Financing Constraints and Corporate Value in China: The Moderating Role of Multinationality and Ownership Type

Abstract

Drawing on institutional theory and agency theory, this study examines the relationship between financing constraints and corporate value in China. In addition, we provide solutions for negative effects of financing constraints on corporate value in China. Chinese firms tend to utilize costly informal institutions to gain legitimacy and necessary resources from external stakeholders. This can lead to Chinese firms’ assuming higher financing transaction costs, negatively influencing corporate value. The multinational strategy of Chinese firms can further increase the financial burden of the company, and agency problems of state-owned enterprises (SOEs) can negatively affect the enthusiasm of managers, exacerbating the restraining effects of financing constraints on corporate value. We empirically analyze the non-financial companies listed on the Chinese stock market from 2011 to 2020 by using the methods of fixed effects and dynamic regression, heterogeneity analysis, and instrumental variables. The results show that financing constraints significantly inhibit corporate value. Accounting for the impact of differing degrees of multinationality and different types of ownership in enterprises, we empirically present the positive moderating effects of multinationality and ownership type in reducing corporate value in circumstances of financing constraints. Finally, we suggest ways for Chinese firms to overcome the negative effects of financing constraints on corporate value.

1. Introduction

The global outbreak of COVID-19 has threatened people’s lives and livelihood, and consequently, affected the normal production and operations of enterprises. In addition to the impact on the real economy, the pandemic has also greatly impacted the global financial markets [1], causing an increasing number of firms to experience financing dilemmas and constraints. In emerging markets, in particular, such as Brazil, both countries and companies [2,3] report difficulties in using different financing sources to reduce financing constraints [4]. Research in the Indian market shows that financing constraints not only directly affect the outward foreign direct investment and exports of enterprises [5,6], but also the group affiliation performance and production practices [7,8]. These problems usually require more capital and longer periods of financing, and firms need to overcome more serious financing constraint problems [9]. With the unprecedented challenges, emerging market firm managers must overcome the financing constraints, improve the financing capacity of enterprises, and steadily promote the improvement of corporate value. Notably, China is implementing strict epidemic prevention policies, which will affect the normal production and operations of enterprises. As Lăzăroiu et al. [10] shows, social and environmental factors will inevitably limit the sustainable development of enterprises.

The effects of financing constraints in emerging markets have been widely investigated by scholars [11,12]. Regarding the consequences of financing constraints, several factors are considered such as firm investment [13,14], firm size [15], and firm performance; including firm growth [12,16], firm productivity [8,17,18], and firm innovation [19,20]. The literature has expanded our understanding of diverse impacts of financing constraints on firms in emerging economies. Among those consequences, corporate value has been especially investigated by scholars [11]. However, only limited scholarly attention has been paid to how financing constraints in China affect the corporate value of Chinese firms and how they can overcome local financing constraints. In addition, the theoretical mechanisms of the negative effects of financing constraints on corporate value have not yet been fully explained. These research gaps raise two research questions: (1) whether financing constraints have negative effects on corporate value in China, and (2) how Chinese firms can overcome the negative effects of financing constraints on corporate value.

To answer these questions, in this study, we draw upon institutional theory [21,22] and agency theory [23,24]. To describe the research settings of the study, first, we discuss the particular institutional characteristics of China where institutions are not well developed. We further discuss how Chinese firms utilize informal institutions such as guanxi [25] and bribery [26,27] to respond to such institutional voids [28], gaining legitimacy and necessary resources from powerful external stakeholders such as the government and banks [21,22]. In particular, we explain the institutional deficiencies of the Chinese financial markets, which lead to difficulties in formal financing through market institutions such as capital markets and banks.

Based on the fundamental understanding of such institutional voids in Chinese financial institutions, we discuss how Chinese local firms highly utilize informal institutions, such as building connections with external stakeholders and bribing them [29,30], to overcome weak financial institutions, get legitimized, and obtain financial resources. We point out, however, that the use of informal institutions is costly, considerably increasing Chinese firms’ transaction financing costs. Thus, we argue that such Chinese firms are less likely to secure enough resources to invest in the sustainable competitive advantages of firms [31,32], leading to investors’ low evaluation of these firms.

To examine how Chinese firms can overcome the negative influence of financing constraints on corporate value, we focus on the specific features of Chinese firms including multinationality [33] and ownership type [34]. First, we discuss how the internationalization of Chinese firms can lead to substantial financial investments for several purposes such as establishing facilities [35], adapting to a host market [36], managing subsidiaries [37], acquiring overseas assets [38], and coping with the foreign exchange and political risks of a host market [39]. Although internationalization requires significant levels of financing, Chinese firms suffer from poorly developed domestic financial institutions and need to utilize informal institutions to channel financing. We further discuss how Chinese firms can also have a hard time securing financing from foreign financial markets due to a lack of information on the overseas financial system [40] and home country origin legitimacy [41]. Finally, we argue that Chinese firms with a greater level of multinationality will show lower corporate value when there are financing constraints because of complex financing environments that increase the transaction costs of firms. Furthermore, we apply agency theory and discuss how Chinese SOEs have agency problems arising from the often unclear requirements imposed by state and monitoring authority that are responsible for SOEs’ shirking behaviors [42]. Since SOE managers must follow sociopolitical objectives such as regional employment growth and infrastructure building [42,43], they have fewer incentives to overcome financing constraints and improve SOEs’ performance [34]. Moreover, we argue that too many monitoring agencies [44] can lead to the ineffective investigation of SOE managers’ efforts to develop financing channels in China.

