Industrial policy is a crucial instrument employed by the Chinese government to promote high-quality development of enterprises (HQDE). This study leverages the quasi-natural experiment formed by China's Chain Chief System (CCS) industrial policy, utilizing data from Chinese A-share listed companies during 2017–2022 and a difference-in-differences method to explore the relationships among industrial policy, HQDE, and business-government relations. Our findings indicate that the CCS policy can significantly promotes HQDE, with alleviating financing constraints and reducing agency costs identified as potential channels. Business-government relations play a pivotal role in moderating this positive relationship. Specifically, increases in state-owned equity proportions and geographical proximity between enterprises and government both enhance the promotional effect of CCS policy on HQDE. Furthermore, heterogeneity tests reveal that this promotional effect is more pronounced in state-owned enterprises, firms operating in high-tech industries, and those located in regions with less unfavorable business environments. These findings contribute to advancing debates on the effectiveness of industrial policies and deepens our understanding of the critical role of business-government relations.
Research Article
Industrial policy and high-quality development of enterprise: The moderating role of business-government relations
https://doi.org/10.21203/rs.3.rs-4698581/v1
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Industrial policy
Chain Chief System
High-quality development of enterprise
Business-government relations
Supply chain resilience
The Chinese economy is currently transitioning from a stage of high-speed growth to a stage of high-quality development. Enterprises are the primary entities of the market economy, and high-quality development of enterprises (HQDE) forms the micro foundation for high-quality development of the Chinese economy. At present, China has made significant progress in enhancing the quality of economic development, primarily evidenced by improvements in efficiency and sustainable development (Luo et al., 2024; Yang et al., 2024). Through technological innovation, managerial reforms, and business restructuring, Chinese enterprises have augmented their economic and social benefits, leading the Chinese economy into a "new normal" (Lei et al., 2024). However, domestic firms still face challenges such as insufficient available capital, principal-agent conflicts, and information asymmetry, impeding further advancements in their economic and social value creation (Li, 2023).
Existing literature suggests that the Chinese government has successfully intervened in enterprises' production, operations, and investment behavior through industrial policies (Mao et al., 2021). Industrial policies refer to strategies that guide the development direction of an industry in a country or region (Tian, 2020). These policies utilize instruments like credit facilities, government subsidies, and tax incentives to allocate production factors and adjust industrial structures, exerting significant influence on enterprise development (Kollmann et al., 2012; Musacchio et al., 2015). Current research primarily focuses on the impact of industrial policies on specific aspects of enterprise development, such as production and operations (Pan et al., 2023), technological innovation (Yan et al., 2023), digital transformation (Xie and Wu, 2024), and green development (Chen et al., 2021). However, there is a notable lack of comprehensive study on HQDE.
HQDE embodies China's new development philosophy of innovation, coordination, green, openness, and shared development (Lei et al., 2024). It requires enterprises to not only pursue profit growth but also prioritize efficiency and sustainable development (Wang et al., 2022). Existing literature has extensively researched the measurement of HQDE and its determinants. Regarding the measurement of HQDE, scholars have yet to reach a universally consistent view. Some studies use total factor productivity (TFP) as a proxy indicator for HQDE (Lee et al., 2023; Zhou et al., 2024). Others construct multidimensional composite indicators to measure HQDE from perspectives such as financial performance, innovation capability, corporate governance, and environmental and social responsibility (Lei et al., 2024; Luo et al., 2023). Therefore, accurately defining HQDE and selecting appropriate indicators to scientifically measure it are crucial for the validity of research conclusions. In addition, constructing a more comprehensive HQDE indicator is one of the contributions of this article. As for the determinants of HQDE, existing research can be categorized into macro and micro levels. The former includes factors such as environmental regulation (Lei et al., 2024), digital finance (Li et al., 2023), digital infrastructure (Guo et al., 2024), economic policy uncertainty (Li et al., 2021), government subsidies (Lin and Zhang, 2024), interest rate and tax rate adjustments (Xue et al., 2022). The latter involves factors such as enterprises' size (Hanousek et al., 2015), ownership type (Kang and Kim, 2012), capital structure (Atta Mills et al., 2021), financialization (Siming Liu et al., 2021), governance ability (Sun et al., 2024), accounting information quality (Atta Mills et al., 2021). In particular, many scholars have pointed out that financing constraints and agency costs are significant factors constraining HQDE (Baxamusa and Jalal, 2024; Feng et al., 2024).
Since 2017, to enhance industrial chains' innovation capacity, green development, digital transformation, and resilience, thereby achieving high-quality development of both industrial chains and enterprises, local governments in China have successively implemented the Chain Chief System (CCS) industrial policy. The CCS policy comprises two core elements: "leading enterprises" and "chain chiefs". "Leading enterprises" refer to companies that have a significant impact on the development of industrial chains. These enterprises can leverage their key positions to adjust the pace of industrial chain development, eliminate internal excess capacity, and connect upstream and downstream enterprises within the industrial chain. "Chain chiefs" are typically senior local administrative officials responsible for supervising, planning, and maintaining the development of the entire industrial chain. They preside over the formulation and implementation of major industrial projects and coordinate government departments such as science, technology, finance, and banking to jointly support industrial development. While traditional industrial policies primarily emphasize the government's role in resource allocation and provide selective support for specific industries, the CCS policy emphasizes the combination of "effective markets" and "capable government." Market-oriented "leading enterprises" occupy a central position, while "chain chiefs" play a guiding and coordinating role. The CCS policy functions more as a mechanism for responsibility allocation, mobilization, and factor guarantee. Furthermore, in terms of policy objectives, the CCS policy places greater emphasis on enhancing supply chain resilience, which is another key difference from traditional industrial policies. As the CCS policy has been gradually implemented across China, it has become an important institutional pillar for local governments to promote HQDE. However, research on the impact of the CCS policy on HQDE is currently lacking.
