j={\left(y_{1j},y_{2j},\dots ,y_{sj}\right)}^T,j=1,2,\dots .,n$$

(2)

The input vector is $x_{ij}\ge 0,i=1,2,\dots ,m$, where $x_{ij}$ represents the amount of the $j$th decision-making unit’s investment in the $i$th input type. Similarly, $y_{kj}\ge 0,k=1,2,\dots ,s$, and $y_{kj}$ represents the amount of output of the $j$th DMU for the $k$th output type. In the selection of radial distance and non-radial distance, this paper refers to relevant literature on financing efficiency measurement. At the same time, the BCC and CCR models for radial distance are classic models for measuring efficiency. Therefore, we use radial distance to measure efficiency. Meanwhile, the sample size of this paper is relatively large, with a total of 496 enterprises and 3170 valid samples obtained, so it can effectively avoid the problem of small sample bias. The CCR model for measuring the static financing efficiency of listed high-tech manufacturing companies is as follows:

$$\left\{\begin{array}{c}min \theta -\epsilon \left({\displaystyle \sum }_{i=1}^m{s}_i^{-}+{\displaystyle \sum }_{k=1}^s{s}_k^{+}\right)\\ {{\displaystyle \sum }}_{j=1}^n{\lambda }_j{x}_{ij}+s_i^{-}=\theta x_{i{j}_0}\\ s.t. {{\displaystyle \sum }}_{j=1}^n{\lambda }_j{y}_{kj}-s_k^{+}=y_{k{j}_0}\\ {\lambda }_j\ge 0,j=1,2,\dots n\\ s_i^{-}\ge 0,s_k^{+}\ge 0\end{array}\right.$$

(3)

On the basis of the CCR model, the BCC model, with variable returns to scale in financing for companies, can be obtained by incorporating the condition of variable returns to scale.

$$\left\{\begin{array}{c}min \theta -\epsilon \left({\displaystyle \sum }_{i=1}^m{s}_i^{-}+{\displaystyle \sum }_{k=1}^s{s}_k^{+}\right)\\ {{\displaystyle \sum }}_{j=1}^n{\lambda }_j{x}_{ij}+s_i^{-}=\theta x_{i{j}_0}\\ s.t. {{\displaystyle \sum }}_{j=1}^n{\lambda }_j{y}_{kj}-s_k^{+}=y_{k{j}_0}\\ {\lambda }_j\ge 0,j=1,2,\dots n\\ {\lambda }_1+{\lambda }_2+\dots +{\lambda }_j=1,j=1,2\dots ,n\\ s_i^{-}\ge 0,s_k^{+}\ge 0\end{array}\right.$$

(4)

In Equations (3) and (4), $\epsilon$ represents the non-Archimedean infinitesimal, ${\lambda }_j$ represents the weights, $s^{-}$ represents input redundancy, and $s^{+}$ represents output insufficiency. The optimal solution $\theta$ in the model represents the efficiency value of the decision-making unit, with a larger $\theta$ indicating a more reasonable input–output ratio and higher financing efficiency for the company. In the estimation of the model, output orientation is adopted. The DEA–CCR model in Equation (3) can be used to obtain the comprehensive financing efficiency of the company, and the DEA–BCC model in Equation (4) can calculate the pure technical efficiency of the company’s financing. The scale efficiency value can be further calculated based on the comprehensive efficiency value and the pure technical efficiency value.

For Dynamic Financing Efficiency, the dynamic trend of financing efficiency in high-tech manufacturing is solved using the DEA–Malmquist model. The DEA–CCR and DEA–BCC models reflect the efficiency values of decision-making units in the current period, but the production technology level of enterprises will change over time, and the frontier will also change in different periods. The Malmquist index is widely used for evaluating the dynamic efficiency. Campisi et al. [37,38] calculate the MPI to measure the evolutions of the knowledge-intensive business services’ productivities. Giacalone et al. [39] evaluate the dynamic efficiency of the Italian judicial system using DEA-based Malmquist productivity indexes. Yan [40] uses the Malmquist productivity index model to measure the dynamic efficiency of China’s rural water conservancy investment. Song et al. [41] employ the Malmquist productivity index model to measure the dynamic efficiency of China’s tradable green certificate market. ** et al. [35] use the DEA–Malmquist method to measure the dynamic financing efficiency of listed companies in the energy conservation and environmental protection industry.

The dynamic financing efficiency lies in comparing the changes in efficiency values between adjacent periods. Considering that the external environment faced by enterprises in adjacent phases is relatively similar, the comparability of financing efficiency is higher. Meanwhile, referring to current literature on dynamic financing efficiency, we ultimately chose to use traditional methods to calculate the dynamic financing efficiency. The basic idea of the Malmquist index is to use one set of isoquant curves as a reference set, when comparing efficiency values at different times, and to measure the changes in input–output productivity by comparing distance function ratios at different times. Its specific expression is as follows.

$$\begin{gathered} M\left(y_{t+1},x_{t+1},y_t,x_t\right)=\sqrt{[D^t\left(x_{t+1},y_{t+1}\right)/D^t\left(x_t,y_t\right)\left]\ast [D^{t+1}\left(x_{t+1},y_{t+1}\right)/D^{t+1}\left(x_t,y_t\right)\right]} \\ =[D^{t+1}\left(x_{t+1},y_{t+1}\right)/D^t\left(x_t,y_t\right)]\ast \sqrt{\frac{D^t\left(x_{t+1},y_{t+1}\right)}{D^{t+1}\left(x_{t+1},y_{t+1}\right)}\ast \frac{D^t\left(x_t,y_t\right)}{D^{t+1}\left(x_t,y_t\right)}} \\ =SECH\left(x_{t+1},y_{t+1};x_t,y_t\right)\times PECH\left(x_{t+1},y_{t+1};x_t,y_t\right)\times TECH\left(x_{t+1},y_{t+1};x_t,y_t\right) \end{gathered}$$

(5)

Among them, $M\left(y_{t+1},x_{t+1},y_t,x_t\right)$ is the Malmquist index, which represents the change in total factor productivity of input and output from period $t$ to $t+1$. The D represents the matrix of inputs and outputs, $y_t$ and $y_{t+1}$ represent the output vectors of period $t$ and $t+1$, and $x_{t+1}$ and $x_t$ represent the input vectors of period $t$ and $t+1$.

$SECH\left(x_{t+1},y_{t+1};x_t,y_t\right)\times PECH\left(x_{t+1},y_{t+1};x_t,y_t\right)$ represents the efficiency change index ($EFFCH$), which is the change in static comprehensive efficiency. $SECH\left(x_{t+1},y_{t+1};x_t,y_t\right)$ represents the scale efficiency change index, $PECH\left(x_{t+1},y_{t+1};x_t,y_t\right)$ represents the pure technical efficiency change index, and $TECH\left(x_{t+1},y_{t+1};x_t,y_t\right)$ represents the technical progress efficiency index. The Malmquist index can be decomposed into the comprehensive efficiency change index and the technical progress efficiency index, where the former reflects the speed of catching up to the production frontier curve from period $t$ to $t+1$, that is, the change of financing efficiency, and it can be further decomposed into the scale efficiency change index and the pure technical efficiency change index.

