Can Mandatory Disclosure of CSR Information Drive the Transformation of Firms towards High-Quality Development?

Abstract

This paper establishes a quasi-natural experiment grounded in the exogenous shock occasioned by the implementation of a compulsory corporate social responsibility (CSR) information disclosure policy. It investigates the ramifications of this mandated CSR information disclosure policy on firms’ total factor productivity (TFP) through the integration of the difference-in-differences (DID) methodology. The investigation reveals that obligatory disclosure of CSR information significantly augments firms’ total factor productivity (TFP) by mitigating agency conflicts and financial constraints. Further analysis elucidates investment efficiency and innovation enhancement as pivotal conduits through which the mandatory CSR information disclosure policy fosters firms’ TFP. The study explores the impact of mandatory CSR information disclosure on firms’ TFP mechanism, which has significant policy value and can provide useful reference for the high-standard development of China’s corporate economic transformation.

1. Introduction

China has sustained a period of swift economic expansion over numerous decades, leveraging abundant resources and cost-effective inputs. However, such a crude approach to economic growth has also led to issues like excessive resource consumption and environmental degradation. Concurrently, as economic magnitude expands and developmental impediments surface, the perpetuation of high-speed economic growth solely through a GDP-centric production paradigm becomes untenable. The deceleration in overall economic growth primarily stems from lackluster industrial productivity. Therefore, the Communist Party of China (CPC) clearly proposed at the 20th People’s Congress to accelerate the development of a modernized economic system and strive to enhance TFP. Firms, as the main micro-body of economic operation, play a pivotal role in economic transformation and change. Therefore, relying on boosting a firm’s productivity to promote high-quality economic development and transformation is undoubtedly an important way to achieve China’s strategic goal of sustainable development. Given this background, the search for rational policies to promote firms’ TFP has emerged as a paramount consideration amidst China’s ongoing economic transformation as the world’s second-largest economy.

Owing to escalating global apprehensions regarding economic and environmental sustainability, there exists a burgeoning inclination among nations to enforce mandatory disclosure of CSR activities by firms. Over the past two decades, Chinese officials have instituted policies pertaining to the disclosure of CSR endeavors aimed at propelling firms towards high-quality development and transformation. Diverging from the voluntary disclosure norms prevalent in Europe and the United States, China adopted a mandatory disclosure framework for select firms as early as 2008. Specifically, in December 2008, the Chinese government promulgated the “Notice on Improving the 2008 Annual Reports of Listed Firms” (hereinafter referred to as the “Notice”), which mandated that over 20% of listed firms furnish their CSR reports alongside their annual reports and delineated explicit standards and contents for CSR report disclosure. The content of the disclosure is closely related to the sustainable development of the firm; in particular, the Notice requires the regulated firm to make qualitative or quantitative disclosures of information from ten aspects, such as shareholder relations, creditor relations, employee relations, supplier relations, customer relations, environmental protection, public relations, philanthropy, CSR policy, working conditions and inadequate performance of CSR, etc., and the more detailed the disclosure of the content, the higher the quality of the firm’s CSR information. In addition, prior to the issuance of the Notice, a mere fraction (less than 3%) of domestically listed firms voluntarily divulged information pertaining to CSR activities [1]. Thus, the implementation of this policy marks the formal inception of CSR disclosure practices in China [2].

As a comprehensive indicator of the production efficiency of firms, the improvement of TFP usually comes from the enhanced technological levels and resource allocation of firms [3]. A large number of studies based on the Notice have pointed out that mandatory disclosure of CSR information usually interferes with resource reallocation, capital investment, and technological innovation in the production process of firms [2,4]. For example, it has been pointed out that CSR expenditure and green transformation undertaken by firms to meet mandatory CSR disclosure requirements will undoubtedly increase their short-term costs and affect their short-term profitability [2]. However, some scholars have also pointed out that the implementation of the Notice may help to improve firms’ external financing constraints and principal–agent conflicts and thus stimulate their R&D and innovation [4]. Therefore, from the point of view of a series of economic consequences triggered by the Notice, it is inevitable that the intervention of mandatory CSR disclosure on firms’ production, investment, and other activities will further affect firms’ TFP, which is of great significance for the high-quality development of macroeconomics. Regrettably, there is a dearth of literature examining the correlation between mandatory CSR disclosure and TFP. Moreover, relying solely on extant literature analyses, it remains inconclusive whether the Notice has a favorable effect on corporate TFP. Therefore, as a crucial instrument for the government to try to promote changes in firms’ development, is the implementation of the Notice beneficial for a firm’s productivity? What are the mechanisms behind its effects? These questions concern the economic transformation of China, the largest developing country, and therefore need to be studied urgently. Drawing upon this premise, the present study formulates a quasi-natural experiment leveraging the exogenous shock resulting from the issuance of the Notice in 2008. It employs the difference-in-differences (DID) methodology to juxtapose alterations in TFP between regulated and non-disclosing firms prior to and following the policy’s publication. This framework serves to examine the influence of mandatory CSR disclosure on the advancement of firms’ high-quality development.

Compared to previous studies, this paper may make marginal contributions in the following three areas: First, although the disclosure of CSR reports is a growing trend worldwide, previous studies on the economic consequences of corporate CSR disclosure have mainly examined data from developed countries [5]. And studies have shown that the implementation of the same CSR policy in different countries may have diametrically opposite economic consequences due to reasons such as economic systems and cultural backgrounds [6,7]. As the largest developing country, China’s unique socialist system has led its economy to take a very different path from that of Western countries. Consequently, investigating the influence of the Notice on the TFP of Chinese firms can contribute to a deeper comprehension of the economic ramifications associated with CSR disclosure within emerging economies. Second, previous studies on TFP by Chinese scholars have mostly been conducted from a macro perspective, such as industry and region [8]. This paper, on the other hand, starts from the micro level, and by investigating the impact of mandatory CSR disclosure on corporate TFP and the role of the logic behind it, it can more purposefully provide empirical references and theoretical foundations for future policy formulation and implementation regarding the transformation of the high-quality development of firms. Third, although the existing literature has examined the implementation of the Notice on corporate surplus management [9], audit efficiency [10], tax avoidance [11], investment efficiency [12], and environmental performance [2], unfortunately, scholars have not yet reached a more consistent conclusion on whether mandatory CSR disclosure is ultimately beneficial to the sustainable and high-quality development of firms [13,14]. Therefore, based on existing studies, this paper provides direct empirical evidence on whether mandatory CSR disclosure improves the development quality of firms by clarifying the impact of the Notice on the TFP of firms.

2. Review of the Literature

2.1. Economic Consequences of CSR Disclosure

Considering the early stage of CSR practices in China, initial research on CSR disclosure primarily drew upon corporate data sourced from developed economies. A multitude of Western scholars have undertaken empirical inquiries into the influence of CSR disclosure on corporate progress, often focusing on information asymmetry as a key perspective. For instance, Dhaliwal et al. identified that CSR information serves as a valuable complement to firms’ financial data, facilitating a more precise assessment of a firm’s current financial standing and future growth potential [15]. Similarly, Christensen et al.’s research underscores that the disclosure of CSR information not only effectively addresses information asymmetry issues but also sheds light on the ethical conduct of corporate executives, thereby mitigating conflicts between firms and stakeholders and fostering the future progress of the organization [16]. Moreover, Escamilla-Solano et al. argued that there is a consensus in the academic community on the improvements brought about by the European Directive in terms of the disclosure of CSR actions by companies and the reduction in information asymmetry between the company and its stakeholders [17]. It is worth noting that in developed economies, most CSR information is voluntarily disclosed by firms [18], with managers being inclined to disclose such information for specific strategic reasons. For example, good firms have an incentive to demonstrate good CSR performance to the outside world by voluntarily disclosing more CSR information in order to enhance their reputation. And the good reputation that comes with a well-disclosed CSR report will attract more media coverage, which in turn boosts corporate financial performance [19], while poorly managed firms may also distract investors by disclosing friendly CSR information to hide management’s self-interested activities [1]. Therefore, studies that rely on the voluntary disclosure of CSR information may face a notable endogeneity problem. In order to effectively address this problem, certain scholars have leveraged the exogenous shock stemming from the issuance of China’s 2008 Notice. They have scrutinized the ensuing economic ramifications of mandatory CSR disclosure by employing the DID approach.

