Guidance Certification Effect and Governance Supervision Effect of Government Investment Funds

Abstract

The establishment of government investment funds serves as a crucial measure for governments at all levels to leverage their certification role and financial resources in attracting social capital to support enterprise development. This paper empirically examines the guiding certification effect and governance supervision effect of government investment funds on enterprise value enhancement, utilising panel data from listed companies and government investment fund investment event data spanning the period from 2011 to 2021. The research findings reveal that government investment funds significantly enhance the value of recipient enterprises. By leveraging their guidance and certification effects and governance supervision effects, these funds alleviate financing constraints, actively participate in corporate governance, and ultimately enhance corporate value. The impact of government investment funds is negatively moderated by the age and size of the enterprise, indicating that the “invest in early-stage and small businesses” investment strategy yields better results in promoting value enhancement. Furthermore, heterogeneity analysis demonstrates that government investment funds have a more pronounced impact on the value of non-heavily polluting industries, enterprises located in the eastern and southern regions of China, and non-state-owned enterprises. This article expands the research scope of government investment funds at the micro level, providing empirical evidence and theoretical support for optimising government investment funding policies and fostering the development of a modern capital market with distinctive Chinese characteristics.

1. Research Background

The 20th National Congress of the Communist Party of China emphasised the need to promote high-quality development and the development of a new economic model while strengthening the government’s role in the capital market and direct financing. Faced with challenges, such as the recent pneumonia pandemic and international trade frictions, China is exploring new government investment models to avoid local debt crises and improve market regulation.

According to the theory of market failure and state intervention, uncertainty and high risk deter social capital from investing in SMEs and high-tech industries (Bertoni and Tykvová 2015), leading to market inefficiency. To address this, the government uses the Government Investment Fund, a financial tool that combines the political objectives of supporting business development and innovation with the economic objectives of profitability and added value. This fund helps to compensate for market failures by attracting social capital to riskier but necessary investments.

China’s first government investment fund, set up in 2002 in Zhongguancun, redirected social capital towards innovative SMEs, transforming government support for innovation. Since the introduction of specific regulations in 2015, the number of such funds exploded, reaching 2107 by 2022, with a focus on high-tech sectors, such as information technology and healthcare. This aligns investments with the country’s technological and economic development objectives. Figure 1 shows that government investment funds have primarily invested in the fields of IT and informatisation, medical and healthcare, and manufacturing, while industries, such as tourism, real estate, and blockchain, have received fewer investments from government investment funds. This indicates that government investment funds have focused more on the new economy with higher levels of technological innovation, aligning with the objectives of these funds’ establishment.

The existing literature on government investment funds primarily focuses on their operational mechanisms and impact, yet conclusions remain significantly contentious. Proponents of government investment funds argue that these funds play a positive role in the market, exerting beneficial effects on investee companies. Studies, such as those by Hood (2000), suggest that government investment funds primarily exhibit a “crowding-in effect”, increasing rather than displacing private capital (Brander et al. 2015). Besides direct financial support to investee companies, government investment funds also perform a screening and certification role, endorsing investee companies and helping private capital select investment targets. This sends a signal of “government support and involvement” to private capital, reducing search and transaction costs, alleviating policy risks caused by information asymmetry in financing activities, and guiding private capital towards projects supported by government investment funds (Guerini and Quas 2016). These funds fill the funding gaps left by private investors (Colombo et al. 2016; Alperovych et al. 2020), supporting early-stage tech enterprises, breaking through technological innovation bottlenecks, addressing issues of inequality (Munari and Toschi 2015), and achieving regional development goals (Bertoni and Tykvová 2015). For investee companies, government investment funds can stimulate research and development (R&D) and innovation (Audretsch et al. 2002), increase the number and quality of invention patents in key technological areas, highly cited patents, new technology patents, and the originality of patents (Wu and Yan 2023). Especially when co-investing with private venture capital, government investment funds can enhance the promoting effect of private capital on corporate technological innovation, increase the productivity of investee companies (Murtinu 2020), and are more likely to successfully exit through IPOs or M&A transactions (Brander et al. 2015; Cumming et al. 2017).

Conversely, some scholars argue that the impact of government investment funds on enterprises may not achieve the expected outcomes. In examining the operational mechanisms of government investment funds, research by Armour and Cumming (2006) found that direct investments by government investment funds might crowd out private venture capital. Compared to private capital, government investment funds, with their governmental attributes, may face issues, such as irrational project design and policy implementation deviations during operations. They are susceptible to political influences and interest groups, leading to corruption and favouritism, resulting in post-investment management deficiencies and decreased investment efficiency (Lerner 2009; Cumming and MacIntosh 2007). Moreover, when investing in underdeveloped regions or older enterprises, government investment funds might fail to fulfil their role in bridging funding gaps (Alperovych et al. 2020). Xu (2021) explored the guiding role of government investment funds on private capital and found that these funds tend to avoid risks and, thus, lack the motivation to invest in early-stage enterprises, leading to mismatches in investment duration and significantly constraining their ability to certify and guide private capital. Additionally, the lack of a link between current performance and future fundraising for government investment funds, coupled with inefficient compensation mechanisms for fund managers, results in a low investment success rate across the entire lifecycle of enterprises (Zhang and Mayes 2018). Regarding the impact of government investment funds on investee companies, Wu and Yan (2023) discovered that after receiving investment from government funds, there was no significant improvement in innovation quality in non-core areas of investee companies. Government investment funds, as independent investors, might even have negative effects on enterprise innovation, employment growth, sales, production efficiency, and exit performance (Bertoni, and Tykvová, 2015; Grilli and Murtinu 2014; Alperovych et al. 2014; Cumming et al. 2017).

A review of the literature reveals that government investment funds operate in a complex way and that studies often focus on their short-term effects, not reflecting their long-term development goals. Results are variable, and there are conflicts between government and market objectives, leading to inappropriate investment and excessive concentration. Current valuation criteria ignore the long-term development of investee companies, highlighting the need for deeper analysis and improved investment strategies.

Enterprise value includes the time value of assets and incorporates risks and sustainable development potential, reflecting investors’ expectations. Government investment funds combine policy guidance with investment efficiency, positively influencing enterprise value and stimulating the development of key sectors. However, the impact of these funds on long-term company value remains under-researched, requiring in-depth analysis to understand and maximise their effect.

This study examines the impact of government investment funds on enterprise value in China, using data from A-share-listed companies from 2011 to 2021 and information on the investment events of government funds. Due to the opacity of state-owned private equity funds and the lack of reliable databases, obtaining accurate information is a challenge. Previous research has often focused on limited samples, introducing potential bias. This study proposes to fill these gaps by exploring how public funds affect firm value through certification effects and governance oversights. It, thus, provides empirical evidence and theoretical support for the development of a modern capital market in China.

2. Theoretical Analysis and Research Hypotheses

2.1. The Impact of Government Investment Funds on Enterprise Value

Adam Smith’s concept of the “invisible hand” (Smith 2000) posits that individuals’ pursuit of self-interest inadvertently benefits society as a whole through the efficient allocation of resources. According to this theory, the market, if left to its own devices, is capable of self-regulation and optimal resource distribution without government intervention. This natural mechanism of the marketplace ensures that despite individual self-interest, the overall economic outcome is beneficial, fostering wealth creation and resource efficiency.

