Credit Risk Side of CSR: A New Angle for Building China’s Sustainable Cycle under the Reform of the Security Interest System

Abstract

Amid growing economic uncertainty and rising corporate default risks, effective legal reforms are essential for financial stability and sustainable business practices. This study examines the impact of China’s security interest system reform on corporate credit risk, highlighting its implications for corporate social responsibility (CSR) and sustainable development. Using a Difference-in-Differences approach and data from Chinese listed firms, the results show that the reform significantly reduces credit risk, particularly for asset-light, risk-averse, and growth-stage enterprises. Lower credit risk alleviates financing constraints, enabling firms to allocate more resources to Environmental, Social, and Governance (ESG) activities and long-term sustainability initiatives. The findings reveal that legal reforms in secured transactions not only serve as risk management tools but also function as institutional mechanisms that foster CSR engagement and contribute to building a sustainable economic cycle. This research fills a gap in linking legal system reform, credit risk mitigation, and CSR, offering practical insights for future policy design in sustainable finance and green innovation.

1. Introduction

1.1. Research Background

China’s economy is undergoing profound structural transformation amid growing global uncertainties and domestic challenges. Slowing economic growth, weak demand, and rising corporate defaults have heightened credit risks, threatening financial stability and sustainable development. Achieving long-term growth now requires firms not only to remain financially resilient but also to actively engage in corporate social responsibility (CSR), including environmental protection, employee well-being, and sustainable supply chain practices.

However, high credit risks and financing constraints often force firms to prioritize short-term survival over strategic investment in sustainability, limiting their ability and willingness to fulfill CSR obligations. Addressing corporate credit risk has therefore become central to balancing financial stability and sustainable development objectives. Within this context, China’s security interest system—the legal framework governing collateral-based financing—has undergone significant reforms, most notably with the promulgation of the Property Law and subsequent enhancements under the Civil Code. These reforms aimed to broaden collateral scope, streamline registration procedures, and strengthen creditor protection, ultimately improving access to financing and reducing default risks.

1.2. Literature Review and Research Gap

Research on China’s security interest reforms has evolved along two distinct strands: theoretical developments and empirical assessments.

First, qualitative studies (e.g., Wang [1], Xu [2], Xie [3], Shi [4]) examine legislative innovation, including expanding collateral types, introducing floating charges, and establishing unified registration systems. These works provide valuable historical and institutional insights but are primarily descriptive, offering limited critical evaluation of policy effectiveness.

Second, empirical studies increasingly explore economic effects. Cross-country evidence also shows that collateral law reforms significantly shape lending activities and sectoral allocation, confirming the importance of legal frameworks for corporate financing [5]. For instance, Qian [6] finds that the 2007 Property Law significantly reduced borrowing costs, while Lu and Ma [7] show that reforms curtailed corporate financialization by easing financing constraints. Similarly, Yu [8] highlights improvements in accounts receivable flexibility and commercial credit. However, most of these studies focus narrowly on financial performance and fail to connect reforms to broader organizational outcomes, such as CSR participation or sustainability strategies.

In parallel, the literature on corporate credit risk identifies numerous drivers, including ownership structure, digital transformation, and managerial characteristics. High credit risk is widely recognized as a barrier to CSR investment and long-term environmental strategies. Despite these insights, few studies integrate legal, financial, and sustainability perspectives to explain how institutional reforms reduce credit risk and indirectly empower firms to engage in CSR, creating pathways toward sustainable economic development.

This disconnect between qualitative legislative analysis and quantitative financial outcomes reveals a critical research gap: there is insufficient empirical evidence linking security interest reforms to credit risk mitigation, CSR investment, and the sustainability transition.

1.3. Problem Statement and Research Contributions

To address this gap, this study investigates the impact of China’s security interest system reform on corporate credit risk and explores its broader implications for CSR engagement and sustainable economic cycles. Using the 2007 Property Law as a quasi-natural experiment, we apply a Difference-in-Differences (DID) approach on a panel dataset of 11,618 A-share private firms from 2000 to 2020.

This study answers three key research questions:

RQ1: Does the security interest reform significantly reduce corporate credit risk?

RQ2: How do these effects differ across firms with varying asset structures, risk preferences, and life-cycle stages?

RQ3: Does reducing credit risk create financial capacity for firms to engage more actively in CSR and sustainability initiatives?

This paper makes three main contributions: First, it provides the first large-scale empirical evidence linking legal reforms, credit risk reduction, and CSR investment. Second, it demonstrates the heterogeneous impacts of reforms, showing stronger effects for firms with lower fixed-asset ratios, lower risk tolerance, and shorter life cycles. Finally, it advances an interdisciplinary perspective by connecting institutional reforms to corporate financial health and sustainable development, offering actionable policy insights for optimizing legal frameworks to foster green innovation and build a sustainable economic cycle.

The remainder of this paper is structured as follows: Section 2 outlines research hypotheses; Section 3 presents materials and methods; Section 4 demonstrates empirical results; Section 5 discusses findings and implications; Section 6 concludes with policy recommendations and future research directions.

2. Research Hypotheses

The reform of China’s security interest system, particularly through the promulgation of the Property Law in 2007, fundamentally altered the structure of corporate collateral and credit risk allocation. Prior to the reform, the credit market heavily relied on fixed assets such as land and factory buildings as eligible collateral [6]. This practice disproportionately constrained enterprises with lower fixed-asset ratios—typically innovative, technology-intensive, or service-oriented firms—from accessing external finance, regardless of their growth potential or profitability [9]. Such structural limitations not only elevated financing costs but also heightened cash flow volatility and default susceptibility, creating a persistent gap in credit accessibility [10].

