Financing Constraints and High-Quality Development of Chinese Listed Firms: Mechanisms of Investment Efficiency and Contingent Factors

Abstract

Against the backdrop of tightened credit conditions, external financing constraints have increasingly become an important factor affecting enterprises’ high-quality development. This study focuses on the impact of financing constraints on the high-quality development of Chinese listed firms and constructs an analytical framework involving investment efficiency as a mediator and contextual factors such as managerial effectiveness and internal control quality as moderators. Using a longitudinal dataset of China’s A-share listed companies from 2007 to 2021, multivariate regression and mediation effect tests are conducted. The observational findings reveal a statistically meaningful U-shaped association between financial constraints and the high-quality development of enterprises. Further analysis confirms that investment efficiency partially mediates the relationship between financing constraints and high-quality development, while managerial effectiveness and internal control quality play significant moderating roles in this relationship. Additionally, the study reveals heterogeneous impacts of financing constraints on high-quality development across different regions. These findings provide insights into how enterprises can mitigate the adverse effects of financing constraints and promote high-quality development.

1. Introduction

Against the backdrop of international turmoil, the quality of development for domestic enterprises assumes critical importance. Historically, over-reliance on extensive growth has led to stagnation, as seen in Latin American countries, which are caught in the “middle-income trap” (Li, 2022); China has similarly grappled with such growth patterns (Murphree & Breznitz, 2025). With the dissipation of the demographic dividend slowing rapid growth (Cai & Lu, 2016), recent policies emphasize avoiding this trap through total factor productivity enhancement and high-quality development (HQD) promotion (Glawe & Wagner, 2020). However, intensified external financing constraints amid tightened credit conditions in recent years have emerged as a key barrier (Li, 2022). Against this backdrop, clarifying how financing constraints shape enterprise HQD, including underlying mechanisms and contextual factors, becomes crucial.

Financing constraints exert complex impacts on enterprise behavior: excessive constraints may disrupt cash flow, forcing enterprises to prioritize short-term survival over long-term development; yet moderate constraints could incentivize improvements in management and resource allocation efficiency, thereby facilitating HQD (Li et al., 2022). In China, such dynamics are further complicated by structural issues—private enterprises face financing discrimination due to ownership attributes (B. Yan et al., 2018). While capital diversion from real sectors to virtual economies (e.g., finance, internet) exacerbates funding shortages for real economy enterprises, hindering their HQD process.

This study focuses on Chinese A-share listed firms to explore the relationship between financing constraints and enterprise HQD. With a framework centered on investment efficiency as a mediator and managerial effectiveness/internal control quality as moderators. Specifically, it examines (1) the non-linear (U-shaped) relationship between financing constraints and HQD, testing whether severe constraints hinder but moderate ones facilitate HQD; (2) the mediating role of investment efficiency in this relationship, complementing existing focus on technological progress; (3) heterogeneous impacts across regions; and (4) the moderating effects of managerial effectiveness and internal control quality.

The contributions lie in (1) confirming the U-shaped relationship between financing constraints and HQD via multivariate regression, highlighting the dual role of constraints in restricting development and driving efficiency under pressure; (2) enriching HQD transmission mechanisms by emphasizing investment efficiency, a less explored path compared to technological factors; and (3) identifying managerial and internal control factors that moderate the relationship, providing actionable insights for enterprises to mitigate financing constraint risks.

2. Literature Review

2.1. Financing Constraints and High-Quality Development

In a perfectly functioning capital market, funds flow freely, and the cost of internal and external financing for enterprises is the same, with no financing constraints (Z. Yang et al., 2023). However, in reality, the separation of ownership and management, along with information asymmetry, creates risks for external investors, who demand higher returns to compensate for these risks (Murphree & Breznitz, 2025). This phenomenon gives rise to financing constraints, typified by inadequate capital, investment deficits, and cash flow shortages, which in turn impact the pursuit of high-quality development. The extant literature, as evidenced by the preponderance of studies in this area, posits that financing constraints impede high-quality development by virtue of their effect on investment. For instance, Benjamin and Meza (2009) found that the 1997 Korean financial crisis led to financing constraints, causing labor and production factors to shift from high-productivity manufacturing to low-productivity wholesale trade, thereby reducing total factor productivity (TFP). In addition, Hsieh and Klenow (2009) developed a structural model to measure internal financing capacity and financial collateral, while Uras (2014) extended this research, showing that low bond ratings and low asset liquidity lead to lower productivity. Chang and Tang (2021) argue that the convergence of the digital economy and the financial system can ease financial constraints across various societal levels, thereby promoting the high-quality development of enterprises. Moreover, Chen et al. (2022) carried out an empirical investigation into China’s Shanghai and Shenzhen A-share listed companies over the period 2011–2020, finding that financial constraints exert a negative impact on enterprises’ total factor productivity (Cao & Cao, 2025). However, they argue that the inclusiveness of digital finance has the potential to alleviate these constraints.

Financing constraints refer to the obstacles or restrictions that enterprises encounter in the process of obtaining external financing (Li, 2022). High-quality development represents a new stage of development characterized by innovation-driven growth, green and low-carbon practices, and inclusive prosperity (Glawe & Wagner, 2020). It emphasizes the quality, efficiency, and sustainability of development rather than mere economic growth rates (Anagnostopoulou & Malikov, 2024). External financing constraints have been shown to impede the advancement of enterprises, as they serve to diminish the funds available for the promotion of productivity. It is important to acknowledge that a certain degree of financing constraints can exert pressure on enterprises to ensure their survival, compelling them to enhance the quality of their development through the optimization of the efficiency of their own resource allocation. Dhawan (2001) analyzed the production technology parameters of small and large firms during the period 1970–1989 in the United States and found that the productivity of small enterprises was significantly higher than that of large enterprises but also more risky. De and Nagaraj (2014) empirically analyzed data from India, finding that small firms, despite having less favorable access to financing than larger firms, can enhance the total factor productivity of firms to a certain extent by virtue of their flexible management abilities, developed in an unfair market competition environment. Y. Wang and Kong (2019) deduced that China’s financing constraints may have a U-shaped relationship with firms’ productivity by constructing a dynamic theoretical model and then empirically rejected this hypothesis based on China’s Shanghai and Shenzhen A-share data from 2000 to 2011 and put forward the view that financing constraints are a driver of firms’ productivity.