If $M\left(y_{t+1},x_{t+1},y_t,x_t\right)>1$, it indicates that the financing efficiency of high-tech manufacturing is improved and enhanced; if $M\left(y_{t+1},x_{t+1},y_t,x_t\right)<1$, it indicates that the financing efficiency of high-tech manufacturing is deteriorating and worsening. The $TECH$ index, $EFFCH$ index, $SECH$ index, and $PECH$ index, if any of them are greater than 1, indicate that the change of the index is conducive to the improvement of dynamic financing efficiency. If they are less than 1, they indicate that the index decreases dynamic financing efficiency.

Financing efficiency refers to the ability of an enterprise to use funds at as low of a cost as possible and fully utilize the funds to create value, which is a manifestation of the ability of an enterprise to create value with funds. The level of financing efficiency is influenced by two factors: the efficiency of fund inflow and the efficiency of fund allocation. Therefore, the selection of financing efficiency indicators needs to consider the accessibility of corporate financing, financing costs, financing risks (operating debt capacity, creditor interest protection level, etc.), profitability, development capability, operational ability, etc.

From the perspective of financing availability, to some extent, the size of a company can reflect the level of collateral for corporate loans and the size of available financing. The financing cost reflects the cost that a company needs to spend on production and operation, and it reflects the company’s ability to raise funds and use them. Financing risk characterizes the ability of a company to operate in debt and the degree of protection for creditors’ interests. The asset–liability ratio can reflect the capital structure of the company and better reflect the financing risk of the company. The development ability reflects the ability of an enterprise to gain competitiveness and achieve sustainable development in production and operation. Profitability reflects the ability of a company to utilize existing resources to obtain profits and its operational efficiency. The operation capability of an enterprise reflects the efficiency of enterprise asset management and the utilization rate of funds, and asset turnover can better measure the operation capability of an enterprise. Based on the above analysis, and referring to relevant research on financing efficiency [3,35,42,43], on the input indicators, financing availability, financing cost, and financing risk are represented by total assets, operating cost, and the asset–liability ratio, respectively. On the output indicators, the development capacity, profitability, and operating capacity are expressed in operating income, return on assets, and asset turnover, respectively. The financing efficiency input–output indicators constructed in this paper are shown in

Table 1. The evaluation index system for financing efficiency in high-tech manufacturing.

Indicator Type	Indicator Significance	Indicator Description
Input indicators	Financing availability	To reflect the scale of financing and financing availability of listed high-tech manufacturing companies, we use total assets as the indicator.
	Financing cost	Reflecting the ability of enterprises to raise and use funds, the total operating cost including sales expenses, management expenses, and financial expenses is used as the indicator.
	Financing risk	This indicator reflects the company’s ability to operate its liabilities and the degree of protection of the interests of creditors, and is represented by the asset-liability ratio.
Output indicators	Development capability	Reflecting the ability of enterprises to achieve sustainable development, represented by operating revenue.
	Profitability	The indicator is represented by the return on assets. It reflects the ability of the enterprise to use its assets to generate profits.
	Operational capacity	The asset turnover ratio is used to reflect the speed of asset turnover in the company, indicating that the stronger the operational capacity of the enterprise, the faster the asset turnover.

.

On the treatment of the outlier, before the calculation of financing efficiency, truncation was performed on all input–output variables at the 1% and 99% levels to eliminate the impact of outliers on the model. Inflation may affect the financial indicators of enterprises. During the sample observation period, 2013–2020, the average growth rate of the CPI index was 1.88%. It can be seen that, during the sample observation period, the inflation rate is relatively low and has little impact on financial data. Secondly, from the perspective of impact of inflation on the production and operation of enterprises, all enterprises face the same inflation. It can be seen that the impact of inflation is systemic. Therefore, inflation has a relatively small impact on the calculation of financing efficiency in this paper. To ensure that all input and output indicators in the data envelopment (DEA) model are positive, and to eliminate the difference in scale between the various indicators, we use a data standardization formula to normalize all input and output indicators, which transforms the original data of each input and output indicator into the [1, 100] interval.

$${\overline{x}}_n=1+99\times \frac{x_n-min{x}_n}{max{x}_n-min{x}_n}$$

(6)

3.3. Typical Case Analysis

3.3.1. Analysis of Financing Efficiency Based on DEA Model

In general, if the comprehensive efficiency value of a decision-making unit (DMU) is 1, it is considered an effective DMU, indicating that the DMU is operating optimally. When the comprehensive efficiency value is less than 1, there are two situations: if the value is between 0.9 and 1, it is considered a mild DEA inefficiency, while if it is significantly less than 0.9, it is considered a severe DEA inefficiency. The comprehensive efficiency evaluation results are shown in

Table 2. Comprehensive efficiency evaluation results.

Year	Number of DMU with a CEV of 1	Proportion	Mean CEV	Number of DMU with CEV ≥ 0.8	Proportion
2013	36	7.23%	0.652	98	19.68%
2014	37	7.43%	0.653	74	14.86%
2015	29	5.82%	0.685	107	21.49%
2016	31	6.22%	0.671	93	18.67%
2017	24	4.82%	0.694	95	19.08%
2018	18	3.61%	0.692	97	19.48%
2019	22	4.42%	0.716	124	24.90%
2020	17	3.41%	0.691	97	19.48%
Total	214	5.37%	0.682	785	19.70%

Table 2 reports the comprehensive efficiency evaluation results of 498 listed high-tech manufacturing companies from 2013 to 2020. CEV represents the comprehensive efficiency value.

. From 2013 to 2020, the maximum number of effective financing companies is 37 in 2014, and the minimum is 17 in 2020, accounting for 7.43% and 3.41%, respectively. The average proportion of companies with comprehensive efficiency values greater than or equal to 0.8 is 19.7%. The average maximum and minimum comprehensive efficiency values are 0.716 and 0.652 in 2019 and 2013, respectively, with an overall mean of 0.682 over the observation period. These results indicate that the financing efficiency of China’s listed high-tech manufacturing companies is generally low from 2013 to 2020, showing a slow upward trend.

The pure technical efficiency reflects the level of management and internal governance ability of an enterprise in terms of resource allocation and investment efficiency. From 2013 to 2020, the maximum number of listed high-tech manufacturing companies with a pure technical efficiency value of 1 is 61 in 2013, while the minimum is 26 in 2020, accounting for 12.25% and 5.22% respectively. The proportion of companies with a pure technical efficiency value greater than or equal to 0.8 is 35.79%. The maximum value of pure technical efficiency is 0.794 in 2017, while the minimum value is 0.735 in 2014. The overall evaluation results are presented in

Table 3. Evaluation results of pure technical efficiency.

Year	Number of DMU with a PTEV of 1	Proportion	Mean PTEV	Number of DMU with PTEV ≥ 0.8	Proportion
2013	61	12.25%	0.738	161	32.33%
2014	42	8.43%	0.735	158	31.73%
2015	46	9.24%	0.746	161	32.33%
2016	48	9.64%	0.768	191	38.35%
2017	43	8.63%	0.794	230	46.18%
2018	39	7.83%	0.772	192	38.55%
2019	36	7.23%	0.776	197	39.56%
2020	26	5.22%	0.736	136	27.31%
Total	342	8.58%	0.758	1426	35.79%

Table 3 reports the evaluation results of pure technical efficiency for 498 listed high-tech manufacturing companies from 2013 to 2020. PTEV represents the pure technical efficiency value.