To empirically examine our theories, we studied a sample of non-financial firms listed in China from 2011 to 2020. To comprehensively measure the main variables, we used: (1) a financing constraints index based on the work of Cleary [45], (2) a multinationality index based on the work of Lee et al. [46], and (3) the Tobin’s Q model to estimate corporate value. For the test of our hypotheses, we adopted several methods including a two-way fixed effects model, dynamic regression, heterogeneity analysis, and two-stage least squares (2SLS) regression analysis. Based on these empirical findings, we demonstrated a negative relationship between financing constraints and the corporate value of Chinese firms and showed the moderating effects of the level of multinationality and ownership type on this relationship.

We make three major contributions to the literature. First, we confirm the limited number of research findings [11] showing that when firms face financing constraints in China, corporate value will decline commensurately. Although previous research provides insight on the impacts of financing constraints in China, there is still a lack of consensus on the negative effect of financing constraints on corporate value, and management or finance theory applications exploring this phenomenon. Hence, we contribute to the literature by introducing institutional theory [21,22] as a theoretical lens to explain how weak Chinese financial institutions force firms to depend on informal institutions to gain legitimacy and necessary resources from external stakeholders [47]. We highlight that informal institution utilization can cause immense strain on a Chinese firm’s budget, limiting its efforts to build sustainable competitive advantages, resulting in investors’ low evaluation on a firm.

Second, drawing upon institutional theory [21,22] and agency theory [23,24], we provide solutions to the negative effects of financing constraints on corporate value in China. We shed light on our findings that the restraining effects of financing constraints on corporate value can be alleviated by reducing a local firm’s multinationality. Internationalization is considered one of the major strategies for firm growth [48,49,50] and Chinese firms have expanded in and exploited the overseas market, achieving continuous firm growth [51,52]. However, our results highlight that internationalization is not always beneficial since it can impose a significant level of further financial hardship on Chinese firms [35] when their corporate value is negatively impacted by local financing constraints. We demonstrate that internationalization can overburden local firms in China where firms must further depend on informal institutions for legitimization and access financial resources from external stakeholders to secure financing for internationalization. In addition, drawing upon agency theory [23,24], we highlight that SOEs tend to suffer stronger restraining effects due to the agency costs they bear. We focus on the fact that the pursuit of social and political objectives required by the state and SOEs’ multiple principals, who fail to effectively monitor SOE executives, can give rise to SOEs’ agency costs [42]. Thus, we stress that, in the case of weak institutions in China [28], firms need to adjust their characteristics such as multinationality and ownership type to overcome the negative effect of financing constraints on corporate value.

Third, this study advances the literature as it is empirically designed to demonstrate our theory in terms of effectiveness and robustness. To begin, we adopt a financing constraint index by various indicators and develop a valuation model for firms with financing constraints. This makes the firm evaluation of financing constraints more comprehensive and objective, and the valuation of corporate value more accurate. We also use the dynamic regression method to further verify the negative impact of financing constraints on corporate value. By analyzing the regression relationship between financing constraints and the t + 1 year of corporate value, the chain reaction of independent variables to dependent variables is emphasized, making the results more credible. In terms of heterogeneity results, we not only refer to Dai and Zhang [53] but also Xue et al. [54] for the analysis methods of different ownership types and firm sizes. We further extend the heterogeneity analysis at the level of financing constraints and multinationality based on the method of Zhao and Su [55]. After the analysis of various grouping methods, we further examine how financing constraints are still the main obstacle to the improvement of corporate value, especially for SOEs and large-scale enterprises. We use creative factors including geographical factors and details about firms’ independent directors as instrumental variables for the 2SLS regression analysis. This method is improved based on Wei and Wu [56] and Di Giuli and Laux [57], using the distance from the corporate headquarters to the Chinese stock market as an instrumental variable, which more closely corresponds to the financing background of this paper. We also use the office location of independent directors as another instrumental variable. Using both instrumental variables makes the results more robust, supporting our hypothesis.

The remainder of our study is organized as follows. In the next section, we present the institutional background, review the relevant literature, and propose three hypotheses for empirical analysis. Section 3 comprises the data, variables, and model design. Statistics and measurements are shown in Section 4. Section 5 reveals the empirical results of the model estimation. Conclusions and implications are summarized in Section 6.