Although numerous scholarly inquiries have affirmed that mandatory CSR disclosure augments the transparency of corporate information, there persists controversy regarding whether the policy positively influences the operations and progress of firms. On the one hand, certain scholars contend that mandatory CSR disclosure can ameliorate principal–agent conflicts and external financing circumstances for firms by mitigating the information asymmetry issue, thereby enhancing investment efficiency and fostering innovation capabilities [4,12].On the other hand, it has been argued by some scholars that mandatory CSR disclosure may have a “masking effect” when the managerial monitoring mechanism is not perfect, which may potentially increase the risk of stock price collapse of listed firms [20]. In addition, based on the market pressure theory, some scholars believe that the implementation of the Notice will force firms to invest excessive resources in CSR activities, which will harm the economic performance of the firms [2]. Not only that, Liang and Chen emphasized that the mandatory CSR disclosure will exacerbate firms’ financial constraints by a plausible channel of controlling shareholder expropriation [21].

2.2. Mandatory CSR Disclosure and Corporate Total Factor Productivity

Compared to a firm’s short-term economic performance, TFP is a long-term indicator that better reflects the firm’s scientific and technological progress and prospects for sustainable development. If the implementation of the Notice can effectively improve the TFP of firms, then mandatory CSR disclosure can be expected to have a positive impact on the high-quality development of the economy. From previous research, scholars have found that macro factors such as the economic environment, market institutions, and local regulations all have some influence on the TFP of firms, but in the end, the TFP of firms mainly depends on their own innovation ability and capital allocation efficiency [3]. In the research of CSR-related fields, mandatory CSR disclosure is usually considered to be able to ameliorate the external financing constraints and principal–agent problems of firms to some degree, which undoubtedly has a positive impact on activities such as investment and innovation [22]. In other words, viewed through the lens of financing constraints and agency costs, the enforcement of the Notice can notably stimulate the TFP of firms. Nevertheless, in the short term, the Notice is bound to induce operational challenges for firms, as it facilitates the transition towards green transformations. As firms are forced by market and environmental regulatory pressures to increase green transformation expenditures, the resources needed for their daily business activities will inevitably be squeezed, which may affect the productivity of the firms [2]. Therefore, based on existing studies for analysis, the implementation of the Notice may have positive and negative effects on a firm’s TFP. Building upon the extant literature, this paper endeavors to offer novel empirical insights into the impact of mandatory CSR disclosure on the quality advancement of firms in China. It achieves this objective by scrutinizing the influence of the Notice on firms’ TFP and dissecting the underlying mechanisms of its effects.

3. Theoretical Analysis and Hypothesis

Financing constraints and agency conflicts are usually regarded as important factors that hinder the development of firms and inhibit their productivity, so a large number of empirical studies have found that improving the external financing conditions of firms and alleviating agency conflicts between firms and stakeholders is a powerful means of promoting the TFP of firms [3]. On the one hand, alleviating financing constraints helps to optimize the layout of firms in terms of business strategy and other aspects and can promote firms’ investment in technological transformations. For example, the study by Zhang et al. found that the reduction of financing constraints can effectively improve the level of risk-taking of firms and encourage firms to become involved in the conduct of research and development and innovation [23]. Additionally, technological advances undoubtedly contribute to the improvement of a firm’s productivity. It has also been pointed out that easing financing constraints can help firms to improve their investment efficiency and resource allocation. This is because, compared with firms with financing difficulties, firms with sufficient capital are more likely to seize the fleeting good investment opportunities, making their investment decisions more timely and effective [12]. In addition, the easing of financing constraints can also assist firms in achieving greater factor productivity by expanding their production scale, thus ensuring a higher production efficiency. On the other hand, in the real economy, agency conflicts and information asymmetry problems are usually considered as the most prevalent frictions, which may not only cause firms to deviate from the optimal level of investment [12] but also create a disincentive for firms’ R&D and innovation activities [24], resulting in lower firm productivity. For instance, Biddle et al.’s research underscores that the agency conflict arising from information asymmetry might result in firms overlooking numerous projects with a positive net present value [25], consequently diminishing their investment efficiency. Furthermore, in instances of informational opacity, managers may leverage their informational edge to pursue risk-averse strategies that prioritize their own interests over those of shareholders [1]. This invariably curtails the R&D as well as innovation endeavors by firms with high-risk profiles, consequently dampening their overall innovation capabilities. Therefore, augmenting the information transparency of firms and mitigating corporate agency conflicts represent pivotal strategies for enhancing firms’ TFP.

And from the point of view of existing research, a CSR report serves as a crucial channel for external stakeholders to collect firm-related information. This not only helps to alleviate the problem of agency conflict but also conveys the firm’s social responsibility to the outside world, which in turn brings social trust capital to the firm [26]. As illustrated by Costa et al., disclosure of CSR information forces organizations to engage with the surrounding environment, making it difficult for firms to conceal negative information [27]. This enhanced transparency ultimately contributes to enhancing firms’ operational efficiency. Furthermore, the act of disclosing CSR information as a means to communicate goodwill and environmental responsibility to the public not only facilitates the broadening of firms’ avenues for financing but also serves to lower financing expenses [15]. Therefore, based on principal–agent theory and signaling theory, we expect that the implementation of the Notice can help improve the productivity of firms and promote their TFP. However, some studies have also pointed out that mandatory CSR disclosure may cause direct or indirect non-operational costs, which in turn could negatively affect the stock price level and economic performance of firms. For example, Chen et al. found that the CSR performance pressure on firms caused by mandatory disclosure of CSR information may force firms to excessively increase their expenditures on CSR-related activities, leading to a sharp increase in their short-term operating costs, which in turn is detrimental to their economic performance [2]. Similarly, Gupta and Chakradhar found that the stock price of regulated firms decreases with the implementation of mandatory CSR policies [6,28]. In turn, the decrease in stock price level and economic performance may negatively affect the firm’s cost of equity, financing efficiency, and therefore discourage activities such as R&D and investment, which may ultimately act as a disincentive to firm productivity. Therefore, in terms of the harmful effects of disclosure requirements on firms’ operating costs, implementing the Notice may also reduce firms’ TFP. In summary, the implementation of the Notice may have two opposite effects on firms’ TFP. Therefore, this paper proposes competing hypotheses H1a and H1b:

H1a. 

The compulsory CSR disclosure policy can significantly increase the TFP of firms that are subject to regulatory disclosure compared to non-disclosing firms.

H1b. 

The compulsory CSR disclosure policy can significantly reduce the TFP of firms that are subject to regulatory disclosure compared to non-disclosing firms.