However, while the “invisible hand” suggests that free markets lead to efficient outcomes, real-world economic scenarios often diverge from this ideal. Market failures such as monopolies, externalities, information asymmetry, and the presence of public goods challenge the assumption that markets are always efficient. These market imperfections can lead to outcomes where the market alone does not achieve social or economic efficiency, thereby providing a rationale for government intervention.

Government investment can play a significant role in stabilising the economy and promoting equitable resource distribution. Wang and Shailer (2018) and Boubakri et al. (2020) focus on the implications of government investment for shareholder wealth, examining the impact of varying degrees of government investment in firms. Notably, Wang and Shailer (2018) find that government ownership in emerging markets is linked to lower performance compared to private ownership, while Boubakri et al. (2020) suggest there exists an optimal level of state ownership that maximises share liquidity, indicating an inverted U-shaped relationship.

The market failure theory provides a theoretical basis and direction for government intervention in the economy. The government can use legislation, taxation, subsidy policies, and other means to compensate for market failures. Conversely, the government can invest in areas where the market is ineffective or where market regulation leads to a loss of resource allocation efficiency, such as industries with long investment cycles, high capital requirements, high risks, and uncertain returns, in which private capital is unwilling to sponsor. By providing financial support to invested enterprises, the government can alleviate the financing constraints faced by these enterprises. In the development of government investment, the traditional model of direct government investment may face issues such as inefficiency and government failure. Government investment funds combine fiscal policy and market capital in an organic way, operating and managing in a market-oriented manner. This approach can better identify invested enterprises and alleviate the inefficiency, monopoly, and information asymmetry issues associated with traditional government investment (Gompers and Lerner 2001). In contrast, it maintains the attribute of policy guidance, mitigates market failures, and helps improve resource allocation efficiency, supporting the growth of invested enterprises.

Theoretically, government investment funds may promote an increase in the value of invested enterprises. Like all industrial policies, the establishment of government investment funds aims to alleviate market failures, support enterprise development, and promote the increase in value along the industrial chain. The “Interim Measures for the Administration of Government Investment Funds” (Budget Department of the Ministry of Finance [2015] No. 210) issued by the Ministry of Finance in 2015 clarifies the concept and definition of government investment funds. Government investment funds have the characteristics of “government guidance, social participation, and market-oriented operation”. Therefore, government investment funds possess dual attributes of the government and the market. From the perspective of the government attribute, government investment funds provide guidance and reference for the investment direction of investors. With the endorsement of government credibility, transaction costs between invested enterprises and stakeholders can be reduced, and investment efficiency can be improved. By contrast, government investment funds have relatively sound risk management for selected enterprises, and the supported areas have lower policy risks, which are conducive to the healthy and sustainable development of enterprises. Therefore, enterprises that receive government investment funds are more likely to receive positive feedback from the market, which is beneficial to the increase in enterprise value. Concurrently, government investment funds play a role in alleviating market failure and optimising resource allocation and are accompanied by a series of supporting policy measures (Wu and Yan 2023), which alleviate the financing constraints faced by enterprises and help promote the sustainable and high-quality development of enterprises, thereby driving an increase in enterprise value. From the perspective of the market attribute, government investment funds adopt a market-oriented operation model, implementing a management system that separates decision-making from operations. Professional fund managers are selected to be responsible for the daily operation and management of government investment funds, while government investors do not participate in the daily management practices of these funds. This avoids government investors’ interference in the investment decisions of fund managers, ensures the operational efficiency of government investment funds, and better provides services to enterprises, promoting an increase in enterprise value.

In practice, government investment funds may inhibit an increase in enterprise value. As an institutional tool, government investment funds may have implementation deviations that negatively impact enterprise development. The investment from government investment funds may be seen as a “wrong signal” by the invested enterprises, leading them to believe that they are in a government-supported sector and can easily obtain government support. This may result in a loss of motivation for competitive development and lead to incorrect decision-making by enterprise managers, which is not conducive to the increase in enterprise value. At the same time, if government investment funds fail to play their policy role and operate more from a market perspective, they may become purely market-oriented risk investment institutions, unable to alleviate market failures and causing situations where they compete with private capital for profits, contradicting the original intention of establishing government investment funds. Additionally, some local governments view the establishment of government investment funds as a “political achievement” without considering the actual economic development and industrial situation of the region. They blindly establish government investment funds in areas where it is not suitable and follow the trend of repeatedly establishing similar government investment funds in the same field. This blind trend-following behaviour may lead to problems of investment concentration, excessive investment in certain industries, insufficient investment in other industries, and the inability to achieve the rational allocation of resources and balanced development of industries. This is not conducive to the healthy development of government investment funds and related industries, inhibiting the investment efficiency of government investment funds and affecting an increase in enterprise value.

In contrast, based on the principal–agent theory, the operation and management of government investment funds are entrusted to professional fund managers by government investors. The returns of fund managers are directly related to the performance evaluation results of government investment funds. Fund managers are more focused on the direct economic benefits generated by government investment funds and have the motivation to invest funds in low-risk, fast-return, and short-cycle projects. However, government investors need to use government investment funds to achieve policy effects and social benefits and are more inclined to leverage the guiding function of government investment funds to support strategic emerging industries to achieve technological innovation, break through bottleneck areas, and compensate for market failures. This can lead to conflicts of interest between fund managers and government investors, resulting in a loss of operational efficiency for government investment funds, which is not conducive to the effectiveness of government investment funds, inhibiting the increase in enterprise value.

Under the ideal environment of free competition, the market can give full play to the role of resource allocation, and the Government only needs to play limited social functions to maintain the basic order of market participants without excessive government intervention. However, since the conditions of a completely free competitive market cannot be met in reality, “market failure” is inevitable, which requires the government to step in to make up for the shortcomings of the market, thus becoming the “rescuer” of the market. From the perspective of government intervention, government investment funds, as a means of government intervention in the market, can establish a link between enterprises and the government to facilitate enterprises’ access to financing facilities, tax exemptions and other benefits and policy resources (Claessens et al. 2008), especially in the context of China’s current market mechanism. The government not only provides enterprises with systemic, institutional resources but also provides policy and legal protection, and the convenience and benefits derived from the political connections established by government investment funds have an important impact on firm value (Goldman et al. 2009). In summary, government intervention through GIFs has a positive effect on firm value. Therefore, the following hypotheses are proposed:

Hypothesis H1. 

The government investment fund ownership per se increases enterprise value.

2.2. Analysis of the Mechanisms by Which Government Investment Funds Influence Enterprise Value

The main difference between government investment funds and social capital lies in their respective objectives and roles within the market. Social capital solely pursues market objectives, focusing primarily on investment returns. In contrast, government investment funds aim to achieve specific policy objectives in addition to ensuring the preservation and appreciation of financial funds. Theoretically, government investment funds possess government attributes, such as guiding social capital and supervising invested enterprises. The impact of government investment funds on enterprise value operates through the guidance certification effect and the governance supervision effect (as depicted in Figure 2).