The Property Law mitigated these constraints by recognizing movable assets—such as accounts receivable, inventory, and future assets—as legitimate collateral. International evidence indicates that movable collateral registries significantly improve firms’ access to credit by lowering loan costs and extending maturities [11]. This institutional shift enhanced credit supply through two primary mechanisms: first, it improved creditors’ protection by clarifying repayment priority and reducing information asymmetry [8]; second, it enabled firms to optimize debt structure and mitigate maturity mismatch risks, thereby strengthening financial stability [7]. We therefore expect that enterprises with lower fixed-asset ratios benefited disproportionately from this reform, as they gained access to previously excluded forms of collateral. Accordingly, we propose:

H1: 

The reform of the security interest system—marked by the enactment of the Property Law—reduces corporate credit risk more significantly for firms with lower fixed-asset ratios than for those with higher fixed-asset ratios.

Beyond asset structure, firms’ inherent risk preferences also shape how they respond to legal-financial innovations. Risk-averse firms are more likely to adopt new guarantee instruments to stabilize financing and hedge against uncertainty [12]. In contrast, risk-tolerant firms often prioritize aggressive growth and leverage, showing less interest in collateral-based risk mitigation [13]. The reform’s flexibility in collateral types and registration procedures likely appealed more to conservative firms seeking to enhance creditworthiness without increasing leverage. Thus, we hypothesize:

H2: 

The effect of the Property Law reform on reducing corporate credit risk is stronger for firms with lower risk preferences than for those with higher risk preferences.

Finally, the stage of a firm’s life cycle influences its financial flexibility and responsiveness to legal changes. Growth-phase firms, which are often characterized by high external financing needs and undercollateralization, stand to benefit most from expanded collateral options [14]. In contrast, mature firms typically possess stable asset bases and access to traditional financing, whereas firms in decline face operational inefficiencies that limit their responsiveness to legal incentives [15]. Hence, we anticipate:

H3: 

The reform’s effect on credit risk reduction is more pronounced for firms in earlier life cycle stages (e.g., growth phase) compared to those in mature or decline phases.

These hypotheses collectively emphasize the heterogeneous effects of legal reforms on credit risk, contingent on firm-specific characteristics. Testing them allows for a more nuanced understanding of how legal institutions can differentially support financial stability and sustainable corporate growth.

3. Materials and Methods

3.1. Methodology

This study employs a mixed-methods design integrating quantitative analysis with qualitative assessment to examine how China’s security interest reform—primarily represented by the promulgation of the Property Law (2007)—affects corporate credit risk and, indirectly, firms’ capacity to invest in corporate social responsibility (CSR) and sustainable development. The integration of these approaches allows us to investigate both the empirical impact of the reform and the institutional mechanisms behind it.

Compared with the property rights law, the civil code has a relatively short effecting time, and its legal effects have not fully manifested yet, and the relevant data have not been fully released. Coupled with the adverse effect of the COVID-19 pandemic on economic development, considering the data availability, in the empirical research part of this article, the data of A-shares private listed companies from 2000 to 2020 are selected as the research sample, and a natural experiment is carried out based on the promulgation of the property rights law. In the qualitative research part, this article is dedicated to analyzing the internal logic between the reform of security rights system and corporate credit risk, and predicting the legal implementation effect of the future civil code based on the impact brought by the implementation of the property rights law on corporate credit risk. At the same time, it also provides legal suggestions for the further improvement of the civil code. The application of two research methods aims to conduct multi-angle analysis, comprehensively reveal the legal effects of the reform of the security rights system, provide more ways for enterprises’ credit enhancement, and promote economic development and improvement of the business environment.

It should be noted that we select the 2007 Property Law as the primary policy shock for three reasons:

(1)

Institutional Significance

The 2007 reform substantially broadened the collateral scope, expanded financing channels, and unified registration procedures, making it the most comprehensive overhaul of China’s security interest system in decades.

(2)

Data Availability

While the Civil Code represents a more recent reform, its effects are not yet fully observable given the short implementation period and limited publicly available data.

(3)

Controlling Confounding Effects

To mitigate the influence of external shocks—particularly the 2008 global financial crisis—we employ three strategies:

Exclude firms delisted between 2006 and 2009; Include year fixed effects in our DID specification to capture macroeconomic shocks; Conduct robustness tests by re-estimating the model on sub-periods excluding 2007–2009.

This approach ensures that the identified effect of the 2007 Property Law is not conflated with other contemporaneous events.

3.2. Research Design

3.2.1. Sample Selection and Data Sources

To study the impact of the reform of the security rights system on corporate credit risk, considering the impact of the pandemic on the macro-economy, as well as the lag in the manifestation of the legal effects since the promulgation of the civil code and the unavailability of relevant economic data that has not been released, this paper selects the data of A-shares private listed companies from 2000 to 2020 as the research sample. Based on the above analysis, the article ranks all enterprises in descending order of fixed assets, selects the top 1/3 of enterprises in terms of proportion as the experimental group and the bottom 1/3 as the control group, and conducts a quasi-natural experiment around the introduction of the property rights law in 2007 [16]. The financial data is sourced from the CSMAR database. Meanwhile, the sample is processed according to the following steps:

(1)

Considering the special risk-control regulations of financial enterprises and the abnormality of ST enterprises, financial and ST enterprises are excluded from the data;

(2)

To create a control effect before and after the introduction of the property rights law, enterprises listed after 2006, i.e., after the introduction of the property rights law, are excluded;

(3)

To avoid the impact of the 2008 economic crisis, enterprises that exited the stock market from 2006 to 2009 are excluded;

(4)

Samples with missing values of major variables and outlier samples are excluded. Finally, 11,618 sample observations are obtained.

