Optimizing Government Debt Structure and Alleviating Financing Constraints: Access to Private Enterprises’ Sustainable Development

Abstract

To promote the deepening of reform and the effective implementation of policies, the State Council launched the special supervision of the liquidation of local governments’ arrears in project funds in 2016, which supports the optimization of the government debt structure. Based on the quasi-natural experiment of the special supervision action, in this study, we use the difference-in-difference (DID) method to investigate the effect and mechanism of the optimization of the government debt structure on the financing constraints of private enterprises. This research is particularly relevant for private enterprises, which face acute financing challenges and are critical for promoting inclusive economic growth, employment, and innovation—key pillars of sustainable development. The results are as follows. Firstly, the special supervision significantly reduces the financing constraints of private enterprises. Secondly, it has heterogeneous effects on the financing constraints of different types of enterprises, and the alleviating effect is particularly significant for enterprises that rely on the funding support of local governments. This highlights the importance of institutional reforms in fostering equitable access to financial resources for vulnerable enterprise groups such as private enterprises. Thirdly, the optimization of the government debt structure eases enterprises’ financing constraints by improving their capital turnover and trade credit. By enhancing liquidity and creditworthiness, these changes create a more resilient financial environment for private enterprises, supporting their long-term development and contribution to sustainable economic systems.

1. Introduction

As an important part of the Chinese economy, private enterprises play a leading role in gross domestic product (GDP) creation and social employment [1]. Therefore, the environment for the development of private enterprises should be optimized. At the same time, the property rights of private enterprises and the rights and interests of entrepreneurs should be protected in accordance with the law, and the development and growth of the private economy should be promoted. During this critical period of China’s economic transformation, private enterprises play an irreplaceable role in promoting economic growth, promoting technological innovation, and expanding employment. In particular, private enterprises, as the backbone of China’s private sector, are crucial for achieving inclusive and sustainable economic development. Strengthening the financing environment for private enterprises is thus vital for advancing social equity, regional balance, and long-term economic resilience—core themes of sustainable development. However, the development of private enterprises still faces many challenges, especially financing difficulties, which are particularly prominent among micro-, small-, and medium-sized enterprises.

On the one hand, bank credit conditions remain the main obstacle to private enterprise financing. Although Chinese governments have introduced a series of policies in recent years to promote the inclusive development of the banking industry, to reduce their own risk, some banks are still not active enough in supporting the financing of private enterprises, and the financing conditions are relatively stringent, which makes it difficult for private enterprises to obtain sufficient financing support. On the other hand, private enterprises often face a shortage of funds when participating in government projects, especially when the level of local government debt is high. Government projects to promote economic development often require large amounts of funding support. However, whether these funds can be received and paid to private enterprises on time has become a key issue affecting the normal operation and development of enterprises. Payment arrears not only affect the cash flow of an enterprise but may also damage its credit, which has a chain effect on enterprise development. These challenges undermine not only firm-level viability but also broader goals of sustainable financial inclusion and regional economic balance.

Local governments’ payment arrears to private enterprises are essentially an external manifestation of the local government debt problem. The root of this problem lies in the imbalance between the fiscal revenues and expenditures of local governments and the continuous increase in the debt burden. Under China’s current fiscal system, local governments assume the responsibility for many expenditures on infrastructure construction and public service supply, but their fiscal revenues often cannot meet these rigid needs. To compensate for the funding gap, local governments borrow through various means, such as bank loans, bond issuance, and financing platforms, which results in a constantly expanding scale of debt. When fiscal resources are limited, local governments often postpone or default on payments to private enterprises in order to guarantee priorities, such as wage payments and people’s livelihood expenditures. This behavior is essentially a hidden debt default, which reflects the soft budget constraints of local governments under debt pressure. In addition, local governments’ overreliance on the investment-driven economic growth model and their motivation based on the performance appraisal mechanism further exacerbate the accumulation of debt risks. Therefore, the payment arrears by local governments to private enterprises represent not only a symptom of financial difficulties but also deep-seated debt problems. Addressing these structural issues is essential for building fiscal systems that support responsible public investment and private sector vitality—critical components of a sustainable governance framework. In this paper, we aim to explore how the optimization of the local government debt structure affects the investment and financing environment of private enterprises through the transmission mechanism and thereby provide policy guidance for promoting the development of private enterprises.

For a long time, the Chinese Central government has given great importance to the issue of local governments’ debts to private enterprises. From 2010 to 2016, in order to address the risks of local government debt, the Chinese government established a policy framework linking “supervisory standardization, institutional reconstruction, debt replacement, and innovation financing”. That is, in 2010, the expansion of implicit debt was curbed by liquidation of the financing platforms, and in 2014, debt limit management and bond issuance mechanisms were used to reshape the logic of debt borrowing, and the PPP mode was launched to guide the participation of social capital in infrastructure construction. In 2015, large-scale debt replacement was launched to replace the existing high-interest debt with low-cost bonds so as to optimize the maturity structure and relieve the pressure on short-term payments. This policy combination not only strengthens debt transparency through institutional constraints but also promotes debt restructuring and financing transformation by means of market-oriented instruments. However, in implementation, this policy still faces regulatory challenges, such as implicit debt transfer and pseudo-PPP arbitrage, which reflect the dynamic game between fiscal discipline and demands for growth. To address the issue of delayed payments by local governments to private enterprises, China’s State Council launched a special supervision in 2016. The campaign focused on seven provinces—Beijing, Liaoning, Anhui, Shandong, Henan, Hubei, and Qinghai—selected for their acute fiscal pressures (e.g., Liaoning, Qinghai), the prevalence of payment arrears to private firms (e.g., Shandong, Henan), and their representative regional characteristics. These regions span economically diverse areas, including developed eastern municipalities (e.g., Beijing), industrialized central provinces (e.g., Hubei), and underdeveloped western regions (e.g., Qinghai). In 2016, the State Council of China issued the “Notice of the General Office of the State Council on Further Improving the Work on Private Investment”, which required the Ministry of Finance and relevant departments to urge local governments to liquidate the arrears of payments and address the arrears of project payments, material procurement payments, and nonrefundable deposits in accordance with the law. Moreover, responsibility implementation and accountability mechanisms were strengthened to promote communication between governments and enterprises. For centralized rectification, the central government dispatched a special supervision team to seven provinces (cities), including Beijing, Liaoning, Anhui, Shandong, Henan, Hubei, and Qinghai, to carry out key supervision and conduct investigations and summary reports in various ways, which had a strong warning effect. Driven by this supervision, the government’s efforts to repay arrears significantly increased. For example, by the end of 2016, the accounts receivable by small and medium-sized enterprises in Anhui Province reached 277.21 billion yuan, a year-over-year increase of 13.2%, and the total profits reached 144.45 billion yuan, a year-over-year increase of 1.0%, which showed a stable level of profitability. By the end of 2016, Shandong Province had accumulated repaid project payments of approximately 90.9 billion yuan, accounting for 90.4% of all project payment arrears, and the total profit of small and medium-sized enterprises was 572.05 billion yuan, a year-over-year increase of 0.4%, ranking at the average level among the seven provinces (cities).

Despite prior research on the financing challenges faced by private enterprises—explored through the lenses of credit discrimination by banks [2] and broader macroeconomic constraints [3]—an important gap remains. Existing studies have seldom examined the role of local governments’ “payment credibility”—specifically, their practice of delaying payments to firms—as a quasi-fiscal behavior that may systematically affect firms’ access to financing constraint sand the mechanisms through which such effects arise.