2.2. Financing Constraints and Investment Efficiency

Before proceeding, we need to clarify the role of the principal-agent problem in enterprises’ long-term development and investment behavior. This issue significantly impacts corporate operations. From a long-term perspective, conflicts arise due to misaligned interests between managers and shareholders (Tsaban & Shavit, 2024). When managers prioritize personal gain maximization, they may overlook long-term strategic planning, thereby impeding sustainable growth. In investment activities, the principal-agent problem often leads to either overinvestment or underinvestment (X. Wang et al., 2024). Managers may excessively allocate resources to expand business scale and enhance their status, even if projects yield low returns. Conversely, risk-averse behaviors driven by concerns over personal compensation may cause them to abandon promising opportunities, resulting in insufficient investment (Anagnostopoulou & Malikov, 2024). Both scenarios ultimately erode long-term corporate value.

Financing constraints, arising from the pressure of cash flow, have been shown to influence business decisions. Concurrently, financing constraints inherent in the normal operation of the enterprise often result in the utilization of higher cost capital (Myers & Majluf, 1984). In such circumstances, enterprise managers are predominantly concerned with the enterprise’s short-term profitability. Consequently, in decision-making, managers often prioritize projects that offer a short-term return, despite their net present value being negative or low in the long term (Panousi & Papanikolaou, 2012). This approach, however, reduces their investment efficiency (Liu & Lin, 2018). Lin (2019) conducted a study of Chinese cultural and creative enterprises’ date, which revealed a pervasive financing constraint situation in China, consequently resulting in underinvestment. Y. Wang et al. (2014) empirical study utilized data from 2009 to 2019 in China and identified that financing constraints can impede the degree of inefficient investment by enterprises.

2.3. Financial Constraints, Investment Efficiency and Firms’ High-Quality Development

Financial constraints and investment efficiency are pivotal to firms’ high-quality development. Financial constraints hinder investment efficiency—supply chain finance eases this by reducing constraints and information asymmetry (Dou & Zhao, 2024), while carbon regulations raise costs for non-SOEs, curbing productive investment (Z. Yang et al., 2023). Maietta and Sena (2010) analyzed the nature of the relationship between financial constraints and technical efficiency for traditional firms and producer cooperatives specializing in wine production in Italy over the period 1996 to 2001. Their findings indicate that firms coping with financing constraints can increase total factor productivity in the form of, among other things, improving their own investment efficiency.

2.4. Brief Review

In summary, this paper conducts a comprehensive review of the existing literature on the topic of financing constraints and the high-quality development of enterprises. The scope of this review not only covers the relationship between financing constraints and the investment efficiency of enterprises but also delves into the transmission mechanism through which financing constraints affect the high-quality development of enterprises.

The existing body of literature examining the relationship between financing constraints and the high-quality development of enterprises mainly approaches the issue from the perspective of investment in scientific and technological innovation. It is a widely accepted view that financing constraints force enterprises to adopt a short-sighted strategy under the pressure of survival. As a result, these enterprises tend to overlook long-term productivity growth and instead prioritize projects with immediate returns. However, some of the existing literature contends that, under a certain level of financing constraints, enterprises will concentrate on improving their internal management efficiency, which may render the negative impacts of financing constraints less significant. It has also been proposed that financing constraints can have a dual impact on the high-quality development of enterprises: initially, they can stimulate enterprises to enhance their business management capabilities, contributing positively to development; yet, once a certain threshold is reached, financing constraints begin to exert negative effects. Through theoretical and empirical analysis, this paper endorses the third perspective, arguing that there exists an inverted U-shaped relationship between the degree of financing constraints faced by enterprises and their high-quality development.

In the existing literature regarding the financing constraints and investment efficiency of enterprises, the dominant viewpoint posits that overly severe financing constraints have a negative impact on investment efficiency by decreasing the amount of available capital. Certain scholars have extended Richardson’s inefficient investment model, classifying inefficient investment into two types: underinvestment and overinvestment. From these two aspects, they have investigated the linear relationship between financing constraints and the investment efficiency of enterprises. In contrast, this paper aims to explore the nonlinear relationship between financing constraints and enterprise investment efficiency, taking into account both scenarios of underinvestment and overinvestment.

In examining the transfer mechanism linking financial constraints and enterprises’ high-quality development, scholars typically anchor their research on the dimension of innovation investment. Specifically, financial constraints impede firms’ allocation to innovation activities, which in turn elicits adverse effects on these enterprises’ high-quality development trajectory. Consequently, this relationship is widely acknowledged in existing empirical studies as one where financial constraints undermine enterprises’ high-quality development. Nevertheless, it is worth noting that financial constraints not only impact enterprises’ investment efficiency but also, in subsequent stages, facilitate their high-quality development. This study will investigate the transmission pathway between financial constraints and high-quality development from the perspective of firms’ investment efficiency, with the objective of offering a more nuanced perspective on this intricate relational dynamic.

3. Mechanism Analysis, Hypotheses and Research Design

3.1. Mechanism Analysis and Hypotheses

3.1.1. Financing Constraints, Investment Efficiency, and High-Quality Development

The principal-agent theory posits the separation of ownership and operation of enterprises, whereby capitalists entrust operation and decision-making to those with professional expertise to optimize benefits. This arrangement enables the actual operators of enterprises to engage more closely with their operations, acquiring deeper insights into the enterprise, which can potentially give rise to agency issues. In addition to the global financial crisis, investors are expected to adopt a more cautious approach when allocating capital. The Modigliani-Miller theorem is unable to provide a comprehensive representation of the ideal enterprise financing structure in the capital market, leading to a financing constraint. Consequently, enterprises are compelled to shoulder elevated costs of capital utilization in external financing, exerting pressure on their cash flow requirements for production and operations. This, in turn, jeopardizes the survival and growth of enterprises. Consequently, enterprise managers may be compelled to curtail investment in innovation and other projects with low short-term returns when making decisions, thereby adversely impacting the enterprise’s overall development (Farre-Mensa & Ljungqvist, 2016).

However, under a certain degree of financing constraints, enterprises will value the funds they obtain, and they will select projects with higher rates of return in their decision-making. Conversely, when confronted with external financing constraints, enterprise shareholders, creditors and other stakeholders will increase their oversight of the enterprise to mitigate the agency problem and enhance the quality of the enterprise’s development by reducing the form of overinvestment. Consequently, this paper posits the argument that enterprises, confronted with mounting financing constraints, will enhance their development quality by optimizing their resource allocation efficiency. However, beyond a critical juncture, the enterprise’s impact on diminishing its long-term income will become more pronounced, thereby impeding the enterprise’s growth. The following hypotheses are thus proposed:

H1. 

Financing constraints exhibit an inverted U-shaped relationship with high-quality development.

H2. 

Investment efficiency mediates the relationship between financing constraints and high-quality development.