4. Research Design

4.1. Sample Selection

Considering the effectiveness of the exogenous shock of the release of the Notice in 2008, the initial sample of this paper includes all A-share listed companies from 2006 to 2013. First, we exclude non-regularly traded listed firms (including ST, ST*, and PT). In addition, to mitigate the endogeneity problem, we also exclude firms which voluntarily disclose CSR information during the period and test only regularly disclosing firms (experimental group) and non-disclosing firms (control group). Finally, we also exclude the sample of firms with missing relevant financial data. In total, we obtain a panel data set consisting of 7963 firm-year observations, of which 1750 belong to the experimental group and 6213 to the control group. In addition, to address the issue of non-random sample selection, this paper employs the propensity score matching (PSM) method to screen the sample. Specifically, following Chen et al. and Monica [2,9], this paper matches the experimental group with the control group in a put-back nearest-neighbor match in a ratio of at most 1:3 based on the firms’ data in the pre-notification period (2006–2008). Among these, we selected firms’ market value (Mv), operating profit growth rate (Growth), return on net assets (Roe), gearing ratio (Lev), and cash holdings (Cash) as covariates for the match and set the caliper to 0.25. Finally, after ensuring that the deviations in any given covariate between the experimental group and the control group are less than 10%, we obtain a total of 4909 covariates. This gives us a panel dataset consisting of a total of 4909 firm-year observations, which are used for robustness tests.

4.2. Model Design

Referring to existing studies [4,12], this paper investigates the impact of a compulsory CSR disclosure policy on the TFP of firms by constructing a DID model:

$${TFP}_{i,t+1}={\beta }_0+{\beta }_1{Treated}_i\times {Post}_t+{\theta }^{\prime }{X}_{i,t}+{\alpha }_i+{\gamma }_t+{\epsilon }_{i,t+1}$$

(1)

In Equation (1), $i$ and $t$ denote firm and year, respectively. ${\alpha }_i$ and ${\gamma }_t$ denote individual and time fixed effects, respectively, which are used to control for the impact of omitted firm characteristics and time trends that do not vary over time but are unobserved. Given that the effect of the announcement on firms’ TFP may be time-lagged, we use TFP in period t + 1 in Equation (1) for the dependent variable. ${Treated}_i$ is a grouping dummy variable, where firms in the experimental group take the value of 1, otherwise 0. ${Post}_t$ is a time dummy variable, where the years 2009 and later take the value of 1, otherwise 0. $X_{i,t}$ is a set of control variables related to TFP, and ${\epsilon }_{i,t+1}$ is the error term. Of interest in the regression results of Equation (1) is the coefficient ${\beta }_1$ on ${Treated}_i\times {Post}_t$, which examines the change in TFP of firms in the experimental group relative to those in the control group before and after the implementation of the Notice.

4.3. Variable Measure and Data Source

4.3.1. Measuring Total Factor Productivity (TFP)

Most commonly used TFP measurement methods are constructed on the basis of the Cobb–Douglas production function, and the OLS method, fixed-effects method, OP method, and the LP method are all derived from this basis and are all widely used. Among them, the fixed-effects method is an improvement on the OLS method, solving the endogeneity problem to some extent by further decomposing the residuals in the OLS regression. The LP method is an improvement over the OP method that uses intermediate inputs as investment proxies, overcoming the limitation of the OP method which requires investment by firms to be positive, and can minimize the loss of sample size [29,30]. In this study, total factor productivity TFP_F and TFP_L, assessed using the fixed effects method and the LP method, respectively, are selected to be the dependent variables.

4.3.2. List of Firms That Disclose CSR Reports

The list of firms that are forced to disclose CSR reports is defined in accordance with the corresponding normative disclosure firms in the 2008 Notice, including listed firms in the “Corporate Governance Category”, firms issuing foreign shares listed abroad, financial firms, and all firms in the “SZSE 100 Index”. In this paper, the list of relevant regulated disclosure firms was manually collected from the websites of the Shanghai and Shenzhen stock exchanges.

4.3.3. Control Variables

With reference to Zhang’s research [4], this paper has identified and chosen the following control variables: firm size (Size), debt level (Lev), return on assets (Roa), current asset ratio (Cr), firm age (Age), firm ownership (Soe), ownership concentration (Top1), and operating income growth rate (Growth). In general, the larger the size of a firm, the more resources it has at its disposal, which creates strong support for R&D activities and the long-term productivity of the firm. In addition, some studies have shown that the age of the firm’s listing may be positively correlated with its innovation awareness [4]. Additionally, firms with better profits usually have a higher level of competition, so their need for innovation is more pronounced, which has a positive effect on both the firms’ TFP. Finally, the shareholding ratio of major shareholders and revenue growth rate of enterprises are related to the management efficiency and growth of enterprises, which will affect the productivity of enterprises to a certain extent [3]. The data for these variables are sourced from the CSMAR database. Subsequently, all the continuous variables undergo Winsorization at the 1st percentile and 99th percentile to mitigate the influence of outliers. Detailed definitions of these variables are provided in

Table 1. Description of variables.

Variable	Symbol	Definition
Total factor productivity (LP)	TFP_L	TFP measured using the LP method
Total factor productivity (FE)	TFP_F	TFP measured using the FE method
Time dummy variable	Post	Post = 1 (sample period in 2009–2013), post = 0 (sample period in 2006–2008)
Subgroup dummy variable	Treated	Treated = 1 (experimental group firms), treated = 0 (control group firms)
Firm scale	Size	The logarithm of total assets
Listed years	Age	The logarithm of the firm’s listing years
Profitability	Roa	The net profit divided by total assets
Current asset ratio	Cr	Total current assets/total assets
Asset–liability ratio	Lev	The ratio of total debt to total assets
Major shareholders	Top1	The fraction of shares held by the largest shareholders
Revenue growth	Growth	Year-end sales/prior year-end sales
Ownership	Soe	Soe = 1 (state-owned firms), Soe = 0 (non-state-owned firms)

.

5. Test Results

5.1. Descriptive Statistics

Table 2. Summary statistics.

	N	Mean	Min	Median	Max	SD
TFP_L	7963	8.891	5.790	8.906	12.611	1.121
TFP_F	7963	11.511	7.905	11.299	15.278	1.332
Post	7963	0.573	0.000	1.000	1.000	0.494
Treated	7963	0.322	0.000	0.000	1.000	0.469
Size	7963	20.942	15.577	21.711	28.482	1.295
Lev	7963	0.471	0.007	0.502	0.992	0.193
Age	7963	2.221	0.693	2.398	3.178	0.532
Roa	7963	0.042	−0.232	0.036	0.229	0.054
Soe	7963	0.621	0.000	1.000	1.000	0.485
Top1	7963	0.362	0.090	0.354	0.753	0.151
Cr	7963	0.543	0.017	0.550	1.000	0.217
Growth	7963	0.221	−0.580	0.135	2.412	0.418

displays the findings of the descriptive statistics. In the sample of 7856 firms from 2006 to 2013, we find that the mean of TFP_L (TFP_F) is 8.891 (11.511), the median is 8.906 (11.299), the maximum is 12.611 (15.278), the minimum is 5.790 (7.905), and the standard deviation is 1.121 (1.332). This indicates that there is some variation in the TFP between firms, but that they are all within a reasonable range of values. As for the control variables, the mean (median) of Size is 20.942 (21.711), with a maximum of 28,482 and a minimum of 15,577, which indicates that most of the firms in our sample are medium to large. And the mean (median) of Lev is 0.471 (0.502), which indicates that the debt of the sample firms is about 50% of their total assets. The remaining variables fall within a reasonable range of values; hence, we refrain from further elaboration.