Business-government relations constitute a crucial non-market environment for enterprise development (Tian et al., 2019). In China, local governments not only control critical resources such as industry entry permits, land approvals, loan guarantees, and preferential policies, but also bear significant responsibilities for regional economic development and public goods investment (Jia et al., 2024; Qiao and Fei, 2022). When firms seek resources from the government, one consideration for the government is "What is our relationship?" (Su and Fung, 2013). Consequently, positive business-government relations facilitate enterprises, particularly private ones, in accessing government-controlled resources (Juntao and Haitao, 2023). Existing literature indicates that favorable business-government relations can bring more tax incentives, credit support, fiscal subsidies, and government contracts to enterprises (Abdurakhmonov et al., 2020; Faccio, 2010), thereby promoting international expansion (Fornes et al., 2021), technological innovation (Tian et al., 2019), strategic transformation (Juntao and Haitao, 2023), and industrial agglomeration (Cammett, 2007). In recent years, the Chinese government has endeavored to construct a new type of "closeness" and "integrity" business-government relations, improving the government's ability to provide public services, and thereby promoting high-quality development of the Chinese economy (Tian et al., 2019). Currently, there is limited research on the role of business-government relationships in the interaction between industrial policies and HQDE. Therefore, in this study, drawing on the quasi-natural experiment formed by the CCS policy, we attempt to discover: Does industrial policies affect HQDE? How does the business-government relations moderate this relationship?
Considering the importance of the CCS policy and HQDE, we employ data from Chinese A-share listed companies during 2017–2022 and utilize the difference-in-differences (DID) method to evaluate their relationship. The results indicate that the CCS policy significantly enhances HQDE, and this effect persists after policy implementation. This conclusion remains valid after a series of robustness tests. Our mechanism analysis reveal that the CCS policy can enhance HQDE by alleviating financing constraints and reducing agency costs. Furthermore, our moderating effect analysis shows that business-government relations can positively moderate the relationship between the CCS policy and HQDE. Specifically, increases in state-owned equity proportions and geographical proximity between enterprises and government both enhance the promotional effect of CCS policy on HQDE. Finally, heterogeneity examinations find that the promoting effect is more pronounced for state-owned enterprises, firms operating in high-tech industries, and those located in regions with unfavorable business environments.
This study contributes to the literature in three ways. First, it enriches the literature on industrial policy. To the best of our knowledge, there is limited research investigating the impact of industrial policy on HQDE, particularly regarding the effects of the CCS policy, which we may be the first to explore. Scholars have explored the effects of industrial policies on certain aspects of firm performance, such as production efficiency, technological innovation, green transformation, or digitalization (Aghion et al., 2015; Dai and Wang, 2019), but comprehensive research on HQDE as a whole is limited. Our study fills this gap. Additionally, the exploration of mechanisms such as financing constraints and agency costs enhances our understanding of how industrial policies influence enterprise development in complex ways.
Second, our study enriches the literature on business-government relations. While scholars have examined the impact of such ties on the allocation of public and market resources (Faccio, 2007; Kang and Park, 2012), as well as their subsequent effects on firm technological innovation, expansion, and industrial upgrading (Fornes et al., 2021; Tian et al., 2019), they have overlooked the role of business-government relations as a non-market institutional force in the process through which industrial policies influence firm development. Our research confirms that business-government relationships can positively moderate the relationship between industrial policies and enterprise development, deepening our understanding of the government's important role in economic development.
Finally, we construct a new indicator to measure HQDE. High-quality development embodies China's new development concepts of innovation, coordination, green development, openness, and shared growth. Its rich connotations suggest that its evaluation indicators should be multidimensional and complex. Based on these concepts and existing research (Lei et al., 2024; Luo et al., 2023), and in light of the CCS policy's important goal of enhancing supply chain resilience, we construct new indicators to measure HQDE from six dimensions: financial performance, innovation capability, green development, shared development, digitalization level, and supply chain resilience. This effort provides a reference for subsequent empirical research related to HQDE.
The remainder of this paper proceeds as follows. Section 2 reviews relevant literature and develops our research hypotheses. Section 3 describes the data sources, variable definitions, and model specifications. Section 4 presents the empirical results and related analyses. Section 5 concludes by summarizing our findings, discussing their policy implications, and addressing limitations and avenues for future research.
2.1. The impact of the CCS policy on HQDE
Industrial policy refers to the economic intervention measures taken by the government to promote the development of specific sectors (White, 2008). As an important means to compensate market failures and optimize resource allocation, industrial policy has played a significant role in promoting China's economic growth and HQDE (An et al., 2016). The CCS policy is an institutional innovation made by local governments in China against the backdrop of changes in the international economic landscape and the transition of domestic economic development stages. In addition to employing traditional policy tools such as government subsidies, tax incentives, credit facilitation, and development zones (Damayati et al., 2024; Qiao and Fei, 2022), the implementation of the CCS policy exhibits two salient features: First, local senior officials act as supporters and coordinators for industrial chain development, promoting the agglomeration of production factors and cross-sector collaboration. Second, the policy objectives encompass growth, efficiency, sustainability, and supply chain resilience, striving to propel the high-quality development of enterprises and industrial chains.