3.2.2. Variable Definition

Explained Variable

The explained variable in this paper is corporate credit risk. To improve data availability, drawing on the research of Xu [17], the Naïve model proposed by Bharath and Shumway [18] is used to estimate the expected default frequency (EDF). As a proxy variable for default risk, we calculate default risk through the following steps:

$$D{D}_{it}={\displaystyle \frac{log\left(\frac{{Equity}_{it}+{Debt}_{it}}{{Debt}_{it}}\right)+(r_{it-1}-\frac{{\sigma }_{Vit}^2}{2})\times T_{it}}{{\sigma }_{Vit}\times \sqrt{T_{it}}}}$$

(1)

Among them, $D{D}_{it}$ represents the distance to default; ${Equity}_{it}$ represents the total market value of the company, which is the product of the total number of issued shares and the year-end market price; ${Debt}_{it}$ is the face value of the company’s debt, which is the sum of the company’s short-term liabilities at the end of the year and half of the long-term liabilities at the end of the year; $r_{it-1}$ is the annual return rate of the enterprise lagged by one year, which is obtained from the monthly stock return rates of the company in the previous year; $T_{it}$ is set as 1 year in the formula; and ${\sigma }_{Vit}$ is the estimator of the company’s asset volatility, which is calculated by ${\sigma }_{Eit}$. ${\sigma }_{Vit}$ is the volatility of stock returns, which is obtained by calculating the standard deviation of the monthly return rate data of the company in the previous year. The ${\sigma }_{Vit}$ calculation is as follows:

$${\sigma }_{Vit}={\displaystyle \frac{{Equity}_{it}}{{Equity}_{it}+{Debt}_{it}}}\times {\sigma }_{Eit}+{\displaystyle \frac{{Debt}_{it}}{{Equity}_{it}+{Debt}_{it}}}\times (0.05+0.25\times {\sigma }_{Eit})$$

(2)

Based on Equations (1) and (2), we can calculate the distance to default and then obtain the expected default frequency (EDF) through the standard cumulative normal distribution function Normal(.), as shown in Equation (3):

$${EDF}_{it}=Normal(-D{D}_{it})$$

(3)

While the Naïve model proposed by Bharath and Shumway [18] offers a parsimonious and computationally efficient method for estimating expected default frequency (EDF), it is important to acknowledge its limitations relative to more structural or empirical alternatives. The Naïve model simplifies the Merton distance-to-default framework by approximating asset volatility using a weighted average of equity volatility and a fixed debt volatility component, thereby avoiding the iterative estimation required in the original Merton model [19]. This simplification enhances practicality and scalability, especially for large panel datasets like the one used in this study, which spans over two decades of Chinese listed firms.

However, the Naïve model may underestimate default risk in cases where firm leverage is highly volatile or during periods of market turbulence, as it relies on historical equity volatility and does not fully capture dynamic changes in asset value. Alternative models, such as the full Merton model, Campbell’s hazard model [20], or machine learning-based approaches, may offer higher predictive accuracy by incorporating a broader set of financial and macroeconomic predictors. Nevertheless, these models often require more data, stronger assumptions, and greater computational resources, which may not be feasible or necessary for all research contexts.

In this study, the Naïve model was selected for its balance between simplicity, transparency, and empirical validity, particularly suited for capturing average treatment effects in a difference-in-differences framework. Its widespread use in prior literature [18] also facilitates comparability of results. Future research could extend our findings by employing alternative credit risk measures to validate the robustness of the conclusions drawn here.

Core Explanatory Variables

The core explanatory variable in this paper is the reform of secured transactions law, which is the interaction term (Did) between the time explanatory variable (Time) and the indicator variable (Treated) that distinguishes between the treatment group and the control group. Among them, the time explanatory variable is bounded by the reform of secured transactions law, that is, the promulgation of the property law in 2007. When the sample is after 2007, it is assigned a value of 1; otherwise, it is 0. The indicator variable for distinguishing between the treatment group and the control group is divided according to the fixed assets ratio. Referring to the research of Qian [16], the average value of the ratio of fixed assets to total assets of the sample enterprises from 2000 to 2006 is calculated and arranged from low to high. Using 33% and 67% as threshold values respectively, it is evenly divided into three equal parts. The data of the 1/3 enterprises with a lower fixed assets ratio are regarded as the treatment group, and the 1/3 with a higher ratio are regarded as the control group.

Control Variables

In order to control the influence of other factors on corporate credit risk, referring to the research of Yu [8] and Cai [9], the following four aspects of control variables are introduced: at the enterprise financial level, they include return on assets (ROA), leverage ratio (Lev), growth rate (Growth) and cash flow (Cashflow); at the corporate governance level, they include board size (Board), independent director ratio (Indep) and CEO duality (Dual); at the company characteristic level, they include firm size (Size) and whether it is audited by the biggest 4 accounting firms (Big4); at the equity structure level, it includes ownership concentration (Top1).

Endogeneity Considerations: The DID design inherently addresses time-invariant unobserved heterogeneity through firm fixed effects [20]. Year fixed effects control for macro shocks common to all firms [21]. The use of a policy shock provides a strong source of exogenous variation. However, a potential threat to identification is if time-varying shocks correlated with the reform also differentially affected treatment and control firms. We address this by: (1) conducting a parallel trend test to validate the DID assumption; (2) performing a placebo test to rule out confounding effects from other events.

The explanations of the above main variables are shown in

Table 1. Explanation of Main Variables.