2. Theory and Hypotheses

2.1. Institutional Background of Emerging Markets and China

An institutional perspective provides an appropriate theoretical lens to examine unique issues of emerging market economies [58]. Institutions are considered the creators of the rules of the game that generate a firm’s transaction costs [58]. Emerging markets are characterized by low quality market institutions that hinder effective market transactions [59]. For instance, the rule of law, contracts, and property rights are not well-enforced; and adequate infrastructure and investments are not provided [60,61]. Under such circumstances, weak institutions, which often refers to institutional voids [28], can result in inequitable information flow, distribution of resources, and enforcement of laws, leading to a firm’s stagnation, corruption, lack of rigor around investment and production, and low performance [62,63]. Prior studies have mainly examined how emerging market firms try to facilitate market transactions, become legitimate, and obtain limited resources from stakeholders as they utilize informal institutions such as bribing and networks [47] instead of formal institutions such as laws and regulations [64] under weak institution circumstances.

Among emerging countries, China’s institutional features have been a primary interest of the institutional perspective, including the institutional voids [28] of Chinese political, legal, and economic systems [65]. Compared with other emerging market firms, Chinese firms highly depend on informal institutions to deal with poorly developed market institutions [47,59]. For instance, guanxi—an interpersonal connection containing implicit mutual understanding and obligations in China—has become a critical practice of Chinese firms to obtain necessary and limited resources such as governmental subsidies and workforce [25]. Corruption and bribery, measured by entertainment and travel expenditures, are other examples of informal institutions that Chinese firms utilize to build relationship capital and overcome domestic weak institutions [26,27]. Utilizing such informal institutions, Chinese firms can build legitimacy as stakeholders around them to support a focal firm’s activities and existence, resulting in obtaining resources [21,22,65].

Firms in several Chinese sectors have suffered from institutional voids [65]. In particular, institutional deficiencies of the Chinese financial market have been pointed out as one of the major obstacles local firms have to manage [66,67]. Chinese financial systems such as governmental control, accounting standards, the legal system, and investor protection remain underdeveloped compared with those of other countries [29,66,67,68]. Under these circumstances, access to formal financing through market institutions such as banks and capital markets in China is significantly limited for firms [47], certainly leading to financing constraints. Therefore, Chinese firms are increasingly under pressure to effectively overcome the constraints produced by weak financial institutions to ensure continuous growth in corporate value. We take into account Chinese firm’s specific features of multinationality [69] and ownership structure [70] as critical factors that can affect the negative relationship between financing constraints and corporate value. We select multinationality and ownership structure as our moderators since a rapidly increasing number of studies have observed that many Chinese firms have been internationalized in recent years [33]; and compared with non-SOEs, SOEs in China face acute agency problems arising from fewer incentives for managers to maximize SOEs’ profits [34]. This study is designed to examine how these two particular features representative of Chinese firms are compounded by financing constraints which, in turn, affect the corporate value of Chinese firms.

2.2. Related Literature and Development of Hypotheses

2.2.1. The Effect of Financing Constraints

Prior studies have examined various impacts of emerging market firms’ financing constraints largely on firm investment, firm size, and firm performance; including a firm’s productivity, growth, innovation, and corporate value. Regarding the effect of financing constraints on a firm’s investment, increased financing constraints can reduce corporate investment in China [13]. Studied have found that while firms in China with financing constraints tend to show inefficient investment behavior [14], firms without financing constraints tend to overinvest [71]. In particular, when firm size is considered, Angelini and Generale [72], focusing on the effects of financial constraints on firm size distribution in different countries, found a negative correlation between financial constraints and firm size. Especially in emerging economies such as Vietnam, small enterprises are more affected by financing constraints [15]. As for firm productivity—a sub-category of firm performance—financing constraints can reduce the total factor productivity of Chinese firms [18] and hinder the growth of productivity of Turkish firms [17]. The research in India shows that when companies face credit constraints, they often choose to decentralize production outsourcing to increase productivity [8]. For firm growth, the work of Du and Nguyen [16] reported on how cognitive financial constraints restrict the growth of Vietnamese firms. Wang et al. [12] further observed how formal financing constraints hinder the growth of enterprises in China. As for firm innovation, especially in the face of high financing constraints, firms’ investment in green technology innovation was greatly reduced in China [19,20].

Regarding the effect of financing constraints on corporate value, the primary interest of this study, there is little discussion about whether and how financing constraints have a negative relationship on corporate value, particularly in China. Some studies only discuss the relationship between financial constraints and firm financial performance. For example, Wu and Huang [73] found a negative relationship between financial constraints and the financial performance of Chinese firms. Hai et al. [74] examined financial constraints can moderate the relationship between the innovation output and financial performance of Chinese firms. One of the exceptions is Deng and Zhao [11] who found that compared with financially unconstrained companies, there is a clear positive relation between cash flow and the value of financially constrained companies in China. However, the current literature still lacks consensus on the negative relationship between financing constraints and Chinese firms’ corporate value and an explanation of the theoretical mechanisms of the negative impacts of financing constraints on corporate value in China.