5.2. Dynamic Trend Test

Before proceeding to the double difference estimation, we first examine the dynamic impact of the Notice on firms’ TFP to test the feasibility of the double difference model in this paper. Specifically, with reference to the study of Dai et al. [31], we construct several-year dummy variables, i.e., ${Year}_{-1}$, ${Year}_0$, ${Year}_{+1}$, ${Year}_{+2}$, ${Year}_{+3}$, ${Year}_{+4}$, and ${Year}_{+5}$, which correspond to the year before the disclosure of the Notice, the year of disclosure, the year after the disclosure, two years after the disclosure, three years after the disclosure, and four years after the disclosure, respectively. Then, we multiply the year dummy variable ${Year}_t$ and subgroup dummy variable ${Treated}_i$ to obtain the interaction term ${Treated}_i\times {Year}_t$ and construct the following model for regression:

$$\begin{array}{r}{TFP}_{i,t+1}={\beta }_0+{\beta }_1{Treated}_i\times {Year}_{-1}+{\beta }_2{Treated}_i\times {Year}_0+{\beta }_3{Treated}_i\times {Year}_{+1}+{\beta }_4{Treated}_i\times {Year}_{+2}+\\ {\beta }_5{Treated}_i\times {Year}_{+3}+{\beta }_6{Treated}_i\times {Year}_{+4}+{\beta }_7{Treated}_i\times {Year}_{+5}+{\theta }^{\prime }{X}_{i,t}+{\alpha }_i+{\gamma }_t+{\epsilon }_{i,t+1}\end{array}$$

(2)

The regression outcomes for Equation (2) are delineated in

Table 3. Dynamic trend test.

	(1)	(2)
	TFP_L	TFP_F
Year − 1 × Treated	0.038	0.014
	(0.70)	(0.30)
Current × Treated	0.054	0.048
	(1.01)	(1.03)
Year + 1 × Treated	0.220 ***	0.078 *
	(4.07)	(1.65)
Year + 2 × Treated	0.337 ***	0.258 ***
	(6.23)	(5.45)
Year + 3 × Treated	0.384 ***	0.359 ***
	(7.08)	(7.58)
Year + 4 × Treated	0.357 ***	0.337 ***
	(6.58)	(7.11)
Year + 5 × Treated	0.197 ***	0.167 ***
	(5.51)	(8.01)
Size	0.223 ***	0.157 ***
	(8.72)	(7.05)
Lev	−0.243 **	−0.224 ***
	(−2.47)	(−2.60)
Roa	0.094	−0.460 **
	(0.46)	(−2.56)
Growth	0.006	−0.010
	(0.28)	(−0.60)
Age	0.142 ***	0.113 ***
	(3.36)	(3.07)
Top1	−0.437 ***	−0.074
	(−2.96)	(−0.58)
Soe	−0.108 **	−0.069
	(−2.08)	(−1.53)
Constant	−4.597 ***	−3.314 ***
	(−8.57)	(−7.07)
Firm FE	Yes	Yes
Year FE	Yes	Yes
N	5801	5801
Adj-R2	0.689	0.688

Note: Values within parentheses denote t-values; significance levels are denoted by ***, **, and *, representing statistical significance at the 1%, 5%, and 10% levels, respectively.

. In every column of the regression results, the coefficients of ${Treated}_i\times {Year}_{-1}$ and ${Treated}_i\times {Year}_0$ are both insignificant, indicating that the trends in the TFP of the experimental group and the control group are similar prior to the implementation of the policy. After the implementation of the Notice, we can see that the coefficients of ${Treated}_i\times {Year}_{+1}$, ${Treated}_i\times {Year}_{+2}$, ${Treated}_i\times {Year}_{+3}$, ${Treated}_i\times {Year}_{+4}$, and ${{Treated}_i\times Year}_{+5}$ are all significantly positive in both columns of the regression results, which shows that the TFP of the experimental group firms increased significantly in the five years after the implementation of the Notice compared to the firms in the control group. Therefore, the results of the above dynamic trend test satisfy the premise of double difference estimation.

5.3. Preliminary Regression Results

The regression outcomes for Equation (1) are provided in

Table 4. Benchmark regression.

	(1)	(2)	(3)	(4)
	TFP_L	TFP_F	TFP_L	TFP_F
Treated × Post	0.119 ***	0.196 ***	0.139 ***	0.206 ***
	(5.19)	(6.75)	(5.19)	(6.75)
Size			0.252 ***	0.340 ***
			(11.87)	(13.59)
Lev			0.696 ***	0.907 ***
			(7.76)	(8.97)
Roa			1.191 ***	0.957 ***
			(8.17)	(5.81)
Age			0.176 ***	0.239 ***
			(4.13)	(4.84)
Soe			0.107 **	0.138 **
			(2.03)	(2.23)
Top1			0.700 ***	0.865 ***
			(3.75)	(3.91)
Cr			0.897 ***	0.489 ***
			(8.31)	(3.81)
Growth			0.215 ***	0.228 ***
			(12.61)	(12.47)
Constant	3.371 ***	5.444 ***	3.471 ***	4.444 ***
	(9.66)	(11.10)	(9.15)	(10.10)
Firm effect	Yes	Yes	Yes	Yes
Year effect	Yes	Yes	Yes	Yes
N	7963	7963	7963	7963
Adj. R2	0.349	0.347	0.348	0.347

Note: Values within parentheses denote t-values; significance levels are denoted by *** and **, representing statistical significance at the 1% and 5% levels, respectively.

. In columns (1) and (2), the coefficients for Treated × Post, excluding the control variables, are 0.119 and 0.196, respectively, both demonstrating significance at the 1% level and indicating a positive effect. Similarly, in columns (3) and (4), the coefficients for Treated × Post with the control variables are 0.139 and 0.206, respectively. These are also significant at the 1% level, indicating that, relative to the control group, the firms in the experimental group experienced an increase in TFP_L and TFP_F following the implementation of the Notice of 11.9% and 19.6%, respectively. These findings suggest a significant improvement in the TFP of the firms within the experimental group following the implementation of the Notice compared to those in the control group, thus supporting Hypothesis H1a.

Furthermore, the direction and statistical significance of the regression coefficients associated with the control variables generally align with the findings documented in the extant literature [3]. Among them, the regression coefficients of Size and Roa are significantly positive. The reason may be that larger firms typically have lower financing constraints and are therefore more favorably disposed to R&D and innovation activities or high-quality investment projects, while more profitable firms are typically more eager to innovate and conduct R&D in order to maintain their long-term competitiveness, both of which are beneficial for driving firms’ TFP. In addition, the coefficients of Soe and Growth also have a significant positive value, which may be due to the fact that the nature of state ownership and higher industrial prosperity are conducive to firms’ external financing, which in turn contributes to the increase in firms’ TFP.

5.4. Robustness Test

In order to assess the robustness of the regression results, we carry out robustness tests using the following methods: (1) as mentioned above, we rescreen the sample of firms using the PSM method; (2) we consider that the transmission of policy effects and firms’ technological development and application may all have time lags, and such time lags are difficult to observe. Therefore, we also used TFP in period T + 2 as the dependent variable in the regression. (3) To rule out the influence of other policy shocks on our results, this paper also assumes that 2007 is the year of the Notice and carries out a placebo test with a sample of firms during 2006–2008.

Table 5. Robustness tests.