. The findings suggest that the majority of listed high-tech manufacturing companies in China showed relatively low pure technical efficiency from 2013 to 2020, and they exhibited a slow and minor fluctuating development trend, highlighting the need for improvement in their management level and internal governance ability.

The scale efficiency reflects the impact of the financing and investment scale of the enterprise on its overall performance. During the period from 2013 to 2020, the maximum number of companies with a scale efficiency value of 1 is 147 in 2013, while the minimum is 45 in 2018, accounting for 29.52% and 9.04%, respectively. The proportion of companies with a scale efficiency value greater than or equal to 0.8 is 84.71%. The maximum value of scale efficiency is 0.936 in 2020, and the minimum is 0.871 in 2016. The overall evaluation results are presented in

Table 4. Scale efficiency evaluation results.

Year	Number of DMU with a SEV of 1	Proportion	Mean SEV	Number of DMU with SEV ≥ 0.8	Proportion
2013	147	29.52%	0.881	367	73.69%
2014	134	26.91%	0.888	387	77.71%
2015	101	20.28%	0.917	463	92.97%
2016	86	17.27%	0.871	382	76.71%
2017	61	12.25%	0.872	383	76.91%
2018	45	9.04%	0.893	424	85.14%
2019	58	11.65%	0.920	478	95.98%
2020	46	9.24%	0.936	491	98.59%
Total	678	17.02%	0.897	3375	84.71%

Table 4 reports the scale efficiency evaluation results of 498 listed high-tech manufacturing companies from 2013 to 2020. SEV represents the scale efficiency value.

. The findings suggest that the listed high-tech manufacturing companies in China demonstrated a relatively high scale efficiency from 2013 to 2020, with a substantial financing and investment scale, and they displayed a positive trend of continuous improvement.

The trend in the comprehensive efficiency of high-tech manufacturing, as well as its further decomposition into pure technical efficiency and scale efficiency, is depicted in Figure 1. In general, the financing efficiency of listed high-tech manufacturing companies in China is relatively low, with a mean comprehensive efficiency of only 0.682 from 2013 to 2020, a mean pure technical efficiency of 0.758, and a mean scale efficiency of 0.897. Furthermore, the scale efficiency is consistently higher than the pure technical efficiency throughout the observation period. This indicates that China’s high-tech manufacturing sector has a relatively low capacity to adjust the input–output structure, to achieve the maximum financing efficiency, without taking into account the scale factor. However, the increasing trend in scale efficiency suggests a strong financing support for high-tech manufacturing in China to some extent.

3.3.2. A Dynamic Analysis of Financing Efficiency Based on the DEA-Malmquist Model

This study employs the DEA–Malmquist model to measure the dynamic changes in the financing efficiency of listed high-tech manufacturing companies in China from 2013 to 2020. Compared with static analysis, this approach provides a better understanding of changes over time. During the observation period, the comprehensive efficiency change index, pure technical efficiency change index, and scale efficiency change index are 1.028, 1.012, and 1.015, respectively, and they are all greater than 1. The comprehensive efficiency change is influenced by both pure technical efficiency and scale efficiency, with the latter having a higher impact (1.015 > 1.012), which is consistent with previous research findings.

The Malmquist index, a measure of dynamic financing efficiency, was 0.996 from 2013 to 2020, indicating slow improvement in financing efficiency in China’s high-tech manufacturing industry. However, the averages of comprehensive efficiency change index, pure technical efficiency change index, and scale efficiency change index were all greater than 1, suggesting a positive impact on financing efficiency in the industry. The average technical progress index was slightly lower than 1, with values of 1.013 and 1.015 in the periods of 2015–2016 and 2019–2020, respectively.

The annual changes analysis reveals that the improvement of corporate financing efficiency is fluctuating during the observation period. The Malmquist index was greater than or equal to 1 in the 2013–2014, 2015–2016, and 2018–2019 periods, but it was less than 1 in other years. The decrease in production technology explains the lower Malmquist index in the periods of 2014–2015 and 2016–2017. The decrease in production technology and pure technical efficiency explains the lower Malmquist index in the 2017–2018 period. The decrease in comprehensive financing efficiency was the main factor in the 2019–2020 period. These findings suggest that, while the financing efficiency of China’s high-tech manufacturing industry is continuously improving, the overall dynamic financing efficiency remains relatively low (

Table 5. The Malmquist index and its decomposition evaluation results.

Year	Comprehensive Efficiency Change Index	Pure Technical Efficiency Change Index	Scale Efficiency Change Index	Technical Progress Index	Malmquist Index
2013–2014	1.035	1.015	1.016	0.970	1.000
2014–2015	1.071	1.028	1.039	0.932	0.995
2015–2016	0.996	1.044	0.952	1.013	1.006
2016–2017	1.060	1.048	1.009	0.948	0.996
2017–2018	1.008	0.978	1.030	0.976	0.983
2018–2019	1.051	1.015	1.036	0.957	1.004
2019–2020	0.973	0.956	1.020	1.015	0.987
Total	1.028	1.012	1.015	0.973	0.996

Table 5 reports the Malmquist index and its decomposition evaluation results for 498 listed high-tech manufacturing companies from 2013 to 2020.

).

Based on the analyses presented above, the following findings can be highlighted. Firstly, the financing efficiency of China’s high-tech manufacturing industry is relatively low, with the scale efficiency being higher than the pure technical efficiency. The mean values of comprehensive efficiency, pure technical efficiency, and scale efficiency from 2013 to 2020 are 0.682, 0.758, and 0.897, respectively. The proportion of enterprises with efficiency values greater than or equal to 0.8 is 19.7%, 35.79%, and 84.71%, respectively. The mean value of the scale efficiency is the highest, followed by the pure technical efficiency, and the mean value of comprehensive efficiency is the lowest. This indicates that the high-tech manufacturing industry needs to improve its management level and internal governance capacity to optimize resource allocation and investment efficiency, and to a certain extent, it presents an extensive development mode. However, relevant policies that favor high-tech manufacturing, such as credit financing, have promoted the improvement of scale efficiency.

Secondly, the financing efficiency of China’s high-tech manufacturing industry is in a fluctuating and continuously improving state. From 2013 to 2020, the mean value of the Malmquist index was 0.996, the mean value of the comprehensive efficiency change index was 1.028, and the mean value of the technical progress index was 0.973. The Malmquist index was greater than or equal to 1 in the 2013–2014, 2015–2016, and 2018–2019 periods, but it was less than 1 in other years. These indicate that the financing efficiency is fluctuating during the observation period. China’s high-tech manufacturing industry needs to work towards improving its overall dynamic financing efficiency by further enhancing technical progress and optimizing resource allocation.

4. Materials and Methods

4.1. Data Processing and Data Sources

To ensure the reliability and validity of the results, the sample data are carefully screened and processed in accordance with the research needs. Firstly, research samples with special treatments, such as ST and *ST, are excluded. Secondly, all research samples with missing data are removed. Thirdly, truncation was performed on continuous variables at the 1% and 99% levels to eliminate the impact of outliers on the model. Lastly, to ensure the accuracy of the dynamic financing efficiency calculation, the data are balanced and the panel is processed. As a result, a total of 496 enterprises and 3170 valid samples are obtained. All financial data in this paper are extracted from the Choice database, and the economic policy uncertainty index is obtained from the China economic policy uncertainty index constructed by Baker et al. [44].