According to institutional theory [21,22], to gain the necessary resources for firm operations and survival, firms have to conform to institutional pressures and gain legitimacy from powerful external stakeholders such as the state. In China, especially in capital markets, the existence of weak institutions often leads to higher transaction costs [75]. Chinese institutions for efficient market transactions such as legal and financial systems or the protection of investors are not well developed [66]. In this situation, firms face difficulties obtaining financing such as bank lending, central and local governmental subsidies, and investments from venture capitalists [66].

To obtain such financial resources, Chinese firms have to put in tremendous efforts to utilize informal institutions to be legitimized by the powerful external stakeholders around a focal firm, including financial regulators, banks, investors, and the central and local governments [47]. For instance, Chen et al. [29] stressed that, in China, large local banks are corrupted and highly affected by government intervention. Loan approval kickbacks, insiders’ theft, and large-scale fraud are common in these banks [29]. To access bank loans from these banks, even firms with high performance have to pay bribes to banks [29]. Li et al. [30] also found that a Chinese firm’s political connections such as affiliation with the Communist Party of China (CPC) can lead to building networks with key economic and political figures, securing access to financial resources including bank loans. However, utilizing informal institutions is costly and can significantly increase the transaction costs of Chinese firms’ financings. In this situation, a firm’s limited resources can restrict a firm’s efforts in constructing its sustainable competitive advantage, including investment in R&D, efficient production, and staff training, leading to low firm growth and performance [31,32]. It can severely negatively affect corporate value because financial capital is necessary for a firm’s operation, performance, and growth [76]. Investors will give such Chinese firms with financing constraints a low evaluation. Hence, we develop our first hypothesis:

Hypothesis 1.

Financing constraints are negatively related to corporate value in China.

2.2.2. Moderating Effect of Multinationality

Multinationality is defined as “a firm’s expansion beyond the borders of its home country across different countries and geographical regions” [77]. As Chinese firms have substantially internationalized and are becoming important global players [33], the multinationality of Chinese firms has become one of the most important factors to consider when examining Chinese firms. Although the level of multinationality is known to influence a firm’s financing such as credit ratings [78] and fundraising [79] in advanced markets, how the multinationality of Chinese firms moderates the negative relationship between financing constraints and corporate value is still unknown.

We propose that the level of multinationality of a Chinese firm positively moderates the negative relationship between financing constraints and corporate value. Internationalization of a firm requires a considerable level of financial investment. For example, establishing production, technical, and marketing facilities abroad requires substantive financing [35]. To expand to foreign countries, Chinese firms also have to deal with uncertainties arising from cultural, institutional, and linguistic differences between China and a host country, as well as the local economic and political factors [80] to adapt to a host market [36]. Such differences require firms to incur costs to adjust to a host market and manage subsidiaries [37]. To address their relative disadvantages, Chinese multinational firms sometimes make enormous investments to acquire overseas strategic assets [38]. Furthermore, dealing with additional foreign exchange and political risks in a host country can induce increased costs [39].

As such, internationalization can significantly increase the financial burden of multinational firms from China where financing is limited due to weak institutions. In this situation, firms face considerable financial problems in expanding and internationalizing. When Chinese firms invest overseas, they have to exert a great deal of efforts to overcome domestic financing difficulties created by a weak domestic financial system [81]. For effective financing for internationalization and adjusting of financing channels in a timely manner, Chinese firms need to further utilize informal institutions such as guanxi [25] and corruption [29]. These informal channels should increase the financial distress of Chinese multinational firms. In addition, when these firms enter a foreign country to develop a multinational business, they can face changes in a foreign financial environment. Financing through the financial market of a host country is also challenging due to a lack of information on the financial system [40] and home country origin legitimacy of Chinese firms [41]. Thus, compared with domestic firms, Chinese multinational firms operate in a more complex environment [82], having trouble in dealing with not only domestic but also foreign financings. These challenges can put a further financial strain on Chinese multinational firms. Indeed, investors would give a low valuation to enterprises in cases where Chinese enterprises with financing constraints adopt multinational strategies. Thus, we develop a second hypothesis as follows:

Hypothesis 2.

Multinationality in firms positively moderates the relationship between firms’ financing constraints and corporate value in China.

2.2.3. Moderating Effect of Ownership Type

Scholars have widely explored the effect of a firm’s ownership type in China. Among several organizational types that can be determined by ownership types, SOEs are well known to be inefficient and face considerable complex institutional pressures in China [83]. In comparison to Chinese non-SOEs, SOEs are less efficient and profitable [34,84] mainly due to the agency problems they bear. According to agency theory, the agency problem arises because agents do not behave in the best interest of the principals [24]. In that sense, principals need to monitor managers and align incentives to ease the conflicting goals within firms, inducing agency costs including the monitoring expenditures by the principal [23,24]. In China, SOEs’ agency problems mainly arise from two aspects: the state pursues socio-political objectives through SOEs and applies an unclear hierarchical structure to the monitoring of SOEs [42]. When SOEs with such agency problems face financing difficulties, they will be less likely to be evaluated highly by investors.