	(1)	(2)	(3)	(4)	(5)	(6)
	PSM		TFP (T + 2)		Placebo test (2006–2008)
	TFP_L	TFP_F	TFP_L	TFP_F	TFP_L	TFP_F
Treated × Post	0.112 ***	0.164 ***	0.071 ***	0.106 ***	0.009	0.021
	(6.25)	(8.74)	(3.95)	(6.15)	(0.01)	(0.81)
Size	0.243 ***	0.331 ***	0.262 ***	0.349 ***	0.122 **	0.197 ***
	(14.17)	(16.65)	(17.27)	(20.57)	(2.48)	(3.83)
Lev	0.655 ***	0.912 ***	0.933 ***	1.115 ***	0.594 ***	0.630 ***
	(8.75)	(11.36)	(13.47)	(14.79)	(4.45)	(4.54)
Roa	1.130 ***	0.873 ***	0.604 ***	0.488 ***	0.777 ***	0.956 ***
	(6.97)	(4.79)	(3.80)	(2.80)	(3.43)	(3.85)
Age	0.212 ***	0.299 ***	0.115 ***	0.130 ***	0.217 *	0.258 **
	(4.80)	(5.94)	(3.52)	(3.62)	(1.89)	(2.18)
Soe	0.128 ***	0.176 ***	0.087 **	0.114 ***	0.058	0.079
	(3.26)	(3.92)	(2.37)	(2.75)	(0.55)	(0.73)
Top1	0.567 ***	0.742 ***	0.456 ***	0.561 ***	−0.231	−0.260
	(4.48)	(5.02)	(3.79)	(4.17)	(−0.97)	(−1.02)
Cr	0.899 ***	0.503 ***	0.764 ***	0.442 ***	0.335 **	0.174
	(11.49)	(5.48)	(9.64)	(4.95)	(2.22)	(1.11)
Growth	0.224 ***	0.235 ***	0.112 ***	0.117 ***	0.072 ***	0.071 **
	(12.13)	(11.94)	(7.16)	(6.87)	(2.66)	(2.43)
Constant	3.680 ***	4.565 ***	3.672 ***	4.783 ***	5.308 ***	6.060 ***
	(12.22)	(13.17)	(13.80)	(16.05)	(5.01)	(5.46)
Firm effect	Yes	Yes	Yes	Yes	Yes	Yes
Year effect	Yes	Yes	Yes	Yes	Yes	Yes
N	4909	4909	7963	7963	2136	2136
Adj. R2	0.321	0.329	0.247	0.265	0.089	0.103

Note: Values within parentheses denote t-values; significance levels are denoted by ***, **, and *, representing statistical significance at the 1%, 5%, and 10% levels, respectively.

displays the outcomes of the robustness examinations. Specifically, columns (1) and (2) of Table 5 present the regression findings for the PSM sample. The regression coefficients of the interaction terms are 0.112 and 0.164, respectively, both of which are significant at the 1% level, illustrating the persistence of robust regression results following the application of the PSM method to the firms’ data. In columns (3) and (4), when the firms’ TFP in period T+2 is utilized as the dependent variable, the regression coefficients of the interaction terms remain notably positive at 0.071 and 0.106, respectively, underscoring the robustness of our findings even after accounting for the time lag of R&D activities and policy transmission. In columns (5) and (6), it is observed that the regression coefficients of the interaction terms in both columns are statistically insignificant under the placebo test involving changes in the time of policy implementation. This absence of evidence indicates that the TFP of firms within the experimental group did not undergo significant alterations around 2007 relative to the control group, implying that the baseline regression results are not solely contingent upon temporal dynamics.

6. Further Analysis

6.1. Heterogeneity Analysis of External Financing Constraints and Agency Costs

On the basis of the above analysis, this paper has preliminarily verified that the implementation of the Notice can effectively improve firms’ TFP, while in the theoretical analysis section, we believe that the improvement of agency costs and external financing constraints may be an important influencing mechanism of the Notice to promote firms’ TFP. Therefore, in this section, we first examine the cross-sectional differences in firms’ agency costs and external financing constraints to initially test the positive effect and potential influence channels of the Notice on firm’s TFP.

6.2. Nature of Ownership

Given the large share of state-owned banks’ assets in the overall Chinese banking system, it is inevitable that banks will have ownership preferences that make non-SOEs more vulnerable to external financing constraints than SOEs. Consequently, we anticipate that the enforcement of the Notice will exert a more pronounced effect in alleviating the financing constraints faced by non-SOEs, thereby imparting a more substantial enhancement to their TFP compared to the relatively well-capitalized SOEs. To examine this hypothesis, we partitioned the sample of firms into SOE and non-SOE groups and conducted distinct regression analyses for each.

The results of the segmented regressions are presented in

Table 6. Heterogeneity of property rights in firms.

	(1)	(2)	(3)	(4)
	SOE	Non-SOE	SOE	Non-SOE
	TFP_L	TFP_L	TFP_F	TFP_F
Treated × Post	0.097 ***	0.181 ***	0.157 ***	0.261 ***
	(3.22)	(2.98)	(5.15)	(5.03)
Size	0.209 ***	0.308 ***	0.287 ***	0.402 ***
	(8.56)	(8.47)	(10.22)	(9.44)
Lev	0.657 ***	0.526 ***	0.857 ***	0.725 ***
	(6.12)	(3.95)	(7.09)	(4.97)
Roa	1.236 ***	1.077 ***	1.038 ***	0.788 ***
	(6.72)	(4.59)	(4.94)	(3.07)
Age	0.194 ***	0.186***	0.256 ***	0.245 ***
	(3.13)	(2.60)	(3.55)	(2.95)
Top1	0.644 ***	0.939 **	0.780 ***	1.114 **
	(3.28)	(2.39)	(3.35)	(2.46)
Cr	0.949 ***	0.806 ***	0.540 ***	0.481 ***
	(7.90)	(5.83)	(3.82)	(2.93)
Growth	0.216 ***	0.203 ***	0.232 ***	0.212 ***
	(10.83)	(6.52)	(10.33)	(6.57)
Constant	4.305 ***	2.536 ***	5.514 ***	3.357 ***
	(9.19)	(4.32)	(10.43)	(4.97)
Firm effect	Yes	Yes	Yes	Yes
Year effect	Yes	Yes	Yes	Yes
N	4867	3096	4867	3096
Adj. R2	0.323	0.371	0.305	0.385
p-value	0.000 ***		0.000 ***

Note: Values within parentheses denote t-values; significance levels are denoted by *** and **, representing statistical significance at the 1% and 5% levels, respectively. “p-values” were employed to assess disparities in coefficients of Treated × Post across groups, obtained using SUEST sampling conducted 1000 times.

. In columns (1) and (2), the coefficients of the interaction terms are observed to be 0.097 and 0.181, respectively, both demonstrating significance at the 1% level. Similarly, in columns (3) and (4), the coefficients (0.157 and 0.261, respectively) of the interaction terms also exhibit significance at the 1% level. These findings indicate that the implementation of the Notice can indeed enhance TFP in both SOEs and non-SOEs. To test the differences in the regression coefficients between different subgroups, following Lian and Liao [32], this paper tests the differences in the above coefficients on the basis of the apparent uncorrelated model (SUEST), and the results are presented in the last row of Table 6. We can see that the differences in the coefficients have an empirical p-value of 0.000, suggesting that the differences in the coefficients of the grouped regressions are significant at the 1% level. These findings imply that the impact of the Notice on enhancing TFP is more pronounced within the non-SOE group. Consequently, the outcomes substantiate that the implementation of the Notice can augment the TFP of firms by ameliorating their external financing constraints.

6.2.1. Regional Financial Development Process

Research shows that in regions with higher levels of financial development (FD), there is less information asymmetry between firms and banks and thus less inefficient investment and underinvestment in R&D by firms due to financing constraints or credit mismatches, making the firms in the region generally more productive. Given the role of mandatory CSR disclosure in increasing the transparency of firms’ information, we expect that the Notice will have a greater impact in mitigating the financial constraints faced by firms located in regions with lower FDs, thus providing a stronger boost to their TFP. In order to examine this hypothesis, we stratify the sample based on the upper and lower quartiles of FD into a high FD group and a low FD group, respectively, and conduct separate regression analyses. Here, FD denotes the ratio of total deposits and loans of financial institutions to GDP in each province.