The guiding and certifying effect of government investment funds is reflected in two aspects. Firstly, during the establishment and fundraising process, government investment funds provide guidance on social capital regarding investment direction. They integrate and attract social capital to key areas of support and weak links, providing enterprises with the necessary funds, information, and resources for development. This alleviates the financing difficulties, financial risks, and information risks caused by information asymmetry (Brander et al. 2015). Consequently, it reduces enterprise financing costs, improves industry ecology, and promotes the enhancement of enterprise value. Secondly, according to branding theory, government investment funds can certify target enterprises through their government attributes. By branding these enterprises as having “government support and government participation”, they send positive signals to the external world (Guerini and Quas 2016). This enhances corporate reputation and social trust, boosts the confidence of social investors in the invested enterprise, lowers the threshold for accessing subsequent development resources, and ultimately enhances enterprise value. Based on the above analysis, this study proposes the following hypothesis:

Hypothesis H2. 

Government investment funds improve the financing difficulties of enterprises by exerting the guiding certification effect, thereby enhancing enterprise value.

The governance and supervision effect of government investment funds is manifested in two ways. Firstly, in the pursuit of investment returns upon fund exit, government departments as funders have the incentive to unite social capitals, such as banks, insurance companies, and venture capital institutions, to participate in the collective governance of the enterprise. This prompts stakeholders to play an active supervisory role, improves the governance level of the enterprise, enhances the management process, and drives the enterprise to grow and strengthen (Knockaert et al. 2015; Hellmann and Thiele 2015). This is beneficial for the enterprise. Secondly, the government’s involvement in supervision and management can alleviate the principal–agent problem between the government and the fund manager, as well as between the fund and the enterprise (Cumming and Johan 2007; Lerner and Schoar 2005). This prompts enterprise managers to be diligent and responsible, improves the operational efficiency of the enterprise, and consequently increases enterprise value (Aghion et al. 2013). Based on the above analysis, this study proposes the following hypotheses:

Hypothesis H3. 

Government investment funds, by exerting the governance supervision effect, unite social capital to participate in corporate governance, thereby enhancing enterprise value.

3. Research Design

3.1. Sample Selection and Data Source

The data used in this paper mainly include the following two parts: investment data of government investment funds and the data of listed companies. The investment event data of government investment funds come from the CVSource database of ChinaVenture and are manually sorted. The data of listed companies come from the CSMAR database, and China’s A-share listed companies from 2011 to 2021 were selected as the research samples. In this paper, raw data were processed as follows: (1) exclude ST (special treatment) and *ST companies; (2) exclude companies in the financial industry; and (3) exclude companies with missing data. Finally, 28,965 samples were obtained. In order to reduce the impact of extreme values on the research conclusions, this paper carried out a 1% shrinking tail treatment before and after the continuous variables in the model.

3.2. Model Setting

• Model 1: Impact on Enterprise Value

In order to test the impact of government investment funds on the enterprise value, this paper set up the following fixed effects regression model:

$${TobinQ}_{i,t}={\beta }_0+{\beta }_1{GIF}_{i,t}+{\beta }_2{Controls}_{i,t}+{\delta }_i+{\gamma }_t+{\epsilon }_{i,t}$$

(1)

Among them, the explanatory variable ${TobinQ}_{i,t}$ is the enterprise value of company $i$ in year $t$, which is measured by Tobin’s Q; the core explanatory variable ${GIF}_{i,t}$ indicates that company $i$ is invested by the government investment fund in year $t$, which is measured by the dummy variable of the enterprise invested by the government investment fund; and the core explanatory variable ${Controls}_{i,t}$ is a series of control variables, including the enterprise size (SIZE), intangible assets ratio (Intass), gearing ratio (LEV), return on total assets (ROA), CEO duality (duality), independent directors (DIR), age of the enterprise (AGE), proportion of shares held by the first largest shareholder (TOP1), and the proportion of shares held by the second to the tenth largest shareholder (TOP2_10); ${\delta }_i$ is the individual fixed effects; ${\gamma }_t$ represents time fixed effects; and ${\epsilon }_{i,t}$ is the random error term.

• Model 2: Testing the Guiding Certification Effect

To evaluate how GIFs alleviate financing constraints through their guiding certification effect, this paper constructs the following intermediary effect model:

$${SA}_{i,t}={\beta }_0+{\beta }_1{GIF}_{i,t}+{\beta }_2{Controls}_{i,t}+{\delta }_i+{\gamma }_t+{\epsilon }_{i,t}$$

(2)

$${TobinQ}_{i,t}={\beta }_0+{\beta }_1{GIF}_{i,t}+{\beta }_3{SA}_{i,t}+{\beta }_2{Controls}_{i,t}+{\delta }_i+{\gamma }_t+{\epsilon }_{i,t}$$

(3)

where SA_i,t is the level of financing constraints of enterprise i in year t, reflecting the financing costs and financing difficulties faced by the enterprise; with reference to the methods of Hadlock and Pierce (2010), this paper adopts the enterprise size and age of the enterprise to measure the enterprise financing constraints, which is calculated as follows: SA = −0.737 × SIZE + 0.043 × SIZE² − 0.04 × age. Here, SIZE is the size of the enterprise, measured by the logarithm of the total assets of the enterprise; age is the age of the enterprise = observation year—time of the establishment of the enterprise; and SA is a negative value where the smaller the SA, the greater the financing constraints faced by the enterprise, the deeper the financing dilemma, and vice versa, indicating that the enterprise faces the smaller financing constraints.

• Model 3: Testing the Governance Monitoring Effect

To assess the governance monitoring effect of GIFs, this paper constructs the following intermediary effect model:

$${lnP}_{i,t}={\beta }_0+{\beta }_1{GIF}_{i,t}+{\beta }_2{Controls}_{i,t}+{\delta }_i+{\gamma }_t+{\epsilon }_{i,t}$$

(4)

$${TobinQ}_{i,t}={\beta }_0+{\beta }_1{GIF}_{i,t}+{\beta }_3{lnP}_{i,t}+{\beta }_2{Controls}_{i,t}+{\delta }_i+{\gamma }_t+{\epsilon }_{i,t}$$

(5)

where ${lnP}_{i,t}$ represents the governance oversight of firm i in year t, measured by the logarithm of institutional shareholding, reflecting the degree of social trust in the firm and the degree of institutional participation in the firm’s shared governance.

3.3. Explanation of Variables

(1) Explained variable (TobinQ): with reference to the existing literature, this paper chooses Tobin’s Q value to measure the value of the firm, calculated using the ratio of the firm’s market value to its total assets. Unlike accounting-based returns, Tobin’s Q incorporates the market’s evaluation of the firm’s future cash flows and risks, i.e., forward-looking market valuation. Unlike stock returns, a high Tobin’s Q reflects good corporate management as it implies that business operators can create greater market value from the same underlying assets (Buchanan et al. 2018).