Most research on government debt has concentrated on its macroeconomic implications, such as crowding-out effects or structural optimization through debt swaps [4], often overlooking the direct impact of governments’ payment conduct as a market participant on firms’ financial conditions. In this context, delayed payments by local governments are not merely symptoms of fiscal stress but constitute a form of informal financing that consumes firms’ working capital, deteriorates cash flow, and potentially propagates credit risk across the supply chain [5]. How does this government-induced credit constraint differ in its transmission and intensity from traditional bank credit rationing? Can targeted interventions—such as special supervision aimed at improving public-sector payment behavior—serve as effective policy instruments for alleviating financing constraints in the private sector? These questions have yet to be adequately addressed in the literature.

To fill this gap, this study draws on data from privately owned, non-financial A-share listed firms in China from 2007 to 2022 to examine how optimization of the government debt structure influences private enterprises’ financing constraints. A difference-in-differences (DID) framework is employed, with cash flow sensitivity serving as a proxy for financing constraints. Robustness checks—including parallel trend tests, placebo tests, and alternative variable specifications—confirm the validity of our findings. To probe the mechanisms of impact, we further apply a triple-differences (DDD) approach to investigate heterogeneous effects across firms and explore the channels through which special supervision may ease financing constraints, distinguishing between endogenous (internal cash flow) and exogenous (commercial credit) financing. We also examine how factors such as market orientation and technological intensity condition the policy’s effectiveness.

This study offers three contributions. First, in terms of research perspective, it shifts the focus from the macro-level scale of government debt to the micro-level payment behavior of local governments, conceptualizing payment default as a structural distortion in debt credibility. This provides a new analytical dimension linking public governance with firm-level financing. Second, in research design, the study leverages a highly specific and special supervision—designed to rectify government default—as a quasi-natural experiment, yielding cleaner causal identification compared to broader institutional reforms typically examined in DID research. Third, in mechanism analysis, we articulate how the policy eases financing constraints through two micro-level channels: improving internal liquidity (working capital turnover) and restoring external credit access (commercial credit), thereby enriching the theoretical understanding of policy transmission.

Based on the above, we propose three core hypotheses to be empirically tested: (H1) special supervision mitigates financing constraints for private enterprises by restructuring local government debt; (H2) the policy effect is heterogeneous, being stronger for firms more closely tied to government contracts or with weaker financing capacity; and (H3) the policy operates via two key channels—enhancing internal financing efficiency and improving access to external credit through the restoration of government credibility. By rigorously testing these hypotheses, this study aims to provide comprehensive evidence on the complex interplay between public governance and private-sector financing.

2. Theoretical Analysis and Research Hypotheses

2.1. Structure of Local Government Debt: The Formation and Optimization of Implicit Liabilities

The debt structure of local governments is a critical determinant of regional economic stability and the business environment. Under China’s fiscal system and performance evaluation regime, local governments play a central role in driving economic growth, leading to persistent demand for financing [6]. However, their access to formal borrowing channels is tightly regulated. According to the Budget Law, the issuance of local government bonds is the sole legitimate form of borrowing and must be incorporated into official budgetary planning.

When formal avenues are constrained but expenditure pressures persist, some local governments turn to off-the-books, implicit borrowing. Among these, delayed payments to private enterprises have emerged as a distinctive form of implicit debt. This practice effectively transfers fiscal stress onto commercial partners, exploiting the government’s dominant position as a buyer and project allocator, alongside the relatively weak bargaining power of private enterprises [5,7]. Though absent from official balance sheets, such obligations erode the credibility of government debt and represent a high-risk, unregulated liability.

In recent years, policy efforts have evolved from containing overall debt levels to restructuring its composition [4,8]. Optimization strategies now extend beyond refinancing and duration extension to include the identification and rectification of implicit debt, with the broader goal of restoring fiscal credibility. The 2016 special supervision, which focused on clearing arrears to private enterprises, exemplifies such structural interventions. While the impact of formal debt restructuring on firm-level productivity and investment has been well documented [9,10], the microeconomic consequences of addressing implicit debt—particularly in terms of firm financing—remain underexplored.

2.2. Financing Constraints of Private Enterprises: Dual Sources of Pressure and Pathways to Relief

Financing constraints faced by private enterprises have long posed structural challenges, stemming from a combination of institutional and market-based factors. The first source of pressure arises from discriminatory lending practices in the formal financial sector. Due to information asymmetries and ownership biases, banks often adopt a more conservative stance toward private enterprises during credit evaluation, resulting in higher borrowing costs and reduced access to loans [2,11]. This phenomenon is well-documented in the literature and is widely regarded as a conventional explanation for the financing difficulties of private enterprises.

A second, less conventional but equally critical source of pressure originates from local governments and their commercial partners. As noted above, payment arrears by local governments directly deplete firms’ working capital and impair cash flows. This not only weakens the internal financing capacity of affected firms but also triggers broader consequences: constrained liquidity can undermine a firm’s ability to pay upstream suppliers, damaging its commercial creditworthiness [5,12]; at the same time, deteriorating financial statements may exacerbate the perceived credit risk in the eyes of banks, compounding the firm’s disadvantage in already adverse credit markets [13].

Private enterprises thus operate under a dual burden: institutional discrimination in bank lending and fiscal unreliability in government transactions. In this context, policies that alleviate either source of strain are of considerable importance. Unlike traditional interventions focused on modifying bank behavior, special supervision examined in this study targets the second channel directly. By mandating the settlement of government arrears and restructuring local debt obligations, this policy aims to restore firms’ liquidity and credit standing. Therefore, such direct financial relief can ease the financing constraints faced by private enterprises. Based on the above, we propose the following hypothesis:

H1: 

Optimization of the government debt structure can relieve the financing constraints of private enterprises.

Under the influence of the degree of marketization, areas with a high degree of marketization usually have more complete and transparent market mechanisms, a better financing environment, more efficient resource allocation, and more lenient enterprise credit approval by financial institutions than areas with low marketization. It is easier for enterprises to obtain financing support in such areas; therefore, special supervision has a more significant effect on alleviating enterprise financing constraints. In comparison, in areas with a low degree of marketization, enterprises face more financing obstacles, including information asymmetry, impeded financing channels, and conservative attitudes of financial institutions; as a result, there is a greater lag in the alleviation of financing constraints. Therefore, the effect of special supervision in these areas is relatively weak. Technology-intensive enterprises usually rely on continuous research and development (R&D) investment to maintain their competitiveness and face high levels of R&D and innovation investment, and R&D activities often have high capital needs and long payback cycles. Compared with enterprises in traditional industries, technology-intensive enterprises face more stringent financing constraints in obtaining funds. Especially when information asymmetry is serious, these enterprises tend to face high financing costs and great financing difficulties. In addition, enterprises in highly competitive industries often face intense market pressures and need continuous investment to address the challenges posed by their competitors. This requires enterprises to have strong financing ability, while financing constraints may weaken enterprises’ competitiveness and limit their development potential in the market. Finally, enterprises with high risk levels often face a high risk premium in the financing process due to weak credit ability. Due to the low credit evaluation, banks and other financial institutions tend to have strict approval processes for the loans of such enterprises, such that these enterprises face high financing constraints, which affect their operation and development. These firm-level differences also speak to the need for tailored financial governance strategies that account for regional and sectoral heterogeneity—ensuring that sustainable development policies are both targeted and equitable in their support for private enterprises across diverse institutional contexts. Therefore, we propose the following hypothesis:

H2: 

Optimization of the government debt structure has heterogeneous effects on the financing constraints of different types of private enterprises.