First, especially in China with weak institutions, SOEs are highly required to follow the sociopolitical objectives of the state [85] such as stabilizing society by increasing regional employment and building infrastructure [42,43]. Managers of such SOEs are concerned about limiting managerial discretion and not highly motivated to focus on making profits by overcoming financing constraints [34]. For SOEs with concentrated ownership, managers may prefer to use debt instruments for financing [86]. Especially in periods of economic uncertainty, companies prefer debt financing to avoid equity dilution [87]. Managers of SOEs tend to be opportunistic; have fewer incentives to change the financing costs; and are less likely to adjust financing channels, improve the efficiency of SOEs, or maximize the firm’s value [88]. Second, SOEs fall under the purview of multiple monitoring agencies in China, such as the State Council’s Supervision and Administration Commission (SASAC), CPC, and the central and local governments. These multiple agencies are less likely to actively and effectively monitor SOEs [84] since it is unclear where the responsibility for the monitoring of SOEs lies [44]. Although executives of Chinese SOEs have conflicting goals with the state, the governance mechanisms to address them are not well developed in China [23]. Accordingly, ownership of enterprises may also have positive moderating effects on financing constraints and corporate value. Chinese SOEs with aforementioned agency problems will have lower performance, resulting in investors’ devaluation of them. We develop a third hypothesis as follows:

Hypothesis 3.

In comparison to non-SOEs, financing constraints have greater effects on the corporate value of SOEs in China.

3. Data and Methodology

3.1. Sample and Data

We collected annual data from 4790 firms listed on the Shanghai Stock Exchange and Shenzhen Stock Exchange from 2011 to 2020 as an initial sample. All financial data came from the China Stock Market and Accounting Research (CSMAR) Database. Geographic data came from Gaode Map, a high-precision mobile application map tool.

During the data cleaning process, all financial firms and firms marked Special Treatment (ST) were excluded. As context, listed firms that are experiencing financial distress have an ST cap imposed on them by the China Securities Regulatory Commission. To reduce the effects of outliers, the main variables were winsorized at the 1st and 99th percentiles. After cleaning the data, our final sample included 1550 non-financial listed firms.

Table 1. Sample Selection and Distribution.

Sample Selection	Number of Firms
SSE- and SZSE-listed data from 2011 to 2020	4790
Less:
Firm-year observations of financial firms	(128)
Firms with business anomalies marked as ST	(229)
Firms with total assets of 0 and an asset-liability ratio greater than 1	(368)
Firms with partial data to compute financing constraints index	(2515)
Final sample	1550

Note: The table shows the data cleaning process. The final sample comprised 15,500 firm-year observations from 2011 to 2020.

shows our data cleaning standards.

3.2. Variables

To test the effects of financing constraints on corporate value, we introduced a dependent variable, an independent variable, moderator variables, instrumental variables, and control variables.

3.2.1. Key Variables

Corporate value is a dependent variable, expressed by Tobin’s Q. The larger the value of Tobin’s Q, the higher the corporate value will be. Financing constraints are the independent variable. A method from the work of Cleary [45] was used herein to measure financing constraints. Our analysis selected six financial indicators: return on equity (ROE), debt asset ratio (Dbt), cash flow adequacy ratio (Cash), current ratio (Curt), dividend per share (Div), and free cash flow (FCF). A probit model was used to construct a financing constraints index (FCI).

According to Hypotheses 2 and 3, we introduced multinationality and ownership type as two moderating variables. Following the work of Lee et al. [46], the multinationality index (MI) was measured with four indicators including foreign sales over total sales (FSTS), foreign assets over total assets (FATA), number of overseas subsidiaries (SUB), and number of countries in which subsidiaries operate (NAT). Chinese enterprises were divided into two types of enterprise based on SOEs and non-SOEs. If ultimate control of an enterprise was state-owned (i.e., the state owned more than 50% of shares), then the enterprise was represented by 1; other enterprises were classified as non-SOEs, which are represented by 0.

3.2.2. Instrumental Variables

Employing a method from the work of Wei and Wu [56], we used the distance from a city to either Shanghai or Shenzhen, whichever was smaller, as the instrumental variable. According to similar study findings, the distance between corporate headquarters to either Shanghai or Shenzhen will affect accessibility to that city, in turn affecting levels of financing difficulty for firms. This instrumental variable is referred to below as Distancemin. We also quantified whether the workplace of independent directors is the same as the registered place of the listed firm by establishing this notion as an instrumental variable. By way of explanation, whether the workplace of independent directors is the same as the registered place of the listed firm has an impact on the extent to which independent directors are able to supervise the operations of the enterprise. When the independent directors and the listed firm were registered in the same place, the assigned value was 1. When the two groups were in different cities, the assigned value was 2. This instrumental variable is referred to below as InDWP.