3.1.2. Moderating Role of Managerial Effectiveness

Managerial effectiveness is defined as a comprehensive reflection of the skills, experience, and values of a firm’s managers (Zhang et al., 2024). It serves as an indicator of whether managers can predict industry development trends based on their experience, make efficient operational decisions, manage employees appropriately, and improve the firm’s input-output ratio (Cao & Cao, 2025). Overall, the stronger the managerial effectiveness, the more likely managers are to have a long-term development perspective. They are better able to utilize the firm’s limited resources, and under the same financing constraints, the development quality of the firms they manage may be superior. Therefore, the following hypothesis is proposed:

H3. 

Managerial effectiveness moderates the relationship between financing constraints and high-quality development.

3.2. Research Design

3.2.1. Variable Selection

The dependent variable is measured by scholars using Total Factor Productivity (TFP) as an indicator of high-quality development, due to its relative objectivity and operational nature (Liang & Zhang, 2024). Regarding the methods for calculating total factor productivity (TFP), scholars typically measure the relative changes between factors such as the number of employees and capital investment and operating income (Wooldridge, 2009). Among these, the Generalized Method of Moments (GMM) approach, which is based on the profit maximization objective, avoids the propagation of estimation errors from the first step to the second step in two-stage methods (Tang, 2017). This has become a widely adopted measurement approach in academic research. The present study will utilize the method proposed of GMM to calculate the TFP of enterprises. The higher its value, the higher the quality of the enterprise’s development.

Independent Variable: Referring to Kaplan and Zingales (1997), this paper constructs the KZ index to measure the degree of financing constraints faced by enterprises. A higher value indicates a greater degree of financing constraints.

Mediating Variable: Qian (2014) built an index to describe the inefficiency of corporate investment, drawing on the research of Richardson (2006). A higher value indicates a greater degree of inefficient investment.

The moderating variable is as follows: J. Yan and Zhao (2025) refined the managerial effectiveness measurement method proposed by Demerjian et al. (2012) based on the actual situation in China. Initially, sales revenue (Sales) is employed as the output variable, with the cost of goods sold (CoGS), selling, general, and administrative expenses (SG&A), property, plant, and equipment (PPE), net intangible assets (Intan), research and development expenditure (R&D), and goodwill (Goodwill) serving as input variables. Subsequently, the production efficiency of the enterprise is determined through the implementation of Data Envelopment Analysis (DEA) (referring to Equation (1)).

$$\mathrm{M}\mathrm{A}\mathrm{X}\left(\mathsf{\theta }\right)={\displaystyle \frac{\mathrm{S}\mathrm{a}\mathrm{l}\mathrm{e}\mathrm{s}}{{\mathrm{v}}_1\mathrm{C}\mathrm{o}\mathrm{G}\mathrm{S}+{\mathrm{v}}_2\mathrm{S}\mathrm{G}\&\mathrm{A}+{\mathrm{v}}_3\mathrm{P}\mathrm{P}\mathrm{E}+{\mathrm{v}}_4\mathrm{O}\mathrm{p}\mathrm{s}\mathrm{L}\mathrm{e}\mathrm{a}\mathrm{s}\mathrm{e}+{\mathrm{v}}_5\mathrm{I}\mathrm{n}\mathrm{t}\mathrm{a}\mathrm{n}+{\mathrm{v}}_6\mathrm{G}\mathrm{o}\mathrm{o}\mathrm{g}\mathrm{w}\mathrm{i}\mathrm{l}\mathrm{l}}}$$

(1)

The Tobit model is utilized to conduct regression of the calculated firm production efficiency onto firm size, market share, free cash flow, years since listing, and business complexity. The residuals obtained from this regression are employed as an indicator of managerial effectiveness (seeing Equation (2)), where a larger residual indicates stronger managerial effectiveness. Furthermore, industry and annual fixed effects are controlled for in order to mitigate the impact of firm-level factors, such as firm size, on the measurement of managerial effectiveness. Clustering effects are applied at both the firm and annual levels to control for cross-sectional and temporal correlations.

$$\mathsf{\theta }=\mathsf{\alpha }+{\mathsf{\beta }}_1\mathrm{S}\mathrm{i}\mathrm{z}\mathrm{e}+{\mathsf{\beta }}_2\mathrm{M}\mathrm{a}\mathrm{r}\mathrm{k}\mathrm{e}\mathrm{t}\mathrm{s}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{e}+{\mathsf{\beta }}_3\mathrm{F}\mathrm{C}\mathrm{F}+{\mathsf{\beta }}_4\mathrm{A}\mathrm{g}\mathrm{e}+{\mathsf{\beta }}_5\mathrm{H}\mathrm{H}\mathrm{I}+\mathsf{\epsilon }$$

(2)

The residuals obtained thus represent the managerial effectiveness of the firm, with larger values indicating stronger managerial effectiveness.

Control variables: The selection of growth ability, equity concentration, and other indicators has been based on prior research. The information of the main variables is listed in Table 1.

Table 1. Variable names.

Variable	Name	Definition
WRDG	Total Factor Productivity (TFP)	Calculated through Wooldridge’s method
MrEst		Calculated through Blundell’s method
KZ	Financing Constraints	Calculated through Hadlock’s method
ME	Managerial Effectiveness	Measured through the DE-Tobit method by Demerjian
ABSINV	Degree of Inefficient Investment	Degree of inefficient investment measured using Richardson’s method
ZScore	Financial Distress	Calculated using Edward’s method
growth	Revenue Growth Rate	(Current period’s total operating revenue—Last year’s total operating revenue)/Last year’s total operating revenue
cash	Cash Flow	Net operating cash flow/Total assets
stock	Equity Concentration	ln(Shareholding proportion of the controlling shareholder)
indro	Proportion of Independent Directors	Number of independent directors/Total number of board members
dual	Duality of CEO and Chairman	Dummy variable for whether the chairman and CEO are the same person (1 if yes, 0 if no)
organ	Institutional Ownership Ratio	Proportion of shares held by institutional Investors in the listed company (%)
age	Firm Age	Years since the firm’s establishment

Source: Organized based on the research design in the text.