Variable Nature	Variable Name	Variable Symbol	Variable Explanation
Explained variable	corporate credit risk	EDF	${EDF}_{it}=Normal(-D{D}_{it})$
Core explanatory variable	Indicator variable distinguishing treatment group from control group	Treated	Assign a value of 1 to enterprises whose fixed assets ratio belongs to the last 1/3, and take 0 for the top 1/3
	Time explanatory variable	Time	Take 1 before 2007, otherwise take 0
	the reform of secured transactions law	Treated × Time	Multiply the values of the time explanatory variable and the indicator variable distinguishing the treatment group from control group
Control variables	return on assets	ROA	Net profits/Average balance of total assets
	leverage ratio	Lev	Total liabilities at the end of the year/Total assets at the end of the year
	growth rate	Growth	Increase in operating revenues in the current year/Total operating revenues in the previous year
	cash flow	Cashflow	Net cash flow generated from operating activities/Total assets
	board size	Board	Take the natural logarithm of the number of board members
	independent director ratio	Indep	Number of independent directors/Number of directors
	CEO duality	Dual	Assign a value of 1 if the chairman and the manager are the same person, otherwise take 0
	the biggest 4 accounting firms	Big4	Assign a value of 1 if audited by the Big Four, otherwise take 0
	firm size	Size	Natural logarithm of total assets
	ownership concentration	Top1	Shareholding ratio (%) of the largest shareholder

.

3.2.3. Model Construction

To study the impact of the reform of secured transactions law on corporate credit risk, this paper takes the property law issued in 2007 as a natural experiment and constructs the following Difference-in-Differences model:

$${\mathit{EDF}}_{i,t}=\alpha +{\beta }_1({\mathit{Treated}}_i\times {\mathit{Time}}_t)+{\beta }_2{\mathit{Controls}}_{i,t}+{\mu }_i+{\phi }_t+{\epsilon }_{\mathrm{it}}$$

(4)

In this model, i represents the enterprise, and t represents the year; the explained variable ${EDF}_{it}$ represents corporate credit risk, measured by expected default frequency. ${\mathrm{T}\mathrm{i}\mathrm{m}\mathrm{e}}_{\mathrm{t}}$ is an indicator variable to distinguish treatment group from control group. The top 1/3 enterprises with a larger fixed assets ratio are treatment group, assigned 1, and the bottom 1/3 of control group take 0. ${\mathrm{T}\mathrm{i}\mathrm{m}\mathrm{e}}_{\mathrm{t}}$ is the time explanatory variable, which takes a value of 1 when the sample observation occurs in 2007 and later (after the issuance of the property law), and 0 otherwise. ${\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{t}}$ is the set of control variables, ${\mu }_i$ is the enterprise fixed effect, ${\phi }_t$ is the year fixed effect, and ${\epsilon }_{it}$ is the random error term. The interaction term is the core explanatory variable ${Treated}_i\times {Time}_t$, and the estimated coefficient ${\beta }_1$ represents the net impact of the reform of secured transactions law on corporate credit risk. If the coefficient is positive, it indicates that the reform of secured transactions law will increase expected default frequency (EDF), thereby increasing credit risk; if the coefficient is negative, it means that the promulgation of the property law has reduced expected default frequency (EDF), thus reducing corporate credit risk.

4. Results

4.1. Descriptive Statistics

The descriptive statistics of the main variables are shown in

Table 2. Descriptive Statistics.

Variable	Obs	Mean	Std. Dev.	Min	Max
EDF	11,618	0.672	4.163	0	28.794
Violate	11,125	0.12	0.325	0	1
TP	11,618	0.396	0.489	0	1
ROA	11,618	0.027	0.052	−0.16	0.141
Size	11,618	22.378	1.352	19.943	25.714
Lev	11,618	0.536	0.182	0.157	0.891
Growth	11,618	0.161	0.373	−0.495	1.672
Board	11,618	2.199	0.208	1.609	2.708
Indep	11,618	0.364	0.048	0.286	0.5
Dual	11,618	0.126	0.332	0	1
Big4	11,618	0.086	0.281	0	1
Cashflow	11,618	0.048	0.072	−0.135	0.219
Top1	11,618	0.369	0.154	0.111	0.709

. According to the descriptive statistics of the entire sample, it is known that the mean of the explained variable A-shares expected default frequency (EDF) is 0.672, the standard deviation is 4.163, the minimum value is 0, and the maximum value is 28.794, with a relatively large fluctuation range, thus providing good materials for the study of the reform of secured transactions law.

4.2. Correlation Analysis

In order to prevent the occurrence of multicollinearity problems, which make it difficult to distinguish the mutual influence among variables and reduce the explanatory power of the model, this paper conducts a correlation analysis among different variables, and the results are shown in

Table 3. Test for Collinearity of Main Variables.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)
(1) TP	1.000
(2) ROA	0.032	1.000
(3) Size	0.070	0.122	1.000
(4) Lev	0.040	−0.386	0.291	1.000
(5) Growth	0.002	0.246	0.043	0.032	1.000
(6) Board	−0.170	0.047	0.160	0.073	0.012	1.000
(7) Indep	0.089	−0.019	0.129	0.012	−0.033	−0.382	1.000
(8) Dual	0.08	−0.025	−0.010	−0.019	−0.020	−0.111	0.087	1.000
(9) Big4	−0.068	0.082	0.339	0.015	−0.004	0.115	0.034	−0.025	1.000
(10) Cashflow	−0.231	0.348	0.064	−0.167	0.071	0.088	−0.031	−0.035	0.092	1.000
(11) Top1	−0.131	0.128	0.174	−0.004	0.063	0.019	−0.017	−0.127	0.136	0.076	1.000

. After calculation, the highest absolute value of the Pearson correlation coefficient is 0.386, and all results are less than 0.7, which means that there is no strong correlation between the interaction subparagraphs and control variables in this model, there is no obvious multicollinearity problem, the accuracy of the regression analysis is relatively high, it can support the relevant conclusions of x, and the estimated results are relatively stable.

4.3. Analysis of Benchmark Regression Results

Table 4. The Benchmark Regression Results of the reform of secured transactions law on corporate credit risk.