The competitive advantage theory posits that integrating factors like labor, capital, and land can create stronger competitive advantages (Porter, 1990). As senior local government officials, "chain chiefs" can mobilize production factors within their jurisdictions and allocate more resources to favored enterprises. Simultaneously, they can coordinate the management work of various government departments, reduce institutional transaction costs, and thereby alleviate the resource constraints faced by enterprises. Furthermore, the supervisory pressure from "chain chiefs" can improve enterprises' internal governance and reduce agency costs. These channels can provide more available resources for enterprises' technological innovation, digital transformation, and sustainable development, thus forming the foundation for HQDE. Consequently, we propose our first hypothesis:
H1. The CCS policy can significantly enhance HQDE.
2.2. Mechanisms of the CCS policy to promote HQDE
First, we suggest that the CCS policy can enhance HQDE by alleviating enterprises' external financing constraints. It is widely acknowledged that enterprises face significant difficulties in securing financing from free competitive markets when undertaking activities such as technological innovation and environmental protection, as these endeavors exhibit externalities wherein the benefits generated cannot fully offset the costs incurred (Ball and Kittler, 2019; Hall and Lerner, 2010). The economic rationale for governments to implement industrial policies is to utilize administrative interventions to mitigate the issue of underinvestment in areas like innovation and environmental protection, thereby overcoming market failures arising from incomplete appropriability of returns (Dai and Wang, 2019). The CCS policy can increase enterprises' access to fiscal funds and credit financing through resource allocation effects and signaling effects, consequently alleviating external financing constraints. On one hand, local Chinese governments command abundant public resources and disposal rights (Tian et al., 2019). By leveraging policy instruments such as fiscal subsidies, policy loans, or tax incentives, the CCS policy can directly augment the allocation of fiscal resources to enterprises, bridging the funding gaps they face in innovation, environmental protection, or other developmental activities (Aghion et al., 2015; Ding et al., 2024). On the other hand, the implementation of the CCS policy sends a signal to the credit market that the government supports the development of specific industries, enhancing commercial banks' expectations of future returns for enterprises in favored industries (Li et al., 2023). Additionally, the preferential treatment under the CCS policy provides enterprises with an implicit government guarantee, incentivizing commercial banks to relax their lending conditions (Fan et al., 2023). The dual effects jointly increase the scale of credit financing accessible to enterprises.
Second, we posit that the CCS policy can enhance HQDE by reducing enterprises' agency costs. Agency costs arise from the separation of ownership and control in enterprises (Williams, 1988), eroding shareholder wealth and impeding enterprises development (Guo and Zhang, 2023). Based on stakeholder theory, the CCS policy can reduce agency costs by increasing stakeholder pressure. The stakeholder theory holds that the success of an enterprises depends on how it manages relationships with stakeholders, including government, banks, communities, suppliers, customers, shareholders and employees (Freeman and Phillips, 2002). One of the crucial tasks for managers is to maintain support from these key groups, balance their interests, and make the company the place where they can maximize their benefits(Jones et al., 2017). These stakeholders serve as sources of constraints on managerial conduct (Bridoux and Vishwanathan, 2018), and the pressure from them can incentivize managers to make greater efforts to improve enterprise performance and reduce behaviors that undermine shareholder wealth, thereby lowering agency costs (Guo and Zhang, 2023). On one hand, when providing enterprises with government subsidies, credit facilitation, or tax incentives, the CCS policy typically requires compliance with conditions like innovation quality or environmental performance. These regulatory pressures from the government and banks force companies to overcome management inertia and resistance, thereby reduce agency costs (Ambec and Barla, 2007). On the other hand, the implementation of the CSS policy strengthens corporate information disclosure, mitigating information asymmetries between managers and shareholders (Zhao et al., 2024). Shareholder pressure curbs managerial self-serving behaviors, such as excessive spending (Ang et al., 2000), risk avoidance (Kennedy, 1994), and procrastination (Ambec and Barla, 2005), thus reducing agency costs.
Numerous studies indicate that alleviating external financing constraints and reducing agency costs contribute to improving enterprises' labor productivity (Baxamusa and Jalal, 2024), technological innovation (Su et al., 2023), green development (Qian, 2024), digital transformation (Xu et al., 2023), and supply chain resilience (Qi et al., 2024). Thus, we propose our second hypothesis:
H2. The CCS policy can enhance HQDE by alleviating financing constraints and reducing agency costs.
2.3. The moderating role of business-government relations
The business-government relations constitute an important external environment for enterprise survival and development, influencing corporate strategy and financial performance (Su and Fung, 2013). Extensive research has demonstrated that favorable business-government relations bring resource advantages to enterprise development, including preferential access to credit (Leuz and Oberholzer-Gee, 2006), more government subsidies (Cheng et al., 2024), tax incentives (Faccio, 2010), government bailouts during financial distress (Faccio et al., 2006), government procurement contracts (So et al., 2007), ensured property rights and intellectual property protection (Shiyuan Liu et al., 2021), IPO privileges (Francis et al., 2009), and improved future stock returns (Cooper et al., 2010).
