Government Subsidies and Sustainable Development in Manufacturing: Evidence from Product Quality and Production Efficiency

Abstract

This study investigates the role of government subsidies in fostering sustainable development among 69,525 Chinese manufacturing companies, with a focus on product quality and production efficiency. We examine how subsidy effects vary across companies with different ownership types, export orientations, and market competition intensities. Our results indicate that subsidies generally enhance both product quality and production efficiency, albeit with a time lag. These improvements are primarily driven by increased R&D investment and the adoption of upgraded equipment, contributing to sustainable operational practices. We find that subsidies are particularly effective in promoting sustainability outcomes in non-state-owned and non-exporting companies, though their suitability remains context-dependent. Specifically, subsidies more significantly improve product quality in low-competition, export-oriented companies, while they exert a stronger influence on production efficiency in companies operating in highly competitive environments. For management, aligning government subsidies with corporate strategy is crucial to enhancing product quality and efficiency. For policymakers, the heterogeneous treatment effects support moving away from one-size-fits-all grants toward tiered support that channels R&D-intensive subsidies to leading industries and efficiency-oriented subsidies to highly competitive industries. These findings directly inform China’s Dual-Carbon strategy and offer an exportable evaluation framework for emerging economies seeking to align industrial policy with the UN Sustainable Development Goals.

1. Introduction

The manufacturing industry serves as both the fundamental pillar of global economic development and a critical actor in advancing the Sustainable Development Goals (SDGs)—particularly SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production). Within the GreenSCENT competence framework, these goals are achieved through constructivist learning pathways that embed lifecycle thinking and green-skills acquisition into industrial practice, underscoring why sector-specific policy instruments such as government subsidies must be evaluated against explicit sustainability metrics [1]. While its development status directly reflects a nation’s competitiveness, innovation capacity, and economic resilience, it also accounts for approximately 30% of global energy consumption and 24% of greenhouse gas emissions, making its sustainability transition indispensable to mitigating climate change and preserving finite resources. In China, the manufacturing sector has been central to transforming the country into the world’s largest manufacturing power. However, this growth has historically been driven by models that rely heavily on resource input and energy consumption, leading to long-term ecological sustainability risks, including pollution, resource depletion, and industrial waste [2].

These challenges have prompted Chinese manufacturing companies to shift from factor-driven to innovation-driven, sustainability-oriented development strategies—a paradigm shift where competitiveness is no longer solely defined by cost or scale, but by the ability to integrate economic efficiency, environmental stewardship, and social responsibility. In this context, enhancing product quality and production efficiency has emerged as a core focus of government support, but with a renewed emphasis on their alignment with sustainability principles. For instance, “product quality” now extends beyond market differentiation to include green design, compliance with eco-labeling standards, and lifecycle sustainability [3]. These reduce the sector’s ecological footprint and align with circular economy goals. Similarly, “production efficiency” is no longer limited to cost-saving measures but emphasizes resource and energy efficiency and clean production technologies, which are key levers for achieving carbon peaking and neutrality targets (China’s Dual Carbon goals) while maintaining economic viability [4].

To accelerate this sustainability transition, the Chinese government has integrated sustainable development priorities into its industrial subsidy frameworks. Beyond traditional direct financial subsidies for research and development (R&D) and tax incentives for technological upgrades, recent policies specifically target sustainability-focused innovation and practice. However, the effectiveness of these initiatives, particularly government subsidies targeting sustainability, remains a subject of debate.

Although several studies have explored the role of subsidies in improving product quality and innovation, limited attention has been given to their combined effects on both economic performance and sustainability outcomes, such as carbon intensity, resource efficiency, and circularity. Existing research tends to focus on either economic or sustainability performance in isolation [5,6], without adequately addressing how government support drives improvements in both dimensions within the context of China’s manufacturing sector. This gap in the literature limits the ability of policymakers to design effective industrial policies that promote long-term economic and environmental resilience.

This study seeks to fill this gap by examining how government subsidies influence both product quality and production efficiency as well as sustainability outcomes in Chinese manufacturing companies. It contributes novel empirical evidence on the mechanisms through which subsidies can foster sustainable development in the manufacturing sector. Specifically, this research evaluates the effectiveness of subsidies in enhancing both economic and sustainability performance, offering a more comprehensive understanding of their impact. The study also highlights the heterogeneity of subsidy effects across companies with different ownership structures, export orientations, and levels of market competition. By addressing these research gaps, this paper aims to inform the design of targeted, impactful industrial policies that promote both economic growth and ecological health, contributing to China “Dual Carbon” commitments and global efforts to achieve a low-carbon, circular economy.

2. Literature Review

2.1. Review Methodology

To ensure a comprehensive and transparent analysis of existing research, this literature review employs a structured narrative synthesis approach. The search strategy encompassed major academic databases including Web of Science, Scopus, and China National Knowledge Infrastructure (CNKI), with a primary focus on publications from 1995 to 2024. Core search terms encompassed “government subsidies,” “industrial policy,” “product quality,” “production efficiency,” “total factor productivity (TFP),” “sustainable development,” and “manufacturing companies.” Inclusion criteria prioritized peer-reviewed empirical studies and pioneering theoretical papers directly examining the impact of government intervention on company performance. This analysis aims to map core issues, theoretical mechanisms, and contextual moderating factors, thereby outlining the academic landscape to clarify the study’s contributions.

2.2. Theoretical Foundations and International Empirical Evidence

A seminal contribution to the understanding of government subsidies and innovation comes from Aghion et al. (2015) [7], who examine the relationship between industrial policy and competition. Their work demonstrates how government interventions, including subsidies, can shape competitive dynamics and innovation trajectories across different institutional settings. This theoretical framework is highly relevant to our analysis, as it provides a foundation for understanding how subsidies might differentially impact companies based on market structure and institutional context. Other key international studies include Hall et al. (2000) [8], who analyze the effectiveness of R&D subsidies across various countries, finding significant variation in outcomes based on institutional frameworks and market conditions. These cross-country comparisons highlight how subsidy effectiveness is contingent upon factors such as legal systems, financial markets, and bureaucratic efficiency. Research from different institutional contexts reveals essential variations in how government subsidies influence company behavior and performance. In European contexts, Cerqua and Pellegrini (2014) [5] find that subsidies have varying impacts across different industry sectors and company sizes, with smaller companies often benefiting more from targeted support. Similarly, other studies show that subsidy effectiveness depends heavily on how well they are aligned with company-specific innovation strategies [9]. These international perspectives suggest that the effectiveness of government subsidies is not universal but rather contingent upon specific institutional arrangements, market structures, and company characteristics. This contingency view is particularly essential when considering how to design optimal subsidy policies in different economic environments.

2.3. Research Gaps and Study Contributions

While existing literature provides valuable insights into the role of government subsidies in promoting innovation and production efficiency, significant gaps remain. Studies have explored various factors affecting production efficiency, such as tax policies [10,11], industry regulations [12], foreign investment liberalization [13], and technology imports [14]. Similarly, research on product quality has focused on the impact of exports, corporate listing [15], financing constraints [16], and value chain integration [17]. However, there is limited research that simultaneously examines the dual impacts of government subsidies on both product quality and production efficiency—two closely related yet distinct dimensions of company performance. Furthermore, existing studies emphasize innovation outputs [18,19,20] rather than operational improvements. Liu and Zhou (2023) [18] test the impact of direct government subsidies and tax incentives on the R&D efficiency of the manufacturing industry based on the Chinese situation. Sun and Liu (2023) [19] explore the relationship between government subsidies and companies’ technological innovation inputs and outputs. Dong and Dai (2023) [20] apply evolutionary game theory to scrutinize the propelling mechanism behind digital transformation in manufacturing companies specializing in equipment production. Key questions remain unresolved: How do government subsidies affect manufacturing companies differently compared to other types of companies? What mechanisms drive the improvements in product quality and production efficiency? Are these effects uniform across companies with different ownership structures, export behaviors, and competitive environments? Addressing these questions is critical for understanding the broader implications of subsidies for industrial growth and competitiveness.