4.2. Model and Variable Definition

4.2.1. Model

To test the research hypotheses H_1a, H_1b, and H₂, multiple linear regression models are constructed and analyzed.

$$\begin{array}{cc}SF{E}_{i,t}=& {\alpha }_0+{\alpha }_{1}EP{U}_t+{\alpha }_{2}Siz{e}_{i,t}+{\alpha }_{3}Ro{a}_{i,t}+{\alpha }_{4}Growt{h}_{i,t}+{\alpha }_{5}Cas{h}_{i,t}\\ & +{\alpha }_{6}Leve{l}_{i,t}+{\alpha }_7{Q}_{i,t}+{\alpha }_{8}Shar{e}_{i,t}+{\alpha }_{9}GDP+{\epsilon }_{i,t}\end{array}$$

(7)

$$\begin{array}{cc}DF{E}_{i,t}=& {\beta }_0+{\beta }_{1}EP{U}_t+{\beta }_{2}Siz{e}_{i,t}+{\beta }_{3}Ro{a}_{i,t}+{\beta }_{4}Growt{h}_{i,t}+{\beta }_{5}Cas{h}_{i,t}\\ & +{\beta }_{6}Leve{l}_{i,t}+{\beta }_7{Q}_{i,t}+{\beta }_{8}Shar{e}_{i,t}+{\beta }_{9}GDP+{\delta }_{i,t}\end{array}$$

(8)

where $i$ represents the listed companies in the high-tech manufacturing industry, while $t$ represents time. The constant term is denoted by ${\alpha }_0$. The static financing efficiency is represented by $SF{E}_{i,t}$, while the dynamic financing efficiency is represented by $DF{E}_{i,t}$. The economic policy uncertainty index is denoted by $EPU$. The error terms are represented by ${\epsilon }_{i,t}$ and ${\delta }_{i,t}$. In model (1), the parameter of interest is ${\alpha }_1$. Based on relevant studies and theoretical analysis, it is hypothesized that the sign of ${\alpha }_1$ may be positive or negative, indicating that economic policy uncertainty may either promote or inhibit static financing efficiency in the high-tech manufacturing industry. Similarly, in model (2), the parameter of interest is ${\beta }_1$, and it is hypothesized that ${\beta }_1$ is positive, indicating that an increase in economic policy uncertainty will improve dynamic financing efficiency. In the selection of estimation models, through the Hausman test, this paper selects individual fixed effects on models (7) and (8).

4.2.2. Dependent Variable

Static financing efficiency (SFE) is also called comprehensive financing efficiency, as mentioned above. It reflects the current input and output efficiency of the enterprise, and it is calculated using DEA–BCC and DEA–CCR models.

Dynamic financing efficiency (DFE) reflects the improvement of financing efficiency brought about by enterprise technology. We use the DEA–Malmquist model to calculate the Malmquist index to evaluate dynamic financing efficiency.

4.2.3. Independent Variable

The economic policy uncertainty index is measured using the Baker Index. Baker et al. [44] construct the China economic policy uncertainty index using text analysis by searching for keywords such as “China”, “economy”, and “uncertainty” in the South China Morning Post. This method enables a continuous and quantitative description of economic policy uncertainty.

4.2.4. Control Variable

We extend the model by adding other control variables that may affect the company’s financing efficiency, and we control the variables from both the micro enterprise level and macro level perspectives. At the corporate level, we control the size of the enterprise, profitability, revenue growth rate, cash flow level, asset liability ratio, shareholder shareholding, and Tobin Q value, as these variables significantly affect the financing efficiency of the enterprise [34,35,36]. At the macro level, we control the GDP growth rate to eliminate macro level interference. Additionally, to avoid the influence of individual characteristics, industry, regional differences, and time trends on the estimation results, we further control for individual fixed effects, industry fixed effects, province fixed effects, year fixed effects, and enterprise-level cluster standard errors. For further details on the definitions and calculation methods of the aforementioned variables, please refer to

Table 6. Variable descriptions.

Variable	Symbol	Calculation Methodology
Static financing efficiency	SFE	DEA-BCC Model
Dynamic financing efficiency	DFE	DEA-Malmquist Model
Economic Policy Uncertainty	EPU	Converting relevant indices constructed by Baker et al. [44] into annual data divided by 100
Enterprise profitability	ROA	Corporate profit divided by total assets
Enterprise scale	Size	Natural logarithm of total assets
Revenue growth rate	Growth	(Current revenue-previous revenue)/previous revenue
Cash flow level	Cash	Net cash flow from operating activities divided by total assets
Asset-liability ratio	Level	Total liabilities divided by total assets
Shareholder holding	Share	Proportion of shares held by the largest shareholder
Tobin Q	Q	Market value of equity plus net debt divided by the book value of total assets
GDP growth rate	GDP	Sourced from the National Bureau of Statistics

.

4.3. Descriptive Statistics

Table 7. Descriptive statistics.

Variable	Mean	Standard Deviation	Minimum Value	Maximum Value	Percentile			Skewness	Kurtosis
					25%	50%	75%
SFE	0.682	0.146	0.250	1.000	0.571	0.667	0.765	0.469	2.719
DFE	0.996	0.179	0.446	2.155	0.914	1.000	1.056	0.951	7.994
EPU	3.935	2.466	1.139	7.919	1.525	3.644	6.042	0.475	1.840
Level	34.894	17.169	6.337	76.393	20.348	33.056	48.291	0.321	2.135
Growth	18.007	27.703	−45.753	164.685	1.935	13.942	28.834	1.465	7.278
ROA	7.515	7.015	−14.650	32.750	2.860	6.323	11.135	0.587	4.180
Size	21.532	1.146	19.163	24.426	20.665	21.476	22.322	0.236	2.478
Cash	0.066	0.071	−0.136	0.321	0.019	0.059	0.106	0.442	3.637
Q	3.373	2.162	0.950	13.205	1.866	2.709	4.150	1.718	6.180
Share	33.126	13.546	8.480	74.860	22.680	30.910	41.650	0.563	2.805
GDP	0.063	0.016	0.023	0.077	0.064	0.068	0.072	−1.902	5.260

presents the descriptive statistics of the main variables in this study. Regarding the dependent variables, the mean values of static financing efficiency and dynamic financing efficiency are 0.682 and 0.996, respectively, and the median values are 0.667 and 1, respectively. The mean value of static financing efficiency is higher than the median, indicating that some enterprises have relatively high static financing efficiency values, but overall, the static financing efficiency in China’s high-tech manufacturing industry is low. The median value of dynamic financing efficiency is higher than the mean, and the median value is 1, indicating that 50% of the samples have relatively high dynamic financing efficiency values. The standard deviation of static financing efficiency is 0.146, while the standard deviation of dynamic financing efficiency is 0.179, indicating that the variability of dynamic financing efficiency in China’s high-tech manufacturing industry is higher than that of static financing efficiency.