Chinese local governments command substantial administrative resources (Tian et al., 2019), and business-government relations can moderate the relationship between the CCS policy and HQDE by influencing the government's propensity to allocate resources to firms. Resource dependence theory posits that organizational survival requires acquiring resources from the surrounding environment, and dependence on external resources influences the power dynamics between organizations (Emerson, 1962). If organization A's survival and development rely on resources provided by organization B, then B can significantly influence A's behavior (Hillman et al., 2009). Activities such as technological research and development, environmental protection, and digital transformation require larger initial investments and longer payback periods, making them difficult to finance in capital markets (Fagerberg, 2017). Consequently, companies undertaking these efforts become more dependent on public resources controlled by the government (Jia et al., 2024). Given that "chain chiefs" are government officials, robust business-government relations will enhance the government's inclination to allocate resources to favored enterprises during CCS policy implementation. This, in turn, amplifies the promotional effect of the CCS policy on HQDE. Accordingly, we propose our third hypothesis:
H3. Business-government relations can positively moderate the relationship between the CCS policy and HQDE.
The conceptual model of our study is shown in Fig. 1.
3.1. Data source and sample selection
Our sample comprises Chinese firms listed on the A-share market from 2017 to 2022. To ensure data validity, we implement the following procedures: (1) We exclude data from companies designated as ST or *ST; (2) We omit samples with missing data; (3) We winsorize all continuous variables at the 1st and 99th percentiles to mitigate the influence of outliers. Firm-level data are obtained from the CSMAR and CNRDS databases, province-level data are sourced from the China City Statistical Yearbook, and information on the CCS policy is collected from publicly available documents issued by provincial governments.
3.2. Variable definition
The dependent variable: High-quality development of enterprises (HQDE). HQDE is a multidimensional concept. We employ the standard entropy method to develop the HQDE index, allocating weights to each dimension based on the actual distribution of data. Guided by China’s new development philosophy encompassing innovation, coordination, green development, openness, and sharing, and drawing from existing literature (Luo et al., 2023), we construct the HQDE index across six dimensions: financial performance, innovation capacity, green development, sharing development, digitalization level, and supply chain resilience. These dimensions respectively represent the enterprise's basic operational status, innovation concept, green concept, openness concept, sharing concept, and coordination concept. Table 1 provides details of the index construction.
It is noteworthy that we use the digitalization level to represent the concept of openness, as digital technologies enhance information transmission efficiency, reduce transaction costs, increase cooperation between enterprises and regions (Myovella et al., 2020), and promote foreign direct investment and market expansion (Qi et al., 2023). Furthermore, enhancing supply chain resilience is a crucial policy objective of the CCS policy. We employ supply chain resilience to represent the coordination concept and reflect the characteristics of the policy, as it reflects the level of collaboration between enterprises and their upstream and downstream partners (Qi et al., 2024).
Supply chain resilience refers to a supply chain's ability to withstand external shocks and recover to its original operational state (Gölgeci and Kuivalainen, 2020). It manifests in an enterprise's capacity to flexibly adjust production lines and collaborate with upstream and downstream supply chain partners in resource allocation. Qi et al. (2024) divide supply chain resilience into two aspects: supply chain resistance and supply chain recovery. Supply chain resistance ability is measured by the ratio of accounts receivable to operating income, as greater accounts receivable pressure deteriorates relationships between suppliers and customers. Supply chain recovery ability is represented by the "deviation" between production fluctuation and demand fluctuation. The formula is: Mathi,t=Productioni,t/Demandi,t; Productioni,t=Demandi,t+Inventoryi,t-Inventoryi,t−1, where Production represents enterprise output, Demand represents enterprise demand (measured by cost of sales), and Inventory represents net inventory at year-end. If Match exceeds 1, it indicates that supply fluctuations upstream in the supply chain are greater than demand fluctuations downstream, suggesting lower supply chain recovery ability.
|
Dimension |
Indicator |
Calculation |
Data source |
|---|---|---|---|
|
Financial performance |
Total asset growth rate |
Asset growth/total assets |
CSMAR |
|
Return on Total Assets |
Net profit/total assets |
CSMAR |
|
|
Net operating profit margin |
Net profit/operating income |
CSMAR |
|
|
Total asset turnover rate |
Operating income/total assets |
CSMAR |
|
|
Innovation |
Number of invention patent applications |
Sum of invention patents |
CNRDS |
|
Number of non-invention patent applications |
Sum of non-invention patents |
CNRDS |
|
|
R&D expenses |
R&D expenses/operating income |
CNRDS |
|
|
Green development |
Pollutant treatment capacity |
Emission weight of exhaust gas, wastewater and solid waste |
CNRDS |
|
Environmental governance costs |
Environmental governance costs |
CNRDS |
|
|
Environmental management disclosure |
The social responsibility report discloses environmental related information as 1, otherwise it is 0 |
CNRDS |
|
|
Digitization |
Digital Transformation Index |
Frequency count of digital transformation-related terms in the company annual report. |
CSMAR |
|
Sharing development |
Employee wages |
Employee wages/operating income |
CSMAR |
|
Tax |
Corporate income tax/operating income |
CSMAR |
|
|
Supply chain resilience |
Supply chain resistance ability |
Accounts receivable/operating income |
CSMAR |
|
Supply chain recovery ability |
(Cost of sales + current year inventory - previous year inventory)/Cost of sales |
CSMAR |
The independent variable: The CCS policy (CCS). Following the approach of Zhao et al. (2024), the dummy variable CCS represents the interaction term\(treat \times post\). Here, \(treat\) is a group dummy variable, where \(treat\) equals 1 if an enterprise belongs to an industry targeted by the local government's implementation of the CCS policy, and 0 otherwise. \(post\) is a time dummy variable, taking the value of 0 before the implementation of the CCS policy and 1 thereafter.