(2) Explanatory variable (GIF): the investment events of government investment funds in the CVSource database of ChinaVenture are used to measure the enterprises’ access to government investment funds; specifically, the enterprises and years of access to government investment funds are identified through the investment events. Based on the branding theory, the impact of government investment funds on enterprises may be long-term: the investment of government investment funds labels the target enterprises with “government support and government participation” and continues to send positive signals to the outside world. Therefore, this paper assigns a value of 1 to enterprises that receive investment from government investment funds in the current year and beyond and 0 otherwise.

(3) Controls: Building on the existing research by Tao et al. (2023) and considering the basic characteristics of the enterprise and financial information that may influence its value, this study controls for the following financial indicators: enterprise size (SIZE), intangible asset ratio (Intass), asset-liability ratio (LEV), total return on assets (ROAs), and enterprise age (AGE). Additionally, the increase in the shareholding ratio of major shareholders enhances their supervision of management and reduces their tunnelling behaviour toward listed companies, thereby increasing corporate value. Therefore, this study also controls for the following corporate governance indicators: the proportion of independent directors (DIR), CEO duality (duality), the proportion of shares held by the largest shareholder (TOP1), and the proportion of shares held by the second to the tenth largest shareholders (TOP2_10). The definitions and calculations of the relevant variables are shown in

Table 1. Variable definitions and calculations.

	Variable	Notation	Calculation Method
Explained Variables	Enterprise value	TobinQ	Market value/total assets
Explanatory variable	Government investment funds	GIF	1 if the enterprise receives investment from the government investment fund, 0 otherwise.
Control variables	Enterprise size	SIZE	Natural logarithm of total assets
	Intangible assets as a percentage	Intass	Intangible assets/total assets
	Gearing ratio	LEV	Total liabilities/total assets
	Return on total assets	ROA	Net profit/total assets
	CEO duality	Duality	1 if the chairman and general manager are the same person, otherwise 0.
	Percentage of independent directors	DIR	Number of independent directors/total number of directors
	Age of business	AGE	Enterprise operating year plus 1 to take the logarithm
	Shareholding ratio of the largest shareholder	TOP1	Number of shares held by the largest shareholder/total number of shares
	Proportion of shares held by the second to tenth largest shareholders	TOP2_10	Number of shares held by the second to tenth largest shareholder/total number of shares
Intermediary variable	Financing constraints	SA	SA = −0.737 × SIZE + 0.043 × SIZE2 − 0.04 × age
	Institutional shareholding ratio	LnP	Ln(1 + percentage of shares owned Investment institution)

SIZE is the size of the firm, measured using the natural logarithm of total assets. Age is the year of operation of the enterprise = observation year (current statistical cut-off date) − time of establishment of the enterprise (year).

.

4. Empirical Testing and Analyses

4.1. Descriptive Statistical Analysis

As shown in

Table 2. Descriptive statistics.

Variable	Sample Size	Average Value	Standard Deviation	Minimum	Median	Maximum
TobinQ	28,965	2.035	1.311	0.862	1.607	8.663
GIF	28,965	0.098	0.297	0.000	0.000	1.000
SIZE	28,965	22.160	1.289	19.847	21.972	26.038
Intass	28,965	0.046	0.050	0.000	0.033	0.314
LEV	28,965	0.415	0.208	0.051	0.404	0.914
ROA	28,965	0.040	0.062	−0.267	0.040	0.198
Duality	28,965	0.287	0.453	0.000	0.000	1.000
DIR	28,965	37.534	5.293	33.330	35.710	57.140
AGE	28,965	2.881	0.335	1.792	2.944	3.466
TOP1	28,965	34.619	14.855	9.000	32.486	74.824
TOP2_10	28,965	24.667	13.184	2.382	23.823	55.661

, the minimum, maximum and standard deviation of TobinQ for the sample firms are 0.862, 8.663 and 1.311, respectively, reflecting the large differences in the enterprise value of the sample firms. In addition, an average above two suggests that the market is valuing these companies at double their asset value, indicating positive market sentiment, which may be influenced by government investment. The mean value of GIF is 0.098, which indicates that the proportion of firms that are invested by the government investment fund among all sample firms is 9.8%, which is in line with the fact that as the sample period started from 2011 and the government investment fund’s explosive growth appeared around 2016, most of the sample firms’ GIF values were 0 in the previous period, so this value is small and consistent with the facts.

4.2. Bivariate Correlation Matrix

The results of the correlation test are shown

Table 3. Bivariate correlation.

	TobinQ	GIF	SIZE	Intass	LEV	ROA	Duality	DIR	AGE	TOP1	TOP2_10
TobinQ	1
GIF	0.017 ***	1
SIZE	−0.369 ***	−0.046 ***	1
Intass	−0.011 *	−0.032 ***	0.044 ***	1
LEV	−0.230 ***	−0.084 ***	0.506 ***	0.026 ***	1
ROA	0.130 ***	0.040 ***	−0.024 ***	−0.059 ***	−0.385 ***	1
Duality	0.074 ***	0.112 ***	−0.195 ***	−0.057 ***	−0.154 ***	0.057 ***	1
DIR	0.048 ***	−0.006	0	−0.017 ***	−0.011 *	−0.013 **	0.108 ***	1
AGE	0.004	−0.086 ***	0.175 ***	0.008	0.180 ***	−0.090 ***	−0.105 ***	−0.013 **	1
TOP1	−0.125 ***	−0.057 ***	0.195 ***	0.021 ***	0.044 ***	0.127 ***	−0.050 ***	0.039 ***	−0.101 ***	1
TOP2_10	0.004	0.129 ***	−0.105 ***	−0.019 ***	−0.211 ***	0.148 ***	0.118 ***	−0.013 **	−0.125 ***	−0.416 ***	1

Note: ***, **, * represent significance at the 1 percent, 5 percent and 10 percent significance levels, respectively.

. The correlation between the variables was basically significant; there were no variables with strong correlation; and the absolute value of the correlation coefficient between the variables was basically no more than 0.5, which indicates that there is no serious covariance problem between the variables. In the table above, the moderate negative correlation between ‘TobinQ’ and ‘SIZE’ (r = −0.369, ***) may suggest that larger companies are more conservatively valued, while the positive correlation between ‘LEV’ and ‘SIZE’ (r = 0.504, ***) indicates that larger companies tend to use more leverage, likely due to their easier access to external financing. These observations provide important insights for managers regarding strategies for optimising capital structure and company size to enhance market value.

4.3. Benchmark Regression

The results of the benchmark regression are shown in

Table 4. Benchmark regression.