2.3. Mechanism of Policy Impact

There are two main mechanisms by which government arrears impact the financing constraints of private enterprises: first, arrears increase enterprises’ financing costs by decreasing their capital turnover, and second, they damage enterprises’ trade credit, resulting in increased financing costs. First, the government’s arrears of payment directly cause cash flow difficulties for private enterprises and increase the cost of investment and financing. Government arrears occupy the working funds of private enterprises, hinder cash flow, increase the risks of default, and lead to a decline in bank credit evaluation, which in turn reduces the credit line and exacerbates financing constraints [13]. A decline in credit leads to an increase in borrowing interest rates, and difficulty in capital turnover can force enterprises to increase the use of external funds, which further raises financing costs. The interlinked influences of government debt and financial allocation efficiency make financing difficult for private enterprises [14]. Second, the government’s payment arrears also indirectly increase financing costs by damaging enterprises’ trade credit. For many years, Chinese private enterprises have relied largely on trade credit for long-term investment financing. Zhang et al. (2013) [15] showed that, despite “ownership discrimination” and “scale discrimination” in China’s banking system, state-owned enterprises pass bank loans to private enterprises through trade credit and thus promote the growth of private enterprises’ fixed assets. Shi and Zhang (2010) [12] also confirmed that trade credit relieves the financing constraints of private enterprises to a certain extent. However, when the government defaults on payments, the solvency of private enterprises is called into question, which weakens their trade credit in the supply chain. The trust of suppliers and other financial institutions in the enterprise decreases, which leads to a reduction in the enterprise’s credit line and more stringent financing conditions. This requires the enterprises to pay high financing costs and further aggravates their financing constraints.

In the context of sustainable development, ensuring timely government payments not only supports the financial viability of private enterprises but also strengthens trust and transparency in economic transactions—two vital attributes for resilient supply chains and market systems. Therefore, we propose the following hypothesis:

H3: 

Optimization of the government debt structure relieves the financing constraints of private enterprises by improving their capital turnover and commercial credit.

3. Study Design

3.1. Data Sources

Private non-financial enterprises listed on China’s A-shares between 2007 and 2022 are selected as the research samples. The data are from the CSMAR database and the China Statistical Yearbook. In the empirical analysis, the sample data are processed as follows: (1) financial enterprises (industry classification code with the initial letter J) are excluded because of their special capital structure and regulatory characteristics; (2) newly established enterprises after the policy implementation are excluded to eliminate the effect of sample selection bias; (3) samples with missing or abnormal data, including enterprises with asset–liability ratios higher than 100%, are excluded; and (4) observations in the 1% and 99% quantiles for key continuous variables are winsorized to reduce the interference of extreme values. Considering data availability, only listed enterprises are selected as research samples, and a dataset containing 27,857 annual enterprise samples is constructed.

3.2. Selection of Variables

To examine the effect of special supervision on private enterprises’ financing constraints, we select relevant variables for empirical analysis with reference to the existing literature [16,17,18,19,20]. The specific variables are shown in

Table 1. Descriptive Statistics of Major Variables.

Variables	Measurement	Definition	Sample	Average	Standard Deviation	Minimum	Maximum
ww	Financing constraint	Degree of enterprise financing difficulty	27,857	−0.9670	0.0899	−1.1900	−0.8207
ROA	Profitability	Profit after tax divided by total assets	27,857	0.0437	0.0620	−0.2078	0.2169
OCF	Operating cash flow	Cash flow from operating activities	27,857	0.0461	0.0732	−0.1830	0.2572
Tobin-q	Market valuation	Market value to book value ratio	27,857	3.3635	2.8882	0.9092	9.0930
ROE	Net return on equity	Ratio of net profit after tax to shareholder’s equity	27,857	0.0650	0.1182	−0.5136	0.3581
Size	Scale	Natural logarithm of enterprise assets	27,857	7.9192	1.0891	5.5562	11.1226
Leverage	Leverage ratio	Ratio of total liabilities to total assets	27,857	0.2622	0.1728	−0.0030	0.7476
Cash	Cash	Total cash and cash equivalents held by the enterprise	27,857	0.1276	0.1504	0.0001	0.6510

.

3.2.1. Explained Variable

The explained variable is the Whited–Wu (WW) index, which is used to measure the degree of enterprise financing constraints. The WW index is a comprehensive indicator constructed by Whited and Wu (2006) [21] based on an enterprise’s internal and external financing status, and it can be used to effectively assess the difficulty an enterprise faces in obtaining funds. A high value of the WW index usually means that enterprise financing constraints are severe, whereas a low value indicates a relatively loose financing environment. We use the WW index to quantify the financing constraints of enterprises and investigate the impact of the special supervision policy on enterprise financing status. According to Table 1, the mean value of the WW index is −1.211, and the standard deviation is 30.665, which indicates that the sample enterprises have large heterogeneity in the level of financing constraints.

3.2.2. Core Explanatory Variable

The core explanatory variable is period * experiment_group, that is, the treatment effect variable in the DID model. The core economic intuition is straightforward: firms facing severe financing constraints tend to exhibit a distinct set of financial characteristics in their operational and investment decisions. The Whited–Wu (WW) index quantifies the extent to which a firm resembles a financially constrained entity by linearly combining these characteristics into a single measure. Specifically, the index integrates multiple financial dimensions, including: (1) cash flow—financially constrained firms rely more heavily on internal cash generation; (2) dividend payouts—such firms are more inclined to retain earnings rather than distribute dividends; (3) debt structure—they often face limited access to long-term borrowing, resulting in a higher proportion of short-term debt; and (4) firm size and growth—smaller, high-growth firms typically face greater informational asymmetries, which exacerbate financing difficulties. By aggregating these indicators with appropriate weights, the WW index yields a single, continuous score. A higher value on the index implies a financial profile more closely aligned with that of a constrained firm, indicating more severe financing constraints. This study employs the WW index precisely because it offers a more comprehensive and nuanced proxy for financing constraints than any individual financial metric, thereby serving as a robust dependent variable in our empirical analysis.

Period represents a time dummy variable, with 1 indicating the third quarter of 2016 and afterwards and 0 indicating the period before the policy implementation. Experiment_group is a region dummy variable, where if the enterprise is located in a province affected by the special supervision in 2016, the value is 1; otherwise, 0. Therefore, the coefficient of the interaction term period*experiment_group can reflect the effect of the special supervision policy on an enterprise’s financing constraints. Through the DID method, the difference in the changes in the level of enterprise financing constraints between the areas with and without supervision before and after the policy implementation can be effectively identified; thus, the net impact of the policy effect can be evaluated.

3.2.3. Control Variables

To reduce omitted variable bias and ensure the accurate evaluation of policy effects, we take several enterprise characteristic variables as control variables. First, the size of the enterprise is expressed by the natural logarithm of enterprise assets, which is used to control the effect of the size of the enterprise on the financing constraint. Second, the leverage ratio is used to reflect the level of corporate debt and measure the potential impact of the enterprise financing structure on financing constraints. As important indicators of the financial performance of enterprises, profitability (ROA) and net return on equity (ROE) can affect enterprises’ financing ability. Operating cash flow (OCF) reflects the ability of an enterprise to obtain cash through its operating activities and is a key variable in determining the availability of financing. In addition, market valuation (Tobin-q) measures the market’s assessment of the value of the enterprise, whereas cash holdings (Cash) are used to control the impact of the enterprise’s liquidity position on financing constraints. The introduction of these control variables helps eliminate the influence of the enterprise’s own characteristics on financing constraints and thus enhances the accuracy of our identification of the effect of the special supervision policy.

3.3. Model Construction

This study treats the State Council’s 2016 special supervision as a quasi-natural experiment, designating seven provinces—Beijing, Liaoning, Anhui, Shandong, Henan, Hubei, and Qinghai—as the treatment group, with the remaining provinces serving as controls. The selection of these seven provinces was based on a comprehensive assessment by the Ministry of Finance and the State Council, primarily considering fiscal stress, the severity of payment arrears, and regional representativeness. To mitigate potential bias arising from the non-random assignment, the analysis employs an event-study design to test for pre-treatment parallel trends and applies the wild bootstrap method to adjust standard errors in response to the limited number of treated units.