Control Variables: This study includes control variables at both the enterprise and provincial levels. Enterprise-level control variables include: Enterprise size (Size), measured as the natural logarithm of total assets; Enterprise age (Age), measured as the natural logarithm of the year of establishment; Leverage (Lev), measured as the ratio of total debt to total assets; Capital intensity (Inten), measured as the ratio of total assets to total operating income; Fixed asset growth rate (Fix); Industry competition level (HHI), the Herfindahl-Hirschman Index, measured as the sum of squares of market shares of all enterprises within the industry. Provincial-level control variables include: Provincial GDP (GDP), measured as the natural logarithm of GDP for each province; Industrial structure level (Indu), measured as the ratio of value added in the tertiary industry to value added in the secondary industry for each province; Government intervention level (Inter), measured as the ratio of provincial fiscal expenditure to GDP. Descriptive statistics are shown in Table 2.
|
Variable |
N |
Mean |
Std. Dev. |
Min |
Max |
|---|---|---|---|---|---|
|
HQDE |
6300 |
0.119 |
0.063 |
0.040 |
0.433 |
|
CSS |
6300 |
0.203 |
0.402 |
0.000 |
1.000 |
|
Size |
6300 |
22.104 |
1.139 |
20.116 |
25.619 |
|
Age |
6300 |
1.867 |
0.960 |
0.000 |
3.258 |
|
Lev |
6300 |
2.185 |
2.320 |
0.979 |
17.378 |
|
Inten |
6300 |
1.402 |
0.995 |
0.773 |
7.748 |
|
Fix |
6300 |
0.329 |
0.726 |
0.014 |
5.563 |
|
HHI |
6300 |
6.191 |
0.744 |
5.015 |
7.759 |
|
GDP |
6300 |
10.947 |
0.473 |
9.641 |
11.664 |
|
Indu |
6300 |
1.533 |
1.020 |
0.873 |
5.297 |
|
Inter |
6300 |
0.166 |
0.042 |
0.124 |
0.292 |
3.3. Model setting
This study treats the implementation of the CSS policy as a quasi-natural experiment and constructs the following DID model (1) to analyze its impact on HQDE:
4.1. Baseline regression
Table 3 presents the baseline regression results for the impact of the CCS policy on HQDE. Column (1) excludes both control variables and fixed effects, column (2) omits control variables, column (3) omits fixed effects, and column (4) includes both control variables and fixed effects. Across columns (1)-(3), the CSS coefficients are both significantly positive at the 5% level, and in column (4), the CSS coefficient is significantly positive at the 1% level. Therefore, it can be concluded that the implementation of the CCS policy significantly enhances the HQDE of favored enterprises. Therefore, hypothesis H1 is confirmed.
| Variable | (1) | (2) | (3) | (4) |
|---|---|---|---|---|
| CSS | 0.004** | 0.005** | 0.005** | 0.007*** |
| (2.210) | (2.116) | (2.236) | (2.641) | |
| Size | −0.006*** | −0.008*** | ||
| (− 5.879) | (− 8.863) | |||
| Age | −0.009*** | −0.008*** | ||
| (− 7.793) | (− 7.073) | |||
| Lev | 0.001** | 0.001*** | ||
| (2.133) | (2.597) | |||
| Inten | 0.000 | −0.001 | ||
| (0.535) | (− 0.618) | |||
| Fix | 0.000** | 0.000 | ||
| (2.213) | (1.490) | |||
| HHI | 0.013*** | 0.009 | ||
| (10.770) | (1.474) | |||
| GDP | 0.017*** | 0.009 | ||
| (3.612) | (1.568) | |||
| Indu | 0.001 | −0.000 | ||
| (1.299) | (− 0.261) | |||
| Inter | 0.238*** | 0.137** | ||
| (4.396) | (2.071) | |||
| Constant | 0.118*** | 0.118*** | −0.048 | 0.126 |
| (140.113) | (139.316) | (− 0.761) | (1.384) | |
| Firm FE | No | Yes | No | Yes |
| Year FE | No | Yes | No | Yes |
| Ind FE | No | Yes | No | Yes |
| N | 6300 | 6300 | 6300 | 6300 |
| Adj. R2 | 0.001 | 0.104 | 0.057 | 0.155 |
| Note: (1) The robust-adjusted t-statistics clustered at provinces are in parentheses. (2) *, **, *** denote significance levels at 10%, 5%, and 1%, respectively. (3) Firm FE, Year FE, Ind FE represent firm fixed effects, year fixed effects, and industry fixed effects, respectively. | ||||
4.2. Robustness tests
4.2.1 Parallel trends test
Based on model (2), we discuss the results of parallel trend tests and dynamic effects tests, as depicted in Fig. 2. Prior to the implementation of the CSS policy, the coefficients of the pre-policy dummy variable (pre4-pre1) fail to achieve significance within the 95% confidence interval. This result indicates that the HQDE between the treatment group and the control group satisfies the parallel trend assumption. After the policy implementation, the coefficients of the post-policy dummy variables (post1-post3), are significantly positive and gradually increasing, suggesting that the CCS policy has a sustained and positive impact on HQDE, illustrating the dynamic effects of this policy.