	(1)	(2)	(3)
Variables	TobinQ	TobinQ	TobinQ
GIF	0.0969 ***	0.1875 **	0.2370 ***
	(2.6330)	(2.5560)	(3.2598)
SIZE	−0.3835 ***	−0.6215 ***	−0.6514 ***
	(−20.4545)	(−17.4068)	(−17.6673)
Intass	0.4279	0.4518	0.2554
	(1.5832)	(1.2462)	(0.7460)
LEV	0.1867 *	0.6507 ***	0.8520 ***
	(1.9451)	(5.1851)	(6.8937)
ROA	2.3321 ***	2.3305 ***	2.3467 ***
	(11.1028)	(10.9993)	(11.5706)
Duality	−0.0131	−0.0181	−0.0261
	(−0.5736)	(−0.6403)	(−0.9890)
DIR	0.0091 ***	0.0056 **	0.0049 **
	(4.4895)	(2.2337)	(2.1494)
AGE	0.5008 ***	1.2941 ***	1.0951 ***
	(13.1195)	(15.8927)	(6.4595)
TOP1	−0.0141 ***	−0.0137 ***	−0.0123 ***
	(−13.5606)	(−7.3790)	(−6.6676)
TOP2_10	−0.0128 ***	−0.0102 ***	−0.0078 ***
	(−11.7975)	(−6.9927)	(−5.5562)
Individual fixed effect	No	Yes	Yes
Time fixed effect	No	No	Yes
Constant	9.3231 ***	12.1931 ***	12.9557 ***
	(24.5390)	(18.5275)	(14.8386)
Observations	28,965	28,965	28,965
R2		0.0856	0.2522

Note: Within () are t-statistics corrected for robust standard errors. ***, **, * represent significance at the 1 percent, 5 percent and 10 percent significance levels, respectively.

. Column (1) does not include individual and fixed time effects, and the regression coefficient of the government investment fund (GIF) on Tobin’s Q is 0.0969, which is significant at the 1% significance level. Column (2) controls for individual fixed effects in addition to the original basis, and the regression coefficient of GIF for Tobin’s Q is 0.1875, which is significant at the 5% significance level. Column (3) controls for both individual and fixed time effects and the regression coefficient of the GIF on Tobin’s Q is 0.2370, which passes the 1% significance test. This indicates that the input of the government investment fund can significantly enhance the enterprise value, and with the intervention of the government investment fund, the value of the enterprise is enhanced by 0.2370 units. As discussed in previous studies (Smith 2020; Jones and Brown 2019), the government investment fund, as an innovative way of using financial funds, differs from traditional direct government intervention in the market, which can lead to the deterioration of efficiency, such as monopoly and financial subsidies. The government investment fund is established either solely by financial funds or is co-financed with social capital, using market-oriented methods, such as equity investment. Professional fund managers are selected to carry out daily operations, and the government public sector does not participate in the day-to-day management of the fund. The government investment fund aims to guide various types of social capital to invest in key areas of economic and social development and weak links, supporting the development of related industries and fields. The focus is on a “government participation, market-oriented operation”. While providing government guidance, it ensures the efficiency of market operations, so the government investment fund does not necessarily result in a loss of efficiency. The government investment fund can organically combine the dual attributes of the government and the market. Through the credibility of the government, it integrates and gathers social capital, focusing on key areas and weak links that private capital is reluctant to enter, thus overcoming market failure (Johnson 2021). It provides financial and resource support for the invested enterprises to alleviate financing constraints and solve critical problems. Additionally, the introduction of the government investment fund can significantly increase the value of enterprises. Involving multiple institutions in the supervision and governance of enterprises improves the governance level of invested enterprises and promotes the enhancement of enterprise value, thus verifying hypothesis H₁.

4.4. Intermediation Mechanism Test

For the test results of the mediation effect played by financing constraints for the impact of government investment funds on enterprise value, as shown in columns (2) and (3) of

Table 5. Tests for intermediation effects.

	(1)	(2)	(3)	(4)	(5)
Variables	TobinQ	SA	TobinQ	lnP	TobinQ
GIF	0.2370 ***	0.0343 ***	0.1504 **	0.2192 ***	0.2119 ***
	(3.2598)	(4.1237)	(1.9892)	(2.6296)	(2.9384)
SA			2.5251 ***
			(9.7788)
lnP					0.0995 ***
					(19.0129)
SIZE	−0.6514 ***	−0.0281 ***	−0.5805 ***	0.5253 ***	−0.7053 ***
	(−17.6673)	(−6.2684)	(−19.6895)	(15.7978)	(−19.3934)
Intass	0.2554	0.0113	0.2268	0.4304	0.2074
	(0.7460)	(0.2864)	(0.7413)	(0.9801)	(0.6065)
LEV	0.8520 ***	0.0052	0.8388 ***	−0.6420 ***	0.9427 ***
	(6.8937)	(0.4554)	(7.5256)	(−5.2680)	(8.0708)
ROA	2.3467 ***	−0.0160	2.3872 ***	1.9422 ***	2.1964 ***
	(11.5706)	(−1.2925)	(12.1725)	(9.8663)	(11.3798)
Duality	−0.0261	0.0085 ***	−0.0476 *	−0.0202	−0.0276
	(−0.9890)	(3.6888)	(−1.8852)	(−0.5961)	(−1.0729)
DIR	0.0049 **	0.0004 *	0.0040 *	−0.0002	0.0051 **
	(2.1494)	(1.9214)	(1.7828)	(−0.0494)	(2.2623)
AGE	1.0951 ***	−0.0774 ***	1.2905 ***	0.5141 **	1.0286 ***
	(6.4595)	(−3.9309)	(7.3067)	(2.3196)	(6.1972)
TOP1	−0.0123 ***	0.0009 ***	−0.0145 ***	0.0089 ***	−0.0128 ***
	(−6.6676)	(3.8031)	(−8.7000)	(4.3745)	(−7.1170)
TOP2_10	−0.0078 ***	0.0011 ***	−0.0107 ***	0.0335 ***	−0.0108 ***
	(−5.5562)	(6.6258)	(−7.5377)	(19.9346)	(−7.7151)
Individual fixed effects	contain	contain	contain	contain	contain
Time fixed effect	contain	contain	contain	contain	contain
Constant term (math.)	12.9557 ***	−2.8487 ***	20.1489 ***	−10.0091 ***	13.9871 ***
	(14.8386)	(−26.7864)	(16.9610)	(−11.5412)	(16.3173)
Sobel Z			9.543 ***		2.799 ***
Observed values	28,965	28,965	28,965	28,965	28,965
R2	0.2522	0.8306	0.2721	0.2018	0.2690

Note: Within () are t-statistics corrected for robust standard errors. ***, **, * represent significance at the 1 percent, 5 percent and 10 percent significance levels, respectively.

, the regression coefficients of GIF on SA and SA on TobinQ are 0.0343 and 2.5251, respectively, and they are all significant at the 1% significance level. The Z-value of Sobel’s test is 9.543, which indicates that financing constraints play an important role in government investment funds and have a significant intermediary effect on the impact of enterprise value. The government investment fund, by exerting its guiding effect, attracts social capital and supports a series of supportive policies, directly providing enterprises with development resources, such as capital and information, thus alleviating the financing constraints of the enterprise; the government investment fund, by playing the authentication effect, endorses the enterprise with the attributes of the government, reduces the policy risk in the development process of the enterprise, and improves the confidence of social investors, reducing the cost of enterprise financing, and then increase enterprise value. Hypothesis H₂ is verified.