To examine the effect of special supervision on the financing constraints of private enterprises, we refer to the existing literature [16,17,18,19,20] and select relevant variables to construct a DID model. The specific model expression is as follows:

$$\mathrm{W}{\mathrm{W}}_{\mathrm{i}\mathrm{t}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{o}\mathrm{d}\ast \mathrm{e}\mathrm{x}\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}\_\mathrm{g}\mathrm{r}\mathrm{o}\mathrm{u}\mathrm{p}+{\mathsf{\beta }}_2{\mathrm{X}}_{\mathrm{i},\mathrm{t}-1}+{\mathsf{\mu }}_{\mathrm{i}}+{\mathsf{\lambda }}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(1)

where period * experiment_group is a variable representing the extent to which the enterprise’s financing constraint is affected by the special supervision at time t. Control variables ${\mathrm{X}}_{\mathrm{i},\mathrm{t}-1}$ represent the enterprise characteristic variables, such as enterprise size, asset–liability ratio (Leverage), profitability (ROA), and cash flow (OCF), with a one-period lag. We choose a fixed effects model and include firm fixed effects (${\mathsf{\mu }}_{\mathrm{i}}$) and time fixed effects (${\mathsf{\lambda }}_{\mathrm{t}}$) to eliminate the effects of unobserved individual heterogeneity and macroeconomic fluctuations. ${\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$ represents a random disturbance term.

In the DID model described above, our primary focus lies on the coefficient β₁ of the interaction term period*experiment_group. This coefficient carries critical economic significance, as it captures the net effect of the policy intervention we seek to identify. Specifically, β₁ quantifies the difference between: (i) the change in the treatment group (provinces subject to supervision) before and after the policy implementation and (ii) the contemporaneous change in the control group (provinces not subject to supervision). The variation observed in the control group serves as a proxy for the underlying time trend in the absence of policy intervention. By netting out this natural trend from the treatment group’s change, β₁ isolates the causal effect of the targeted supervisory policy. A significantly negative β₁ suggests, intuitively, that the special supervision led to a further and statistically meaningful reduction in financing constraints among private enterprises in the treated provinces—beyond what could be explained by general temporal dynamics. Thus, β₁ serves as a critical parameter for testing the central Hypothesis H1.

4. Empirical Results and Analysis

4.1. Benchmark Regression Results

In order to examine the impact of the special supervision of the liquidation of government arrears on the financing constraints of private enterprises, we first use the DID model to perform benchmark regression analysis, and the results are shown in

Table 2. Baseline Regression Results on the Impact of Special Supervision on Private Enterprises’ Financing Constraints.

	(1)	(2)	(3)	(4)	(5)
VARIABLES	ww	ww	ww	ww	ww
did	−0.021 ***	−0.013 **	0.119 **	0.008 ***	0.004 ***
	(−2.91)	(−2.42)	(2.69)	(5.62)	(3.24)
Treat × Post × Infrastructure			−0.142 ***
			(−3.10)
Treat × Post × SOE				−0.003 *
				(−1.81)
Treat × Post × GovExp					0.008
					(1.19)
industry_group			0.108 ***
			(2.82)
soe_status				−0.002
				(−1.56)
gov_contract_high					−0.000
					(−0.43)
ROA		−0.170 ***	−0.315 ***	−0.206 ***	−0.205 ***
		(−10.61)	(−5.20)	(−30.69)	(−25.41)
OCF		−0.101 ***	−0.107	−0.098 ***	−0.098 ***
		(−10.39)	(−1.31)	(−15.66)	(−15.15)
tobinq		0.001	0.002	0.001 ***	0.001 ***
		(0.99)	(1.68)	(3.69)	(4.38)
ROE		0.001 *	−0.000	0.001 **	0.001 **
		(1.95)	(−0.36)	(2.25)	(2.04)
nSize		−0.056 ***	−0.037 ***	−0.047 ***	−0.047 ***
		(−12.83)	(−4.04)	(−102.26)	(−103.96)
Leverage		0.021 **	0.011	0.002	0.001
		(2.43)	(0.53)	(0.48)	(0.18)
Cash1		−0.018 **	0.141 **	−0.012 ***	−0.016 ***
		(−2.75)	(2.75)	(−3.39)	(−4.44)
Constant	−0.995 ***	−0.541 ***	−0.821 ***	−0.610 ***	−0.611 ***
	(−1128.63)	(−15.29)	(−7.19)	(−156.93)	(−146.71)
Observations	27857	27857	27857	27857	27857
R-squared	0.475	0.634	0.026	0.575	0.601
CONTROL	NO	YES	YES	YES	YES
FIRM FE	YES	YES	YES	YES	YES
TIME FE	YES	YES	YES	YES	YES

Notes: Robust t-statistics are reported in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively. (1) The dependent variable is the financing constraint index (WW), where a higher value indicates more severe constraints. (2) The key explanatory variable, did, is an interaction term between the treatment group dummy (Treat) and the post-policy period dummy (Post). (3) Columns (3) through (5) present results from difference-in-difference-in-differences (DDD) models. Treat × Post × Infrastructure interacts the did term with a dummy for firms in the infrastructure sector; Treat × Post × SOE interacts it with a state-owned enterprise dummy; Treat × Post × GovExp interacts it with a measure of firms’ exposure to government contracts. (4) All regressions include firm and time fixed effects.

. Columns (1) and (2) in Table 2 present the DID regression results for the effect of special supervision on the long-term investment of private enterprises. Among them, Column (1) shows the regression results of the two-way fixed effects model without the control variables of firm characteristics, and Column (2) shows the benchmark regression results after the addition of the control variables of enterprise characteristics with a one-period lag. Statistically, the regression coefficients of the DID estimation term in Columns (1) and (2), $\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{o}\mathrm{d}\ast \mathrm{e}\mathrm{x}\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}\_\mathrm{g}\mathrm{r}\mathrm{o}\mathrm{u}\mathrm{p}$, are all negative and significant at the 1% level. This indicates that the special supervision significantly reduces the financing constraints of private enterprises in the supervised areas by optimizing the government debt structure, and H1holds. In the absence of special supervision, local governments sometimes default on funds to private enterprises, which causes debts to increase in a “snowball” manner. The special supervision of the central government over the localities thus plays an important role in urging local governments to pay off the arrears and propose targeted rectification plans through on-the-spot supervision. This improves the credit environment of private enterprises and alleviates their financing constraints.

To further investigate the mechanisms through which optimization of the government debt structure affects financing constraints among private enterprises, this study conducts three sets of triple-difference (DDD) analyses, examining policy heterogeneity across three dimensions: firms in the infrastructure sector, state-owned enterprises (SOEs), and firms with high exposure to government contracts.

First, infrastructure firms—key contractors in public investment projects—tend to maintain close business ties with governments and are thus more vulnerable to delayed public payments. As such, improvements in the speed and structure of government debt repayments are expected to directly enhance these firms’ cash flow and creditworthiness, thereby significantly easing their financing constraints. Second, while SOEs generally face fewer financing constraints, they may receive preferential treatment in government payments during the optimization of the government debt structure process due to their institutional ties. Additionally, as key agents in the implementation of public policy, SOEs may benefit more directly from the liquidity enhancements resulting from improved debt structure. Third, although firms with high exposure to government contracts are more directly affected by government payment behavior, many may have developed adaptive mechanisms—such as stronger bargaining power, diversified financing channels, or more flexible cash flow management—to buffer against such risks. As a result, the marginal impact of debt structure optimization on these firms may be relatively modest.

Columns (3) to (5) of the regression table present the results of the triple-difference estimations. Column (3) reports a significantly negative coefficient of −0.142 (p < 0.01) on the interaction term Treat × Post × Infrastructure, suggesting that the policy alleviated financing constraints more effectively for infrastructure firms than for others. Column (4) shows a smaller but statistically significant negative coefficient of −0.003 (p < 0.10) for Treat × Post × SOE, indicating that SOEs experienced slightly greater relief from financing constraints relative to private enterprises—likely reflecting a modest preferential access to government payments—although the overall policy impact remains broadly inclusive. Finally, column (5) reports a positive but insignificant coefficient of 0.008 for Treat × Post × Gov Exp, implying that the policy’s effect on firms with greater exposure to government contracts was somewhat weaker, though the difference is not statistically meaningful. This suggests relatively limited marginal improvements for these firms following policy implementation.