Table 7. Heterogeneity of regional financial development level.

	(1)	(2)	(3)	(4)
	Higher FD	Lower FD	Higher FD	Lower FD
	TFP_L	TFP_L	TFP_F	TFP_F
Treated × Post	0.075	0.147 ***	0.127 **	0.229 ***
	(1.49)	(2.73)	(2.22)	(3.72)
Size	0.201 ***	0.239 ***	0.291 ***	0.333 ***
	(5.13)	(5.86)	(6.45)	(6.93)
Lev	0.800 ***	0.426 ***	1.015 ***	0.649 ***
	(5.45)	(2.88)	(6.20)	(3.72)
Roa	1.930 ***	0.903 ***	1.529 ***	0.720 **
	(5.49)	(3.38)	(3.85)	(2.36)
Age	0.185 **	0.183 **	0.258 **	0.258 ***
	(2.07)	(2.24)	(2.52)	(2.69)
Soe	−0.080	0.131 **	−0.060	0.172 **
	(−0.67)	(2.45)	(−0.42)	(2.52)
Top1	0.922 **	0.935 ***	1.123 **	1.210 ***
	(2.42)	(2.64)	(2.49)	(2.88)
Cr	0.590 ***	0.820 ***	0.163	0.396 **
	(3.38)	(5.27)	(0.79)	(2.15)
Growth	0.203 ***	0.230 ***	0.224 ***	0.240 ***
	(5.61)	(7.25)	(5.99)	(7.08)
Constant	4.456 ***	3.692 ***	5.331 ***	4.526 ***
	(6.06)	(5.44)	(6.37)	(5.56)
Firm effect	Yes	Yes	Yes	Yes
Year effect	Yes	Yes	Yes	Yes
N	1944	1936	1944	1936
Adj. R2	0.322	0.312	0.313	0.345
p-value	0.000 ***		0.001 ***

Note: Values within parentheses denote t-values; significance levels are denoted by *** and **, representing statistical significance at the 1% and 5% levels, respectively. “p-values” were employed to assess disparities in coefficients of Treated × Post across groups, obtained using SUEST sampling conducted 1000 times.

shows the results of the grouped regressions. The coefficients of the interaction terms in columns (1) and (2) are 0.075 and 0.147, respectively. While the former lacks statistical significance, the latter demonstrates significance at the 1% level. This indicates that the implementation of the Notice notably enhances TFP_L within the lower FD group. In column (3) and column (4), the coefficients of the interaction terms are 0.127 and 0.229, respectively, both exhibiting statistical significance. Furthermore, a comparative analysis reveals that the coefficients on the interaction terms are considerably greater in the lower FD group compared to the higher FD group, with identical dependent variables (the first two columns yield an empirical p-value of 0.000, while the last two columns yield an empirical p-value of 0.001). This underscores that the Notice exerts a more pronounced effect on the TFP of firms within the lower FD group. Thus, the findings presented in Table 7 furnish supplementary evidence supporting the notion that the implementation of the Notice can augment firms’ TFP by ameliorating their external financing constraints.

6.2.2. Equity Incentive

Scholarly findings suggest that equity incentives afford managers the opportunity to partake in the firm’s profit sharing as shareholders, thereby aligning their interests with those of shareholders and mitigating the principal–agent conflict. Consequently, equity incentives are commonly recognized as a critical mechanism for resolving agency issues. Building upon this premise, we anticipate that the implementation of the Notice will be particularly impactful in addressing the agency problem within firms characterized by a lower management shareholding ratio (Msr), thereby exerting a more pronounced influence on their TFP. To evaluate this proposition, we partition the dataset into high and low Msr categories using the upper and lower quartiles of Msr, conducting distinct regression analyses. Msr denotes the fraction of shares held by management in relation to the total outstanding shares of the firm.

The outcomes are delineated in

Table 8. Heterogeneity of equity incentive.

	(1)	(2)	(3)	(4)
	Higher Msr	Lower Msr	Higher Msr	Lower Msr
	TFP_L	TFP_L	TFP_F	TFP_F
Treated × Post	0.094	0.160 ***	0.142 **	0.239 ***
	(1.49)	(3.01)	(2.00)	(3.91)
Size	0.184 ***	0.174 ***	0.240 ***	0.231 ***
	(3.38)	(5.19)	(3.97)	(4.92)
Lev	0.593 ***	0.491 ***	0.688 ***	0.712 ***
	(2.99)	(4.68)	(3.11)	(5.96)
Roa	1.514 ***	1.227 ***	1.293 ***	1.157 ***
	(4.66)	(5.19)	(3.74)	(4.57)
Age	0.444 ***	0.128 *	0.557 ***	0.208 **
	(3.47)	(1.71)	(3.77)	(2.40)
Soe	0.038	0.118	0.049	0.128
	(0.42)	(1.64)	(0.46)	(1.63)
Top1	0.946 ***	0.590 **	1.166 ***	0.717 **
	(2.67)	(2.12)	(2.71)	(1.97)
Cr	1.226 ***	0.651 ***	0.999 ***	0.267
	(6.04)	(3.91)	(4.39)	(1.28)
Growth	0.217 ***	0.263 ***	0.235 ***	0.264 ***
	(5.47)	(10.13)	(5.59)	(9.51)
Constant	3.568 ***	5.001 ***	4.822 ***	6.425 ***
	(3.68)	(9.51)	(4.51)	(9.30)
Firm effect	Yes	Yes	Yes	Yes
Year effect	Yes	Yes	Yes	Yes
N	1738	1734	1738	1734
Adj. R2	0.323	0.321	0.323	0.372
p-value	0.000 ***		0.000 **

Note: Values within parentheses denote t-values; significance levels are denoted by ***, **, and *, representing statistical significance at the 1%, 5%, and 10% levels, respectively. “p-values” were employed to assess disparities in coefficients of Treated × Post across groups, obtained using SUEST sampling conducted 1000 times.

. In the grouped regressions within columns (1) and (2), it is noted that the coefficients on the interacting terms are 0.094 and 0.160, respectively. While the former lacks statistical significance, the latter demonstrates significance at the 1% level. In the subgroup regressions depicted across columns (3) and (4), the coefficients of the interaction terms stand at 0.142 and 0.239, demonstrating significant positivity at the 5% and 1% significance levels, respectively. Furthermore, a comparative analysis reveals that the coefficients on Treated × Post in both regressions are markedly greater for the lower Msr group compared to the higher Msr group (with a p-value of 0.000 in both instances), as evidenced by the SUEST test. This indicates that the Notice serves as a more potent driver of firms’ TFP within the lower Msr group. Thus, the findings presented in Table 8 furnish additional evidence supporting the proposition that the implementation of the Notice can augment firms’ TFP by effectively mitigating their agency conflict problem.