Regarding the independent variables, the average value of the economic policy uncertainty index is 3.935, with a minimum value of 1.139 and a maximum value of 7.919, indicating significant fluctuations in the macroeconomic environment faced by enterprises during the sample observation period. Other control variables remain within reasonable ranges. These statistics are reported to provide a comprehensive understanding of the distribution and variability of the variables in the sample, as well as to inform the subsequent analysis and interpretation of the results.

From the perspective of skewness and kurtosis, most variables are distributed symmetrically, and some variables have high kurtosis. Figure 2 shows the probability density distribution of static financing efficiency, dynamic financing efficiency, and the economic policy uncertainty index. The figure shows that the distribution of core variables in this paper is approximate to normal distribution.

5. Empirical Results

5.1. The Impact of EPU on Enterprise Financing Efficiency

Table 8. The empirical results of the impact of EPU on static–dynamic financing efficiency.

	(1)	(2)	(3)	(4)
	SFE	SFE	DFE	DFE
EPU	0.006 ***	0.023 ***	−0.002	0.010 ***
	(4.53)	(12.58)	(−1.09)	(3.28)
Level		−0.002 ***		−0.002 ***
		(−4.76)		(−4.75)
Growth		0.000 ***		0.002 ***
		(3.75)		(11.02)
ROA		0.007 ***		0.001
		(10.42)		(1.02)
Size		−0.022 **		−0.040 ***
		(−2.14)		(−2.84)
Cash		0.118 ***		0.232 ***
		(3.13)		(2.81)
Q		0.006 ***		0.005
		(3.10)		(1.58)
Share		0.000		−0.001
		(0.57)		(−1.30)
GDP		0.355 ***		0.093
		(2.99)		(0.38)
Constant	0.645 ***	0.986 ***	1.003 ***	1.881 ***
	(87.41)	(4.49)	(89.95)	(6.10)
Year FE	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes
Province FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
Firm Cluster	Yes	Yes	Yes	Yes
R-squared	0.039	0.234	0.279	0.271

Table 8 reports empirical results of the impact of EPU on the static–dynamic financing efficiency of enterprises. SFE represents the static financing efficiency, and DFE represents the dynamic financing efficiency; ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Robust t-statistics are reported in parentheses.

presents the empirical results of the impact of economic policy uncertainty on static and dynamic financing efficiency in the high-tech manufacturing industry. Columns (1) and (3) control for individual fixed effects, time effects, industry fixed effects, province fixed effects, and enterprise-level clustering standard errors. In column (1), the coefficient of economic policy uncertainty on static financing efficiency is 0.006, which is significant at the 1% level. In column (2), the coefficient of economic policy uncertainty on static financing efficiency is 0.023, which is significant at the 1% level. From an economic perspective, this means that a 1 percentage point increase in the economic policy uncertainty index leads to a 0.023 percentage point increase in financing efficiency in the high-tech manufacturing industry. These results confirm the competitive hypothesis H_1a, which posits that an increase in economic policy uncertainty will promote an increase in static financing efficiency in the high-tech manufacturing industry.

In column (3), the coefficient of economic policy uncertainty on dynamic financing efficiency is negative, but it is not significant. However, as shown in column (4), an increase in economic policy uncertainty significantly promotes an increase in dynamic financing efficiency in the high-tech manufacturing industry, which is consistent with hypothesis H₂. This coefficient is significant at the 1% level. These results provide important insights for policymakers and investors who are interested in understanding the impact of economic policy uncertainty on financing efficiency in the high-tech manufacturing industry.

5.2. Endogenous Test

The main regression results provide evidence that an increase in economic policy uncertainty can incentivize enterprises to improve both static and dynamic financing efficiency. However, endogeneity issues may arise, as economic policy uncertainty is a macro-level variable that can also be influenced by the performance of individual enterprises. To address this issue, we employ the 2SLS method with instrumental variables. Firstly, following the approach of Gu et al. [1], we use the U.S. economic policy uncertainty index, as constructed by Baker et al., as an instrument variable for the Chinese economic policy uncertainty index. Secondly, following Peng et al. [45], we select the economic policy uncertainty index of seven major trading partners of China, namely the U.S., Japan, South Korea, the U.K., France, Germany, and Italy, and use their trade shares as weights to construct a weighted instrument variable. The results of the regression using these two-stage instrument variables are presented in

Table 9. Results of the EPU-IV instrument variable.

	(1)	(2)	(3)	(4)
	SFE	DFE	SFE	DFE
EPU_G	0.020 ***	0.009 ***
	(8.77)	(2.86)
EPU_US			0.020 ***	0.009 ***
			(8.77)	(2.86)
Level	−0.002 ***	−0.002 ***	−0.002 ***	−0.002 ***
	(−4.76)	(−4.75)	(−4.76)	(−4.75)
Growth	0.000 ***	0.002 ***	0.000 ***	0.002 ***
	(3.75)	(11.02)	(3.75)	(11.02)
ROA	0.007 ***	0.001	0.007 ***	0.001
	(10.42)	(1.02)	(10.42)	(1.02)
Size	−0.022 **	−0.040 ***	−0.022 **	−0.040 ***
	(−2.14)	(−2.84)	(−2.14)	(−2.84)
Cash	0.118 ***	0.232 ***	0.118 ***	0.232 ***
	(3.13)	(2.81)	(3.13)	(2.81)
Q	0.006 ***	0.005	0.006 ***	0.005
	(3.10)	(1.58)	(3.10)	(1.58)
Share	0.000	−0.001	0.000	−0.001
	(0.57)	(−1.30)	(0.57)	(−1.30)
Constant	1.017 ***	1.889 ***	1.017 ***	1.889 ***
	(4.72)	(6.23)	(4.72)	(6.23)
Year FE	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes
Province FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
Firm Cluster	Yes	Yes	Yes	Yes

We employ the instrumental variable method to retest the model (7) and model (8). Table 9 shows the instrumental variable 2SLS regression results. EPU_G represents an instrumental variable calculated based on the economic policy uncertainty of seven trading countries; EPU_US represents an instrumental variable calculated based on the economic policy uncertainty of the U.S.; ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Robust z-statistics are reported in parentheses.

. Columns (1) and (2) show the regression results using the weighted instrument variable, while columns (3) and (4) show the results using the U.S. economic policy uncertainty index as the instrument variable.

As shown in Table 9, economic policy uncertainty is positively correlated with both static and dynamic financing efficiency, and the relationship is statistically significant at the 1% level, which is consistent with the main regression results. These findings support the argument that enterprises of the high-tech manufacturing industry can respond to economic policy uncertainty by improving their financing efficiency.

5.3. Robustness Test

5.3.1. Lagged Core Explanatory Variable

We have empirically tested the impact of economic policy uncertainty on the static and dynamic financing efficiency of firms. However, considering the time lag required for policy implementation, which involves coordination among various government departments and administrative divisions, policy effects may not be immediately felt by firms. Moreover, firms may experience a lag in receiving and interpreting information on economic policies. Therefore, we conduct a robustness test to examine whether economic policy uncertainty has a cumulative effect on financing efficiency and to address potential endogeneity concerns. We use lagged one and two-period economic policy uncertainty indexes to test the robustness of the static and dynamic financing efficiency.