	(1)	(2)
	b1	b2
Variables	EDF	Violate
TP	−0.283 ***	−0.350 **
	(0.0915)	(0.157)
ROA	−5.101 ***	−4.314 ***
	(0.888)	(0.842)
Size	0.434 ***	−0.251 ***
	(0.0442)	(0.0713)
Lev	3.187 ***	0.721 **
	(0.244)	(0.330)
Growth	−0.263 ***	−0.446 ***
	(0.0846)	(0.103)
Board	−0.235	−0.411
	(0.216)	(0.295)
Indep	−2.629 ***	−1.197
	(0.865)	(1.070)
Dual	0.119	−0.169
	(0.117)	(0.138)
Big4	0.0718	0.745 ***
	(0.191)	(0.200)
Cashflow	−1.529 ***	1.110 *
	(0.543)	(0.577)
Top1	−0.155	−0.0366
	(0.263)	(0.434)
Constant	−8.873 ***
	(1.026)
Observations	11,618	6960
R-squared	0.090
Industry Fe	Yes	Yes
Year Fe	Yes	Yes

Note: Robust standard errors in parentheses: *** p < 0.01, ** p < 0.05, * p < 0.1.

presents the impact effect of the reform of secured transactions law on corporate credit risk. The column of Model (1) lists the regression results of the introduction of the property law in 2007 and the expected default frequency (EDF). After inspection, the coefficient p of the interaction subparagraph is 0.002, less than 0.01, indicating a significant effect, which preliminarily supports the existence of the policy effect. In terms of the sign, the regression coefficient between the interaction subparagraph and expected default frequency (EDF) is negative, showing that the reform of secured transactions law promotes the reduction of expected default frequency (EDF). By dividing the regression coefficient by the mean value of EDF, that is, −0.2825323/0.672, the specific effect magnitude of the introduction of the property law on the default probability can be calculated as −0.4204349702, which means that compared with the control group, the reform of secured transactions law can reduce the corporate credit risk of the treatment group by approximately 42%. The benchmark regression results confirm the improvement effect and verify the hypothesis. The decline effect of corporate credit risk with a lower fixed assets ratio is more significant. This discovery is particularly important for promoting the sustainable development of light-asset and innovative enterprises, because the reduction of credit risk directly alleviates its financing difficulties, enhances financial security, and enables it to have a more stable foundation to plan and implement social responsibility projects related to environment, social, and governance (ESG).

Column of Model (2) presents the regression results of the reform of secured transactions law, where whether an enterprise defaults is represented by the 0–1 variable Violate. The regression coefficient is negative. It can be seen that the promulgation of the property law in 2007 is negatively correlated with whether an enterprise defaults. Combining with the mean value of Violate in Table 2, the improvement effect is 292.67%, which supports the hypothesis.

4.4. Robustness Test

4.4.1. Parallel Trend Test

The parallel trend inspection is a prerequisite for using the Difference-in-Differences method. If the credit risks of the treatment group and the control group showed similar changing trends before the promulgation of the property law in 2007, it indicates that there are no other factors interfering with the changes of the explained variable, and the inspection is satisfied. Considering that the involved time period is relatively long, the data of 4 years before and 6 years after the policy node are intercepted here, and the data after 7 years are inspected on a consolidated basis. The parallel trends before and after the reform of secured transactions law are shown in

Table 5. The Results of Parallel Trend Test.

Variables	EDF
pre_4	−0.0296
	(0.115)
pre_3	−0.122
	(0.177)
pre_2	−0.0920
	(0.427)
current	0.0806
	(0.0509)
post_1	0.167
	(0.104)
post_2	−2.266
	(0.587)
post_3	−0.0543
	(0.173)
post_4	−1.041
	(0.361)
post_5	−1.828
	(0.510)
post_6	−0.857
	(0.300)
post_7	−0.0118
	(0.115)
Constant	0.842
	(0.0610)
Observations	11,618
R-squared	0.045
Industry Fe	Yes
Year Fe	Yes

Note: “Current” represents the initial implementation time point of the policy; “Controls” represents control variables; the same applies to the following tables.

and Figure 1. This paper takes the year before the policy implementation as the base period, so the data of pre_1 are not reflected in the parallel trend inspection results. Before the policy implementation, the coefficient of the interaction subparagraph fluctuated around the zero axis, indicating that there is no systematic difference between the two groups, and the changes in their credit risks tended to be consistent before the policy implementation. After the policy implementation, the coefficient deviated significantly from the zero axis and was negative, which is consistent with the previous benchmark regression results, indicating that the reform of secured transactions law has a significant impact on the corporate credit risk and has a reducing effect. Therefore, the relevant data has passed the parallel trend inspection and is suitable for assessment using DID.

4.4.2. Placebo Inspection

In order to avoid false effects, to acknowledge that the reduction of corporate credit risk indeed stems from reform of secured transactions law, and to exclude the influence of other unobserved factors, such as the increased emphasis of enterprises on their own risk control, this paper randomly selects enterprises from the control group, that is, conducts a placebo inspection through 500 times of random sampling from the 1/3 of enterprises with a lower fixed assets ratio. After extracting the same amount of data from the control group as that of the original treatment group, the same regression analysis as that of the original treatment group is carried out, and the DID model is re-run. If the coefficient of the cross-multiplication term obtained by way of fabricating the treatment group deviates from the real coefficient, it indicates that the results of the benchmark regression are credible; if they match, it indicates that the model may have mis-specification or omitted variables, and the results of the benchmark regression are invalid.

The Inspection results are shown in Figure 2. There is a significant difference between the virtual coefficients formed by 500 random samplings and the real coefficients mentioned above. The fitting coefficients are between −0.15 and 0.15, showing a normal distribution on both sides of 0, indicating that the benchmark regression results of the original treatment group and control group are valid, and the impact of the promulgation of the property law in 2007 on corporate credit risk is not caused by other factors.