3.2.2. Model Specification

In order to examine the impact of financing constraints on the high-quality development of enterprises, models are constructed in order to analyze the relationship between financing constraints and high-quality development (seeing Equations (3) and (4)). Equation (3) represents the linear relationship between financing constraints and high-quality development, while Equation (4) tests the non-linear relationship between financing constraints and high-quality development.

$${\mathrm{T}\mathrm{F}\mathrm{P}}_{\mathrm{i}\mathrm{t}}=\mathsf{\alpha }+{\mathsf{\beta }}_1{\mathrm{K}\mathrm{Z}}_{\mathrm{i}\mathrm{t}}+\mathsf{\beta }{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{t}}+\sum \mathrm{i}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}+\sum \mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}+\mathsf{\epsilon }$$

(3)

$${\mathrm{T}\mathrm{F}\mathrm{P}}_{\mathrm{i}\mathrm{t}}=\mathsf{\alpha }+{\mathsf{\beta }}_1{\mathrm{K}\mathrm{Z}}_{\mathrm{i}\mathrm{t}}^2+{\mathsf{\beta }}_2{\mathrm{K}\mathrm{Z}}_{\mathrm{i}\mathrm{t}}+\mathsf{\beta }{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{t}}+\sum \mathrm{i}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}+\sum \mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}+\mathsf{\epsilon }$$

(4)

This paper employed the stepwise regression method for testing, as outlined in Equations (5) and (6). If the coefficient ${\mathsf{\beta }}_1$ in Equation (5) and the coefficient ${\mathsf{\beta }}_1$ in Equation (6) are both found to be significant, it can be concluded that the investment efficiency of the enterprise plays a mediating role in the linear relationship between financing constraints and the development quality of the firm.

$${\mathrm{A}\mathrm{B}\mathrm{S}\mathrm{I}\mathrm{N}\mathrm{V}}_{\mathrm{i}\mathrm{t}}=\mathsf{\alpha }+{\mathsf{\beta }}_1{\mathrm{K}\mathrm{Z}}_{\mathrm{i}\mathrm{t}}+\mathsf{\beta }{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{t}}+\sum \mathrm{i}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}+\sum \mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}+\mathsf{\epsilon }$$

(5)

$${\mathrm{T}\mathrm{F}\mathrm{P}}_{\mathrm{i}\mathrm{t}}=\mathsf{\alpha }+{\mathsf{\beta }}_1{\mathrm{A}\mathrm{B}\mathrm{S}\mathrm{I}\mathrm{N}\mathrm{V}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_2{\mathrm{K}\mathrm{Z}}_{\mathrm{i}\mathrm{t}}+\mathsf{\beta }{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{t}}+\sum \mathrm{i}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}+\sum \mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}+\mathsf{\epsilon }$$

(6)

The construction of non-linear mediation effect models is imperative for the analysis of the relationship between financing constraints and the development quality of firms (seeing Equations (7) and (8)). The significance of the coefficient ${\mathsf{\beta }}_1$ in Equation (7) and the coefficient ${\mathsf{\beta }}_1$ in Equation (8) is crucial for the demonstration of the mediating role of investment efficiency in this non-linear relationship.

$${\mathrm{A}\mathrm{B}\mathrm{S}\mathrm{I}\mathrm{N}\mathrm{V}}_{\mathrm{i}\mathrm{t}}=\mathsf{\alpha }+{\mathsf{\beta }}_1{\mathrm{K}\mathrm{Z}}_{\mathrm{i}\mathrm{t}}^2+{{\mathsf{\beta }}_2\mathrm{K}\mathrm{Z}}_{\mathrm{i}\mathrm{t}}+\mathsf{\beta }{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{t}}+\sum \mathrm{i}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}+\sum \mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}+\mathsf{\epsilon }$$

(7)

$${\mathrm{T}\mathrm{F}\mathrm{P}}_{\mathrm{i}\mathrm{t}}=\mathsf{\alpha }+{\mathsf{\beta }}_1{\mathrm{A}\mathrm{B}\mathrm{S}\mathrm{I}\mathrm{N}\mathrm{V}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_2{\mathrm{K}\mathrm{Z}}_{\mathrm{i}\mathrm{t}}^2+{\mathsf{\beta }}_3{\mathrm{K}\mathrm{Z}}_{\mathrm{i}\mathrm{t}}+\mathsf{\beta }{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{t}}+\sum \mathrm{i}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}+\sum \mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}+\mathsf{\epsilon }$$

(8)

The construction of a model is required in order to test the moderating effect of managerial effectiveness on the linear relationship between financing constraints and corporate development quality (seeing Equation (9)). If the coefficient ${\mathsf{\beta }}_1$ in Equation (9) is significantly positive, it is indicative of the fact that managerial effectiveness mitigates the negative impact of financing constraints on corporate development quality.

$${\mathrm{T}\mathrm{F}\mathrm{P}}_{\mathrm{i}\mathrm{t}}=\mathsf{\alpha }+{\mathsf{\beta }}_1{\mathrm{K}\mathrm{Z}}_{\mathrm{i}\mathrm{t}}\ast {\mathrm{M}\mathrm{E}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_2{\mathrm{K}\mathrm{Z}}_{\mathrm{i}\mathrm{t}}+\mathsf{\beta }{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{t}}+\sum \mathrm{i}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}+\sum \mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}+\mathsf{\epsilon }$$

(9)

The construction of a model is required to test the moderating effect of managerial effectiveness on the non-linear relationship between financing constraints and corporate development quality (seeing Equation (10)). If the coefficient ${\mathsf{\beta }}_1$ in Equation (10) is significantly positive, it indicates that the involvement of managerial effectiveness can make the impact of financing constraints on corporate development quality smoother. Furthermore, if ${\mathsf{\beta }}_1\ast {\mathsf{\beta }}_4$ − ${\mathsf{\beta }}_2\ast {\mathsf{\beta }}_3$ > 0 in Equation (10), it can be deduced that the involvement of managerial effectiveness shifts the inflection point to the right.

$${\mathrm{T}\mathrm{F}\mathrm{P}}_{\mathrm{i}\mathrm{t}}=\mathsf{\alpha }+{\mathsf{\beta }}_1{\mathrm{K}\mathrm{Z}}_{\mathrm{i}\mathrm{t}}^2\ast {\mathrm{M}\mathrm{E}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_2{\mathrm{K}\mathrm{Z}}_{\mathrm{i}\mathrm{t}}\ast {\mathrm{M}\mathrm{E}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_3{\mathrm{K}\mathrm{Z}}_{\mathrm{i}\mathrm{t}}^2+{\mathsf{\beta }}_4{\mathrm{K}\mathrm{Z}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_5{\mathrm{M}\mathrm{E}}_{\mathrm{i}\mathrm{t}}+\mathsf{\beta }{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{t}}+\sum \mathrm{i}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}+\sum \mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}+\mathsf{\epsilon }$$

(10)

4. Empirical Analysis

4.1. Data Sources and Processing

The accounting standard was implemented in 2007, and the rule became one of the most commonly observed rules in China’s capital market. The reporting requirements of capital markets ensure the comparability of data from listed companies. Therefore, we use data from listed companies as our research sample. This paper adopts the data of China’s Shanghai and Shenzhen A-shares for the period from 2007 to 2021, and treats them as follows:

(1)

Exclusion of ST and *ST Companies: These companies, facing the threat of delisting, are likely to have abnormal financial data, which could affect the empirical results.