Table 10. Robustness results based on lagged variables.

	(1)	(2)	(3)	(4)
	SFE	SFE	DFE	DFE
L.EPU	0.060 ***		0.028 ***
	(12.24)		(3.28)
L2.EPU		0.044 ***		0.038 ***
		(10.57)		(5.59)
Level	−0.002 ***	−0.002 ***	−0.002 ***	−0.003 ***
	(−5.09)	(−6.15)	(−4.75)	(−5.49)
Growth	0.000 ***	0.000 ***	0.002 ***	0.002 ***
	(3.50)	(3.29)	(11.02)	(10.99)
ROA	0.007 ***	0.007 ***	0.001	0.001
	(10.58)	(10.86)	(1.02)	(0.97)
Size	−0.037 ***	−0.039 ***	−0.040 ***	−0.032 **
	(−3.21)	(−2.99)	(−2.84)	(−2.16)
Cash	0.110 **	0.102 **	0.232 ***	0.173 **
	(2.50)	(2.29)	(2.81)	(2.11)
Q	0.006 ***	0.003	0.005	0.004
	(2.81)	(1.31)	(1.58)	(1.27)
Share	0.000	0.001	−0.001	−0.001
	(0.58)	(0.72)	(−1.30)	(−1.02)
GDP	5.782 ***	1.656 ***	2.665 ***	1.137 ***
	(13.17)	(10.29)	(3.03)	(3.16)
Constant	0.906 ***	1.331 ***	1.670 ***	1.597 ***
	(3.88)	(4.65)	(5.87)	(4.94)
Year FE	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes
Province FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
Firm Cluster	Yes	Yes	Yes	Yes
Observations	2890	2577	2890	2577
R-squared	0.227	0.217	0.098	0.102
Number of Code	495	495	495	495

Table 10 reports the one-period lag and two-period lag EPU index to retest the model (7) and model (8); ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Robust t-statistics are reported in parentheses.

shows that lagged one-period economic policy uncertainty has a significant positive effect on both static and dynamic financing efficiency. This suggests that economic policy uncertainty not only affects financing efficiency in the current period but also has an impact in the subsequent period. The coefficients of lagged two-period economic policy uncertainty are 0.044 and 0.038 for static and dynamic financing efficiency, respectively, and both are significant at the 1% level. These results suggest that economic policy uncertainty has a cumulative effect on static and dynamic financing efficiency in the high-tech manufacturing industry. In other words, financing efficiency is not only affected by current economic policy uncertainty but also by previous periods. These findings confirm the main test results and ensure the reliability of our conclusions.

5.3.2. Replacing Economic Policy Uncertainty Index

Considering the potential measurement errors and biases in the economic policy uncertainty index, we use a new index, based on keyword searches of “uncertainty”, “vagueness”, “unpredictable”, and “finance” in the People’s Daily and Guangming Daily newspapers, constructed by Davis et al. [46]. The new index is transformed into annual data using the average method, and the regression results are shown in columns (1) and (2) of

Table 11. Robustness test results based on EPU index replacement and remeasurement.

	(1)	(2)	(3)	(4)
	SFE	DFE	SFE	DFE
EPU_ML	0.064 ***	0.025 ***
	(12.58)	(3.28)
EPU_N			0.023 ***	0.010 ***
			(12.58)	(3.28)
Level	−0.002 ***	−0.002 ***	−0.002 ***	−0.002 ***
	(−4.76)	(−4.75)	(−4.76)	(−4.75)
Growth	0.000 ***	0.002 ***	0.000 ***	0.002 ***
	(3.75)	(11.02)	(3.75)	(11.02)
ROA	0.007 ***	0.001	0.007 ***	0.001
	(10.42)	(1.02)	(10.42)	(1.02)
Size	−0.022 **	−0.040 ***	−0.022 **	−0.040 ***
	(−2.14)	(−2.84)	(−2.14)	(−2.84)
Cash	0.118 ***	0.232 ***	0.118 ***	0.232 ***
	(3.13)	(2.81)	(3.13)	(2.81)
Q	0.006 ***	0.005	0.006 ***	0.005
	(3.10)	(1.58)	(3.10)	(1.58)
Share	0.000	−0.001	0.000	−0.001
	(0.57)	(−1.30)	(0.57)	(−1.30)
GDP	1.075 ***	0.385	0.377 ***	0.102
	(8.94)	(1.38)	(3.19)	(0.42)
Constant	0.888 ***	1.847 ***	0.986 ***	1.881 ***
	(4.14)	(6.08)	(4.49)	(6.10)
Year FE	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes
Province FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
Firm Cluster	Yes	Yes	Yes	Yes
R-squared	0.234	0.098	0.234	0.098

We use a new EPU index and recalculate it to retest the model (7) and model (8), respectively. EPU_ML represents new index; EPU_N represents recalculated index; ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Robust t-statistics are reported in parentheses.

, which are consistent with the main results.

5.3.3. Recalculating Economic Policy Uncertainty Index

In the main regression, the data are annual, while the economic policy uncertainty index is based on monthly data. To ensure the reliability of our main results, we use the geometric average method to transform the monthly data into annual data. The empirical results, presented in columns (3) and (4) of Table 11, indicate that economic policy uncertainty has a similar effect on the static and dynamic financing efficiency of high-tech manufacturing firms as in the main regressions.

5.3.4. Changing the Model Estimation Method

Considering that the value of static financing efficiency is between 0 and 1, the robustness test is carried out with the Tobit model, and the regression results are shown in

Table 12. Robustness test results based on the Tobit model.

	(1)	(2)
	SFE	SFE
EPU	0.006 ***	0.018 ***
	(4.09)	(13.24)
Level		−0.001 ***
		(−3.41)
Growth		−0.000
		(−1.60)
ROA		0.009 ***
		(12.85)
Size		0.006
		(1.22)
Cash		0.257 ***
		(5.60)
Q		0.006 ***
		(2.79)
Share		0.000
		(1.42)
GDP		0.539 ***
		(4.59)
Year FE	Yes	Yes
Province FE	Yes	Yes
Industry FE	Yes	Yes
Firm Cluster	Yes	Yes
Constant	0.670 ***	0.346 ***
	(41.39)	(3.13)

We use the Tobit method to re-estimate model (7). ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

. In column (1), only industry fixed effects, province fixed effects, time fixed effects, and company-level clustering standard errors are controlled. The coefficient of economic policy uncertainty on the static financing efficiency of enterprises is positive and significant at the 1% level. Adding other control variables in column (2), the coefficient of economic policy uncertainty on financing efficiency is 0.018, which is significant at the 1% level, indicating that an increase in economic policy uncertainty can improve enterprise financing efficiency. This is consistent with the benchmark regression results.

6. Additional Analyses

6.1. The Transmission Mechanism of EPU on Corporate Financing Efficiency

This part investigates the influence of economic policy uncertainty on static financing efficiency, within the high-tech manufacturing sector, by examining its internal mechanisms. Static financing efficiency can be broken down into pure technical efficiency and scale efficiency for further analysis. Empirical findings from columns (1) and (2) in

Table 13. Empirical results of the transmission mechanism of EPU on firm financing efficiency.