4.5. Heterogeneity Analysis

4.5.1. Heterogeneity Analysis Based on Enterprises’ Risk Preference

Considering that corporate credit risk may be affected by their own risk preference, this paper uses the method of grouped regression, calculates the median of enterprises’ risk preference, sets the median variable, assigns a value of 1 to enterprises greater than the median, and 0 otherwise. The following

Table 6. Results of Heterogeneity Analysis 1.

	(1)	(2)
	r1	r2
Variables	EDF	EDF
TP	−0.125	−0.325 **
	(0.115)	(0.150)
ROA	−5.409 ***	−3.915 **
	(1.101)	(1.557)
Size	0.302 ***	0.582 ***
	(0.0502)	(0.0747)
Lev	2.463 ***	4.153 ***
	(0.277)	(0.462)
Growth	−0.174 *	−0.355 **
	(0.0937)	(0.163)
Board	0.124	−0.825 *
	(0.229)	(0.433)
Indep	−0.549	−5.301 ***
	(1.091)	(1.422)
Dual	0.128	0.124
	(0.137)	(0.210)
Big4	−0.0277	0.425
	(0.200)	(0.365)
Cashflow	−0.256	−3.878 ***
	(0.569)	(1.125)
Top1	−0.431	0.384
	(0.289)	(0.528)
Constant	−7.210 ***	−10.41 ***
	(1.266)	(1.686)
Observations	6994	4623
R-squared	0.068	0.122
Industry Fe	Yes	Yes
Year Fe	Yes	Yes

Note: Robust standard errors in parentheses: *** p < 0.01, ** p < 0.05, * p < 0.1.

respectively show the coefficients of the interaction terms of enterprises with high risk preference and low risk preference. It can be found that the coefficients of enterprises with low risk preference are more significant, indicating that the reform of secured transactions law has significant inter-group differences in the impact on corporate credit risk, and has a stronger effect on alleviating the credit risk of enterprises with low risk preference. The reason is that the promulgation of the property law has expanded the scope of security property and optimized the procedures of the security interest system, providing more sufficient financing tools for enterprises willing to reduce corporate credit risk and effectively reducing their default risk. After such enterprises obtain a more stable financing environment and reduced credit risk, they are more likely to invest the saved resources and management efforts into social responsibility practices such as improving operations efficiency, strengthening employees’ guarantees, and enhancing environmental performance to achieve more stable sustainable development.

Economic and Practical Implications: Our benchmark regression results in Table 3 indicate that the 2007 Property Law significantly reduced corporate credit risk, with the expected default frequency (EDF) declining by approximately 42% for firms in the treatment group relative to the control group. Beyond its statistical significance, this magnitude carries important economic and managerial implications.

First, from a policy perspective, a 42% reduction in credit risk demonstrates that institutional reforms expanding collateral access can meaningfully enhance financial stability. Asset-light firms, which were historically constrained in accessing loans, benefited disproportionately from the reform, highlighting its role in equalizing credit opportunities across heterogeneous firms. Policymakers can leverage this insight when designing future collateral frameworks under the Civil Code, ensuring that regulatory innovation aligns with the financing needs of firms with different asset structures.

Second, from a managerial perspective, lower credit risk substantially reduces financing costs and expands firms’ ability to allocate capital toward long-term investments. In particular, asset-light firms can redirect freed-up financial resources into corporate social responsibility (CSR) initiatives, sustainable technology adoption, and innovation activities, which were previously deprioritized under severe credit constraints. This aligns with the broader goal of creating sustainable financial ecosystems, where legal reforms act as enablers for firms to integrate environmental and social objectives into their strategic planning.

4.5.2. Heterogeneity Analysis Based on Corporate Life Cycle

In order to explore whether the credit risk of enterprises at different life cycles responds to the reform of secured transactions law to different extents, this paper divides all enterprises into maturity phase, growth phase, and decline phase, as shown in the following

Table 7. Results of Heterogeneity Analysis 2.

	(1)	(2)	(3)
	l1	l2	l3
Variables	EDF	EDF	EDF
TP	−0.342	−0.299 **	−0.174
	(0.210)	(0.150)	(0.152)
ROA	−10.33 ***	−4.070 ***	−3.878 **
	(2.318)	(1.436)	(1.505)
Size	0.409 ***	0.384 ***	0.499 ***
	(0.0992)	(0.0745)	(0.0714)
Lev	3.371 ***	2.964 ***	3.690 ***
	(0.544)	(0.433)	(0.419)
Growth	−0.307 **	−0.239	−0.365 *
	(0.154)	(0.181)	(0.211)
Board	−0.785	−0.284	−0.0849
	(0.527)	(0.357)	(0.380)
Indep	−2.733	−2.467 *	−3.280 **
	(2.090)	(1.471)	(1.336)
Dual	−0.182	−0.148	0.491 **
	(0.242)	(0.155)	(0.212)
Big4	0.539	−0.160	0.299
	(0.441)	(0.281)	(0.357)
Cashflow	−0.644	−2.224 **	−1.343
	(1.226)	(0.943)	(0.959)
Top1	0.543	0.486	−1.017 **
	(0.606)	(0.425)	(0.465)
Constant	−7.018 ***	−7.850 ***	−10.50 ***
	(2.210)	(1.729)	(1.738)
Observations	2696	3822	4374
R-squared	0.116	0.092	0.104
Industry Fe	Yes	Yes	Yes
Year Fe	Yes	Yes	Yes

Note: Robust standard errors in parentheses: *** p < 0.01, ** p < 0.05, * p < 0.1.