(2)

Exclusion of Financial Firms: Financial institutions have unique business models and financing channels compared to other industries, making them unsuitable for this analysis.

(3)

Exclusion of Incomplete Data Samples: Samples with discontinuous data or missing values are excluded to ensure data integrity.

The first 1% reduction was then applied to minimize the effect of extreme values. The data used in this study are sourced from the China Stock Market & Accounting Research Database (CSMAR). Some indicators are manually processed and calculated using EXCEL 2021, DEAP 2.1, and Stata 17, with the final data analysis conducted using Stata 17.

4.2. Descriptive Statistics

As illustrated in Table 2, the standard deviation of financing constraints (KZ) is 2.4, indicating significant variation in financing constraints among Chinese firms. The mean Z-score is 4.73, with a median of 2.99, suggesting that the majority of firms have a healthy financial status. The mean value of dual leadership (dual) is 0.26, indicating that while some firms have the CEO and chairman roles combined, which is not a widespread issue.

Table 2. Descriptive statistics.

Variable	N	SD	Mean	Min	p50	Max
WRDG	35,404	0.6143768	3.042223	1.239892	3.032217	4.8106
MrEst	35,404	0.6105293	2.525631	0.7288052	2.518295	4.278914
KZ	35,404	2.447005	1.387525	−6.624826	1.622986	7.54728
ME	33,715	0.2474179	−0.0158838	−0.9237927	0	0.7929062
ABSINV	28,285	0.052932	0.043116	0.000525	0.026629	0.328065
ZScore	35,404	5.659845	4.728312	−1.035653	2.985295	36.49113
growth	35,404	0.4879556	0.1818652	−0.643399	0.106404	3.317019
stock	35,404	0.4583701	3.505749	2.128232	3.55934	4.317355
indro	35,404	0.0533835	0.3736158	0.3	0.3333	0.5714
dual	35,404	0.4398782	0.2622764	0	0	1
age	35,404	7.191605	10.25571	0.1068493	9.321918	26.41918
size	35,404	1.311315	22.01696	19.30672	21.8449	26.04762

Source: Organized based on data of China’s Shanghai and Shenzhen A-shares for the period from 2007 to 2021. https://data.csmar.com/ (accessed date: 12 March 2025).

4.3. Correlation Analysis

As demonstrated in Table 3, the correlation analysis reveals a substantial negative correlation between financing constraints and enterprise high-quality development. This finding serves as preliminary verification of hypothesis 1. Additionally, the correlation analysis demonstrates a negative relationship between enterprise high-quality development and inefficient investment in the enterprise. was significantly negatively correlated, and the degree of financing constraints and the degree of inefficient investment in the enterprise in the linear relationship showed a significant negative correlation, i.e., the degree of financing constraints will slow down the inefficiency of the enterprise’s investment; in addition to the following. Furthermore, the growth ability of the enterprise and the development quality of the enterprise are significantly positively correlated. It can be concluded that independent directors can improve the development quality of the enterprise by improving the internal supervision. However, the degree of shareholding concentration and the unity of the two positions will lead to the weakening of the supervision effect of the enterprise and thus negatively affect the development quality of the enterprise. Initially, the aforementioned hypotheses are found to be valid, with correlation coefficients falling below 0.6, thereby indicating the absence of a shared variance in the preliminary assessment of each index.

Table 3. Correlation analysis.

	WRDG	KZ	ABSINV	Growth	Stock	Indro	Dual	Age	Size
WRDG	1
KZ	−0.015 ***	1
ABSINV	−0.108 ***	−0.046 ***	1
growth	0.144 ***	−0.053 ***	0.164 ***	1
stock	0.105 ***	−0.181 ***	−0.033 ***	0.032 ***	1
indro	−0.019 ***	−0.002	0.017 ***	−0.003	0.034 ***	1
dual	−0.045 ***	−0.138 ***	0.049 ***	0.008	−0.013 **	0.107 ***	1
age	0.013 **	0.353 ***	−0.106 ***	−0.045 ***	−0.191 ***	−0.017 ***	−0.230 ***	1
size	0.140 ***	0.069 ***	−0.120 ***	0.036 ***	0.133 ***	0.016 ***	−0.161 ***	0.346 ***	1

Standard errors in brackets. ** p < 0.05, *** p < 0.01. Source: Organized based on data of China’s Shanghai and Shenzhen A-shares for the period from 2007 to 2021. https://data.csmar.com/ (accessed date: 12 March 2025).

4.4. Benchmark Regression

Prior to conducting the regression analysis, a variance inflation factor (VIF) test was performed on the primary regression model. The results indicated that the maximum VIF value was 1.29 (The results are shown in Table 4). Considering its relationship with financing constraints, it was preliminarily concluded that no significant multicollinearity exists between the independent variables, control variables, and the dependent variable. In order to control the factors that do not change individually over time and thereby reduce the endogeneity problem caused by the disturbance term, this paper controls the time, individual fixed effects. The Hausman test rejected the original hypothesis at the 1% level, thereby indicating that the individual, time double fixed effects model should be used.

Table 4. Expansion factors.

Variable	VIF	1/VIF
KZ	1.29	0.7734
age	1.29	0.7761
size	1.17	0.8543
dual	1.09	0.9143
stock	1.09	0.9171
indro	1.01	0.9862
growth	1.00	0.9951
Mean VIF	1.14

Source: Organized based on data of China’s Shanghai and Shenzhen A-shares for the period from 2007 to 2021. https://data.csmar.com/ (accessed date: 12 March 2025).

As illustrated in Table 5, the multiple regression results for Models (1) and (2) are presented. In Column (1), the regression results are shown without control variables, while Column (2) presents the results with control variables included. It is evident that the absolute value of financing constraints (KZ) in Column (2) is 0.00716, which is significantly smaller than 0.0128 in Column (1). Furthermore, the incorporation of control variables into the model resulted in an enhancement of its goodness of fit from 0.0177 to 0.1028. This observation signifies a substantial negative linear relationship between the degree of financing constraints (KZ) and corporate development quality (WRDG).

Table 5. Benchmark regression.