	SFE Mechanism		DFE Mechanism
	(1)	(2)	(3)	(4)	(5)	(6)
	Pure Technical Efficiency	Scale Efficiency	Comprehensive Efficiency Change Index	Pure Technical Efficiency Change Index	Scale Efficiency Change Index	Technology Progress Efficiency Index
EPU	0.015 ***	0.012 ***	0.016 ***	0.014 ***	0.002	−0.006 ***
	(7.70)	(11.37)	(4.94)	(5.79)	(0.97)	(−6.72)
Level	−0.000	−0.002 ***	−0.003 ***	−0.001 *	−0.002 ***	0.000
	(−1.39)	(−9.13)	(−4.91)	(−1.81)	(−6.43)	(0.87)
Growth	0.000 ***	−0.000	0.002 ***	0.002 ***	0.000	0.000 **
	(4.60)	(−0.20)	(10.05)	(12.15)	(1.55)	(2.28)
ROA	0.009 ***	−0.002 ***	0.002	0.004 ***	−0.003 ***	−0.000
	(15.69)	(−5.37)	(1.46)	(3.95)	(−4.19)	(−1.23)
Size	−0.008	−0.020 ***	−0.064 ***	−0.070 ***	0.006	0.022 ***
	(−0.77)	(−3.69)	(−3.98)	(−5.18)	(0.90)	(4.92)
Cash	0.081 **	0.059 **	0.239 ***	0.160 **	0.071	0.000
	(2.42)	(2.32)	(2.79)	(2.28)	(1.51)	(0.02)
Q	0.002	0.006 ***	0.005	0.003	0.003 *	−0.000
	(1.03)	(5.75)	(1.64)	(0.97)	(1.71)	(−0.09)
Share	−0.000	0.001 ***	−0.001	−0.001	−0.000	−0.000
	(−0.16)	(2.68)	(−1.03)	(−0.88)	(−0.48)	(−0.21)
GDP	0.883 ***	−0.634 ***	1.639 ***	1.080 ***	0.475 ***	−1.490 ***
	(7.19)	(−8.15)	(6.29)	(4.72)	(3.26)	(−14.20)
Constant	0.738 ***	1.327 ***	2.308 ***	2.370 ***	0.928 ***	0.619 ***
	(3.29)	(11.79)	(6.60)	(7.93)	(6.17)	(6.48)
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes	Yes	Yes
Province FE	Yes	Yes	Yes	Yes	Yes	Yes
Industry	Yes	Yes	Yes	Yes	Yes	Yes
Firm Cluster	Yes	Yes	Yes	Yes	Yes	Yes
R-squared	0.279	0.271	0.111	0.141	0.109	0.267

Table 13 reports the transmission mechanism of the impact of EPU on corporate financing efficiency. Columns (1) and (2) are results of static financing efficiency; columns (3) to (6) are results of dynamic financing efficiency; ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Robust t-statistics are reported in parentheses.

reveal that the coefficient of economic policy uncertainty on pure technical efficiency is 0.015, which is significant at the 1% level, while the coefficient on scale efficiency is 0.012, which is also significant at the 1% level. These results suggest that economic policy uncertainty drives improvements in both pure technical efficiency and scale efficiency, thereby promoting advancements in management capabilities, internal governance, and financing scale effects within high-tech manufacturing firms. Consequently, this leads to an overall increase in static financing efficiency.

The Malmquist index can be decomposed into the comprehensive efficiency change index and the technical progress efficiency index, with the comprehensive efficiency change index able to be broken down further into the pure technical efficiency change index and the scale efficiency change index. Building on these components, we analyze the mechanism by which EPU affects dynamic financing efficiency in the high-tech manufacturing industry. The empirical results in columns (3) to (6) of Table 13 indicate that the coefficient of EPU on the comprehensive efficiency change index is 0.016, which is significant at the 1% level, the coefficient on the pure technical efficiency change index is 0.014, which is also significant at the 1% level, and the coefficient on the scale efficiency change index is 0.002, but it is not significant. Additionally, the coefficient on the technical progress efficiency index is −0.006, which is significant at the 1% level. These findings suggest that EPU primarily promotes comprehensive efficiency change, thereby enhancing dynamic financing efficiency. However, an increase in EPU does not stimulate improvement in production technology within high-tech manufacturing firms. The absolute value of the coefficient of EPU on the comprehensive efficiency change index is significantly greater than that on the technical progress efficiency index. Consequently, EPU exhibits a stimulating effect on dynamic financing efficiency in the high-tech manufacturing industry.

6.2. Heterogeneity Analysis on the Impact of EPU on Static Financing Efficiency

Financing channels play a crucial role in affecting financing efficiency. Consequently, we perform a differential analysis of static financing efficiency based on different financing channels. These channels can be categorized into external financing and internal financing. External financing can be further divided into equity financing and debt financing, with debt financing including bank credit and commercial credit. This paper, therefore, classifies financing channels into equity financing (EF), commercial credit (Credit), bank borrowing (Loan), and internal financing (IF), which are measured by the sum of paid-in capital and capital reserves, the sum of notes payable, accounts payable, advance receipts, the sum of short-term loans and long-term loans, and the sum of retained earnings, depreciation, and amortization for the current period. To account for differences in enterprise size, these variables are standardized using total assets. In model (7), the interaction terms of economic policy uncertainty and different financing channel indicators, as well as different financing channel indicators, are introduced to conduct heterogeneity tests. The empirical results are presented in

Table 14. Empirical results of heterogeneity tests based on different financing channels.

	(1)	(2)	(3)	(4)
	SFE	SFE	SFE	SFE
EPU	0.021 ***	0.017 ***	0.022 ***	0.029 ***
	(9.03)	(5.96)	(7.43)	(9.89)
EPU × Credit	0.003
	(0.31)
EPU × EF		0.013 **
		(2.00)
EPU × Loan			−0.004
			(−0.37)
EPU × IF				−0.026 ***
				(−3.25)
Credit	0.258 ***
	(3.31)
EF		0.011
		(0.21)
Loan			−0.137
			(−1.43)
IF				0.150 **
				(2.22)
Level	−0.002 ***	−0.001 **	−0.000	−0.002 ***
	(−6.65)	(−2.37)	(−0.50)	(−4.88)
Growth	0.000 ***	0.000 ***	0.000 ***	0.000 ***
	(3.14)	(3.60)	(3.24)	(4.20)
ROA	0.006 ***	0.007 ***	0.006 ***	0.006 ***
	(8.94)	(10.31)	(6.73)	(9.13)
Size	−0.021 **	−0.018 *	−0.008	−0.019 *
	(−2.16)	(−1.90)	(−0.58)	(−1.93)
Cash	0.094 **	0.118 ***	0.108 *	0.107 ***
	(2.29)	(3.13)	(1.82)	(2.85)
Q	0.006 ***	0.007 ***	0.007 **	0.006 ***
	(2.68)	(3.36)	(2.45)	(2.90)
Share	0.000	0.001	0.000	0.000
	(0.33)	(1.01)	(0.70)	(0.66)
GDP	−0.059	0.349 ***	0.408 ***	0.372 ***
	(−0.28)	(2.95)	(2.84)	(3.14)
Constant	0.994 ***	0.885 ***	0.638 **	0.892 ***
	(4.85)	(4.33)	(2.24)	(4.19)
Year FE	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes
Province FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
Firm Cluster	Yes	Yes	Yes	Yes
R-squared	0.233	0.239	0.301	0.243

Table 14 reports the empirical results of heterogeneity tests based on different financing channels. Credit represents commercial credit; EF represents equity financing; Loan represents bank credit; IF represents internal financing; ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Robust t-statistics are reported in parentheses.