3.2.3. Control Variables

This study controlled for firm characteristics, firm growth, and firm profitability. For instance, we used firm age (Age), firm size (Size), return on equity (ROE), total income (Total income), number of employees (Employee), total assets turnover (TATOR), and growth rate of total assets (Growth asset) as control variables. Appendix A provides specific definitions for all variables [89,90,91].

3.3. Model Design

3.3.1. Modeling of Financing Constraints

In empirical studies, univariate or multivariate financing variables are usually used to construct a financing constraints index to measure the financing constraints. Following this idea, Fazzari et al. [92] first proposed this method, with research by Cleary [45] and Lamont et al. [93]. Kliestik et al. [94] found that the current ratio is the most important financial ratio in the financial distress model of each country. In this study, we adopted Cleary’s approach and referred to the paper of Gu et al. [95] for appropriate modification. According to the regression Equation (1), we obtained the fitted value of FCI. The term is used as the independent variable of this study.

$$\begin{array}{c} \mathrm{FCI}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_1{\mathrm{ROE}}_{\mathrm{it}}+{\mathsf{\alpha }}_2{\mathrm{Dbt}}_{\mathrm{it}}+{\mathsf{\alpha }}_3{\mathrm{Cash}}_{\mathrm{it} }+{\mathsf{\alpha }}_4{\mathrm{Curt}}_{\mathrm{it} }+{\mathsf{\alpha }}_5{\mathrm{Div}}_{\mathrm{it}}\\ +{\mathsf{\alpha }}_6{\mathrm{FCF}}_{\mathrm{it}}+\mathsf{\epsilon }\end{array}$$

(1)

3.3.2. Modeling of Multinationality Index

In reference to the work of Lee et al. [46], a Sullivan-style composite index was formed using four measures of single indicators: FSTS, FATA, SUB, and NAT. The model is as follows.

$$\begin{array}{c}\mathrm{MI}=\mathrm{FSTS}+\mathrm{FATA}+\mathrm{SUB}+\mathrm{NAT}\end{array}$$

(2)

3.3.3. Modeling of Corporate Value

To investigate the moderating effects of financing constraints on corporate value, Tobin’s Q model was selected for the regression analysis. Among the terms, FCI was the financing constraints index, MI was the multinationality index, and the control variables (Controls) included Size, Total income, TATOR, Age, and other variables described above.

$$\begin{gathered} {\mathrm{Tobin}\prime \mathrm{s} \mathrm{Q}}_{\mathrm{it}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{FCI}}_{\mathrm{it}}+{\mathsf{\beta }}_2{\mathrm{FCI}}_{\mathrm{it}}\times {\mathrm{MI}}_{\mathrm{it}}+{\mathsf{\beta }}_3{\mathrm{FCI}}_{\mathrm{it}}\times \mathrm{Ownership} \\ +{\mathsf{\beta }}_4{\mathrm{MI}}_{\mathrm{it}}+{\mathsf{\beta }}_5\mathrm{Ownership}+{\mathsf{\beta }}_6{\mathrm{Controls}}_{\mathrm{it}}+\mathsf{\epsilon } \end{gathered}$$

(3)

4. Statistics and Measurements

Table 2. Descriptive Statistics.

Variable	Mean	Std. Dev.	Min	Max
Tobin’s Q	1.918	1.109	0.906	5.922
ROE	0.062	0.099	−0.306	0.257
Ownership	0.394	0.489	0	1
Distancemin	543.145	483.556	0	1670.407
Age	18.004	6.045	2	45.5
Size	22.348	1.417	13.763	28.635
Total income	21.788	1.593	11.599	28.720
MI	0.255	0.341	0	3.259
Employee	5854.222	9080.993	211	45361
TATOR	0.624	0.404	0.113	1.94
Growth asset	0.141	0.239	−0.209	1.05

Note: The sample comprised 15,500 firm-year observations from 2011 to 2020. The main variables were winsorized at the 1st and 99th percentiles. See Appendix A for definitions of variables.

reports basic statistics to depict the data for comprehensive understanding. Table 2 shows that the main indicators are quite different among Chinese-listed firms, including Tobin’s Q, ROE, Total income and employee, and other differing variables. Tobin’s Q ranged from 0.906 to 5.922 across firms, and the mean was 1.918. A mean value greater than 1 indicates that the market value of most selected firms is higher than the replacement costs of their assets. However, the standard deviation of ROE was slightly higher than the mean value, indicating that the data had a large degree of variation and return on assets of some firms was negative.

As shown in

Table 3. Correlations Analysis.