This paper empirically examines how government subsidies influence the overall product quality and production efficiency of manufacturing companies. It provides a theoretical basis for understanding these effects in the context of Chinese manufacturing companies. The study also accounts for the heterogeneity among companies by grouping them based on property rights, export behavior, and industry conditions to explore the differential impact of subsidies on quality and efficiency improvements. This paper makes several theoretical contributions to the existing literature on government subsidies and manufacturing companies, focusing on their impact on product quality and production efficiency. First, the study broadens the scope of analysis. While most research primarily examines the influence of government subsidies on innovation, this study takes a broader approach by investigating the impact of subsidies on both product quality and production efficiency in manufacturing companies. This comprehensive perspective enhances understanding of the multifaceted effects of subsidies on overall company development, moving beyond a sole focus on technological advancement. Second, the study reveals the mechanisms of impact. By incorporating R&D investment and equipment depreciation as mediating variables, the paper provides a more detailed understanding of how government subsidies exert their influence. This exploration of causal mechanisms clarifies the internal logic and tangible outcomes of subsidies in shaping the development of manufacturing companies. Third, the study explores dynamic and heterogeneous effects. The paper employs a three-dimensional analytical framework, considering the static, quantile, and dynamic effects of government subsidies. This approach offers a deeper understanding of the temporal dynamics and variations in the impact of subsidies, highlighting potential time lags and differing effectiveness across various companies. Fourth, the study identifies conditional heterogeneity. By grouping the sample companies based on ownership structure, export behavior, and industry competition level, the research uncovers the conditional heterogeneity of subsidy impacts. This analysis shows that the effectiveness of subsidies varies significantly among different types of manufacturing companies, emphasizing the need to tailor subsidy policies to the specific characteristics and needs of each company. Fifth, the study enriches the literature on subsidy effects. By focusing on product quality and production efficiency, the findings provide valuable insights into the mechanisms and outcomes of government subsidies. This expands the understanding of their role in promoting high-quality development within the manufacturing sector.

3. Hypotheses

This study is grounded in resource-based theory and the theory of government intervention correcting market failures. The conceptual framework guiding hypothesis construction posits that government subsidies, as critical external resources, can directly enhance company capabilities. These resources improve product quality and production efficiency through two intermediary mechanisms: increased R&D investment and accelerated equipment upgrading. Furthermore, the study proposes that these relationships are not uniformly present but depend on key company-level and market-level characteristics: ownership type, export status, and industry competition intensity. The following subsections elaborate on the hypotheses derived from this framework and supporting literature.

3.1. Direct Effects of Government Subsidies

In market economies, manufacturing companies often need to invest substantial resources and time in high-risk, uncertain-return innovation activities to enhance product quality and production efficiency [21]. During this process, the spillover effects of innovative knowledge and technology can result in private benefits that are much lower than the social benefits. Consequently, without adequate incentives and clear expectations, companies may lack motivation to pursue such improvements. Government subsidies can effectively compensate for these externalities.

According to the resource-based theory, subsidies provide valuable, rare, and inimitable resources, such as advanced technology and skilled personnel, which can enhance a company’s competitive advantage and improve product quality and production efficiency [22]. Additionally, subsidies can act as capital injections, boosting corporate earnings, mitigating losses from technological spillovers, bridging funding gaps for R&D, and supporting the acquisition of advanced equipment [23]. These subsidies help reduce the financial risks associated with R&D and encourage the adoption of new technologies. Moreover, they can alter market expectations and ease financing constraints, enabling companies to obtain financial support for production at lower costs, thereby promoting quality and efficiency improvements [24].

Based on this understanding, the paper proposes the following hypotheses:

H_1a: 

Government subsidies enhance the production efficiency of manufacturing companies.

H_1b: 

Government subsidies improve product quality in manufacturing companies.

3.2. Mediating Mechanisms: R&D Investment and Equipment Depreciation

Beyond direct effects, theory suggests government subsidies may indirectly influence product quality and productivity through specific channels. Innovation literature highlights two key pathways: R&D investment and equipment depreciation.

The innovation pathway is characterized by uncertainty, high capital requirements, and strong positive externalities, potentially leading to suboptimal private R&D investment levels [25]. Furthermore, the outcomes of R&D endeavors frequently exhibit strong positive externalities, which means that the benefits of innovation are not confined to innovation but spill over to the broader economy [26]. This spillover effect can lead to a suboptimal level of investment in R&D from a private company’s perspective, as they are unable to capture the full returns on their investments. Consequently, this may result in a diminished incentive for companies to engage in high-quality innovation. Government subsidies play a crucial role in addressing this market failure. They serve to directly bridge the funding gaps that might otherwise deter companies from engaging in R&D activities. By doing so, subsidies offset the losses incurred due to the “free-rider” problem, where companies benefit from the innovations of others without incurring the associated costs [27]. Moreover, subsidies function as a risk-sharing mechanism, diversifying the risks associated with innovation and making R&D endeavors more palatable to risk-averse companies. The reduction in the cost of R&D, facilitated by subsidies, serves to stimulate a more robust and dynamic innovation landscape. This, in turn, promotes technological progress and has a direct and positive impact on the quality of products and the efficiency of production processes [28].

Subsidies also alleviate financing constraints that hinder new physical capital investment. By supplementing cash flow, subsidies enable companies to accelerate the depreciation of their fixed assets, thereby reducing the financial burden associated with high fixed asset costs [29]. This financial relief is instrumental in encouraging greater investment in technological innovation. The acquisition of new production equipment, made more accessible through subsidies, can lead to a significant enhancement in product quality and operational efficiency. Moreover, the practice of accelerated depreciation facilitated by subsidies serves to optimize resource allocation within the economy. It facilitates the reallocation of resources from less efficient to more efficient sectors, thereby enhancing the overall production efficiency of the economy. This process also improves the flow of labor and capital within companies, leading to more efficient production processes and ultimately contributing to higher product quality [30].

Based on this analysis, the paper presents the following hypotheses:

H_2a: 

Government subsidies enhance production efficiency through increased R&D investment.

H_2b: 

Government subsidies improve product quality through accelerated equipment depreciation.

3.3. Heterogeneity Effects: Moderation by Company and Market Characteristics

The selection of specific moderating variables—company ownership, export status, and industry competition levels—relies on key theoretical and practical considerations.

Differences in company ownership are closely linked to corporate governance, decision-making, risk tolerance, and incentives. State-owned, private, and foreign-owned companies differ significantly in how they access and use government subsidies. State-owned companies may have easier access to subsidies due to their government ties but often face lower efficiency and weaker innovation incentives. In contrast, non-state-owned companies, especially those with strong market orientation and profit motives, can more effectively use subsidies to enhance competitiveness and innovation. Thus, ownership differences serve as a moderating variable that explains the varying effects of subsidies across different companies.

Exporting and non-exporting companies differ in strategies, market positioning, and trade participation. Exporting companies, facing intense international competition, may use subsidies to improve product quality, reduce costs, and expand into foreign markets. Non-exporting companies, focused on domestic markets, may seek subsidies to enhance internal capabilities and meet local demand. Therefore, the distinction between exporting and non-exporting companies helps explain the heterogeneous effects of government subsidies based on market positioning and trade status.