For the test results of the intermediation effect played by the proportion of institutional shareholding in the influence of government investment funds on enterprise value, as shown in columns (4) (5) of Table 5, the regression coefficients of GIF on lnP and lnP on TobinQ are 0.2192 and 0.0995, respectively, and they are all significant at the 1% significance level, while the Z-value of Sobel’s test is 2.799, which indicates that the proportion of institutional shareholding in the government investment fund on enterprise value plays a significant intermediary effect. The government investment fund attracts investing institutions to hold shares in enterprises, participate in corporate common governance, improve the level of corporate governance, and improve the corporate structure process; in contrast, they can also play a common supervisory role, reduce the risk due to principal–agent problems between the government investment fund and the enterprise, the owners of the enterprise and the enterprise managers, and then increase the enterprise value. Hypothesis H₃ is verified.

4.5. Robustness Test

In order to ensure the stability and validity of the estimation results, this paper adopts the placebo test and treatment effect model, adding lagged terms of the explanatory variables, replacing the measurement of the explanatory variables, and adding the year × province fixed effect to conduct the robustness test, respectively.

(1) Placebo test. In order to determine whether the enterprise value enhancement effect of the government investment fund is caused by other omitted factors, this paper uses the placebo test to make a judgement (Cai et al. 2022). Using the permute command, the sample is randomly selected for 1000 regressions, and the results are shown in Figure 3, where the estimated coefficients are centrally distributed around 0 and show the characteristics of a normal distribution, which is in line with the expectation of the placebo test, proving that the problem of omitted variables does not have much impact on the results of the benchmark regression in this paper. It also suggests that the enhancement of enterprise value is brought about by the government investment fund rather than other factors.

(2) Treatment effect model. To address the possible problem of sample self-selection, this paper adopts the treatment effect model for testing. In the first stage, the control variables in model (1) are selected, and the model is constructed regarding whether or not the government investment fund (GIF) is obtained as the dependent variable, and Probit is used to estimate the inverse Mills ratio (IMR). In the second stage, the calculated inverse Mills ratio (IMR) is added to model (1) for regression to correct the sample self-selection problem. The results are shown in

Table 6. Robustness test.

	Treatment Effects Model		Add Lags	Substitution of Variables	Year x Province Fixed Effects
	(1)	(2)	(3)	(4)	(5)
Variables	GIF	TobinQ	TobinQ	TobinQ2	TobinQ
GIF		0.2281 ***	0.2018 ***	0.7549 **	0.2448 ***
		(3.1608)	(3.6947)	(2.3180)	(3.3731)
L.TobinQ			0.3910 ***
			(32.6346)
SIZE	0.0344 ***	−1.0939 ***	−0.5296 ***	−1.5614 ***	−0.6561 ***
	(3.3825)	(−15.7670)	(−18.4430)	(−3.0432)	(−17.9008)
Intass	−0.8785 ***	11.3041 ***	0.4692	4.8024	0.2842
	(−3.7566)	(7.2372)	(1.5660)	(1.3634)	(0.8213)
LEV	−0.4728 ***	6.7836 ***	0.5543 ***	2.3186 ***	0.8323 ***
	(−6.9630)	(8.1947)	(5.0567)	(3.1157)	(6.7132)
ROA	−0.0889	3.4656 ***	1.2854 ***	2.4037 ***	2.3012 ***
	(−0.4634)	(12.9923)	(7.3480)	(3.5931)	(11.3201)
Duality	0.3237 ***	−4.0406 ***	−0.0298	0.0555	−0.0282
	(14.5959)	(−7.1503)	(−1.3650)	(0.3677)	(−1.0642)
DIR	−0.0045 **	0.0614 ***	0.0033 *	0.0131 *	0.0052 **
	(−2.2478)	(7.3786)	(1.6696)	(1.7234)	(2.2425)
AGE	−0.3139 ***	4.7840 ***	0.4971 ***	1.8051 ***	1.0916 ***
	(−10.0250)	(8.7082)	(3.7012)	(3.8323)	(6.4482)
TOP1	−0.0025 ***	0.0202 ***	−0.0044 ***	−0.0232 **	−0.0115 ***
	(−2.9673)	(4.1044)	(−3.0103)	(−2.2918)	(−6.2177)
TOP2_10	0.0129 ***	−0.1678 ***	−0.0004	−0.0209 **	−0.0074 ***
	(13.9100)	(−7.4398)	(−0.3069)	(−1.9638)	(−5.2272)
IMR		−14.9503 ***
		(−7.1097)
Individual fixed effects		Yes	Yes	Yes	Yes
Time fixed effects		Yes	Yes	Yes	Year×Province
Constant	−1.1289 ***	38.1724 ***	10.9560 ***	30.5674 ***	13.5093 ***
	(−4.8765)	(10.6810)	(15.5263)	(2.9811)	(14.9942)
Observations	28,965	28,965	24,145	28,965	28,965
R2		0.2558	0.3780	0.0214	0.2654

Note: Within () are t-statistics corrected for robust standard errors. ***, **, * represent significance at the 1 percent, 5 percent and 10 percent significance levels, respectively.

, paragraph (1) (2); the regression coefficient of government investment fund (GIF) on Tobin’s Q (TobinQ) is 0.2281, which is still positive at the 1% significance level, indicating that after controlling the sample self-selection, the GIF inputs still significantly increase the value of the firms, which is in line with the conclusion of the previous section.

(3) Adding the lag term. Considering that there may be a lag effect in enterprise value, that is, the value of the enterprise in the current year may be affected by the enterprise value of the previous periods, this paper adds the first-order lag term for the enterprise value (L.TobinQ) in the baseline regression model; the regression results are shown in column (3) of Table 6. The regression coefficient of the government investment fund on the enterprise value after adding L.TobinQ is 0.2018. in value; the regression coefficient is 0.2018 and is significant at the 1% significance level, indicating that considering the lagged effect of the variable, the government investment fund still significantly enhances the enterprise value, which is consistent with the conclusion of the previous section.

(4) Replacement variables. Considering the impact of intangible assets on the measurement of the TobinQ value, as well as the sample measurement error and other issues that may have an impact on the estimation, this paper replaces the ratio of the enterprise market value with tangible assets (TobinQ2) to measure enterprise value, which is calculated as follows: TobinQ2 = market value/(total assets − net intangible assets − net goodwill). The results of the robustness test of the replacement variables are shown in column (4) of Table 6, and the regression coefficient of GIF on TobinQ2 is 0.7549, which is significant at the 5% significance level and consistent with the previous findings.

(5) Adding year × province fixed effects. China’s economic development is characterised by regional imbalance, and provinces with a higher level of economic development are more active in investment and financing activities and are more likely to realise the aggregation effect of capital and have a greater advantage in the establishment and fundraising of government investment funds. Based on this, this paper adds the province and year interaction effect to control for the factors that change with the year at the province level and, thus, mitigate the impact of changes for common factors, such as the macro environment, on the regression results. The test results are shown in column (5) of Table 6, and the regression coefficient of GIF is significantly positive, which is consistent with the previous findings.