6.3. The Impact of Mandatory CSR Information Disclosure on Financing Constraints and Agency Costs

In the above analyses, we have firstly verified that agency conflicts and the improvement of external financing constraints are two important reasons why CSR disclosure increases firms’ TFP. To further demonstrate the existence of these two roles, we next directly examine the role of disclosure on firms’ agency costs and external financing constraints. Specifically, we construct the following models using firms’ agency conflicts (Ac) and external financing constraints (Kz index) as the dependent variables for testing:

$${Ac}_{i,t}={\beta }_0+{\beta }_1{Treated}_i\times {Post}_t+{\theta }^{\prime }{X}_{i,t}+{\alpha }_i+{\gamma }_t+{\epsilon }_{i,t}$$

(3)

$${Kz}_{i,t}={\beta }_0+{\beta }_1{Treated}_i\times {Post}_t+{\theta }^{\prime }{X}_{i,t}+{\alpha }_i+{\gamma }_t+{\epsilon }_{i,t}$$

(4)

In Equation (3), Ac is quantified using a firm’s overhead ratio. Here, Ac denotes the ratio of the firm’s overhead expenses to its operating revenue [3], a commonly employed metric for assessing the first type of agency cost. Higher values of Ac correspond to increased agency costs within the firm. Equation (4) employs the Kz index to gauge the firm’s external financing constraints, as developed by Kaplan and Zingales [33]. This index, derived from financial metrics such as operating cash flow, debt–equity ratio, and Tobin’s Q value, serves as a widely accepted indicator of financing constraints. A higher Kz index value reflects heightened financing constraints faced by the firm. The regression results for Equations (3) and (4) correspond to columns (1) and (2) of

Table 9. The impact of the Notice on agency costs and financing constraints.

	(1)	(2)
	Ac	Kz
Treated × Post	−0.018 ***	−0.114 ***
	(−3.68)	(−3.01)
Size	0.005	−0.004 **
	(0.96)	(−2.26)
Lev	0.043	−0.005
	(0.83)	(−0.80)
Roa	−0.246 ***	−0.020
	(−3.72)	(−1.26)
Age	−0.018	−0.002
	(−1.10)	(−0.44)
Soe	−0.028 *	0.004
	(−1.71)	(1.03)
Top1	−0.211 *	−0.015
	(−1.80)	(−1.59)
Cr	−0.022	0.014 *
	(−0.92)	(1.80)
Growth	−0.051 **	0.006 ***
	(−2.05)	(3.85)
Constant	0.164 *	0.115 ***
	(1.82)	(4.24)
Firm effect	Yes	Yes
Year effect	Yes	Yes
N	7963	7963
Adj. R2	0.191	0.322

Note: Values within parentheses denote t-values; significance levels are denoted by ***, **, and *, representing statistical significance at the 1%, 5%, and 10% levels, respectively.

, respectively. Notably, the coefficients of Treated × Post in the two columns are −0.018 and −0.114, respectively, and both of them were significant at the 1% level. This reaffirms that the implementation of the Notice effectively diminishes both the agency conflicts and funding constraints encountered by firms.

6.4. The Influence Path Test of Firm’s Investment Efficiency and Innovation Capability

6.4.1. Mandatory CSR Disclosure, Investment Efficiency, and TFP

In the section of the theoretical analysis, we argue that the Notice’s alleviation of agency costs and external financing constraints can effectively improve firms’ investment efficiency, which in turn benefits TFP. Therefore, to test whether the improvement of investment efficiency is an important channel for the Notice to promote firms’ TFP, this paper next further tests the role of investment efficiency in the process of the Notice’s impact on TFP. Based on the study by Chen et al. [31], we first construct the following model to calculate firms’ investment efficiency:

$${Invest}_{i,t}={\beta }_0+{\beta }_1{NEG}_{i,t-1}+{\beta }_2{SalesGrowth}_{i,t-1}+{\beta }_{3}NEG\times {SalesGrowth}_{i,t-1}+{\epsilon }_{i,t}$$

(5)

In Equation (5), the dependent variable ${Invest}_{i,t}$ represents the aggregate of expenditures on fixed assets, intangible assets, and other long-term assets incurred by the firm during the fiscal year subtracted by the cash inflows from asset sales and subsequently divided by the total assets held by the firm at the commencement of the fiscal year. ${NEG}_{i,t-1}$ is a binary indicator that assumes a value of 1 if the growth rate of sales revenue in the previous year (t − 1) is negative, otherwise it takes the value of 0. ${SalesGrowth}_{i,t-1}$ is the percentage change in sales revenue from the $t$ − 1 year to the current year. The residuals obtained from the estimation of Equation (5) by year and by industry are used as a measure of investment efficiency, and the closer the residuals are to 0, the higher the investment efficiency. To facilitate the estimation, the absolute value of investment efficiency (InvEff) is applied as a mediating variable to construct the mediation effect model in this paper:

$${InvEff}_{i,t}={\beta }_0+{\beta }_1{Treated}_i\times {Post}_t+{\theta }^{\prime }{X}_{i,t}+{\alpha }_i+{\gamma }_t+{\epsilon }_{i,t}$$

(6)

$${TFP}_{i,t+1}={\beta }_0+{\beta }_1{Treated}_i\times {Post}_t+{{\beta }_{2}InvEff}_{i,t}+{\theta }^{\prime }{X}_{i,t}+{\alpha }_i+{\gamma }_t+{\epsilon }_{i,t+1}$$

(7)

Column (1) of

Table 10. Mediation analysis.

	(1)	(2)	(3)	(4)	(5)	(6)
	InvEff	TFP_L	TFP_F	Patent	TFP_L	TFP_F
Treated × Post	−0.011 **	0.121 ***	0.184 ***	0.281 ***	0.137 ***	0.197 ***
	(−2.33)	(4.79)	(5.91)	(6.78)	(4.04)	(6.92)
InvEff		−0.381 ***	−0.652 ***
		(−3.82)	(−6.33)
Patent					0.021 ***	0.031 ***
					(2.91)	(3.84)
Size	0.012 ***	0.251 ***	0.342 ***	0.162 ***	0.249 ***	0.336 ***
	(5.49)	(11.40)	(13.20)	(4.93)	(11.73)	(13.48)
Lev	0.025 **	0.679 ***	0.896 ***	0.191	0.691 ***	0.902 ***
	(2.48)	(7.17)	(8.50)	(1.49)	(7.71)	(8.93)
Roa	0.065 ***	1.059 ***	0.835 ***	−0.039	1.191 ***	0.959 ***
	(3.17)	(7.13)	(4.99)	(−0.12)	(8.17)	(5.83)
Age	−0.011	0.164 ***	0.220 ***	0.470 ***	0.168 ***	0.226 ***
	(−1.56)	(3.83)	(4.47)	(4.89)	(3.95)	(4.59)
Soe	−0.009 **	0.141 ***	0.174 ***	−0.098	0.109 **	0.141 **
	(−2.09)	(2.59)	(2.76)	(−1.27)	(2.06)	(2.28)
Top1	−0.012	0.694 ***	0.857 ***	0.042	0.701 ***	0.865 ***
	(−0.72)	(3.56)	(3.72)	(0.17)	(3.80)	(3.97)
Cr	−0.049 ***	0.840 ***	0.407 ***	0.051	0.899 ***	0.489 ***
	(−4.68)	(7.44)	(3.05)	(0.37)	(8.36)	(3.83)
Growth	0.012 ***	0.227 ***	0.244 ***	−0.042 *	0.215 ***	0.229 ***
	(4.39)	(14.58)	(14.51)	(−1.66)	(12.67)	(12.57)
Constant	−0.094 **	3.606 ***	4.574 ***	−2.036 ***	3.508 ***	4.497 ***
	(−2.53)	(9.10)	(9.96)	(−3.78)	(9.26)	(10.27)
Firm effect	Yes	Yes	Yes	Yes	Yes	Yes
Year effect	Yes	Yes	Yes	Yes	Yes	Yes
N	7963	7963	7963	7963	7963	7963
Adj. R2	0.0336	0.341	0.357	0.011	0.352	0.364

Note: Values within parentheses denote t-values; significance levels are denoted by ***, **, and *, representing statistical significance at the 1%, 5%, and 10% levels, respectively.

shows the regression results of Equation (6), where we can see that the coefficient of the interaction term is −0.011, which is significantly negative at the 5% level, suggesting that the implementation of the Notice can make a significant contribution to efficiency of corporate investment. The regression results of Equation (7) are presented in columns (2) and (3) of Table 10. The coefficients of the interaction terms in the two columns are significantly positive at the 1% level, and the coefficients of InvEff are significantly negative at the 1% level, suggesting that the mediation effect is established. In summary, the results in the first three columns of Table 10 suggest that improvements in investment efficiency are an important way in which the Notice promotes corporate TFP.