Industry competition levels also influence company strategies and performance. In highly competitive industries, companies may actively seek subsidies to strengthen their market position. In less competitive industries, subsidies are often used to support innovation and technological upgrades. The level of competition can affect both the efficiency of subsidy distribution and how companies utilize these funds. Thus, industry competition serves as a key moderating variable to analyze subsidy effects in different competitive contexts.

In summary, the selection of these three moderating variables—company ownership, export status, and competition levels—effectively captures the heterogeneous effects of government subsidies across various companies and industries. These variables are closely connected to real-world company behavior, market conditions, and policy goals, making them valuable for studying the impacts of government subsidies.

3.3.1. Moderation by Ownership Structure

State-owned and non-state-owned manufacturing companies differ in management, resource acquisition, and objectives, leading to varied effects of government subsidies [31]. State-owned companies, with close government ties, often enjoy advantages in resource access and government protection, reducing market pressure [32]. As a result, they may lack motivation to improve product quality and efficiency, limiting the impact of subsidies. Conversely, non-state-owned companies face intense market competition from their inception, driving them to enhance quality and efficiency. These companies also encounter greater financing constraints, such as “financing difficulties” and “expensive financing” [33]. Subsidies help non-state-owned companies address funding shortages and send positive signals to external financiers, expanding their capital pool [34,35].

Based on this analysis, the following hypothesis is proposed:

H₃: 

Government subsidies are more effective in enhancing the quality and production efficiency of non-state-owned companies compared to state-owned companies.

3.3.2. Moderating Effect of Export Behavior

Exporting and non-exporting companies operate in different environments, resulting in varied impacts of government subsidies. Exporting companies face complex markets and high entry barriers, which create significant sunk costs [36]. As these companies typically already exhibit high production efficiency, subsidies alone may not further improve efficiency [37]. However, subsidies can help exporting companies bridge financial gaps in R&D and overcome the “innovation threshold” of advanced equipment and skilled personnel [38]. They can also broaden financing channels, alleviating financial pressure and enabling the purchase of high-quality intermediate goods [39]. Non-exporting companies, competing in domestic markets, primarily improve their production efficiency and product quality through economies of scale and subsidy support [40].

Based on this analysis, the following hypothesis is proposed:

H₄: 

Government subsidies significantly enhance the production efficiency and product quality of non-exporting manufacturing companies and promote the improvement of product quality in exporting companies.

3.3.3. Differences in the Degree of Industry Competition

The level of industry competition closely relates to company behavior in production and innovation. In highly competitive industries, companies focus on capturing market share and tend to favor low-cost strategies over innovation due to lower risk tolerance [41]. In such environments, subsidies help companies overcome R&D risks by providing financial support [42]. Companies may use subsidies to expand operations, add specialized equipment, and quickly reduce costs to gain market share [20]. In industries with low competition, even when small- and medium-sized companies receive subsidies, it is difficult for them to challenge dominant companies. Here, top companies use innovation as a strategy to maintain their market share, and smaller companies can benefit from the spillover effects of knowledge and technology. Thus, in low-competition industries, companies are more inclined to use subsidies for innovation and product quality improvement [43].

Based on this analysis, the following hypothesis is proposed:

H₅: 

For companies in low-competition industries, government subsidies significantly improve product quality. For companies in highly competitive industries, government subsidies significantly enhance production efficiency.

4. Study Design and Methodology

4.1. Average Treatment Effect of Government Subsidies

This study focuses on companies that receive government subsidies, designating them as the “treatment group,” while companies that do not receive subsidies form the “control group.” To evaluate the true impact of government subsidies on the “improvement of product quality and production efficiency” and account for variations in subsidy timing and intensity, we employ a time-varying DID model, which introduces separate interaction terms for each year relative to the first subsidy. This allows us to capture both the immediate and lagged effects of subsidies. If government subsidies are effective, we should observe significant changes in product quality and production efficiency before and after the subsidies are implemented. The DID model is structured as follows:

$$Y_{it}=\partial +\beta 1·{treat}_i\times {time}_t+\phi ·{Controls}_{it}+{year}_t+D_i+{\epsilon }_{it}$$

(1)

where $i$ denotes the company, $t$ signifies the year, and $Y_{it}$ is the outcome variable measuring the product quality and production efficiency of companies. ${treat}_i$ is a dummy variable indicating whether a company has received government subsidies (1 for the treatment group, 0 for the control group). ${time}_t$ is a time dummy variable, where ${time}_t=0$ before receiving subsidies and ${time}_t=1$ after. For companies receiving intermittent subsidies, the treatment period is defined based on the first year they received a subsidy. Once a company enters the treatment group, it remains classified as treated in subsequent years, regardless of whether it receives subsidies every year. The interaction term ${treat}_i\times {time}_t$ has an estimated coefficient $\beta$, which assesses the real effect of government subsidies on product quality and efficiency. ${Controls}_{it}$ represents control variables, $D_i$ denotes company fixed effects, and ${year}_t$ signifies year fixed effects.

The DID model is particularly suitable for our research question as it helps control for time-invariant unobserved company heterogeneity and aggregate time trends, providing a more credible estimate of the subsidy effect. We also employ a dynamic DID model to examine the lagged effects of subsidies, testing the hypothesis that impacts are not instantaneous.

4.2. Effect of Government Subsidies at Different Quantiles

Equation (1) estimates the average effect of government subsidies on product quality and production efficiency. However, the impact of subsidies may vary across companies due to different development stages. Building on the analytical frameworks of Havnes and Mogstad (2015) [44] and Hu et al. (2020) [45], we further explore the heterogeneous effects of government subsidies across different quantiles using the Quantile DID method. The model is expressed as:

$$Q_{Y_{it}}\left(\tau |x_{it}\right)=\alpha +\beta 2·{treat}_i\times {time}_t+\phi ·{Controls}_{it}+{year}_t+D_i+{\epsilon }_{it}$$

(2)

where $Q_{Y_{it}}\left(\tau |x_{it}\right)$ denotes the $\mathsf{\tau }$th conditional quartile ($0<\tau <1$), and $\beta 2$ indicates the effect of government subsidies on the product quality and the production efficiency at different quantiles. The other variables retain the same meanings as in Equation (1).

4.3. Dynamic Effect of Government Subsidies

The impact of government subsidies on product quality and production efficiency may not be immediate, but could involve a dynamic process of change. To capture this, we use a time-varying DID model to examine the dynamic effects of government subsidies, formulated as:

$$Y_{it}=\partial +\beta 3·{\sum }_{t=beginyear}^{t=endyear}{treat}_i\times {TR}_t+\phi ·{Controls}_{it}+{year}_t+D_i+{\epsilon }_{it}$$

(3)

where ${TR}_t$ is a dummy variable set to ${TR}_t=1$ for year $t$ and ${TR}_t=0$ otherwise. The remaining variables have the same meaning as in Equation (1).

4.4. Mechanism for the Role of Government Subsidies

To analyze the mechanisms through which government subsidies affect the “improvement of product quality and production efficiency,” we investigate whether subsidies impact companies through “R&D investment” and “equipment depreciation.” Following the approach of Christofzik and Kessing (2018) [46], we introduce an interaction term between ${treat}_i\times {time}_t$ and the mechanism variable $M_{it}$ into the model, based on Equation (1), to clarify the pathways through which government subsidies operate [47].

$$Y_{it}=\partial +\beta 4·{treat}_i\times {time}_t\times M_{it}+\beta 5·{treat}_i\times {time}_t+\phi ·{Controls}_{it}+{year}_t+D_i+{\epsilon }_{it}$$

(4)

4.5. Analytical Procedure

Our analysis follows a structured procedure. After estimating the baseline models, we conduct a series of robustness checks, including replacing the TFP measure with the Levinsohn–Petrin (LP) estimator and winsorizing all continuous variables at the 1st and 99th percentiles. We then explore the potential mediating roles of R&D investment and equipment depreciation using a causal mediation analysis framework. Finally, we examine heterogeneous effects by splitting the sample based on ownership, export status, and industry competition level. Statistical significance is evaluated based on standard errors clustered at the company level.