4.2.2. Placebo test
After satisfying the parallel trends assumption, we must consider whether the changes in HQDE are driven by random factors. To rule out this possibility, we conduct a placebo test. First, we randomly assign firms into treatment and control groups and perform the baseline regression, repeating this process 1,000 times while recording the coefficient and p-value of the pseudo-policy variable (CSS) in each iteration. Second, using these data, we plot the kernel density of the regression coefficients and the distribution of the p-values, as shown in Fig. 3. The horizontal dashed line in Fig. 3 represents the 10% critical significance level, while the vertical dashed line indicates the baseline regression coefficient of 0.007 from column (4) of Table 3.
The results show that the coefficients of the pseudo-policy variable (CSS) are concentrated around 0, while the coefficient of the real policy variable, 0.007, is a significant outlier. Meanwhile, the majority of pseudo-policy variables have p-values greater than the critical value of 10%, indicating that they are not significant. These findings affirm that the observed baseline regression results are not derived from random factors, and demonstrate the robustness of our research conclusions.
4.2.3. Heterogeneous treatment effects
Heterogeneous treatment effects (HTE) refer to the variation in treatment effects across individual subgroups and over time (de Chaisemartin and D’Haultfœuille, 2020). Potential HTE could undermine the validity of the staggered DID estimator (Baker et al., 2022). Goodman-Bacon (2021) demonstrates that the staggered DID estimator is a weighted average of four distinct two-group/two-period (2×2) DID estimators, including earlier treated units versus untreated units, later treated units versus untreated units, earlier treated units versus later treated units, and later treated units versus earlier treated units. The primary source of bias stems from the fourth pair, which treats earlier treated units as a control group for later treated units, where the former already includes the policy effect. When HTE exists, the policy effects for earlier treated units differ from those for later treated units, leading to estimates from this pair that are contrary to the actual effect.
Following Goodman-Bacon (2021), we decompose the staggered DID estimator (from baseline regression) into three pairs: earlier treated group versus later treated group, later treated group versus earlier treated group, and treated group versus untreated group. The decomposition results reveal that the weights for these three pairs of estimates are 30.9%, 2.3%, and 66.8% respectively, with coefficients of 0.002, 0.005, and 0.005, as depicted in Fig. 4. The weight for later treated group versus earlier treated group is very small (2.3%), indicating that the estimation bias caused by HTE is relatively insignificant. Therefore, our staggered DID estimates are robust.
4.2.4. Additional robustness tests
Furthermore, we employed two additional methods for robustness testing: using total factor productivity (OP and LP methods) as alternative outcome variables, and PSM-DID method. The consistent results indicate that our conclusions are highly robust. These findings are detailed in Appendix Table A1 and Fig. A1-A2.
4.3. Heterogeneity analysis
The efficacy of the CSS policy may vary depending on the individual characteristics of enterprises, the industry context, and the macroeconomic environment of the region. This subsection will explore the heterogeneous effects of the policy on HQDE across three dimensions: enterprise ownership, industry technology level, and regional business environment.
4.3.1. Enterprise ownership
Ownership is a crucial factor influencing the allocation of credit resources. In credit markets, state-owned enterprises (SOEs) are more likely to obtain bank loans due to their stronger political connections, with the government effectively serving as their implicit guarantor (Lee et al., 2023). Conversely, non-state-owned enterprises (NSOEs) are more susceptible to encountering credit discrimination (Cheng et al., 2020). With this in mind, we first categorize the sample into SOEs and NSOEs, and then conduct subgroup regressions.
The results in columns (1) and (2) of Table 4 demonstrate that for SOEs, the coefficient of CSS is significantly positive at the 5% statistical level, while for NSOEs, the coefficient of CSS is significantly positive at the 10% statistical level. Further comparing the magnitudes of the coefficients on CSS between SOEs and NSOEs, we find that the impact of the CCS policy is more pronounced for SOEs. In summary, the effect of the CCS policy on HQDE exhibits firm-level heterogeneity, with SOEs experiencing a more significant impact than NSOEs. This conclusion aligns with the findings of Guo and Zhang (2023).
4.3.2. Industry technology level
Given that research and development (R&D) expenditures are sunk costs with uncertain returns, high-tech firms face more stringent financing constraints (Miller et al., 2011), rendering them more reliant on fiscal support (Bai et al., 2024). According to the "Classification of High-tech Industries (Manufacturing Industry) (2017) " issued by the Chinese government, we categorize enterprises operating in the following six industries as high-tech: electronic and communication equipment manufacturing, aerospace manufacturing, computer and office equipment manufacturing, information chemical manufacturing, pharmaceutical manufacturing, and medical equipment and instrument manufacturing. Enterprises in other industries are classified as non-high-tech.
The results of subgroup regression are presented in columns (3) and (4) of Table 4. It can be observed that for high-tech enterprises, the coefficient of CSS is significant at the 1% level, whereas for non-high-tech enterprises, the CSS coefficient is not significant. These results indicate that the CCS policy enhanced HQDE among firms in high-tech industries, while it had no significant impact on firms in non-high-tech industries. This conclusion is consistent with the findings of Xiang et al. (2019).