	Tobin’s Q	FCI	ROE	Age	Size	Total Income	Employee	TATOR	Growth Asset
Tobin’s Q	1.000
FCI	−0.330 ***	1.000
ROE	0.089 ***	−0.652 ***	1.000
Age	−0.056 ***	0.151 ***	−0.029 ***	1.000
Size	−0.267 ***	0.195 ***	0.084 ***	0.112 ***	1.000
Total income	−0.235 ***	0.145 ***	0.133 ***	0.074 ***	0.848 ***	1.000
Employee	−0.200 ***	0.106 ***	0.122 ***	0.041 ***	0.728 ***	0.792 ***	1.000
TATOR	−0.013	−0.037 ***	0.172 ***	−0.024 ***	−0.004	0.321 ***	0.202 ***	1.000
Growth asset	0.030 ***	−0.113 ***	0.277 ***	−0.149 ***	−0.056 ***	−0.020 **	−0.016 *	−0.065 ***	1.000

Note: Results of correlations analysis. *, **, and *** denote significance at the 10%, 5%, and 1% levels. See Appendix A for definitions of variables.

, most of the correlation coefficients of the variables were significant at the 1% level. As the dependent variable, the correlation coefficients between Tobin’s Q and other variables ranged from −0.330 to 0.089. Most of the values were significant at the 1% level.

This study cites the approach of Cleary [45] and used six variables to describe our financing constraints index. The total sample of 15,500 was pre-grouped by “interest coverage ratio” into smaller sets. After deleting outliers, data were divided into three groups in descending order. The first 33% comprised the low financing constraints group (assigned value 0) and the last 33% comprised the high financing constraints group (assigned value 1). Following model estimation, the fitting value of FCI was obtained by preliminary regression calculation. In the initial regression, the p-value of Cash was 0.552, which was not significant. Accordingly, Cash was deleted, and the regression calculation was conducted for the remaining variables. It turns out that all variables were significant at the 1% level. In this way, the financing constraints index equation was established.

$$\begin{array}{c} \mathrm{FCI}=-2.161-56.167{\mathrm{ROE}}_{\mathrm{it}}+13.246{\mathrm{Dbt}}_{\mathrm{it}}-0.424{\mathrm{Curt}}_{\mathrm{it} }-3.566{\mathrm{Div}}_{\mathrm{it}}\\ +0.218{\mathrm{FCF}}_{\mathrm{it}}\end{array}$$

(4)

5. Model Estimation

The regression model is as follows. Equation (5)—where Tobin’s Q represents corporate value and FCI is the financing constraints index to measure the degree of financing difficulty in firms—was used to assess whether financing constraints influence corporate value (i.e., Hypothesis 1). If β₁ was significantly lower than 0, the hypothesis that corporate financing constraints have negative marginal effects on corporate value was confirmed. Equation (6) further explores the marginal effects of financing constraints on corporate value after adding control variables.

$${\mathrm{Tobin}’\mathrm{s} \mathrm{Q}}_{\mathrm{it}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{FCI}}_{\mathrm{it}}+\mathsf{\epsilon }$$

(5)

$${\mathrm{Tobin}’\mathrm{s} \mathrm{Q}}_{\mathrm{it}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{FCI}}_{\mathrm{it}}+{\mathsf{\beta }}_2{\mathrm{Controls}}_{\mathrm{it}}+\mathsf{\epsilon }$$

(6)

$${\mathrm{Tobin}’\mathrm{s} \mathrm{Q}}_{\mathrm{it}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{FCI}}_{\mathrm{it}}+{\mathsf{\beta }}_2{\mathrm{FCI}}_{\mathrm{it}}\times {\mathrm{MI}}_{\mathrm{it}}+{\mathsf{\beta }}_3{\mathrm{MI}}_{\mathrm{it}}+{\mathsf{\beta }}_4{\mathrm{Controls}}_{\mathrm{it}}+\mathsf{\epsilon }$$

(7)

$${\mathrm{Tobin}’\mathrm{s} \mathrm{Q}}_{\mathrm{it}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{FCI}}_{\mathrm{it}}+{\mathsf{\beta }}_2{\mathrm{FCI}}_{\mathrm{it}}\times \mathrm{Ownership} +{ \mathsf{\beta }}_3\mathrm{Ownership}+{\mathsf{\beta }}_4{\mathrm{Controls}}_{\mathrm{it}}+\mathsf{\epsilon }$$

(8)

$$\begin{array}{l}{\mathrm{Tobin}’\mathrm{s} \mathrm{Q}}_{\mathrm{it}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{FCI}}_{\mathrm{it}}+{\mathsf{\beta }}_2{\mathrm{FCI}}_{\mathrm{it}}\times {\mathrm{MI}}_{\mathrm{it}}+{\mathsf{\beta }}_3{\mathrm{FCI}}_{\mathrm{it}}\times \mathrm{Ownership} +{ \mathsf{\beta }}_4{\mathrm{MI}}_{\mathrm{it}}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\mathsf{\beta }}_5\mathrm{Ownership}+{\mathsf{\beta }}_6{\mathrm{Controls}}_{\mathrm{it}}+\mathsf{\epsilon }\end{array}$$