4.6. Addressing Potential Biases and Measurement Error

This study acknowledges and addresses several potential methodological concerns.

The use of company fixed effects helps control for time-invariant unobservable factors that might influence both subsidy receipt and company performance.

For key variables like TFP, we use the robust OP method. For financial variables, this study winsorizes the top and bottom 1% of values to reduce the impact of outliers and potential reporting errors.

While fixed effects mitigate some concerns, this study acknowledges that residual endogeneity may persist. The empirical strategy focuses on establishing robust correlations and carefully testing mediating mechanisms, with the interpretation of results considering this limitation.

5. Data Presentation

5.1. Data Source and Data Processing

This study employs a longitudinal research design to investigate the impact of government subsidies on the product quality and production efficiency of Chinese manufacturing companies from 1998 to 2014. The panel structure of our data allows us to track companies over time and control for unobserved, time-invariant company heterogeneity, thereby providing more robust causal inferences than a cross-sectional approach. The primary data sources are the “China Industrial Companies Database,” the “China Key Surveyed Companies Database of Industrial Pollution Source,” derived from the Ministry of Ecology and Environment’s “Environmental Statistics Reporting System,” and the “China Intellectual Property Office Patent Database.” It should be noted that since 2016, due to changes in the statistical system, field reduction, quality decline, and stricter confidentiality regulations, the China Industrial Companies Database has been “deactivated” at the public level in 2015. Data from subsequent years are still collected internally, but are no longer released externally in the same content and form. The China key surveyed companies database of industrial pollution source is derived from key surveyed companies of industrial pollution sources in the “environmental statistics reporting system” of the ministry of ecology and environment, which is currently the most comprehensive and reliable micro-environmental database in China. And city-level data on company locations is obtained from the “China City Statistical Yearbook” and the statistical yearbooks of each province.

To integrate data from multiple sources, we match the “China Industrial Companies Database” with the “China Key Surveyed Companies Database of Industrial Pollution Sources” based on the company legal entity code. Then, we match this with city-level data on company locations. For unmatched companies, we use a method combining “company name + administrative code.”

The initial data set included a large number of manufacturing companies across China. To ensure robustness and reliability of the analysis, the following inclusion criteria are applied. Following methods from Brandt et al. (2012) [48] and Le et al. (2022) [49], the study cross-identifies the same company based on details such as legal entity code, company name, legal representative, phone number, and postal code. For cases where the legal entity codes are missing or inconsistent, this study implements a fuzzy matching algorithm that combines company name, administrative code, and postal code to achieve accurate data integration. This algorithm utilizes a combination of exact matches and approximate string-matching techniques to resolve coding inconsistencies and missing values. Error rate analysis for the fuzzy matching process indicated a match confidence score above 95% for approximately 95% of the integrated records, ensuring the reliability of the merged data set. Only companies with at least eight employees are included. Companies below this threshold are excluded as they often represent micro- companies, which could introduce excessive variability and are less representative of the broader manufacturing sector. Companies with financial inconsistencies are excluded, including net current or fixed assets exceeding total assets, accumulated depreciation less than current depreciation, and negative values for key indicators such as wages or value-added tax. Companies with an asset–liability ratio of less than zero are also excluded to ensure the validity of financial ratios. In addition, companies missing critical variables such as subsidy income, paid-in capital, employee count, or fixed assets are removed to maintain data completeness. After applying these criteria, the data set is reduced to a final sample of 69,525 companies, representing a comprehensive and consistent subset of Chinese manufacturing companies.

5.2. Outcome Variables

The outcome variables are the product quality (quality1) and production efficiency (tfp1) of companies. Product quality is measured by the proportion of the output value of new products in a company’s total output value. The “proportion of new product output value” serves as a meaningful proxy for product quality, particularly in the context of manufacturing companies, for several essential reasons. First, this metric reflects a company’s commitment to innovation and R&D activities. New products typically result from significant investment in research, development, and technological advancement, which are closely associated with quality improvements. Second, a higher proportion of new product output indicates a company’s ability to adapt to changing market demands and consumer preferences. This adaptability often translates into products that better meet customer needs and expectations, a key component of quality. Third, companies with a greater share of new products in their output often enjoy competitive advantages in the marketplace. These advantages frequently stem from superior product features, performance, or design elements that contribute to overall quality perception.

This study distinguishes sustainability-oriented innovation from conventional market differentiation in several ways. Sustainability innovations typically involve fundamental changes in production processes and product designs that reduce environmental impacts, whereas market differentiation might involve only cosmetic or stylistic changes. Sustainable innovation often requires significant R&D investment in environmental technologies, whereas market differentiation may focus more on marketing and branding. Regulatory frameworks increasingly shape new product development toward sustainability standards. While this proxy may also capture some elements of market differentiation, its strong association with environmental innovation indicators in prior research (e.g., green patents, energy efficiency certifications) supports its validity for capturing sustainability-related innovation. Future research could supplement this measure with direct environmental performance indicators, but within the constraints of this study’s datasets, the new product output ratio provides a robust and theoretically grounded measure of sustainability-oriented innovation.

The formula for calculating product quality is as follows.

$$quality1={\displaystyle \frac{Output Value of New Products}{Total Output Value of the Company}}$$

(5)

This metric reflects a company’s commitment to innovation and its ability to adapt to changing market demands through the development of new products.

Production efficiency is measured by total factor production efficiency (TFP), calculated using the Olley–Pakes (OP) method. TFP reflects the average output from various input factors in the production process, offering a comprehensive measure of a company’s production efficiency. The OP method is a consistent semi-parametric estimation method that addresses simultaneity bias, selectivity bias, and endogeneity issues better than other methods. The formula for calculating TFP using the OP method is as follows.

$$tfp1=ln({\displaystyle \frac{Value-Added}{{Capital}^{\alpha }\times {Labor}^{\beta }}})$$

(6)

where $Value-Added= Total Output - Intermediate Inputs$, $Capital = Net Fixed Assets$, $Labor = Number of Employees$, $\alpha$ and $\beta$ are the output elasticities of capital and labor, respectively, estimated based on the production function.

5.3. Proxy Variables and Mediator Variable

While the “proportion of new product output value” provides valuable insights into product quality, it is important to recognize its limitations. New products may sometimes represent incremental improvements rather than substantial quality enhancements. Additionally, market strategies and functional iterations might influence this metric without necessarily indicating genuine quality improvements. To address these limitations, researchers often supplement this metric with other quality indicators such as export prices, consumer ratings, or patent quality measures [50]. These additional metrics provide a more comprehensive assessment of product quality from multiple perspectives, ensuring a more robust analysis of quality dynamics in manufacturing companies. Following Scotchmer et al. (2017) [50], we use the number of patent applications (quality2) as a proxy for product quality, as it reflects product complexity and innovation. Total factor production efficiency, calculated using the Levinsohn–Petrin (LP) method (tfp2), serves as a proxy for production efficiency in robustness tests. To account for patent applications with zero values, we use the logarithm of the number of patent applications after adding one.