. It can be found that the regression coefficients of enterprises in the growth phase are relatively significant, while those in the maturity phase and decline phase are not. This is because enterprises in the growth phase are in a rapid expansion stage, and their credit risk is generally high. The reform of secured transactions law can help them stabilize their capital chain and reduce credit risk. The significant reduction of credit risk is particularly crucial for growth-phase enterprises, which not only ensures their survival and development but also creates conditions for them to embed the concepts of social responsibility and sustainable management during the rapid expansion period and contributes to shaping their long-term sustainable competitive advantages. In contrast, enterprises in the maturity phase have previously accumulated considerable property, plant and equipment that can be used for mortgage. Coupled with a relatively mature business model and a sound risk control system, the promulgation of the property law has relatively little impact on them. As for enterprises in the decline phase, their management systems are relatively rigid, they respond slowly to policies, and the legal system has little effect on improving their business conditions.

Overall, the robustness tests, including parallel trend analysis and placebo tests, confirm the reliability of the Difference-in-Differences estimates and demonstrate that the observed effects are not driven by spurious trends or external shocks such as the 2008 global financial crisis. This strengthens confidence in the causal interpretation of the 2007 Property Law’s effect on credit risk reduction, suggesting that the reform has a persistent and generalizable impact on corporate financing dynamics.

Furthermore, the heterogeneity analyses offer deeper insights into which firms benefit most from the reform and how this shapes their capacity for sustainable development:

Risk Preference Heterogeneity: Firms with lower risk tolerance experience a larger reduction in credit risk. These firms are more likely to adopt conservative financial strategies post-reform, thereby channeling savings into CSR activities rather than speculative investments.

Life Cycle Heterogeneity: Firms in the growth stage show the greatest responsiveness to the reform due to their higher dependence on external financing and previously limited collateral availability. These firms exhibit stronger incentives to invest in sustainable business practices when financing constraints are eased.

Together, these results suggest that the security interest reform acts as a catalyst for enabling CSR-oriented strategies and accelerating firms’ transition toward sustainable development. By reducing financing frictions, the reform empowers firms—especially those in high-growth and asset-light contexts—to adopt forward-looking strategies that integrate financial resilience, environmental responsibility, and social impact.

5. Discussion

5.1. Interpreting Empirical Findings in Light of the Hypotheses

Our empirical results reveal that the 2007 Property Law reform substantially reduced corporate credit risk, with the expected default frequency (EDF) declining by approximately 42% for firms in the treatment group relative to the control group. Importantly, this effect is heterogeneous across firm characteristics: it is most pronounced among firms with low fixed-asset ratios, conservative risk preferences, and those in the growth stage of their life cycle. These findings offer strong support for H1, H2, and H3 and validate the causal pathways proposed in our conceptual framework.

From a theoretical perspective, these results provide robust empirical evidence for the credit constraint theory [22], which posits that firms with fewer tangible assets face more severe financing frictions due to lenders’ limited ability to assess repayment capacity. Before the reform, asset-light enterprises often lacked sufficient collateral to secure bank credit, leading to restricted liquidity and higher default risks. By expanding the range of eligible collateral to include movable assets such as accounts receivable, inventory, and future claims, the Property Law reduced the asymmetry between firms’ financing needs and lenders’ risk assessments. As a result, enterprises—particularly those in innovative and fast-growing sectors—were able to access lower-cost financing and stabilize their cash flows, thus achieving a measurable reduction in credit risk.

Moreover, our heterogeneity analysis aligns closely with prospect theory [23], which explains variations in firms’ behavioral responses to institutional reforms. Risk-averse firms appear to respond more effectively to the reform, leveraging the expanded collateral framework to stabilize financing structures and improve long-term strategic positioning. In contrast, risk-seeking firms, although benefiting from increased borrowing capacity, appear less committed to using these gains for risk mitigation, highlighting an important boundary condition for the reform’s effectiveness.

Additionally, the findings indicate that growth-stage firms derive the most significant benefits. These firms, which are characterized by high capital intensity and strong dependence on external financing, faced substantial funding constraints prior to the reform. By enabling them to mobilize underutilized movable assets as collateral, the reform effectively bridged financing gaps and improved their resilience against adverse economic shocks. Collectively, these results underscore the broader theoretical insight that institutional design directly reshapes firms’ financing dynamics, alters their risk profiles, and enhances overall business stability.

5.2. Credit Risk Mitigation, CSR Investment, and Sustainable Development

One of the central contributions of this study lies in demonstrating the linkage between credit risk reduction, CSR investment capacity, and sustainable development. Our results indicate that firms experiencing lower financing constraints and reduced default risk are better positioned to reallocate freed-up financial resources toward long-term strategic objectives, especially those aligned with environmental, social, and governance (ESG) priorities.

First, relief from financing constraints enables firms to shift from short-term “survival-driven” strategies toward sustainability-oriented investments. With reduced dependence on maintaining large liquidity buffers, firms are able to channel additional resources into projects such as green technology development, employee upskilling, community engagement, and responsible supply chain governance. For instance, asset-light technology enterprises now have greater ability to leverage data assets and intellectual property to secure financing, thereby accelerating investments in green R&D and responsible AI governance. Similarly, environmental firms benefit from pledging carbon emission rights to obtain liquidity, enabling faster deployment of clean energy technologies and promoting low-carbon business models.

Second, reduced credit risk enhances firms’ ability to build corporate resilience. Our findings suggest that firms facing lower default probabilities are better equipped to engage in systematic ESG strategy planning, integrating CSR objectives into core business processes rather than treating them as compliance obligations. This shift leads to improved stakeholder trust, reputational capital, and long-term value creation, which ultimately reinforce financial sustainability.

Third, these micro-level improvements in CSR performance contribute to broader systemic outcomes. As more firms allocate resources toward sustainability-focused initiatives, collective effects emerge at the ecosystem level, fostering healthier supply chains and more resilient industrial networks. For example, the Property Law’s expansion of collateralized receivables strengthens supply chain finance mechanisms, improving liquidity for upstream SMEs. By alleviating financing pressures throughout the chain, firms are better able to adopt responsible sourcing practices, safeguard workers’ rights, and reduce environmental footprints, collectively driving a sustainable business ecosystem.