4.3.3. Regional business environment
The business environment exerts a significant influence on the efficiency of market resource allocation (Zhang et al., 2024). In unfavorable business environments, enterprises encounter higher transaction costs and financing constraints, particularly when engaging in innovative activities (Chen et al., 2023). Consequently, they become more reliant on public resource support, just like the situation faced by high-tech enterprises. In light of this consideration, we refer to the approach of Yu et al. (2023) to construct a provincial business environment index for China, dividing the sample into regions with favorable and unfavorable business environments based on provincial averages.
The results in columns (5) and (6) of Table 4 indicate that for enterprises located in regions with favorable business environment, the CSS coefficient is positive and significant at the 10% level, while for enterprises located in regions with unfavorable business environments, the CSS coefficient is positive and significant at the 5% level. Further comparison indicates that the coefficient for the unfavorable business environment sample is larger in magnitude than that for the favorable business environment sample. This suggests that in unfavorable business environments, the CCS policy has a more pronounced impact on HQDE, potentially because firms in such environments are more reliant on government resources. To verify the robustness of this conclusion, we follow the approach ofZhang et al. (2023) and substitute the business environment index with the marketization index from the "China Provincial Marketization Index Report (2021)," and re-estimate the subgroup regressions. The results remain consistent.
| Variable | Ownership | Technical level | Business environment | |||
|---|---|---|---|---|---|---|
| (1) | (2) | (3) | (4) | (5) | (6) | |
| SOEs | NSOEs | High-tech | Non-high-tech | Favorable | Unfavorable | |
| CSS | 0.013** | 0.005* | 0.015*** | −0.002 | 0.006* | 0.009** |
| (2.457) | (1.924) | (3.315) | (− 0.829) | (1.944) | (2.123) | |
| Control | Yes | Yes | Yes | Yes | Yes | Yes |
| Firm FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Year FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Ind FE | Yes | Yes | Yes | Yes | Yes | Yes |
| N | 1387 | 4702 | 2602 | 3698 | 3240 | 3059 |
| Adj. R2 | 0.254 | 0.166 | 0.179 | 0.138 | 0.178 | 0.142 |
| Note: Due to space limitations, control variables are not listed. | ||||||
4.4. Mechanism analysis
The theoretical analysis in Section 2 suggests that alleviating external financing constraints and reducing internal agency costs are potential channels through which the CCS policy enhances HQDE. Consequently, we must demonstrate that the CCS policy indeed impacts these factors.
We employ two indicators to measure financing constraints: bank credit obtained by firms (Credit) and government subsidies received by firms (Subsidies), both of which are natural log-transformed. We posit that the CCS policy can increase firms' access to bank credit through signaling effects and augment government subsidies through resource allocation effects. Evidently, larger values of Subsidies and Credit indicate lower financing constraints faced by firms. For agency costs, we follow Ang et al. (2000) and utilize the administrative expense ratio (AER) as an indicator of Type I agency costs. AER, defined as the ratio of administrative expenses to operating income, reflects agency costs arising from managerial on-the-job consumption. A higher AER value signifies greater Type I agency costs. Following Bae et al. (2002), we employ the other receivables ratio (ORR) as an indicator of Type II agency costs. ORR, calculated as the ratio of other receivables to total company assets, captures the "tunneling behavior" of major shareholders who maximize their own interests at the expense of minority shareholders. A higher ORR indicates greater Type II agency costs.
Table 5 presents the results of our mechanism tests. In columns (1) and (2), the CSS coefficients are positive and statistically significant at the 1% level, indicating that the CSS policy increases both bank credit and government subsidies received by firms, effectively alleviating external financing constraints. The CSS coefficient in column (3) is negative and significant at the 1% level, suggesting that the CSS policy tends to reduce Type I agency costs. The insignificant CCS coefficient in column (4) implies that the CCS policy does not mitigate Type II agency costs. These findings support the mechanisms we propose in our study, demonstrating that the CCS policy can promote HQDE by reducing external financing constraints and internal agency costs. Hypothesis H2 is assumed to be validated.
| Variable | Financing constraints | Agency cost | ||
|---|---|---|---|---|
| (1) | (2) | (3) | (4) | |
| Credit | Subsides | AER | ORR | |
| CSS | 0.327*** | 0.212*** | −0.004*** | 0.001 |
| (7.279) | (4.287) | (− 2.963) | (1.367) | |
| Control | Yes | Yes | Yes | Yes |
| Firm FE | Yes | Yes | Yes | Yes |
| Year FE | Yes | Yes | Yes | Yes |
| Ind FE | Yes | Yes | Yes | Yes |
| N | 6300 | 6300 | 6300 | 6291 |
| Adj. R2 | 0.469 | 0.447 | 0.322 | 0.080 |
4.5. Moderating effect analysis
In this section, we incorporate interaction terms between the CCS policy variable and business-government relations variable into our baseline regression model to construct a moderating effect model. This approach allows us to analyze the moderating role of business-government relations. Specifically, we utilize two indicators to measure business-government relations: the proportion of state-owned capital shareholding (Soeshare) and the geographical proximity between provincial governments and corporate headquarters (Closer). Soeshare is defined as the percentage of state-owned capital shareholding in an enterprise. The proportion of state-owned capital shareholding reflects the government's influence on enterprises (X. Li et al., 2023); higher state-owned equity participation indicates stronger business-government relations. Geographical proximity facilitates formal and informal communication and cooperation between governments and enterprises (Lu et al., 2024), also representing stronger business-government relations. Given that provincial government officials generally serve as the "chain chiefs" of the CCS policy, we define Closer as a dummy variable, taking the value of 1 if a firm's headquarters is located in the provincial capital, and 0 otherwise.