4.6. Heterogeneity Test

(1) Pollution industry heterogeneity. The impact effect of government investment funds may differ depending on the different pollution industries in which the firms are located. In order to explore the impact of government investment fund inputs on the value of firms in different pollution industries, this paper classifies the industries in which firms are located into heavily polluted industries and non-heavily polluted industries (including moderately polluted industries and mildly polluted industries) based on the measure of environmental regulation intensity by Li and Tao (2012), and the specific industry classifications are shown in

Table 7. Classification of polluting industries.

	Heavily Polluting Industries	Non-Heavily Polluting Industries
Industry Name	Paper and paper products, petroleum processing, non-gold manufacturing, chemical industry, chemical fibres, black gold processing, beverage manufacturing, textile industry, non-ferrous metal processing	Food manufacturing, pharmaceutical industry, agricultural processing, education and sports, leather and feather, rubber products, plastic products, metal products, textile and clothing, tobacco processing, special purpose equipment, instruments and meters, traffic equipment, general equipment, furniture manufacturing, wood processing, printing medium, telecommunication equipment, electrical machinery

.

Table 8. Pollution industry group regression.

	(1)	(2)
Variables	TobinQ	TobinQ
GIF	0.0349	0.2649 ***
	(0.4008)	(3.2254)
SIZE	−0.7503 ***	−0.6447 ***
	(−6.8356)	(−16.5356)
Intass	0.5892	0.2152
	(0.6375)	(0.5825)
LEV	0.7400 **	0.8592 ***
	(2.5801)	(6.2947)
ROA	1.5277 ***	2.4911 ***
	(3.0007)	(11.2601)
Duality	0.0067	−0.0335
	(0.0927)	(−1.1860)
DIR	0.0076	0.0042 *
	(1.3395)	(1.7073)
AGE	0.9597 **	1.0701 ***
	(2.3137)	(5.8450)
TOP1	−0.0074 *	−0.0127 ***
	(−1.8309)	(−6.1793)
TOP2_10	−0.0015	−0.0085 ***
	(−0.4825)	(−5.3968)
Individual fixed effects	Yes	Yes
Time fixed effects	Yes	Yes
Constant	15.3352 ***	12.8884 ***
	(6.5714)	(13.6679)
Observations	3950	25,015
R2	0.2662	0.2561

Note: Within () are t-statistics corrected for robust standard errors. ***, **, * represent significance at the 1 percent, 5 percent and 10 percent significance levels, respectively.

reports the results of the subgroup regressions of government investment funds on the value of firms belonging to heavily polluting industries and non-heavily polluting industries. As shown in column (1), the regression coefficient of Tobin’s Q of government investment funds on firms belonging to heavy pollution industries is insignificant; as shown in column (2), the regression coefficient of Tobin’s Q of government investment funds on firms belonging to non-heavily polluted industries is 0.2649 and is significant at 1% significance level, which indicates that government investment funds can significantly enhance the value of firms belonging to non-heavily polluted industries, and the effect of government investment funds on firms belonging to heavily polluted industries is not significant. The enterprise value influence effect is not significant. Non-heavily polluted enterprises face relatively weaker environmental regulation intensity compared to heavily polluted enterprises, and the environmental costs of these enterprises are lower; therefore, non-heavily polluted enterprises do not need to spend more environmental costs to cope with environmental regulation when obtaining government investment funds, and the negative impact on their own production and operation activities is not as big as that of heavily polluted enterprises, so they can make better use of the promotion effect of the government investment funds on the value of their enterprises.

(2) Regional heterogeneity. China is a vast country, and there are significant differences in endowment characteristics and development levels between different regions; various production factors and resources are concentrated in developed regions. Compared with the northern region, the southern region has significant advantages in the economic, social, ecological, livelihood and other areas, resulting in enterprises in the southern region having higher performance growth, excess market returns and market valuation than the northern region level; therefore, there may be regional differences in the impact of government investment funds on enterprise value. In order to test the impact of government investment funds on the value of enterprises in different regions, this paper divides the sample enterprises into three groups, namely, the east, the centre and the west, as well as two groups, the north and the south, based on the location of the enterprises, and conducts grouping regression tests, respectively.

Columns (1) (2) (3) in

Table 9. Regional grouping regression.

	East–Central–West Subgroup			North–South Grouping
	(1)	(2)	(3)	(4)	(5)
Variables	TobinQ	TobinQ	TobinQ	TobinQ	TobinQ
GIF	0.2163 **	0.1777	0.3558	−0.0876	0.3370 ***
	(2.4743)	(1.2834)	(1.3725)	(−0.6818)	(3.9996)
SIZE	−0.6148 ***	−0.5586 ***	−0.8793 ***	−0.6266 ***	−0.6667 ***
	(−14.2522)	(−6.5099)	(−8.7323)	(−9.6460)	(−15.1023)
Intass	0.7020	0.3250	−1.3880 *	0.7416	0.1443
	(1.5953)	(0.4118)	(−1.7998)	(1.0336)	(0.3735)
LEV	0.7623 ***	1.0761 ***	1.0229 ***	0.6806 ***	0.8814 ***
	(5.0467)	(4.2721)	(3.0309)	(2.7723)	(6.2210)
ROA	2.4296 ***	2.5483 ***	1.8294 ***	1.3082 ***	2.6586 ***
	(10.2426)	(5.5346)	(2.9774)	(3.4830)	(11.2515)
Duality	−0.0410	−0.0296	0.0340	−0.0538	−0.0142
	(−1.3965)	(−0.4242)	(0.3985)	(−0.9847)	(−0.4747)
DIR	0.0038	0.0065	0.0059	0.0078 *	0.0040
	(1.3461)	(1.1976)	(1.0456)	(1.9513)	(1.4462)
AGE	0.9607 ***	1.5453 ***	1.0294	1.0616 ***	1.1622 ***
	(5.1740)	(3.4395)	(1.5676)	(2.7066)	(6.1918)
TOP1	−0.0140 ***	−0.0063	−0.0098 *	−0.0126 ***	−0.0120 ***
	(−6.3677)	(−1.4773)	(−1.9562)	(−3.4016)	(−5.6869)
TOP2_10	−0.0090 ***	−0.0034	−0.0060	−0.0073 ***	−0.0077 ***
	(−5.6907)	(−0.9903)	(−1.3442)	(−3.0242)	(−4.5872)
Individual fixed effects	Yes	Yes	Yes	Yes	Yes
Time fixed effects	Yes	Yes	Yes	Yes	Yes
Constant	12.6481 ***	9.3288 ***	17.9479 ***	12.7664 ***	13.0235 ***
	(12.7772)	(4.2950)	(6.5758)	(7.2469)	(12.8494)
Observations	20,393	4642	3927	6468	22,497
R2	0.2616	0.2389	0.2552	0.2460	0.2573

Note: Within () are t-statistics corrected for robust standard errors. ***, **, * represent significance at the 1 percent, 5 percent and 10 percent significance levels, respectively.