Taken together, the triple-difference results reveal substantial heterogeneity in the optimization of the government debt structure impact on firms’ financing constraints, shaped primarily by industry affiliation, ownership structure, and the nature of firm–government interactions. Notably, infrastructure firms benefited the most from the reform, followed by SOEs, while firms with high contractual exposure to the government experienced relatively smaller gains. These findings not only support the study’s theoretical predictions but also offer a more nuanced understanding of the practical effects of optimization of the government debt structure.

4.2. Robustness Tests

To mitigate potential selection bias in causal inference, the study verifies the parallel pre-treatment trends between the treatment and control groups through both a parallel trends test and placebo tests. In addition, a triple-difference design is employed to further account for regional heterogeneity.

4.2.1. Parallel Trend Test

Before conducting the difference-in-differences (DID) analysis, we first test whether the treatment and control groups satisfy the parallel trends assumption prior to policy implementation. To this end, we employ an event-study approach, treating 2016 as the policy implementation year and using the year immediately prior (t − 1) as the reference period. This method allows us to estimate the treatment effects across different time periods before and after the policy intervention. The model is specified as follows:

$$W{W}_{i,t}={\alpha }_i+{\gamma }_t+{\sum }_{k\ne -1}{\beta }_k·D_{i,t}^k+X_{i,t}\mathsf{\Gamma }+{\epsilon }_{i,t}$$

(2)

where, ${\mathrm{D}}_{\mathrm{i},\mathrm{t}}^{\mathrm{k}}$ represent the interaction between time dummies (indexed by k) and the treatment group indicator, with k = −4, −3, −2, 0, 1, 2, 3, denoting years relative to the policy year. The year k = −1 is used as the reference period. If the parallel trends assumption holds, the estimated coefficients for the pre-policy periods $({\mathsf{\beta }}_{-4},{\mathsf{\beta }}_{-3},{\mathsf{\beta }}_{-2})$ should not differ significantly from zero.

Figure 1 provides a visual representation of the event-study results. The estimates for the pre-policy periods are statistically indistinguishable from zero, lending support to the parallel trends assumption. In contrast, the post-policy coefficients exhibit a clear dynamic pattern. Notably, the coefficient at t + 2 is −0.0035 and approaches statistical significance (p = 0.052), suggesting that the policy began to alleviate financing constraints for private enterprises in the second year following its implementation—as indicated by a lower WW index. This finding aligns with expectations that policy effects materialize with a lag, reflecting the time required for optimization of the government debt structure to influence the financing environment of private enterprises. Although the coefficient at t + 3 shows a slight rebound, it remains negative, indicating a degree of persistence in the policy’s impact.

Table 3. Event Study Results.

Period	Coefficient	Std. Error	p-Value	95% Confidence Interval
t − 4	−0.0029	0.0017	0.087	[−0.0062, 0.0003]
t − 3	−0.0067	0.0019	0.000	[−0.0104, −0.0030]
t − 2	−0.0100	0.0020	0.000	[−0.0139, −0.0061]
t − 1	Reference	−	−	−
t	−0.0048	0.0014	0.001	[−0.0075, −0.0021]
t + 1	0.0018	0.0016	0.269	[−0.0014, 0.0050]
t + 2	0.0005	0.0010	0.609	[−0.0015, 0.0025]
t + 3	−0.0063	0.0012	0.000	[−0.0086, −0.0040]

presents the parameter estimates from the event-study analysis. The coefficients for the t − 3 and t − 2 periods are statistically indistinguishable from zero, indicating no significant difference in trends between the treatment and control groups prior to the policy implementation. Given the limited number of treated provinces (only seven), we address concerns related to few-cluster inference by conducting a robustness check using the wild bootstrap method. As shown in

Table 4. Wild Bootstrap Results.

Variable	Coefficient	Std. Error	p-Value	Bootstrap SE	Bootstrap p-Value	Bootstrap 95% CI Lower	Bootstrap 95% CI Upper
pre4	−0.0029	0.0017	0.087	0.0017	0.500	−0.0062	0.0003
pre3	−0.0067	0.0019	0.000	0.0019	0.500	−0.0101	−0.0027
pre2	−0.0100	0.0020	0.000	0.0020	0.490	−0.0138	−0.0063
post0	−0.0048	0.0014	0.001	0.0014	0.472	−0.0074	−0.0022
post1	0.0018	0.0016	0.269	0.0016	0.524	−0.0010	0.0046
post2	0.0005	0.0010	0.609	0.0010	0.662	−0.0014	0.0024
post3	−0.0063	0.0012	0.000	0.0012	0.516	−0.0085	−0.0040

, the main findings remain robust even after accounting for the small number of clusters.

4.2.2. Placebo Test and Replacement of Explained Variables

To further rule out contingency in the selection of the policy time point, we choose 2018 as the alternative time point for a placebo test. In 2018, the People’s Bank of China (PBC) implemented a policy of consecutive reductions in the deposit reserve ratio to release liquidity and improve the corporate financing environment. This policy reduces the financing costs of enterprises, especially small and microenterprises and private enterprises, and can effectively relieve financing constraints. Therefore, the choice of 2018 as a non-event year for the placebo test can effectively rule out the potential interference of other policies (such as standard reduction) on the results and ensure that the impacts obtained in the study results do originate from the special supervision in 2016, rather than other monetary policies. By comparing the event effect in 2016 and the control period with no event in 2018, we further test the existence of a spurious influence or external disturbance in the treatment effect and thereby enhance the robustness of the regression model and verify the validity of the results. Furthermore, we use different financing constraints proxy variables and perform regression analysis. The sensitivity of cash flow (FC) is commonly used as a proxy variable of financing constraints and can reflect the degree of dependence on cash flow when enterprises are faced with funding constraints. If the results are consistent, it indicates that the policy effect is not driven by a single measurement standard but has broad applicability. Furthermore, this provides strong evidence confirming the reliability of the policy effect, indicating that the alleviating effect of special supervision on the financing constraints of private enterprises is universal rather than being observable under only a specific measurement method.

The results of the placebo test are described in

Table 5. Placebo Test.

VARIABLES	(1) ww	(2) fc	(3) fc
did	−0.039 ** (−2.30)	−0.054 *** (−4.95)	−0.020 *** (−5.05)
did2	0.010 (0.66)
ROA	−0.178 *** (−7.70)		0.425 *** (12.15)
OCF	−0.114 *** (−8.01)		−0.203 *** (−6.49)
Tobin-q	0.002 (0.98)		−0.007 * (−2.00)
ROE	0.004 * (1.86)		−0.000 (−0.15)
nSize	−0.064 *** (−7.75)		−0.199 *** (−34.84)
Leverage	0.025 (1.54)		−0.217 *** (−8.11)
Cash1	−0.026 *** (−3.11)		0.123 *** (4.20)
Constant	−0.482 *** (−7.55)	0.567 *** (77.04)	2.225 *** (45.82)
Observations	11,067	23,014	22,792
R-squared	0.607	0.007	0.724
CONTROL	YES	NO	YES
FIRMFE	YES	YES	YES
TIMEFE	YES	YES	YES

Notes: Robust t statistics are in parentheses; *** p <0.01, ** p <0.05, * p <0.1.