Table 6 presents the results of our moderating effect tests for business-government relations. Column (1) shows that the interaction term between the state-owned equity share and the CCS policy variable (CSS*Soeshare) has a significantly positive coefficient at the 10% level. This indicates that as the proportion of state-owned equity in enterprises increases, the promotional effect of the CCS policy on HQDE strengthens. Column (2) reveals that the interaction term between government-enterprise geographical proximity and the CCS policy (CSS*Closer) has a significantly positive coefficient at the 5% level. This suggests that when enterprises are located in the same provincial capital city as their corresponding "chain chiefs," the CCS policy exhibits a stronger promotional effect on HQDE. These results demonstrate that business-government relations indeed positively moderate the relationship between the CCS policy and HQDE. Hypothesis H3 is thus corroborated.
| Variable | (1) | (2) |
|---|---|---|
| CSS | 0.006*** | 0.006*** |
| (2.705) | (2.617) | |
| CSS*Soeshare | 0.001* | |
| (1.918) | ||
| Soeshare | 0.000*** | |
| (3.158) | ||
| CSS*Closer | 0.011** | |
| (2.554) | ||
| Closer | −0.010*** | |
| (− 5.107) | ||
| Control | Yes | Yes |
| Firm FE | Yes | Yes |
| Year FE | Yes | Yes |
| Ind FE | Yes | Yes |
| N | 6300 | 6300 |
| Adj. R2 | 0.149 | 0.159 |
5.1. Conclusions
Enterprises are the fundamental cells of a market economy, and achieving high-quality economic development relies on HQDE. How to promote HQDE has emerged as a critical issue for both theoretical and practical realms in China. Against this backdrop, local governments in China have implemented the CCS policy to comprehensively enhance the performance of enterprises in finance, innovation, environmental protection, digitization, and supply chain resilience, thereby fostering HQDE. Given that promoting high-quality economic development is a crucial strategic objective for China at present, studying the impact of the CCS policy on HQDE holds significant theoretical and practical implications.
In this study, we employ data from Chinese A-share listed companies spanning 2017 to 2022 and utilizes a DID approach to elucidate the impact of the CSS industrial policy on HQDE. Our findings reveal that the CCS policy can significantly enhances HQDE, and this effect exhibits considerable persistence. Mechanism analysis reveals that the policy can improve HQDE by alleviating financing constraints and reducing agency costs. Specifically, the implementation of the CCS policy increases government subsidies and bank credit obtained by enterprises, thereby mitigating external financing constraints. Simultaneously, the policy implementation reduces Type I agency costs but does not affect Type II agency costs. Moderating effect analysis indicates that business-government relations can positively moderate the relationship between the CCS policy and HQDE. This is evidenced by the strengthening of the CSS policy's positive effect on HQDE as the proportion of state-owned shareholding increases and as the geographical proximity between enterprises and governments becomes closer. Finally, our documented heterogeneity analysis results demonstrate that the promotional effect of the CCS policy on HQDE is particularly significant in state-owned enterprises, firms operating in high-technology industries, and those located in regions with unfavorable business environments.
5.2. Policy implications
Our research offers several policy implications. Firstly, for the Chinese government, nationwide implementation of the CCS policy is recommended. We find that the CCS policy significantly promotes HQDE. Therefore, the central government should encourage local governments across the country to implement the CCS policy and provide necessary resources and policy support. Additionally, our research reveals that the CCS policy's effect on enhancing HQDE is relatively weaker for non-state-owned and non-high-tech enterprises. Consequently, when implementing the CCS policy, the government should pay more attention to these enterprises to improve the policy's inclusiveness.
Secondly, for enterprises aiming to achieve high-quality development, improving internal governance and building stronger business-government relations are crucial. Our research shows that the CCS policy can promote HQDE by reducing agency costs. Thus, enterprises should strengthen internal governance to minimize inappropriate managerial behaviors such as excessive spending and indolence. Moreover, we find that positive business-government relations enhance the effect of the CCS policy on HQDE. Therefore, it may be beneficial for enterprises to moderately increase state-owned equity participation or strategically locate their headquarters near government offices to facilitate better communication and cooperation with governmental bodies.
5.3. Limitations and future research
Our study has several limitations that warrant further exploration and deepening in future research. Firstly, our research is constrained by the data sample. As Chinese local governments have only recently begun implementing the CCS policy, our study is limited to a sample of Chinese A-share listed companies from 2017 to 2022. This relatively short time period may restrict our ability to accurately assess the dynamic effects of the policy. Future research could consider extending the time frame of the sample, which would allow for a more comprehensive evaluation of the policy's long-term impacts.
Secondly, our exploration of the mechanisms between the CCS policy and HQDE is not comprehensive. While we have identified two important mediating mechanisms - alleviating financing constraints and reducing agency costs - this is clearly not exhaustive. There are potentially more mechanisms to be explored, such as government procurement and promotion of industrial agglomeration. We encourage scholars to uncover additional mechanisms, as this would contribute to constructing a more comprehensive theoretical framework for analyzing the effects of industrial policies.
Conflict of interest:
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Data availability: Data will be available under reasonable request.
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