	Linear Relationship		Non-Linear Relationship
	(1)	(2)	(3)	(4)
	WRDG	WRDG	WRDG	WRDG
KZ2			−0.00341 ***	−0.00337 ***
			[0.0004]	[0.0004]
KZ	−0.0128 ***	−0.00716 ***	−0.00944 ***	−0.00410 **
	[0.0023]	[0.0022]	[0.0020]	[0.0020]
controls	no	yes	no	yes
firm	yes	yes	yes	yes
year	yes	yes	yes	yes
_cons	3.091 ***	3.311 ***	3.123 ***	3.527 ***
	[0.0131]	[0.3005]	[0.0142]	[0.2975]
N	35,404	35,404	35,404	35,404
adj. R2	0.0177	0.1028	0.0238	0.1087

Standard errors in brackets. ** p < 0.05, *** p < 0.01. Source: Organized based on data of China’s Shanghai and Shenzhen A-shares for the period from 2007 to 2021. https://data.csmar.com/ (accessed date: 12 March 2025).

In column (3) of Table 5, the non-linear relationship between financing constraints and high-quality corporate development is examined. The coefficient of the squared term of financing constraints is −0.00341, significant at the 1% level. When relevant control variables are incorporated, the model’s degree of fit increases by 0.0849, the coefficient of the squared term remains largely unchanged. To further assess the non-linear relationship, a U-test was conducted (seeing Table 6). The results show that the inflection point is located at −1.384101, with the observed range of financing constraints spanning from −6.624826 to 7.54728. This implies that the inflection point falls within the data range, and its statistical significance is confirmed at the 1% level. Additionally, the negative coefficient of the squared term in Table 6 indicates an inverted U-shaped relationship between financing constraints and high-quality corporate development.

Table 6. U-test test.

	Lower Bound	Upper Bound
Interval	−6.624826	7.54728
Slope	0.0357425	−0.0609133
t-value	6.381119	−8.013561
p > t	9.75 × 10−11	7.16 × 10−16

Source: Organized based on data of China’s Shanghai and Shenzhen A-shares for the period from 2007 to 2021. https://data.csmar.com/ (accessed date: 12 March 2025).

4.5. Endogeneity Test

In consideration of the potential impact of other unobserved variables on the empirical outcomes, as well as the reciprocal causality between the severity of financing constraints and the advancement of high-quality development, it is acknowledged that enterprises demonstrating superior development under the auspices of promoting enterprises of a similar caliber may experience a diminution in the degree of their own financing constraints (L. Yang & Zhou, 2021). In light of these considerations, this paper proposes the utilization of the lagged first-order financing constraints indicator as an instrumental variable. Additionally, we selected the financial constraints of the firm’s industry excluding its own as an additional instrumental variable. This was incorporated into the test for nonlinear relationships by adding quadratic terms. To this end, an endogeneity test of the benchmark regression results is conducted through the implementation of the second-order least squares method (2SLS) in conjunction with a dynamic panel model. Additionally, considering the time lag effects, we also employed the Generalized Method of Moments (GMM) with lagged instruments in our analysis (columns (6) to (8)). The outcomes of this test are presented in Table 7. As shown in the two-stage regression, the first stage results indicate that the instrumental variables are significantly predictive of the hypothesized endogenous variables. This validates the validity of the instrumental variable selection. In instances where substantial regression results are obtained, the non-identifiable test and the Stock-Yogo weak ID test critical values are employed. Ten percent is 16.8, which is not a weak instrumental variable, through the endogeneity Test.

Table 7. Endogeneity test (GMM-2sls).

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	KZ	WRDG	KZ	KZ2	WRDG	KZ	KZ2	WRDG
KZ2					−0.0406 *			−0.0173 ***
					[0.0224]			[0.0054]
KZ		−0.0139 ***			0.142 *			0.0493 ***
		[0.0037]			[0.0744]			[0.0101]
IV1	0.292 ***		0.255 ***	0.8816 ***		0.255 ***	−0.2011 ***
	[0.0045]		[0.0059]	[0.0306]		[0.0059]	[0.0340]
IV2			0.144 ***	0.3857 ***		0.144 ***	0.5085 ***
			[0.0076]	[0.0390]		[0.0076]	[0.0433]
Cragg-Donald Wald F statistic	3680.685 ***		17.744 ***			53.747 ***
Cragg-Donald Wald F statistic	4232.088		13.872			26.952
Stock-Yogo weak ID test critical values (10%)	16.38		7.03			7.03
controls	yes	yes	yes	yes	yes	yes	yes	yes
firm	yes	yes	yes	yes	yes	yes	yes	yes
year	yes	yes	yes	yes	yes	yes	yes	yes
N		31,737			18,266			18,266
adj. R-sq		−0.0186			−0.0306			0.1301

Standard errors in brackets. * p < 0.1, *** p < 0.01. Note: The two-stage model resulted in a negative adjusted R-squared, which is normal. Source: Organized based on data of China’s Shanghai and Shenzhen A-shares for the period from 2007 to 2021. https://data.csmar.com/ (accessed date: 12 March 2025).

4.6. Robustness

In this paper, we opted to utilize the replacement of explanatory variables to conduct a robustness test, the results of which are presented in Table 8. We employed the method proposed by Mollisi and Rovigatti (2017) to measure the total factor productivity of enterprises, subsequently replacing the method proposed by Wooldridge to measure the total factor productivity of enterprises. The findings presented in Table 8 demonstrate the robustness of the linear and nonlinear relationships between financing constraints and the high quality of enterprises. Furthermore, the linear and non-linear relationship between financing constraints and the high-quality development of firms is also found to be robust. And, considering the multicollinearity issue that may be caused by polynomials in the multiple regression model, we orthogonalized the variable KZ. The regression results are presented in columns (5) to (6) of Table 8, which remain significant and pass the robustness test.

Table 8. Robustness test.

	(1)	(2)	(3)	(4)	(5)	(6)
	MrEst	MrEst	MrEst	MrEst	WRDG	WRDG
KZ	−0.0119 ***	−0.00468 **	−0.00863 ***	−0.00129
	[0.0023]	[0.0023]	[0.0020]	[0.0021]
KZ2			−0.00338 ***	−0.00342 ***
			[0.0004]	[0.0004]
KZ_ortho2						−0.0344 ***
						[0.0042]
KZ_ortho1					−0.0134 **	−0.0165 ***
					[0.0055]	[0.0056]
controls	yes	yes	yes	yes	yes	yes
firm	yes	yes	yes	yes	yes	yes
year	yes	yes	yes	yes	yes	yes
_cons	2.598 ***	2.891 ***	2.629 ***	3.047 ***	3.340 ***	3.468 ***
	[0.0132]	[0.2101]	[0.0143]	[0.2102]	[0.2108]	[0.2108]
N	35,389	35,389	35,389	35,389	35,389	35,389
adj. R-sq	0.02	0.1054	0.026	0.1114	0.1059	0.1122

Standard errors in brackets. ** p < 0.05, *** p < 0.01. Source: Organized based on data of China’s Shanghai and Shenzhen A-shares for the period from 2007 to 2021. https://data.csmar.com/ (accessed date: 12 March 2025).