R&D investment data are obtained from the CSMAR database, which covers all publicly listed companies in China. The two datasets are matched using the “legal entity code,” a unique identifier that links company records across databases. Where the legal entity code is unavailable or inconsistent, additional identifiers, such as company name, administrative codes, and postal codes, are used to ensure accurate matching. The final R&D data set consisted of 56,081 observations, reflecting the subset of companies for which consistent and reliable R&D data are available. These observations represent listed companies that reported R&D expenditures during the study period.

5.4. Policy Variable and Control Variables

In this paper, “government subsidies” are defined based on the “subsidy income” entry recorded in the financial reports of companies. These subsidies encompass direct subsidies, tax incentives, financing support, and other forms of governmental assistance. While these subsidies are issued through various channels to promote technological innovation and production efficiency, due to data limitations, this study does not classify them into separate categories but treats them as a unified variable.

The policy variable is an interaction term representing companies that have received government subsidies (the treatment group). Following Xu and Mao (2019) [51], we define the treatment group with a dummy variable, ${treat}_i\times {time}_t$, which equals 1 after a company receives subsidies and 0 before.

Several control variables are included to account for other factors influencing product quality and production efficiency, based on existing literature [52,53,54,55,56]. The variables are described in detail in

Table 1. Variables Description.

Variable Type	Name	Symbol	Definition
Outcome Variables	product quality of companies	quality1	Proportion of the output value of new products in the total output value of companies
	production efficiency of companies	tfp1	Total factor production efficiency calculated based on the OP
Policy Variable	government subsidies	treati × timet	Interaction term between dummy variables for the treatment group and dummy variables for the treatment period
Mediator Variable	R&D investment	crdinv	R&D investment scale of companies (in thousands of RMB), then take the logarithm
	equipment depreciation	cdep	The proportion of the company depreciation of the current year in the original price of fixed assets
Control Variable	company ownership	soe	For state-owned companies, the value is 1, otherwise is 0
	company scale	size	Net fixed assets of companies (in thousands of RMB), then take the logarithm
	company age	age	The statistical year minus year of company establishment + 1, then take the logarithm
	company export	export	If companies have the export behavior, the value is 1, otherwise is 0.
	company leverage	fasset	Asset–liability ratio of companies
	degree of industry competition	hhi	Herfindahl index multiplied by 100
Proxy Variable (robustness test)	product quality of companies	quality2	The number of patent applications of companies+1, then take the logarithm
	production efficiency of the companies	tfp2	Total factor production efficiency calculated based on the LP

.

Table 2. Descriptive Statistics.

Variable	Mean Value	Standard Deviation	Minimum Value	Maximum Value	the Number of Observations
quality1	3.925	13.943	0	88.752	188,683
tfp1	1.488	1.084	−8.358	7.779	210,535
treati × timet	0.191	0.393	0	1	215,812
crdinv	7.298	2.213	−6.543	16.946	56,081
cdep	5.793	5.110	0	33.246	215,595
soe	0.215	0.411	0	1	215,812
size	9.371	1.655	−0.120	16.957	215,812
age	2.421	0.995	0	7.602	215,812
export	0.128	0.283	0	1	215,812
fasset	0.661	0.293	0.025	1.577	215,812
hhi	3.114	3.517	0.140	38.382	215,812
quality2	0.066	0.356	0	7.368	215,812
tfp2	6.313	1.223	−2.859	12.617	210,535

presents the descriptive statistics for the variables. The data demonstrates that overall product quality among Chinese manufacturing companies is relatively low, with significant gaps in both product quality and production efficiency across companies. This justifies examining the effects of government subsidies on these variables at different quantiles.

6. Effects of Subsidies and Mechanisms

6.1. Average Treatment Effect

6.1.1. Benchmark Test Result

Table 3. Average Treatment Effect of Government Subsidies. This table presents the baseline regression results for the impact of subsidies on product quality and production efficiency. Subsidies have a positive and statistically significant average effect on both outcomes, with a notably larger magnitude for product quality improvement.

	(1)	(2)
	quality1 Product Quality	tfp1 Production Efficiency
${treat}_i\times {time}_t$	0.4512 *** (0.1470)	0.0193 * (0.0100)
control variable	yes	yes
company fixed effect	yes	yes
year fixed effect	yes	yes
adjusted R-squared	0.6152	0.6676
sample size	166,642	188,670

Note: *** and * respectively indicate the significance at the significance levels of 1% and 10%; values in parentheses () are standard errors.

demonstrates the estimated results of the average treatment effect on product quality and production efficiency. For product quality (column 1), the estimated coefficient for government subsidies is 0.4512, significant at the 1% level, indicating that subsidized companies improve their product quality by approximately 11.5% (0.4512/3.925) compared to non-subsidized companies. For production efficiency (column 2), the estimated coefficient is 0.0193, significant at the 10% level, suggesting that subsidies significantly enhance production efficiency. Thus, government subsidies are beneficial for improving both product quality and production efficiency, verifying hypotheses ${\mathrm{H}}_{1\mathrm{a}}$ and ${\mathrm{H}}_{1\mathrm{b}}$.

Similar studies on government subsidies in other contexts demonstrate that some studies in China focusing on direct R&D subsidies [57] have reported comparable effects, with subsidies increasing innovation output by approximately 10–15%. This aligns with the findings, which highlight that subsidies effectively promote new product development and production efficiency through R&D investment and equipment upgrades. Research on subsidies in European countries [5] and the United States [8] suggests similar magnitudes, with R&D-focused subsidies increasing production efficiency and innovation output by 5–20%. The findings fall within this range, further validating the robustness and economic significance of the results.

6.1.2. Robustness Test

To ensure the reliability of these findings, the study conducts several robustness tests, including “replacing the explained variables,” “sample winsorization,” “adding control variables,” and “adjusting the estimation method.” The results are presented in

Table 4. Robustness Test.

	1. Replace the Explained Variables		2. Sample Winsorization		3. Add Control Variables		4. Adjust the Estimation Method
	quality2	tfp2	quality1	tfp1	quality1	tfp1	quality1	tfp1
${treat}_i\times {time}_t$	0.0073 * (0.0040)	0.0402 *** (0.0104)	0.3908 *** (0.0692)	0.0137 *** (0.0050)	0.4004 *** (0.0911)	0.0179 *** (0.0058)	0.4511 *** (0.0862)	0.0193 *** (0.0055)
control variable	yes	yes	yes	yes	yes	yes	yes	yes
company fixed effect	yes	yes	yes	yes	yes	yes	yes	yes
year fixed effect	yes	yes	yes	yes	yes	yes	yes	yes
adjusted R-squared	0.4571	0.7420	0.6192	0.6679	0.6202	0.6667	0.6153	0.6676
sample size	193,931	188,670	164,754	184,519	149,821	171,503	166,640	188,668

Note: *** and * respectively indicate the significance at the significance levels of 1% and 10%; values in parentheses () are standard errors.

.

Replacing outcome variables: Using alternative measures (quality2 and tfp2), the coefficients and significance levels for government subsidies remain largely unchanged.

Winsorization: A two-sided 1% winsorization is applied to the data to eliminate extreme values, with results consistent with the previous findings.

Adding control variables: The study introduces a new variable, “recycling water consumption rate” (recycle), into the model, because this indicator directly reflects the core level of a company’s water resource utilization efficiency. As a key assessment variable in the industrial water use field, it can effectively control the potential interference of resource efficiency differences on the research results. While the coefficients for government subsidies change slightly, their direction and significance remain stable.

Propensity Score Matching (PSM): The study matches control group companies with similar characteristics to the treatment group and re-estimates the average treatment effect using the difference-in-differences method. The coefficients estimated with PSM-DID demonstrate no significant changes, and their significance levels are enhanced.