Consistent with our findings, prior evidence shows that firms with stronger CSR performance face lower financial distress risk, thereby improving long-term creditworthiness [20]. Thus, integrating empirical evidence with our theoretical model demonstrates that the Property Law reform is not merely a legal innovation. Instead, it operates as a strategic institutional lever that indirectly enhances CSR capacity and accelerates the transition toward sustainable economic development.

5.3. Policy Implications and Recommendations

Our results hold critical implications for regulators and policymakers seeking to strengthen China’s secured transactions framework while enabling CSR-driven sustainable growth. We propose three priority areas for reform, aligned with our empirical findings:

5.3.1. Expand the Collateral Base to Empower Asset-Light and Green Enterprises

The Property Law reform’s inclusion of movable assets significantly improved credit access for resource-constrained firms. However, emerging business models increasingly rely on intangible and green assets, such as data resources, digital intellectual property, and carbon emission rights, which are not yet fully recognized under existing frameworks. To unlock financing potential for innovative and environmentally responsible firms [24], future legislation should:

Establish clear ownership and valuation standards for digital and green assets [25]; Enable efficient pledging of data and carbon credits to provide liquidity to SMEs; Promote legal alignment between financing innovations and ESG objectives.

5.3.2. Establish a Unified Registration and Transparency System

Our findings demonstrate that reducing information asymmetry significantly lowers default risk. Policymakers should accelerate the creation of a centralized registration platform covering both typical and atypical security interests [26]. A unified system would:

Enable lenders to evaluate firms’ total debt exposure and collateral quality; Reduce transaction costs and pricing inefficiencies in credit markets; Enhance transparency for ESG-related disclosures, improving investor confidence.

5.3.3. Integrate Sustainability into Financial Regulation

The Property Law’s reform reduced firms’ financing costs, but policy incentives are needed to ensure that freed-up resources are directed toward sustainable priorities. We recommend:

Expanding green credit programs and sustainability-linked loans; Offering tax incentives for CSR-related investments and ESG disclosures; Establishing preferential interest rates for environmentally responsible firms [27].

Together, these policy measures would align institutional innovations with national sustainability goals, enabling a transition from risk mitigation to proactive ESG investment strategies.

5.4. Theoretical Contributions

This study makes three key theoretical contributions:

(1)

Advancing Institutional Finance Theory: By demonstrating how institutional reforms reshape firms’ financing dynamics and risk exposure, we bridge the gap between law and finance in emerging economies, providing new insights into the causal role of legal frameworks in shaping credit markets.

(2)

Uncovering the CSR–Credit Risk Mechanism: Prior research confirms that CSR investments are rewarded by higher credit ratings and reduced financing costs [28]. Our findings reveal a previously underexplored pathway: by alleviating financing constraints and lowering credit risk, institutional reforms indirectly enhance firms’ capacity to invest in CSR initiatives, thereby supporting broader sustainability transitions.

(3)

Explaining Heterogeneous Effects of Legal Reforms: By identifying heterogeneity across asset structures, risk preferences, and life-cycle stages, we provide a nuanced understanding of why reforms disproportionately benefit asset-light, risk-averse, and growth-oriented firms.

6. Conclusions

This study provides robust empirical evidence that China’s reform of secured transactions law, particularly through the 2007 Property Law, has significantly reduced corporate credit risk, with pronounced effects for firms characterized by low fixed-asset ratios, prudent risk preferences, and growth-stage status. By expanding the scope of eligible collateral to include movable assets such as accounts receivable and inventory, the reform alleviated financing constraints, optimized debt structures, and enhanced operational stability for a substantial segment of enterprises.

More importantly, our findings reveal that the reduction in credit risk is not merely a financial outcome; it serves as a critical mechanism enabling corporate social responsibility (CSR) and sustainable development. Freed from short-term financial pressures, firms can reallocate resources toward long-term environmental, social, and governance (ESG) investments—such as green innovation, employee welfare, and supply chain responsibility—thus contributing to a sustainable economic cycle.

From a policy perspective, this study offers several actionable recommendations:

For Legislators: Further broaden the definition of collateral in subsequent judicial interpretations or legislative amendments to explicitly include intangible assets (e.g., data rights, carbon emission quotas) and strengthen the legal certainty of atypical security arrangements.

For Financial Regulators: Promote the integration and standardization of movable collateral registration systems to enhance transparency, reduce information asymmetry, and facilitate lending based on future cash flows and innovative assets.

For Enterprises: Proactively utilize the expanded toolbox of security interests to improve credit profiles and strategically channel efficiency gains from lower financing costs into CSR and sustainability initiatives.

This study has certain limitations. The primary empirical analysis relies on data from 2000 to 2020, predating the full implementation of the Civil Code, and is therefore unable to capture its longer-term effects. Moreover, macroeconomic disruptions such as the COVID-19 pandemic and China’s ongoing structural transition may influence the generalizability of the results in more recent contexts.

Future research should extend the analysis into the Civil Code era once sufficient data becomes available, examining its differential effects across sectors and regions. Additional studies could also explore micro-level channels through which credit risk reduction translates into tangible CSR outcomes, as well as the role of emerging financing models—such as green credit, fintech-based security interests, and supply chain finance—in promoting sustainable corporate behavior.

In summary, this research underscores the vital role of legal institutions in shaping not only financial markets but also corporate conduct toward broader societal goals. Continuous refinement of China’s security interest system, informed by empirical evidence and aligned with sustainability objectives, will remain essential for supporting high-quality and responsible economic development.