5. Analysis of Mediating and Moderating Effects

5.1. Heterogeneity Test

It is evident that the social system environment, historical development and other factors may exert a certain influence on the production and business activities of enterprises. C. Wang et al. (2023) posits that the rapid development of the Yangtze River Delta region’s economy following the reform and opening up is partly attributable to its strong historical business atmosphere. Since the Ming and Qing dynasties, the state has made significant endeavors in this regard. The region’s investment and business activities have a historical legacy that dates back to the Ming and Qing dynasties. Geographically, the Yangtze River Delta region is located at the intersection of the coast and inland, offering significant advantages in transportation. Economically, the region has experienced rapid development since the reform and opening up, providing substantial opportunities for investment and market engagement. Geographically, the Yangtze River Delta serves as the conduit between the coast and the inland regions, offering significant advantages in terms of transportation. Economically, the Yangtze River Delta has undergone rapid development since the country’s reform and opening up, emerging as its foremost economic core area, characterized by vigorous economic activity, accelerated urbanization and industrialization (Quan et al., 2021).

In the Yangtze River Delta (YRD), a region that boasts a favorable business climate and a vibrant economy, the question arises as to whether financing constraints will be alleviated as an inhibiting effect on the quality of firm development. Table 9 reports the effects of financing constraints on firm development quality in the YRD and non-YRD regions. The results presented in columns (1) and (2) of Table 9 indicate a statistically significant linear relationship between financing constraints and the quality of firm development in the non-Yangtze River Delta region. Conversely, column (3) of Table 9 shows that while the linear relationship between financing constraints and the quality of firm development in the Yangtze River Delta region is not significant, the nonlinear relationship remains statistically significant. This paper establishes the nonlinear relationship between the degree of financing constraints and the high-quality development of enterprises in the non-YRD and YRD regions, as illustrated in Figure 1. The overall trend of both regions is that the slopes are negative; however, the relationship graph in the YRD region is smoother compared to that in the non-YRD region. This may be due to the fact that capital flows in the YRD region are more active, and it is easier for enterprises to obtain external financing.

Table 9. Heterogeneity test.

	Non Yangtze River Delta		Yangtze River Delta
	(1)	(2)	(3)	(4)
	WRDG	WRDG	WRDG	WRDG
KZ2		−0.00374 ***		−0.00171 **
		[0.0005]		[0.0007]
KZ	−0.0109 ***	−0.00707 ***	0.00553	0.00624 *
	[0.0026]	[0.0024]	[0.0036]	[0.0035]
controls	no	yes	no	yes
firm	yes	yes	yes	yes
year	yes	yes	yes	yes
_cons	3.348 ***	3.601 ***	4.244 ***	4.313 ***
	[0.3554]	[0.3506]	[0.5286]	[0.5331]
N	25,703	25,703	9701	9701
adj. R2	0.1052	0.1122	0.1211	0.1226

Standard errors in brackets. * p < 0.1, ** p < 0.05, *** p < 0.01. Source: Organized based on data of China’s Shanghai and Shenzhen A-shares for the period from 2007 to 2021. https://data.csmar.com/ (accessed date: 12 March 2025).

Figure 1. Heterogeneity test. Source: Organized based on data of China’s Shanghai and Shenzhen A-shares for the period from 2007 to 2021. https://data.csmar.com/ (accessed date: 12 March 2025).

5.2. Mediating Effects

The present study employs a stepwise regression analysis of columns (1) to (3) of Table 10 to assess the mediating effect of investment efficiency in the linear relationship between financing constraints and the high-quality development of enterprises. The findings reveal a U-shaped relationship between financing constraints and enterprise investment efficiency, thereby validating the hypothesis. The enterprise’s investment efficiency is a mediating factor in the linear relationship between financing constraints and enterprise development. The U-shaped relationship between financing constraints and enterprise development is evident in Table 10 column (5). Between financing constraints and enterprise’s inefficient investment, that is, a certain degree of financing constraints can inhibit the enterprise’s inefficient investment, but when the degree of financing constraints exceeds the inflection point will instead inhibit the enterprise’s investment efficiency by leading to underinvestment, which in turn affects the quality of the enterprise’s development.

Table 10. Intermediation effects.

	Linear Relationship			Non-Linear Relationship
	(1)	(2)	(3)	(4)	(5)	(6)
	WRDG	ABSINV	WRDG	WRDG	ABSINV	WRDG
ABSINV			−0.666 ***			−0.655 ***
			[0.0578]			[0.0575]
KZ2				−0.00337 ***	0.000285 ***	−0.00249 ***
				[0.0004]	[0.0001]	[0.0006]
KZ	−0.00716 ***	−0.00185 ***	−0.0118 ***	−0.00410 **	−0.00247 ***	−0.00630 **
	[0.0022]	[0.0003]	[0.0028]	[0.0020]	[0.0004]	[0.0028]
controls	yes	yes	yes	yes	yes	yes
firm	yes	yes	yes	yes	yes	yes
year	yes	yes	yes	yes	yes	yes
_cons	3.311 ***	0.0334	3.772 ***	3.527 ***	0.0276	3.822 ***
	[0.3005]	[0.0251]	[0.3445]	[0.2975]	[0.0251]	[0.3441]
N	35404	28285	28285	35404	28285	28285
adj. R2	0.1028	0.0699	0.1253	0.1087	0.071	0.1272

Standard errors in brackets. ** p < 0.05, *** p < 0.01. Source: Organized based on data of China’s Shanghai and Shenzhen A-shares for the period from 2007 to 2021. https://data.csmar.com/ (accessed date: 12 March 2025).

5.3. Moderating Effect

The principal-agent theory posits that an enterprise is owned by an individual or entity who, in order to optimize the return on investment, will engage the services of an agent, i.e., a manager, to oversee the enterprise on their behalf. The question that arises from this theory is whether the manager’s ability to perform effectively can serve to mitigate the impact on the enterprise’s development, given the constraints imposed by financing. In the context of proxy, enterprise managers possess greater access to the production and operation of the enterprise, and consequently, they are likely to possess more accurate and indepth knowledge of the enterprise compared with the enterprise owners. This creates an asymmetric information environment, and enterprise managers may be inclined to engage in surplus management for the sake of private interests. The question therefore arises as to whether the quality of enterprise development can be enhanced under the effective internal control of enterprise managers. This issue is empirically analyzed in Table 11.