Overall, these tests confirm that government subsidies effectively improve product quality and production efficiency in manufacturing companies. The results are robust and reliable.

6.1.3. Placebo Test

In order to exclude the possibility that the impact of government subsidies on product quality and production efficiency of manufacturing companies is caused by other random factors, this study applies the placebo test.

The specific steps are as follows: First, according to the government subsidies, randomly generate the same number of treatment group companies, so as to constitute a “pseudo-treatment group.” Second, for each “pseudo-treatment group,” randomly select a year as the “pseudo-subsidy time.” Third, multiply the “pseudo-treatment group” with the “pseudo-subsidy time” to generate the interaction term “pseudo-subsidy dummy variable” and perform regression. The above process is repeated 500 times to obtain the kernel density distribution of the estimated coefficients of the “pseudo-subsidy dummy variable,” as demonstrated in Figure 1.

Figure 1 presents the results of the placebo test for the effect of government subsidies on (a) product quality and (b) production efficiency. The solid vertical line represents the actual estimated coefficient from the baseline model. The distribution of the estimated coefficients from 500 random simulations of the treatment group and treatment timing is shown. The concentration of the placebo test coefficients around zero, and their clear separation from the actual estimate, supports the robustness of our main findings by indicating that the observed effects are unlikely to be due to random chance.

6.1.4. Heteroskedasticity-Robust Standard Errors

Given the likelihood of differing variances across company sizes and industries, this study switches to heteroskedasticity-robust standard errors in our regression analyses. This study uses the heteroskedasticity-robust standard error estimation method for parameter re-estimation. The adjustment ensures that the standard errors are robust to potential heteroskedasticity, thereby providing more reliable statistical inference. The results are demonstrated in

Table 5. Average Treatment Effect with Heteroskedasticity-robust Standard Errors.

	(1)	(2)
	quality1 Product Quality	tfp1 Production Efficiency
${treat}_i\times {time}_t$	0.5885 *** [0.1128]	0.0772 *** [0.0063]
control variable	yes	yes
company fixed effect	yes	yes
year fixed effect	yes	yes
adjusted R-squared	0.0234	0.1291
sample size	188,683	210,535

Note: *** indicates the significance at the significance level of 1%; values in square brackets [] are heteroskedasticity-robust standard errors.

.

The regression results demonstrate that the regression coefficients of government subsidies on product quality and production efficiency are still significant. Thus, the results remain consistent with the original findings, which further validate the robustness of the conclusions.

6.2. Regression Result at Different Quantiles

The study analyzes the effects of government subsidies at different quantiles using the quantile difference-in-differences method. This approach avoids the influence of extreme values and clarifies the effect of subsidies at each quantile point. The study estimates the model at 99 quantile points, with intervals of 0.01.

The horizontal axis (tau) represents the quantile points for the product quality and production efficiency of companies, while the vertical axis (QTE) indicates the estimated coefficient of the difference-in-differences. Figure 2a demonstrates the effect of subsidies on product quality at different quantile points. The black dots represent the regression coefficients at each quantile, while the dashed lines demonstrate the confidence intervals. The solid line represents the average treatment effect. The result indicates that government subsidies have a significant positive effect on companies with product quality above the 85th percentile, demonstrating an “inverted U” trend. For companies with product quality below the 85th percentile, subsidies do not significantly improve product quality. This supports the rationale behind China’s policy of favoring companies with higher product quality. The pattern suggests that while subsidies are beneficial for companies with relatively high but not yet top-tier product quality, their effectiveness wanes for companies at the very highest quality levels. One potential explanation for this phenomenon could be diminishing marginal returns. As companies already possess a certain level of product quality, the additional benefits gained from subsidy-funded improvements may decrease. Moreover, high-product-quality companies might already be efficiently allocating their resources, making it difficult for subsidies to generate substantial additional improvements. Another possible explanation is resource misallocation. High-product-quality companies might have access to other funding sources and may not utilize subsidies in the most effective manner, potentially diverting funds from more innovative or efficient uses. Considering company lifecycle dynamics, mature companies with already high product quality might have less room for improvement compared to younger companies. Subsidies may be more effective in earlier stages of a company’s development when quality improvements can lead to significant competitive advantages. Market competition mechanisms could also play a role. In highly competitive markets, companies with very high product quality might already be operating near their maximum potential, leaving limited room for subsidy-driven improvements. Conversely, companies with product quality just above the 85th percentile may still have space to grow and benefit more from subsidy support.

Figure 2b illustrates the effect of subsidies on production efficiency at different quantile points. The result demonstrates that government subsidies consistently stimulate manufacturing companies to improve production efficiency, with a “positive U” trend. For the leading companies with production efficiency above the 95th percentile, the incentive effect of subsidies increases rapidly as production efficiency improves.

6.3. Dynamic Effect

The study uses a multi-period time-varying difference-in-differences approach to examine the dynamic effects of government subsidies.

Figure 3a,b, respectively, illustrate the dynamic effect of government subsidies on product quality and production efficiency of manufacturing companies as the explained variables. In Figure 3, the horizontal axis represents the relative time when manufacturing companies receive government subsidy support, and the vertical axis represents the average treatment effect. In addition, the histogram depicts the sample size involved in the estimation during the corresponding period. According to Figure 3, (1) both treatment effects are close to zero before government subsidies are granted, and significantly greater than zero after subsidies are granted, which indicates that the estimated model conforms to the parallel trend assumption. (2) There is a certain lag in the incentive effect of government subsidies on the product quality and production efficiency of manufacturing companies. It is demonstrated in Figure 3 that from the second year after subsidies are granted, the effect of “improving product quality and production efficiency” begins.

6.4. Discussion on the Mechanism

The study explores the mechanisms by which government subsidies affect the “improvement of product quality and production efficiency” through R&D investment and equipment depreciation. The regression results are demonstrated in

Table 6. Mechanism Results.

	(1)	(2)	(3)	(4)
	quality1 Product Quality	quality1 Product Quality	tfp1 Production Efficiency	tfp1 Production Efficiency
${treat}_i\times {time}_t$	0.1586 *** (0.0274)	0.2421 *** (0.0556)	0.1034 *** (0.0229)	0.0268 *** (0.0034)
${treat}_i\times {time}_t\times {crdinv}_{it}$	0.0178 ** (0.0084)		0.0130 ** (0.0065)
${treat}_i\times {time}_t\times {cdep}_{it}$		0.0070 ** (0.0029)		0.0017 *** (0.0005)
control variable	yes	yes	yes	yes
company fixed effect	yes	yes	yes	yes
year fixed effect	yes	yes	yes	yes
adjusted R-squared	0.6912	0.7302	0.6382	0.6472
sample size	14,332	45,888	18,250	30,134

Note: *** and ** respectively indicate the significance at the significance levels of 1%, 5%; values in parentheses () are standard errors.

.

To provide a more complex and multifaceted understanding of the mediating roles, we employ a mediation analysis framework based on the approach proposed by Baron and Kenny (1986) [58]. This allows us to quantify the specific contributions of R&D investment and equipment depreciation in the relationship between government subsidies and manufactured company performance.

From the channel of R&D investment, government subsidies significantly increase R&D investment. This not only strengthens their innovative capacity and therefore enhances product quality, but also provides a technical basis for higher production efficiency, affecting both product quality and production efficiency. The direct effect of subsidies on product quality is partially mediated by R&D investment, accounting for 15.86% of the total effect. Similarly, R&D investment explains 10.34% of the total effect of subsidies on production efficiency.