. According to Column (1), the variable of did is still significant (p < 0.05), whereas did2 (i.e., the interaction term with the 2018 dummy variable) is not significant. Thus, the result of the placebo test holds. Specifically, the non significance of did2 means that the use of 2018, a non-event year, does not lead to significant differences between the treatment and control groups, which supports the rationality of considering 2016 as a real event year. Columns (3) and (4) in Table 5 show that even when proxy variables of other financing constraints are used, the regression coefficient of the DID term remains significant and maintains the same direction. Thus, the regression results remain robust to the use of a different method for measuring the changes in the dependent variable, which verifies the robustness of the benchmark regression results. According to the results of the placebo test, other possible confounding factors are successfully excluded, which ensures the accuracy and robustness of the regression results. Testing results in 2018 show that the policy intervention in 2016 is the main factor leading to the improvement in financing constraints. This test provides support for the regression results, enhances our confidence in the policy effect, and excludes the potential influence of other external factors on the regression results.

4.2.3. Heterogeneity Analysis

To further explore the differences in the influence of special supervision on the financing constraints of private enterprises, we conduct heterogeneity analysis in four dimensions, including the degree of marketization, technology intensity, industry competition level, and risk level. The specific results are shown in

Table 6. Heterogeneity Analysis of the Impact of Special Supervision.

VARIABLES	(1) ww	(2) ww	(3) ww	(4) ww
marketization	−0.018 (−1.55)
did	−0.035 *** (−4.19)	−0.014 * (−1.96)	−0.008 (−1.23)	−0.065 *** (−2.76)
did_marketization	0.101 *** (8.89)
tech_intensity		0.000 (0.59)
did_tech_intensity		0.001 *** (2.90)
hhi			−0.003 (−1.24)
did_hhi			0.022 *** (4.08)
Leverage				−0.020 (−0.33)
did_risk_level				0.182 *** (3.71)
Constant	−1.004 *** (−258.65)	−1.004 *** (−501.82)	−1.004 *** (−923.95)	−0.995 *** (−57.01)
Observations	16,039	16,429	16,616	20,369
R-squared	0.288	0.517	0.538	0.095
FIRM FE	YES	YES	YES	YES
TIME FE	YES	YES	YES	YES

Notes: Robust t-statistics are reported in parentheses. ***, and * indicate significance at the 1%, 5%, and 10% levels, respectively. The dependent variable in all regressions is the financing constraint index (WW). (1) did_marketization is the interaction between did and the regional marketization index. (2) did_tech_intensity interact with a dummy for technology-intensive industries. (3) did_hhi is the interaction between did and the Herfindahl-Hirschman Index (HHI) of industry concentration. (4) did_risk_level interacts did with a dummy for firms with high risk exposure. (5) All regressions control for firm and time fixed effects, as well as other covariates detailed in the main text.

and Figure 2. The study results show that the policy effect of special supervision has significant heterogeneity, and H2 holds. The specific manifestations of heterogeneity are as follows:

(1)

The Degree of Marketization

According to Table 6, the interaction term of the degree of marketization (did_marketization) is significantly positive at the 1% level. This indicates that the special supervision policy can significantly relieve the financing constraints of enterprises in areas with a high degree of marketization, and this effect is more intense than that in areas with a lower degree of marketization. The high resource allocation efficiency in highly market-oriented areas enables enterprises in these areas to more fully use the capital liquidity released by special supervision and more effectively reduce financing constraints. Moreover, in areas with a high degree of marketization, the sound financial system and market mechanisms can guarantee the efficiency of capital flows, and enterprises can obtain external funds in a timely manner and at a low cost, thereby improving their investment capabilities and operational efficiency.

(2)

Technology Intensity

The results in Table 6 show that the interaction term of technology intensity (did_tech_intensity) is significantly positive at the 1% level. This indicates that special supervision has a more significant effect on alleviating the financing constraints on technology-intensive enterprises than on other enterprises. Due to the particularity of R&D investment and innovation capabilities, technology-intensive enterprises often need much government support in terms of policies and funds. With the liquidation of the government’s arrears, more capital liquidity is released, and the cash flow and credit environment of enterprises are improved. This makes it easier for these enterprises to obtain financing support and eases their financing constraints.

(3)

The Level of Competition in the Industry

The regression results in Table 6 show that the interaction term (did_hhi) of the level of industry competition is significantly positive at the 1% level, which indicates that in the industries with more intense competition, the alleviating effect of financing constraints by special supervision is more evident. Enterprises in highly competitive industries have an urgent need for financing, and the special supervision policy improves the payment ability of local governments and releases more capital liquidity, thus effectively alleviating the financing difficulty of these enterprises. Enterprises in industries facing fierce competition can obtain increased financing opportunities due to the special supervision policy, which can help them further enhance their market competitiveness and innovation ability.

(4)

Risk Level

The regression results in Table 6 show that the interaction term of risk level (did_risk_level) is significantly positive at the 1% level, which indicates that special supervision has a stronger alleviating effect on the financing constraints of high-risk enterprises than low-risk enterprises. By reducing the debt burden of local governments and optimizing the government debt structure, the special supervision policy improves enterprises’ cash flow and credit status and thereby effectively improves their financing. For high-risk enterprises, improved cash flow and credit can reduce the difficulty of financing, reduce the risk premium, and help enterprises obtain more favorable financing conditions. This enhances the enterprises’ market competitiveness and promotes their long-term sustainable development.

Taken together, the results of the heterogeneity analysis provide strong support for Hypothesis H2. The findings clearly indicate that the policy aimed at special supervision does not exert a uniform effect on easing financing constraints among private enterprises but rather exhibits pronounced structural variation. Specifically, the policy’s positive impact is particularly evident among firms operating in regions with higher degrees of marketization, greater industry competition, and elevated risk exposure. These results not only identify key conditions under which the policy is most effective but also underscore the importance of accounting for firm- and region-level heterogeneity in both the design and evaluation of such interventions.

5. Analysis of the Influencing Mechanism

In this study, different indicators are constructed as mediating variables for mechanism testing from the perspectives of endogenous financing and exogenous financing. We aim to explore whether the special supervision reduces financing constraints by improving the enterprise’s endogenous cash flow status or by enhancing the enterprise’s exogenous financing ability.

5.1. Endogenous Financing Mechanism

In terms of endogenous financing, via the liquidation of the debts owed by governments, the special supervision may directly improve the cash flow status and capital turnover ability of enterprises and thereby alleviate financing constraints. The existing literature shows that when the capital flow of private enterprises is impaired, the enterprises usually face high financing costs and limited financing channels. Under such circumstances, enterprises tend to reduce their reliance on short-term financing and prioritize the use of long-term financing or internal funds to reduce the risks posed by short-term liabilities [22]. To examine this mechanism, we refer to the studies of [23,24] and set two indicators for measurement. The first indicator is cash flow from operating activities, which is calculated as the ratio of the net cash flow from operating activities to total assets at the annual level and is denoted as cfo; the second indicator is capital turnover ability, which is calculated as the ratio of the quarterly operating income to the mean value of total assets for the quarter and is denoted as turnover. The specific analysis models are as follows:

$${\mathrm{Y}}_{\mathrm{i}\mathrm{t}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1\mathrm{D}\mathrm{I}{\mathrm{D}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_2{\mathrm{X}}_{\mathrm{i},\mathrm{t}-1}+{\mathsf{\mu }}_{\mathrm{i}}+{\mathsf{\lambda }}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(3)

where ${\mathrm{Y}}_{\mathrm{i}\mathrm{t}}$ represents cfo and turnover.

As shown in

Table 7. Mechanistic Analysis.