6.4.2. Mandatory CSR Disclosure, Innovation Capacity, and TFP

In the theoretical analysis section, we posit that the Notice’s mitigation of agency costs and external financing constraints can effectively bolster firms’ innovation capacity, a critical factor in enhancing firms’ TFP. Hence, to validate that the enhancement of innovation capacity constitutes an effective mechanism through which the Notice promotes firm’s TFP, we further investigate the Notice’s impact on firm innovation capacity (Patent). Specifically, we employ the natural logarithm of the number of patents filed by the listed firms during the year as a proxy variable for the firms’ innovation capability, which typically reflects the firms’ propensity for innovation [34,35]. The innovation patent data of the listed firms are sourced from the CNRDS database. With Patent serving as the mediating variable, we construct the following regression model:

$${Patent}_{i,t}={\beta }_0+{\beta }_1{Treated}_i\times {Post}_t+{\theta }^{\prime }{X}_{i,t}+{\alpha }_i+{\gamma }_t+{\epsilon }_{i,t}$$

(8)

$${TFP}_{i,t+1}={\beta }_0+{\beta }_1{Treated}_i\times {Post}_t+{{\beta }_{2}Patent}_{i,t}+{\theta }^{\prime }{X}_{i,t}+{\alpha }_i+{\gamma }_t+{\epsilon }_{i,t+1}$$

(9)

The regression outcomes of Equations (8) and (9) are presented in the final three columns of Table 10. Notably, in column (4), the coefficient of Treated × Post stands at 0.281 and is significant at the 1% level. This suggests that the implementation of the Notice substantially enhances the firms’ innovation capacity. Furthermore, the coefficients of the interaction terms in columns (5) and (6) are both significantly positive at the 1% level, alongside the coefficients of patents, which are also significantly positive at the 1% level. These findings indicate the presence of a mediating effect. In conclusion, the results in the final three columns of Table 10 underscore that enhancements in innovation capacity constitute a pivotal pathway through which the Notice fosters firms’ TFP.

7. Discussion

Although many studies have explored the potential impact of CSR disclosure on corporate development, there is no consensus on whether these impacts are all positive or negative. Some studies suggest a potential negative impact of CSR disclosure on a firm’s operations [2], whereas others have identified a positive association between CSR disclosure and firms’ productivity [12]. Consequently, recognizing CSR’s core emphasis on enhancing overall firm quality, this study diverges from prior literature by testing the influence of CSR disclosure on firms’ TFP within the framework of high-quality firm development. The findings reveal that mandatory CSR disclosure can bolster a firm’s TFP by mitigating financing constraints and agency costs. Furthermore, this study identifies enhancing investment efficiency and innovation performance as two pivotal mechanisms through which CSR disclosure drives TFP, thereby broadening the understanding of CSR disclosure’s influence on corporate transformative development.

Additionally, we acknowledge certain limitations that warrant future research enhancement. Firstly, although the TFP measure adopted herein is widely utilized, it possesses inherent limits that may not entirely cover the comprehensive quality of firm development. Hence, future research endeavors could explore alternative methodologies for TFP calculations. Secondly, apart from mechanisms like financing constraints and agency costs, forthcoming research could delve into the influence of external factors that are more pertinent to CSR, such as the firms’ green reputation [36,37] and media sentiment [38,39], on firm development. Thirdly, in developed countries, voluntary disclosure of CSR information is the norm, while China’s CSR process is less developed, so mandatory CSR reports tend to be more reliable than voluntary ones, as reflected in the comprehensiveness, identifiability, and horizontal comparability of disclosures. Therefore, studying the impact of mandatory CSR disclosure on firms can better eliminate the interference of noisy information. In addition, the literature has also examined the improvement of CSR disclosure quality from this perspective [3]. Unlike this literature, we cite the traditional DID model, which is a better measure of the impact of policy shocks than the OLS model, although the model requires that our data must satisfy a certain degree of symmetry, i.e., the timing cannot deviate from policy releases too far back in time, which makes our data potentially slightly outdated. In future research, additional robustness tests using multi-period DID or OLS methods can be considered to compensate for the disadvantage of outdated data caused by the traditional DID model.

8. Conclusions and Policy Implications

Using A-share-listed firms in Shanghai and Shenzhen as the research sample, this study empirically investigates the impact of mandatory CSR information disclosure on the TFP of Chinese firms. The findings reveal that the implementation of the Notice significantly enhances firms’ TFP, primarily attributed to the effective reduction of agency costs and external financing constraints resulting from mandatory CSR information disclosure. Furthermore, supplementary research indicates that enhancements in investment efficiency and innovation capability serve as two significant channels through which the Notice fosters firm TFP. Drawing upon the findings presented in this study, we offer the following recommendations: (1) the level of total factor productivity of enterprises determines the trajectory of China’s future economic development. In order to better guide enterprises towards the path of high-quality development, the government should strengthen and improve the formulation and implementation of policies on disclosure of CSR information as well as support relevant accountability mechanisms as early as possible to further promote the transparency and authenticity of the content of CSR disclosure, thus accelerating the quality transformation of the economy. (2) Financing constraints and agency conflicts remain heavy shackles restricting the development of firms in China. Our research shows that the improvement of external financing constraints and agency conflict issues is an important influence mechanism of the Notice to promote a firm’s TFP. Therefore, the government should further strengthen its support for firms from the financial level, especially focusing on solving the problem of financing difficulties of private firms, in order to create a favorable external financing environment for the improvement of firm productivity in China. In addition, the government may also consider encouraging firms to disclose high-quality CSR information through various means, such as recognition and publicity, which can help further strengthen communication between firms and stakeholders to alleviate the obstacles to firm development caused by agency conflicts and adverse selection problems. (3) Enhancing the quality of firm development is a gradual process requiring sustained efforts. Hence, government incentives and penalties should be directed towards encouraging firms to view social responsibility as a long-term investment. (4) The role of CSR disclosure on TFP may be different among firms that are differentiated in terms of property rights and the level of regional financial development. As far as the external environment of the firm is concerned, the government should enhance the level of financial openness in the location of the firm, increase the competition in the banking sector, and thus enhance the level of financial development in the location of the firm.

In terms of the internal environment of the enterprise, the enterprise should provide managers with the opportunity to participate in the company’s profit-sharing as shareholders to align their interests with those of shareholders and mitigate principal–agent conflicts. Therefore, the government should formulate and implement differentiated CSR policies according to the environment in which the enterprise is located to provide a strong driving force for the enhancement of the enterprise’s total factor productivity through the combination of internal and external means, thus cultivating new quality productivity for the development of the enterprise. (5) As the world’s second-largest economy, China’s CSR changes have the potential to have a significant impact on global economic sustainability and the development of related regulations. Therefore, accelerating China’s CSR process is imperative, as it will help the world’s major economies to integrate and harmonize their concepts of sustainability. This will be significant for the future development of the global climate, society, and humanity together.