Table 11. Moderating effect.

	Linear Relationship		Non-Linear Relationship
	(1)	(2)	(3)	(4)
	WRDG	WRDG	WRDG	WRDG
BC∗KZ2				0.00293 **
				[0.0012]
ME∗KZ2			0.00615 ***
			[0.0015]
BC∗KZ		0.0188 ***		0.00203
		[0.0051]		[0.0042]
ME∗KZ	0.0402 ***		0.0124 **
	[0.0070]		[0.0063]
KZ2			−0.00343 ***	−0.00574 ***
			[0.0005]	[0.0011]
KZ	−0.00958 ***	−0.0228 ***	−0.00105	−0.00421
	[0.0026]	[0.0052]	[0.0025]	[0.0044]
BC		−0.0199		−0.0164
		[0.0133]		[0.0143]
ME	0.118 ***		0.113 ***
	[0.0156]		[0.0163]
controls	yes	yes	yes	yes
firm	yes	yes	yes	yes
year	yes	yes	yes	yes
_cons	3.444 ***	3.513 ***	3.634 ***	3.707 ***
	[0.3259]	[0.3082]	[0.3228]	[0.3065]
N	33,164	33,164	33,164	33,164
adj. R2	0.1252	0.1049	0.1312	0.1106

Standard errors in brackets. ** p < 0.05, *** p < 0.01. Source: Organized based on data of China’s Shanghai and Shenzhen A-shares for the period from 2007 to 2021. https://data.csmar.com/ (accessed date: 12 March 2025).

The moderating role of managerial effectiveness (ME) in the linear relationship between financing constraints and enterprise development quality is reflected in Column (1) of Table 11. The coefficient of financing constraints in the baseline linear regression is significantly negative, while the coefficient of the cross-multiplication term of managerial effectiveness and financing constraints is significantly positive. This indicates that the managerial effectiveness of the enterprise can effectively inhibit the negative impact of financing constraints on the enterprise’s development quality on the linearity. Column (1) of Table 11 reflects the moderating role of ME in the linear relationship between financing constraints and enterprise development quality.

The second column of data reflects the moderating role of corporate internal control (BC) in the linear relationship between financing constraints and the quality of corporate development. The coefficient of the cross-multiplier term between corporate internal control (BC) and financing constraints is significantly positive, i.e., corporate internal control (BC) is able to effectively inhibit the negative impact of financing constraints on the quality of corporate development in the linear relationship (seeing Figure 2).

Figure 2. Moderating effect of ME. Source: Organized based on data of China’s Shanghai and Shenzhen A-shares for the period from 2007 to 2021. https://data.csmar.com/ (accessed date: 12 March 2025).

The third column tests the moderating role of corporate managerial effectiveness (ME) in the nonlinear relationship between financing constraints and firm development quality. It is found that the inverted U-shaped inflection point is shifted to the right under the intervention of higher managerial effectiveness. Figure 2 demonstrates that the curve will be smoother with a higher upper limit.

The fourth column tests the moderating role of internal control (BC) in the nonlinear relationship between financing constraints and the quality of firms’ development. It is found that the inverted U-shaped inflection point is shifted to the left under the intervention of better internal control, and that the curve will be smoother with a higher upper limit (seeing Figure 3).

Figure 3. Moderating effect of BC. Source: Organized based on data of China’s Shanghai and Shenzhen A-shares for the period from 2007 to 2021. https://data.csmar.com/ (accessed date: 12 March 2025).

6. Conclusions and Recommendations

6.1. Conclusions

This paper empirically examines the relationship between financing constraints and the development of Chinese enterprises. It employs a dataset consisting of Shanghai and Shenzhen A-share listed companies spanning from 2007 to 2021. The research findings indicate a negative correlation between financing constraints and the overall development of enterprises. Through in-depth analysis, an inverted U-shaped relationship is identified between financing constraints and the high-quality development of these enterprises. This implies that within a certain range (KZ < 0.6083), financing constraints can act as a catalyst for high-quality development. At this stage, moderate and manageable financial constraints enhance the quality of corporate development by increasing firms’ focus on resource allocation. However, once the inflection point is reached (KZ > 0.6083), further constraints will hinder the improvement of enterprises’ development quality. At this stage, when financial constraints exceed the threshold point that enterprises can bear, the quality of corporate development is impaired by restricting capital utilization. The mechanism testing reveals that financing constraints exert an impact on the development quality of enterprises by influencing their investment efficiency. Moreover, managerial efficiency and the quality of internal control are found to serve as moderating variables in this relationship, shaping the extent and nature of the influence. The enhancement of management efficiency enables enterprises to strengthen their internal resource allocation capabilities under the same financing constraints, thereby improving the quality of development. Regarding the improvement of internal control, it plays a moderating role in the process of high-quality development by reducing principal-agent problems.

6.2. Recommendations

Enterprises are required to strengthen their internal control systems and optimize managerial efficiency. In a well-functioning internal control environment, managers’ actions detrimental to the enterprise’s development, such as earnings management (also known as surplus management), can be promptly detected and rectified. By alleviating the principal-agent problem, this improvement can enhance decision-making efficiency, thereby regulating the influence of financing constraints on the quality of enterprise development.

The government shoulders the responsibility of invigorating capital market activities through the implementation of appropriate policies. Currently, it is evident that Chinese enterprises generally face substantial financing constraints, which exert a negative impact on their development. However, this negative effect is relatively less significant in the Yangtze River Delta region, where economic and financial development is more advanced.

Consequently, it is essential for the government to improve the market financing mechanism. This approach requires a sophisticated strategy that neither involves the indiscriminate relaxation of financing constraints by financial institutions nor the loosening of capital market regulations. Instead, the emphasis should be on alleviating the financing constraints faced by enterprises in the real economy. Additionally, it is crucial to recognize that, in the context of real estate and other industries in the virtual economy, a certain level of financing constraints can actually promote the quality of enterprise development within the real economy.

In summary, both enterprises and the government play vital roles in addressing financing constraints and promoting the high-quality development of enterprises. Enterprises need to focus on internal governance, while the government should implement targeted policies to optimize the financing environment.

6.3. Limitations and Suggestions for Future Research

To ensure the availability and comparability of data, this study focuses on listed companies. Given their capital stock, market position, and political attention, listed companies have distinct advantages over most unlisted companies. However, this leads to heterogeneous impacts of financing constraints on enterprises. Therefore, scholars can conduct research from the perspective of unlisted companies.