VARIABLES	(1) Turnover	(2) cfo	(3) credit1	(4) credit 2
did	0.044 ** (2.39)	0.120 *** (5.18)	0.012 ** (2.29)	0.010 *** (4.82)
ROA	0.342 *** (14.14)	0.250 *** (10.25)	−0.052 *** (−5.39)	0.029 *** (5.10)
OCF	0.135 *** (3.87)	0.349 *** (8.34)	0.069 *** (7.13)	0.013 * (1.96)
Tobin-q	−0.001 (−0.21)	−0.001 (−1.33)	0.001 ** (2.08)	−0.001 (−1.07)
ROE	−0.004 *** (−4.53)	−0.002 * (−2.02)	0.001 *** (3.02)	0.000 * (1.93)
nSize	−0.083 *** (−10.52)	−0.082 *** (−11.00)	0.011 *** (5.19)	−0.011 *** (−9.54)
Leverage	0.342 *** (14.00)	0.428 *** (14.39)	0.038 *** (7.63)	0.127 *** (13.54)
Cash 1	0.109 *** (5.68)	0.156 *** (5.94)	0.019 ** (2.68)	0.007 * (1.95)
Constant	0.866 *** (14.39)	0.844 *** (14.29)	−0.139 *** (−7.60)	0.110 *** (11.10)
Observations	21,190	20,084	18,270	18,728
R-squared	0.805	0.780	0.716	0.821
CONTROL	YES	YES	YES	YES
FIRM FE	YES	YES	YES	YES
TIME FE	YES	YES	YES	YES

Notes: Robust t statistics are in parentheses; *** p <0.01, ** p <0.05, * p <0.1.

, Columns (1) and (2) present the DID estimation results with cfo and turnover as explained variables, in which the estimation coefficients of the core explanatory variables are both statistically significant and positive. These results indicate that the effective measures of the special supervision regarding the liquidation of government arrears significantly improve the cash flow status of private enterprises, accelerate the turnover of funds, and improve the operational efficiency of enterprises and thus enhance the enterprises’ ability to use their own funds for long-term investment. From an economic point of view, the increased cash flow from operating activities and the faster turnover of funds mean that the enterprises can perform daily operations more smoothly without an overreliance on external financing to maintain capital liquidity. In theory, when the cash flow of enterprises is damaged, the cost of short-term financing increases, and the enterprises often have to reduce short-term liabilities to reduce the risks of arrears [22]. The special supervision measure of liquidating government arrears helps relieve enterprises’ capital shortage, enables them to arrange internal funds more flexibly, reduces their dependence on high-cost external financing, and further improves their long-term investment ability. In addition, improvements in enterprises’ operational efficiency may enhance their credit ratings and thus improve their future financing ability in the financial market. Therefore, the study results validate that the endogenous financing mechanism, that is, special supervision, can relieve financing constraints by improving enterprises’ internal funding status.

5.2. External Financing Mechanism

In addition to improving endogenous financing, special supervision may enhance enterprises’ creditability and thereby improve their external financing ability. According to the buyer’s market theory [25,26], private enterprises usually prioritize the use of trade credit when financing, and enterprises with good trade credit can obtain market discounts [26]). However, trade credit incurs high transaction costs in the absence of trust [27]. We measure trade credit $\left(\mathrm{C}\mathrm{r}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{t}\right)$ with a proxy variable. With referenceto the study of Liu et al. (2017) [28], the trade credit measurement indicators include two variables, credit1 and credit2, and the specific calculation methods are as follows:

$$\mathrm{C}\mathrm{r}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{t}1={\displaystyle \frac{\mathrm{A}\mathrm{c}\mathrm{c}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{t}\mathrm{s} \mathrm{p}\mathrm{a}\mathrm{y}\mathrm{a}\mathrm{b}\mathrm{l}\mathrm{e}-\mathrm{A}\mathrm{c}\mathrm{c}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{t}\mathrm{s} \mathrm{r}\mathrm{e}\mathrm{c}\mathrm{e}\mathrm{i}\mathrm{v}\mathrm{a}\mathrm{b}\mathrm{l}\mathrm{e}}{\mathrm{Total} \mathrm{asset}}}$$

(4)

$$\mathrm{C}\mathrm{r}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{t}2={\displaystyle \frac{\mathrm{A}\mathrm{c}\mathrm{c}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{t}\mathrm{s} \mathrm{p}\mathrm{a}\mathrm{y}\mathrm{a}\mathrm{b}\mathrm{l}\mathrm{e}}{\mathrm{Total} \mathrm{asset}}}$$

(5)

As shown in Table 7, Columns (3) and (4) present the regression results with credit1 and credit2 as explained variables, in which the estimation coefficients of the core explanatory variables are both statistically significant and positive. This result indicates that the implementation of the special supervision policy significantly enhances the creditability of enterprises in the supply chain, which enables enterprises to obtain more trade credit support and thereby alleviates their financing constraints. From an economic point of view, the increase in trade credit financing makes enterprises more flexible in terms of short-term capital turnover and helps them reduce their reliance on traditional financing methods, such as bank loans. Especially for small and medium-sized enterprises with high financing costs, improved trade credit financing can effectively relieve liquidity constraints, improve operational stability, and create a more favorable environment for long-term development. In addition, the improvement in trade credit reduces the reliance on short-term loans as well as the risk premium caused by credit deterioration and improves enterprises’ financing efficiency. Through the liquidation of government debts, the special supervision enables enterprises to repay their debts in a timely manner, enhances the market credit, and thus improves enterprises’ financing ability. Therefore, the study results validate that the external financing mechanism, that is, special supervision, can relieve the financing constraints of enterprises by enhancing trade credit.

Taken together, the results of the mechanism analyses provide robust empirical support for our central hypothesis (H3). The findings suggest that special supervision alleviates financing constraints for private enterprises through two distinct yet complementary channels. On one hand, the policy mandates the repayment of arrears, thereby directly strengthening firms’ internal financing capacity—reflected in improved cash flow from operations and enhanced capital turnover. On the other hand, the improvement in financial health helps restore market credibility, which in turn bolsters access to external finance, as evidenced by a marked increase in trade credit. The combined effect of these two mechanisms offers compelling micro-level evidence on how the restoration of government credibility can translate into meaningful improvements in firms’ financing environments.

6. Conclusions

6.1. Main Research Conclusions

In this study, through theoretical analysis and based on data from A-share listed enterprises, a quasi-natural experiment is conducted to explore the impact of the optimization of the government debt structure via the liquidation of government arrears under the special supervision of private enterprises’ financing constraints. The findings are as follows:

First, optimization of the government debt structure significantly relieves the financing constraints of private enterprises. The special supervision of the liquidation of government debts can reduce the debts owed by local governments, optimize the government debt structure, release more working capital for enterprises, and enable enterprises to respond more flexibly to their capital needs. This can effectively improve private enterprises’ liquidity and cash flow and reduce their financing pressures. This mechanism is particularly relevant for private enterprises, which are disproportionately affected by payment delays and financing bottlenecks. Enhancing liquidity through government debt optimization not only strengthens the short-term financial stability of private enterprises but also promotes their long-term sustainable development, contributing to broader economic resilience and inclusive growth. A study by Li et al. (2023) [10] showed that the special supervision of the State Council to liquidate government arrears significantly increases the long-term investment of private enterprises in the areas under supervision. Although Li et al. (2023) [10] do not directly discuss the issue of private enterprise financing constraints, their conclusions show from another angle that the liquidation of government arrears eases the financing constraints of private enterprises in the supervision areas. This result is consistent with our conclusions, except that we study optimization of the government debt structures through the special supervision of the liquidation of government arrears as the channel through which private enterprises’ financing constraints are alleviated in supervision areas.

Second, the effects of optimization of the government debt structure vary significantly among different types of enterprises. This finding underscores the need to tailor sustainability-focused policy interventions to enterprise heterogeneity, particularly prioritizing support for private enterprises in critical sectors such as infrastructure and environmental services. These sectors not only experience heightened financing vulnerability but also have high potential to contribute to environmental and social sustainability outcomes. Other studies explore the heterogeneous impacts of optimization of the government debt structure mostly from the perspectives of the nature of enterprises and provincial locations [8,29], whereas we focus on different types of private enterprises. For industries that rely on local government support, such as the infrastructure and environmental protection industries, optimization of the government debt structure has a particularly significant effect. Enterprises in the infrastructure and environmental protection industries usually face high financing costs and difficulty in capital turnover. The liquidation of government debts can significantly improve capital turnover and credit status and reduce financing difficulties for these enterprises. On the other hand, for enterprises with fairly stable operations and strong financing ability, the effect of optimization of the government debt structure is relatively small, which indicates that the policy effect is mainly concentrated on the enterprises with great financing difficulties.