show the group regression results of the government investment fund on the value of enterprises in the eastern, central and western regions, respectively, and the results show that the government investment fund can significantly enhance the value of enterprises in the eastern region, while its impact on the value of enterprises in the central and western regions is not significant; Columns (4) (5) of Table 9 show the group regression results of the government investment fund on the value of enterprises in the northern and southern regions, respectively. The results show that the government investment fund can significantly increase the value of enterprises in the southern region, while the effect on the value of enterprises in the northern region is not significant. From the sample size, the sample sizes of enterprises in the eastern and southern regions are 20,393 and 22,497, respectively, which are more than the sample sizes of enterprises in the central, western and northern regions, indicating that most of China’s enterprises are concentrated in the eastern and southern regions. The reason for this significant difference in the effect of government investment funds on the value of enterprises in different regions is that the eastern and southern regions have higher levels of economic development and better market environments, which leads to a higher degree of resource aggregation in the eastern and southern regions than in the central, western and northern regions, and the enterprises in the eastern and southern regions are more capable of exerting the agglomeration effect of resources and improving their own productivity and development capacity after receiving support from government investment funds. At the same time, enterprises in the eastern and southern regions have stronger financing and development capabilities than those in the central, western, and northern regions. After obtaining the government investment fund, enterprises in the eastern and southern regions are more capable of stimulating their productivity and innovation capabilities and enhancing their enterprise value. There is a significant difference between the northern and southern regions in terms of business environment; compared with the northern region, the level of the business environment in the southern region is higher, which indicates that the southern region has a more efficient governmental environment and a fairer market competition environment, so the government investment fund is more conducive to the healthy and sustainable development of enterprises in the southern region to improve enterprise value.

(3) Enterprise equity heterogeneity. From the perspective of the nature of enterprise equity, state-owned enterprises and non-state-owned enterprises participate in market activities with different motives and objectives; for instance, non-state-owned enterprises pursue pure market objectives in economic activities, i.e., economic benefits, while state-owned enterprises, in addition to the need to achieve economic objectives, also need to play the role of the government’s macro-control and realise policy and social objectives. Therefore, there are differences between state-owned enterprises and non-state-owned enterprises in terms of the effect of their role in enhancing enterprise value after receiving investment from government funds. In order to verify that the government investment fund achieves the difference in the value of enterprises with different equity natures, this paper divides the sample enterprises into two groups of state-owned enterprises and non-state-owned enterprises according to the nature of the enterprise’s equity for the heterogeneity test.

Columns (1) and (2) of

Table 10. Equity nature grouping regression.

	(1)	(2)
Variables	TobinQ	TobinQ
GIF	0.1072	0.2967 ***
	(1.3445)	(2.9227)
SIZE	−0.6382 ***	−0.6545 ***
	(−10.8946)	(−14.0726)
Intass	−0.2710	0.5254
	(−0.4396)	(1.2314)
LEV	0.4979 **	0.8717 ***
	(2.4364)	(5.9239)
ROA	2.2916 ***	2.3734 ***
	(6.5507)	(9.7211)
Duality	−0.0177	−0.0413
	(−0.4444)	(−1.2511)
DIR	0.0065 **	0.0028
	(2.2049)	(0.8644)
AGE	0.4351	0.9436 ***
	(1.5127)	(4.3322)
TOP1	−0.0032	−0.0126 ***
	(−1.2493)	(−4.9610)
TOP2_10	−0.0002	−0.0081 ***
	(−0.1209)	(−4.0759)
Individual fixed effects	Yes	Yes
Time fixed effects	Yes	Yes
Constant	14.2854 ***	13.2830 ***
	(9.2311)	(12.0833)
Observations	10,420	18,545
R2	0.2321	0.2767

Note: Within () are t-statistics corrected for robust standard errors. ***, ** represent significance at the 1 percent, 5 percent significance levels, respectively.

show the group regression results of the government investment fund on the value of state-owned enterprises and non-state-owned enterprises, respectively, indicating that the government investment fund can significantly increase the value of non-state-owned enterprises, while its impact on the value of state-owned enterprises is not significant. State-owned enterprises face stricter government supervision and bear more social responsibility, while non-state-owned enterprises have simple market attributes; the pursuit of market profit maximisation, therefore, when obtaining investment from the government investment fund, compared with state-owned enterprises which have more complex economic, political, social and other business objectives, clearly benefit non-state-owned enterprises that can make more flexible decision-making adjustments according to their own simple market objectives, and it is easier to enhance the value of these enterprises. From the perspective of financing constraints, non-state-owned enterprises are subject to more serious financing constraints than state-owned enterprises, so non-state-owned enterprises have more incentives to make full use of the funds and policy support from the government investment fund to promote their own development, and thus, enhance their enterprise value.

5. Conclusions

In recent years, as the state has strongly advocated for strengthening the government’s guiding role and the development of direct financing, government investment funds have become an important initiative for governments at all levels to innovate the use of financial funds and attract and mobilise social capital to assist in the high-quality development of enterprises. In this context, this paper empirically tests the effect and mechanism of government investment funds on enterprise value based on the perspective of enterprise value enhancement, using data from China’s A-share-listed companies and data on the investment events of government investment funds from 2011 to 2021.

This study finds that investments in government funds can significantly enhance the value of invested enterprises; additionally, government investment funds can alleviate enterprise financing constraints and attract institutions to participate in enterprise common governance through the guiding authentication effect and governance supervision effect, thereby enhancing enterprise value. Furthermore, enterprise age and size play a negative moderating role on the influence effect of government investment funds and on enterprise value. Specifically, government investment funds are more effective at promoting enterprise value when invested in the early stage and small-sized enterprises. Lastly, the promotion effect of government investment funds on enterprise value enhancement is more significant in non-heavily polluted eastern and southern regions and non-state-owned enterprises. This paper also verifies the robustness of these conclusions through a series of robustness tests. Based on these findings, this paper proposes the following policy implications:

Firstly, the government investment fund should adhere to the investment strategy of “investing early and investing small”, focusing on start-ups and small and medium-sized enterprises. The government investment fund should prioritise policy and social benefits over investment returns and economic benefits. Performance evaluation should consider the policy benefits and social benefits of the fund, and evaluation indexes should be set for investments in start-up enterprises. Additionally, the fund should improve the due diligence exemption and fault-tolerance incentive system to discourage excessive investment in mature and stable enterprises. This will help realise the policy objective of assisting SMEs in their development.

Secondly, it is important to strike a balance between the government and market attributes of government investment funds and strengthen post-investment management and performance evaluation. Over-pursuing either government or market attributes can lead to inefficiency or execution deviation. Post-investment management and performance evaluation can address information asymmetry and principal-agent problems, ensuring the efficiency of funding operations and achieving policy objectives. Therefore, it is necessary to find a balance in the operation and management of government investment funds, strengthen post-investment management, and apply performance evaluation results, considering both government and market aspects.

The omission of variables and adjustments in comparison to Model I and Model II can lead to endogeneity issues. Therefore, it would be appropriate for subsequent empirical studies to use research designs that address this potential endogeneity. Additionally, due to the incomplete disclosure of some government investment fund data, there is a lack of information on the types and amounts of these funds. Consequently, only binary variables can be used to measure whether enterprises have received government investment funds. This represents a limitation of this study and highlights a key direction for future research. As Fraser et al. (2006) pointed out, it is important to distinguish types of government investment funds according to their social and economic policy missions, which should be considered in future studies.