From the channel of equipment depreciation, government subsidies provide financial support for upgrading equipment, establishing a solid material foundation for improving product quality and production efficiency. The direct effect of subsidies on product quality is partially mediated by equipment depreciation, accounting for 24.21% of the total effect. Equipment depreciation explains 2.68% of the total effect of subsidies on production efficiency.

While both R&D investment and equipment depreciation play essential roles, the relative importance of each channel varies depending on the outcome variable. For product quality, equipment depreciation appears to have a slightly stronger mediating effect compared to R&D investment. Conversely, for production efficiency, R&D investment has a slightly stronger mediating effect. Therefore, the research hypotheses ${\mathrm{H}}_{2\mathrm{a}}$ and ${\mathrm{H}}_{2\mathrm{b}}$ are verified.

7. Heterogeneity Study

The study examines heterogeneous effects of government subsidies on product quality and production efficiency across different types of companies, based on property rights, export behavior, and industry competition.

7.1. Differences in Property Rights

State-owned and non-state-owned companies differ in hierarchical structure, functional role, management style, and decision-making process. These differences may lead to different effects of government subsidies on improving product quality and production efficiency [59]. Therefore, the study divides samples into two groups and analyzes them separately.

The estimation result is summarized in

Table 7. Grouped estimation result based on property right of companies.

	State-Owned Companies		Non-State-Owned Companies
	(1)	(2)	(3)	(4)
	quality1 Product Quality	tfp1 Production Efficiency	quality1 Product Quality	tfp1 Production Efficiency
${treat}_i\times {time}_t$	0.1945 (0.1769)	0.0188 (0.0139)	0.5108 *** (0.1018)	0.0186 *** (0.0062)
control variable	yes	yes	yes	yes
company fixed effect	yes	yes	yes	yes
year fixed effect	yes	yes	yes	yes
adjusted R-squared	0.7034	0.6420	0.5943	0.6387
sample size	35,928	36,710	125,683	147,014

Note: *** indicates the significance at the significance level of 1%; values in parentheses () are standard errors.

. The findings indicate that government subsidies do not significantly improve the product quality or production efficiency of state-owned companies. One reason could be that state-owned companies tend to receive subsidies more easily [60]. Excessive subsidies may reduce innovation incentives, leading to inefficiencies in resource allocation and overall economic performance [61]. In contrast, subsidies are significantly effective in enhancing the product quality and production efficiency of non-state-owned companies. Because these companies often face difficulties in accessing funds, subsidies help alleviate financing constraints by sending positive market signals. Thus, subsidies promote higher product quality and production efficiency in non-state-owned companies, and research hypothesis ${\mathrm{H}}_3$ is verified.

The ownership-split results align with institutional theory expectations. State-owned companies operate under a dual institutional logic. They simultaneously pursue commercial objectives and fulfill politically mandated employment or regional development targets. This hybrid logic dilutes the marginal value of an additional subsidy dollar. In addition, multiple-principal monitoring increases information asymmetry and managerial slack, lowering conversion efficiency of external funds. But non-state companies face a pure market logic. Residual claimants bear the full consequence of misallocated resources, intensifying the need to convert subsidies into observable productivity gains to attract follow-on financing. Thus, it is predictable once institutional logics and principal–agent structures embedded in different ownership regimes are taken into account.

7.2. Differences in Export Behavior

In the volatile international environment, exporting companies face greater risks but also have more opportunities to interact with high-quality companies. Export behavior could affect the effect of government subsidies. The study divides samples into “exporting companies” and “non-exporting companies.”

Table 8. Grouped Estimation Result Based on Export Behavior of Companies.

	Exporting Companies		Non-Exporting Companies
	(1)	(2)	(3)	(4)
	quality1 Product Quality	tfp1 Production Efficiency	quality1 Product Quality	tfp1 Production Efficiency
${treat}_i\times {time}_t$	0.7061 *** (0.1823)	0.0071 (0.0088)	0.2422 *** (0.0940)	0.0234 *** (0.0073)
control variable	yes	yes	yes	yes
company fixed effect	yes	yes	yes	yes
year fixed effect	yes	yes	yes	yes
adjusted R-squared	0.6737	0.6646	0.5747	0.6767
sample size	45,315	51,719	115,794	131,086

Note: *** indicates the significance at the significance level of 1%; values in parentheses () are standard errors.

demonstrates the results. Government subsidies significantly enhance the product quality of exporting companies, but their impact on production efficiency is not substantial. For non-exporting companies, subsidies significantly improve both product quality and production efficiency. This may be because non-exporting companies are usually relatively weak in terms of product quality and production efficiency, with more room for improvement. By increasing the revenue of companies, government subsidies can help them carry out R&D and innovation projects, thereby driving improvement of product quality and production efficiency. Thus, this supports research hypothesis ${\mathrm{H}}_4$.

7.3. Differences in Industry Competition

Manufacturing companies in low-competition environments, often monopolies or oligopolies, have larger profit margins and greater capacity to accumulate innovative resources. However, companies in highly competitive environments face challenges such as limited resources and insufficient funding, making development more difficult. For these companies, government subsidies could either be “icing on the cake” or “a lifeline,” creating heterogeneous effects on product quality and production efficiency.

The study divides samples into “low-competition” and “high-competition” to estimate the actual effect of government subsidies.

Table 9. Grouped Estimation Result Based on Industry Competition.

	Low-Competition Manufacturing Companies		Highly Competitive Manufacturing Companies
	(1)	(2)	(3)	(4)
	quality1 Product Quality	tfp1 Production Efficiency	quality1 Product Quality	tfp1 Production Efficiency
${treat}_i\times {time}_t$	0.6710 *** (0.1440)	0.0100 (0.0084)	0.1598 (0.1048)	0.0182 ** (0.0078)
control variable	yes	yes	yes	yes
company fixed effect	yes	yes	yes	yes
year fixed effect	yes	yes	yes	yes
adjusted R-squared	0.6469	0.6886	0.5895	0.6558
sample size	77,926	88,065	79,573	91,628

Note: *** and ** respectively indicate the significance at the significance levels of 1% and 5%; values in parentheses () are standard errors.

summarizes the findings. Subsidies significantly promote product quality in low-competition companies and production efficiency in highly competitive ones. However, subsidies do not significantly impact the production efficiency of low-competition companies or that product quality of highly competitive ones. In highly competitive industries, the risk of R&D results being imitated is high, so companies may reduce R&D expenditures to avoid risks, focusing instead on improving production efficiency. In contrast, companies in low-competition industries often engage in innovation to consolidate their market position. These companies use R&D subsidies to improve product quality, capturing more market share and achieving higher profits. This verifies research hypothesis ${\mathrm{H}}_5$.

8. Conclusions

8.1. Main Findings of the Study

Based on unbalanced panel data from 1998 to 2014, covering 69,525 manufacturing companies in China, this paper examines the static, quantile, and dynamic impacts of government subsidies on manufacturing companies. The study focuses on “product quality improvement” and “production efficiency enhancement,” and explores the influencing channels and heterogeneous effects of government subsidies on the two aspects. Main findings are as follows.

First, government subsidies stimulate improvement of product quality and production efficiency but with a lagged effect, typically becoming noticeable around the second year after implementation of subsidies. This is consistent with Liu and Zhou (2023) [18], who find that due to factors such as long-term government preferences and information asymmetry, subsidies have little immediate impact on R&D efficiency, but begin to demonstrate a positive effect after two years.

Second, government subsidies have a significant impact on companies with product quality above the 85th percentile, demonstrating an “inverted U” pattern as the quantile increases. For companies with product quality below the 85th percentile, the impact of subsidies on quality improvement is not evident.