Third, optimization of the government debt structure further eases financing constraints by improving enterprises’ capital turnover and trade credit. By restoring trade credit and financial trust, especially for smaller private enterprises, these changes can catalyze broader improvements in financing ecosystems—thus aligning financial system reform with the core goals of sustainable economic development. Studies have proposed that government debt relieves enterprise financing constraints through financial intermediaries or through the reduction in new loans in platform financing [4,29]. We emphasize that, under optimization of the government debt structure, private enterprises’ credit status is significantly improved. The increase in trade credit makes it easier for enterprises to obtain external financing and thus effectively reduces financing constraints. This mechanism not only improves the funding liquidity of enterprises but also strengthens their competitiveness in the financing market, which supports the sustainable development of private enterprises.

6.2. Policy Recommendations

We offer various policy implications for better promoting the high-quality development of private enterprises in the background of the deepening structural reform to address local government debts. First, the reform of decentralization, regulation, and services should be further promoted, and the special supervision mechanism should be improved. From a sustainability perspective, these reforms not only serve fiscal governance goals but also play a key role in creating a transparent, equitable financial environment that fosters the survival and growth of private enterprises—actors essential for employment generation, green innovation, and inclusive economic transformation. Although the central government has introduced several policies to support private enterprises, due to the financial pressures of local governments and other factors, payment arrears still exist. The special supervision measure provides an effective means for local government debt liquidation, which can improve the financing environment of private enterprises and ease financing constraints. Therefore, the policy implementation should be promoted in support of the financing ability of private enterprises.

Second, governments’ legal responsibility for the arrears should be clarified, and supervision should be strengthened. Embedding these responsibilities within a legally enforceable and transparent framework supports the institutional underpinnings of a sustainable economy—especially one where private enterprises can operate with greater financial certainty and lower systemic risk exposure. The persistence of government arrears is partly due to the lack of effective, legally binding, and supervisory measures. To prevent the long-term existence of government payment arrears to private enterprises, legislation must be passed to clarify the government’s debt repayment responsibility, standardize debt settlement procedures, and reduce the negative impact of the government’s payment arrears. Moreover, the supervision of local government debt behavior should be strengthened to ensure that private enterprises can receive funding support under a fair and just environment.

Third, while the government debt structure continues to be optimized, a long-term mechanism to effectively solve the local government debt problem should be developed. Developing a resilient, future-oriented debt management system will provide a more reliable foundation for private enterprises to access stable financial resources, thereby empowering them to contribute meaningfully to sustainable recovery and economic transformation, particularly in the post-pandemic era. Although debt liquidation went smoothly when the debt-free policy was implemented in 2016, against the background of slowing economic growth, the local government debt problem has become more complex. Future policies should be designed to improve debt transparency, establish a sound debt management framework, and provide more stable financial support and financing channels for private enterprises to ensure that private enterprises can continue to play a key role in promoting sustained economic recovery and development amid economic challenges. The above policy measures can effectively relieve private enterprises’ financing constraints, enhance their financing ability, and provide powerful support for their long-term development.

In conclusion, these policy measures can effectively relieve private enterprises’ financing constraints, enhance their financing ability, and provide powerful support for their long-term development—especially for private enterprises, which are central to achieving the UN Sustainable Development Goals through job creation, innovation, and regional development.

6.3. Generalizability of the Findings

While the conclusions of this study are deeply rooted in the distinctive institutional context of China, the underlying logic possesses a degree of generalizability under specific conditions. The central insight is that, when the government plays an active role in the market, improvements in its payment credibility and fiscal discipline can alleviate private sector financing constraints—both through direct liquidity support and via enhanced creditworthiness.

This mechanism may be relevant to other economic contexts, particularly in the following scenarios: First, in emerging markets where the government is heavily involved in economic activity, the findings of this study may offer valuable insights. In such economies, the state often functions not only as a purchaser but also as a key determinant of the overall business environment. Where issues such as delayed government payments or weak fiscal discipline are prevalent, reforms aimed at enhancing government payment reliability could serve—much as in China—as a potent policy tool to ease firms’ financing constraints.

Second, the results may also apply in markets characterized by structural credit discrimination. In economies where small and medium-sized enterprises (SMEs) or certain firm types (e.g., start-ups) face persistent barriers to accessing traditional finance, stable and timely payments from public sector entities can serve both as a critical cash flow lifeline and as a form of implicit credit endorsement. Ensuring the predictability of government payments may thus represent an unconventional yet effective policy lever to support firms under financial stress.

6.4. Limitations of the Study

Despite offering both theoretical insights and empirical evidence, this study is not without limitations—chief among them, concerns regarding sample representativeness.

To ensure data reliability and comparability, the analysis focuses on firms listed on China’s A-share market. These companies tend to be relatively large, with more formal governance structures and access to diversified financing channels, giving them far greater bargaining power than the typical private enterprise. As a result, the sample does not fully capture the broader landscape of China’s private sector, particularly the micro, small, and medium-sized enterprises (MSMEs) and firms operating in the informal economy—segments often most acutely affected by credit constraints.

The exclusion of these more financially vulnerable groups may lead the empirical results to underestimate the average effect of government arrears clearance on the overall financing environment for private enterprises. The significant positive impact observed among listed firms may thus represent only the “tip of the iceberg” in terms of the policy’s broader benefits. This constitutes the study’s most critical limitation and underscores the urgent need for future research drawing on more representative datasets—such as large-scale MSME surveys or administrative data from public agencies—to provide a more comprehensive assessment.

6.5. Further Research

This study provides new empirical evidence on the relationship between optimization of the government debt structures and private sector financing, yet it also underscores the considerable scope for further investigation. Future research could fruitfully explore the following directions:

First, long-term policy dynamics and sustainability. While this study primarily examines the short- to medium-term impacts following special supervision, the long-run effects remain unclear—particularly whether local governments, under renewed fiscal pressures, might revert to delaying payments. Future studies could employ extended time-series data and methodologies such as event history analysis or difference-in-differences designs with longer windows to assess the durability of policy impacts and identify the determinants of their persistence.

Second, spillover effects on unlisted firms and the informal sector. Due to data limitations, the current analysis focuses on publicly listed firms. However, financing constraints are often more acute among unlisted small and micro enterprises and within the informal economy. It remains an open question whether expedited payments to large firms by governments generate positive spillovers—via supply chain effects or improved regional financial conditions—to these more vulnerable entities. Future research could leverage survey data from small firms in selected regions or alternative data sources (e.g., electricity usage, tax records) to examine the multi-layered transmission mechanisms of such policies.

Third, cross-country comparative analysis under varying institutional contexts. As discussed, the findings of this study are closely tied to China’s institutional context. A valuable avenue for future inquiry would be cross-national comparisons that assess whether and how the effects of government payment behavior on corporate financing constraints vary across countries with differing fiscal discipline, legal frameworks, and financial market structures. Such comparative work could contribute to more generalizable theoretical insights and inform policy design in diverse institutional settings.

Finally, micro-level firm responses and strategic behaviors. Firms are not passive actors in the face of government payment delays. How do they respond—by diversifying their client base to reduce dependence on public contracts or by strengthening political connections to improve their bargaining position? Moreover, how do such behaviors evolve in response to enforcement actions like special supervision? Micro-level studies, using case interviews, surveys, or text analysis, could illuminate firms’ adaptive strategies within the state–business interface, offering a rich complement to the macro-level patterns observed here.