.

Table 14 demonstrates that the regression coefficient of the interaction term between economic policy uncertainty and commercial credit is 0.003, and the coefficient of the interaction term between economic policy uncertainty and bank borrowing is −0.004. However, both coefficients are not significant, suggesting that commercial credit and bank credit do not moderate the relationship between economic policy uncertainty and static financing efficiency in the high-tech manufacturing industry. This outcome may be attributed to banks becoming more cautious about lending when external environmental uncertainty increases, leading to a decline in credit availability for enterprises—especially long-term loans—and resulting in increased credit costs for businesses. Likewise, when economic policy uncertainty intensifies, enterprises reduce the commercial credit supply to ensure the smooth operation of their cash flow. As shown in columns (2) and (4), the coefficient of the interaction term between economic policy uncertainty and equity financing is 0.013, which is significant at the 5% level, and the coefficient of the interaction term between economic policy uncertainty and internal financing is −0.026, which is significant at the 1% level. These findings indicate that equity financing can significantly enhance the incentive effect of economic policy uncertainty on static financing efficiency in the high-tech manufacturing industry, while internal financing tends to reduce this effect.

7. Empirical Findings and Pertinent Discussion

In this section, we aggregate the empirical findings from the previous sections and provide a comprehensive discussion of the results. We present the main conclusions drawn from our analysis, as well as the implications for high-tech manufacturing firms and policymakers.

Static Financing Efficiency: From 2013 to 2020, the average comprehensive efficiency of high-tech manufacturing was only 0.682, the average pure technical efficiency was 0.758, and the average scale efficiency was 0.897. The financing efficiency of China’s high-tech manufacturing industry is relatively low, with scale efficiency being higher than pure technical efficiency. The benchmark regression results show that the EPU has a positive impact on the static financing efficiency of high-tech manufacturing, supporting hypothesis H_1a. The mechanism analysis results show that a higher EPU leads to improvements in both pure technical efficiency and scale efficiency. These enhancements contribute to better management capabilities, internal governance, and financing scale effects within high-tech manufacturing firms, ultimately increasing the static financing efficiency.

Dynamic Financing Efficiency: The Malmquist index is greater than or equal to 1 in the 2013–2014, 2015–2016, and 2018–2019 periods, but it is less than 1 in other years. The financing efficiency of China’s high-tech manufacturing industry is in a fluctuating and continuously improving state. The benchmark regression results show that EPU promotes dynamic financing efficiency, supporting hypothesis H₂. The mechanism analysis results show that EPU primarily promotes comprehensive efficiency change, which enhances dynamic financing efficiency. However, an increase in EPU does not stimulate improvement in production technology within high-tech manufacturing firms. Overall, EPU exhibits a stimulating effect on dynamic financing efficiency in the high-tech manufacturing industry.

Financing Channels: The heterogeneity analysis results show that commercial credit and bank credit do not moderate the relationship between EPU and static financing efficiency in the high-tech manufacturing industry. This result may be attributed to banks becoming more cautious about lending when external environmental uncertainty increases, leading to a decline in credit availability for enterprises—especially long-term loans—and resulting in increased credit costs for businesses. Similarly, when EPU intensifies, enterprises reduce the commercial credit supply to ensure the smooth operation of their cash flow. We find that equity financing can significantly enhance the incentive effect of EPU on static financing efficiency in the high-tech manufacturing industry, while internal financing tends to reduce this effect.

8. Implications

This study has some implications. First, the financing efficiency of China’s high-tech manufacturing industry is relatively low, and efforts should be made to accelerate the adjustment of the input–output structure and improve financing efficiency. Currently, the pure technical efficiency of China’s high-tech manufacturing industry is far lower than the scale efficiency, which reduces the financing efficiency. Therefore, China’s high-tech manufacturing industry should strengthen internal governance, improve the management level, maximize the efficiency of fund utilization, and improve financing efficiency. Second, enterprises should objectively treat uncertainty and seek development opportunities in it. This study finds that economic policy uncertainty can positively influence the static–dynamic financing efficiency of enterprises, indicating that economic policy uncertainty has multiple effects on economic entities: transmitting uncertainty signals while also hiding new development opportunities. Third, government departments should strengthen their regulatory efforts. Relevant government departments in China have provided significant financing support for high-tech manufacturing industries, such as bank credit support. However, the management ability of the enterprise itself is relatively low, and the financing scale efficiency is higher than pure technical efficiency. Therefore, government departments should increase their supervision efforts and provide more targeted credit policies, to guide funds to flow to enterprises with strong technological innovation capabilities and high resource allocation efficiency, in order to improve financing efficiency and optimize the industrial structure. Finally, in terms of theoretical contributions, some existing studies have shown that economic policy uncertainty has a negative impact on enterprises. This study finds that the impact of economic policy uncertainty on enterprises is multiple and non-monotonic.

9. Conclusions

In the context of increasing economic policy uncertainty, this paper employs the China economic policy uncertainty index, constructed by Baker et al., to empirically investigate the impact of economic policy uncertainty on the financing efficiency of China’s high-tech manufacturing industry. By extending the analysis of financing efficiency from static measures to dynamic ones, this study aims to provide a comprehensive understanding of how external shocks affect China’s high-tech manufacturing industry, offer insights into the potential challenges and opportunities associated with economic policy uncertainty, and explore the implications for policymakers, industry stakeholders, and enterprises. Our results indicate that the static financing efficiency of listed high-tech manufacturing companies in China is low, whereas the dynamic financing efficiency is relatively high, suggesting an ongoing optimization of the financing input–output structure in China’s high-tech manufacturing industry. Economic policy uncertainty has a significant positive relationship with both static and dynamic financing efficiency. This highlights the importance of adapting to the rapidly changing economic environment and capitalizing on the potential opportunities that may arise amidst economic policy uncertainty. In terms of mechanism effect analysis, economic policy uncertainty promotes the improvement of an enterprise’s pure technical efficiency and scale efficiency, thus promoting the improvement of static financing efficiency and, through the channel of comprehensive efficiency change, improving dynamic financing efficiency. In terms of heterogeneity analysis, equity financing significantly enhances the incentive effect of economic policy uncertainty on static financing efficiency, while internal financing inhibits this effect, and bank credit and commercial credit have no significant impact.

Limitations and improvements: Although we hope to conduct a good study on the relationship between economic policy uncertainty and financing efficiency, there are still some shortcomings. In the selection of financing efficiency evaluation methods, although we have consulted a large number of relevant literature sources and selected the classic DEA model to calculate financing efficiency, further exploration may be needed to measure financing efficiency, such as whether using SBM–DEA, Bootstrap DEA, and global Malmquist index models can better measure financing efficiency. The connotation of financing efficiency and the selection of indicators are still worth further exploration. At the same time, the theoretical basis and mechanism of the impact of economic policy uncertainty on financing also merit further research.