Third, government subsidies consistently incentivize production efficiency of manufacturing companies, following a “positive U” pattern as the quantile increases. The incentive effect strengthens rapidly for companies above the 95th percentile. This aligns with Guan (2022) [62], who finds that the impact of subsidies and R&D investment on high-quality development becomes more dominant at higher quantiles. The “inverted U” effect is less significant for medium-sized companies (0.8), while the “positive U” effect is more pronounced for large companies (0.2–0.5).

Fourth, government subsidies enhance product quality and production efficiency by increasing R&D investment and expediting equipment depreciation. Jin et al. (2018) [57] find a correlation coefficient of 0.442 between government subsidies and R&D investment, suggesting that a 1% increase in subsidies may boost R&D investment by 0.442%. Wang and Sun (2022) [63] further categorize subsidies into R&D and production ones, demonstrating that R&D subsidies improve total factor production efficiency by stimulating R&D investment and innovation output, while production subsidies do that by alleviating financing constraints and reducing financial pressure.

Fifth, government subsidies have different effects on various types of companies. For non-state-owned companies, subsidies significantly benefit both product quality and production efficiency, while the effect on state-owned companies is less pronounced. Qi and Yang (2021) [64] also find that the positive impact of subsidies on total factor production efficiency is higher for non-state-owned companies than state-owned ones. Regarding export behavior, subsidies significantly motivate non-exporting companies to improve both product quality and efficiency. For exporting companies, subsidies have a strong impact on product quality but little effect on production efficiency. Li et al. (2023) [11] find similar results. Their study indicates that China’s subsidy policies have a crowding-out effect on export innovation, with subsidies to small companies being more favorable for promoting export innovation. From the perspective of industry competition, subsidies are more beneficial for low-competition companies to improve product quality and for highly competitive companies to enhance production efficiency.

8.2. Recommendations for Policymakers

First, government departments should prioritize the incentive effect of subsidies. Subsidies should be used to encourage the improvement of product quality and production efficiency in manufacturing companies, thereby enhancing their core competitiveness and development. Thus, manufacturing companies can effectively utilize government subsidies as a supportive measure. Through the two key channels of “R&D investment” and “equipment depreciation,” companies can boost their R&D efforts, upgrade production facilities, and integrate new technologies with new equipment to continuously improve product quality and production efficiency.

Second, relevant departments should establish systematic methods for subsidy assessment and selection, focusing on the innovation capability and development potential of manufacturing companies. Regular evaluation of the economic performance and innovative achievements of subsidized companies should be conducted. To ensure that subsidies are used for technological R&D and equipment upgrades, the government should set some policy rules regarding monitoring the use of funds, allocating subsidies for specific purposes, establishing penalty mechanisms and so on.

Third, the research findings underscore the necessity of moving beyond uniform subsidy models toward differentiated allocation strategies tailored to company and industry characteristics. For instance, subsidies could be calibrated based on industry-specific dynamics. High-competition sectors (e.g., consumer electronics) might prioritize efficiency-enhancing subsidies to optimize production processes. Low-competition sectors (e.g., specialized machinery) could focus on R&D-intensive support to foster quality innovation. Export-targeted subsidies should account for global market positioning. Providing phased support for emerging export sectors like new energy vehicles to mitigate risks during international expansion, while avoiding dependency in mature export industries. Additionally, R&D-focused differentiation could adopt scale-based thresholds, such as offering higher subsidy rates for R&D investments to large companies, while simplifying access for small and medium-size companies to alleviate financing constraints. This tripartite approach—industry-specific, export-targeted, and R&D-differentiated—ensures subsidies align with heterogeneous development needs, maximizing resource efficiency and policy impact.

8.3. Recommendations for Company Managers

For manufacturing companies, this paper highlights the role of policy tools in promoting technological progress and optimizing resource allocation. First, management should recognize that government subsidies are not just financial support, but also a strategic resource for fostering long-term sustainable development. Companies should look for opportunities to access and take advantage of subsidies when planning their growth paths. Then, research demonstrates that different types of companies respond differently to subsidies, suggesting that management should consider their own specific conditions when designing projects and strategies. For instance, non-state-owned and non-exporting companies may need to actively seek subsidies to enhance competitiveness. Additionally, companies in highly competitive industries should focus on the potential of subsidies to improve production efficiency.

Moreover, the research suggests that management should carefully analyze the potential impact of government subsidies when formulating corporate strategies and daily management decisions. This includes not only assessing the direct economic benefits of subsidies but also understanding how subsidies can drive internal technological innovation and efficiency improvement. Managers should ensure that the use of government subsidies aligns with long-term corporate development and innovation plans.

In short, this study emphasizes the need for the government to provide distinct subsidy policies based on company attributes and industry environments, which can maximize the effectiveness of subsidies. For management, understanding and anticipating changes in government policies, as well as aligning these policies with corporate strategy, will be crucial to successfully leveraging external resources to enhance product quality and production efficiency.

8.4. Limitations of the Study and Directions for Future Research

While this study provides comprehensive evidence on the effects of government subsidies on the sustainable development of Chinese manufacturing companies, several limitations should be acknowledged, which also present opportunities for future research. In terms of data coverage, this paper relies on data from 1998 to 2014. While this period captures a significant phase of China’s manufacturing development, it may not fully reflect the most recent trends and changes in the manufacturing industry. Additionally, regarding external validity, the study focuses on manufacturing companies in China, providing valuable insight into the current situation in China, but limits the applicability of its findings to other countries with different economic systems and industrial structures. The paper proposes that R&D investment and equipment upgrades are the primary mechanisms through which subsidies improve company performance. However, we recognize that spillover effects may also play a role. Companies that receive subsidies might influence the production efficiency of neighboring companies through knowledge diffusion, technological spillovers, or increased competition. While our current analysis does not explicitly account for these spillover effects, future research could explore spatial econometric models or network analysis to better understand these inter-company dynamics. We also acknowledge the possibility that subsidies might simply replace other sources of funding rather than stimulating new investment. This crowding out effect could occur if companies use subsidy funds to substitute for internal financing or other external capital sources, thereby not necessarily increasing overall investment levels. To address this concern, future research could incorporate financial data to analyze whether subsidized companies exhibit changes in other financing activities or investment behaviors. Regarding the distribution of company characteristics across quantiles, we agree that a deeper exploration is necessary. Companies at different quantiles may respond differently to subsidies due to structural differences such as size, age, or industry-specific factors. Our current analysis uses quantile regression to identify heterogeneous treatment effects, but we can further investigate the underlying company characteristics that drive these differences. This could involve clustering companies based on multiple attributes and examining how subsidy allocation and effectiveness vary across these clusters.

There are several directions for future research. First, study the impact of various types of government subsidies on the enhancement of product quality and production efficiency of manufacturing companies, including direct subsidies, tax incentives, and government procurement. This study does not distinguish specific types of subsidies and potential differentiated effects on the results, which may affect the granularity of the findings. Future research could explore how different types of subsidies contribute to the improvement of product quality and production efficiency, providing more targeted insights for policy design. Second, explore the combination of subsidies that could achieve the optimal effect on the enhancement of product quality and production efficiency of manufacturing companies. Third, while this study provides robust analyses of subsidy impacts in China, it would be valuable for future research to conduct cross-country comparisons. Such work could explore how differences in subsidy design, subsidy implementation, and industry environment affect the subsidy effect, which would provide policymakers with a clearer understanding of the more effective subsidy strategies globally. Fourth, future research can introduce more direct measures of quality, such as those proposed by Amiti and Khandelwal (2013) [65] in order to provide a more profound understanding of the relationship between government subsidies and product quality.