(9)

Equation (7) was combined with Equation (6) and the moderator variable MI was added to evaluate whether multinational strategies play a moderating role between financing constraints and corporate value (i.e., Hypothesis 2). Similarly, Equation (8) was combined with Equation (6) and the moderator variable Ownership was added to test whether ownership structures in enterprises play a moderating role between financing constraints and corporate value (i.e., Hypothesis 3). Equation (9) was combined with Equations (7) and (8), together with the two moderator variables, and was used to comprehensively test the influence of financing constraints on corporate value.

5.1. Main Results

Table 4. Fixed Effects Models.

	(1)	(2)	(3)	(4)	(5)
	Model 1	Model 2	Model 3	Model 4	Model 5
FCI	−0.017 ***	−0.011 ***	−0.009 ***	−0.008 ***	−0.006 **
ROE		0.038 ***	0.038 ***	0.037 ***	0.035 ***
Age		0.487 ***	0.483 ***	0.483 ***	0.479 ***
Size		−0.186 ***	−0.186 ***	−0.184 ***	−0.184 ***
Total income		−0.029	−0.030	−0.029	−0.031
Employee		0.056 ***	0.055 ***	0.055 ***	0.053 ***
TATOR		0.070	0.073	0.071	0.073
Growth asset		0.023	0.025	0.024	0.026
MI			0.027		0.026
FCI × MI			−0.008 *		−0.008 *
Ownership				0.000	0.000
FCI × Ownership				−0.009 ***	−0.010 ***
Cons	0.467 ***	3.545 ***	3.590 ***	3.521 ***	3.564 ***
R2	0.020	0.083	0.084	0.085	0.086
Adj. R2	0.020	0.083	0.083	0.084	0.085
F	95.987	48.001	39.334	44.664	37.469
N	9429	9429	9429	9429	9429

Note: Regression results of Equations (5)–(9). The sample comprises 15,500 firm-year observations from 2011 to 2020. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. See Appendix A for definitions of variables.

shows the main results of the empirical analysis. Each column shows the result of panel regressions and is analyzed with the firm-level fixed effects model. As shown in Table 4, the estimation result of Equation (5) in column (1) is the regression result of the univariate model. Column (1) shows that the FCI coefficient β₁ was −0.017, which was significant at the 1% level. This result proves that financing constraints have a negative effect on corporate value. Column (2) is the estimated result of Equation (6), which is the regression result after adding the control variables. Column (2) shows that the FCI coefficient β₁ was −0.011, which is still significant at the 1% level. Accordingly, Hypothesis 1 is confirmed.

The fixed effects regression analysis after inclusion of the moderating variables is as follows. Column (3) is the estimation result of Equation (7), which is the regression result when only MI is added into the model as a moderating variable. The FCI × MI coefficient β₂ was −0.008, which is significant only at the 10% level. Therefore, MI as a moderating variable is shown to weakly strengthen the negative relationship between the main effects. Column (4) is the estimation result of Equation (8), which is the regression result when only Ownership is added into the model as a moderator variable. The FCI × Ownership coefficient β₂ was −0.009, which is significant at the 1% level. Therefore, the moderating variable was shown to strengthen the negative relationship between the main effects. In other words, this finding indicates that Ownership as a moderating variable had a significant strengthening or promoting effect on the relationship between FCI and Tobin’s Q. Column (5) is the estimation result of Equation (9), which is the regression result when both moderating variables (MI and Ownership) are added into the model. The FCI × MI coefficient β₂ was −0.008 and FCI × Ownership coefficient β₃ was −0.010. Only the latter was significant at the 1% level. These results further confirm that the moderating variables strengthened the negative relationship between the main effects. In other words, both of the moderating variables (MI and Ownership) strengthen the relationship between FCI and Tobin’s Q. Thus, Hypotheses 2 and 3 are confirmed.

5.2. Dynamic Panel Regression

Until this point, we have conducted a regression analysis with fixed effects models. For robustness in results, we forecasted the dependent variables for one year ahead. In our sample, the firms were from various industries. To avoid possible influence on the results, we employed a two-way fixed effects model for empirical analysis, with year (Year) and industry (Indus) effects fixed. Column (1) in

Table 5. Dynamic Panel Regression.

	(1)	(2)	(3)	(4)	(5)
	Tobin’s Qt+1	Tobin’s Qt+1	Tobin’s Qt+1	Tobin’s Qt+1	Tobin’s Qt+1
FCI	−0.023 ***	−0.016 ***	−0.016 ***	−0.013 ***	−0.013 ***
Roe		0.029 ***	0.029 ***	0.027 ***	0.027 ***
Age		0.043 ***	0.043 **	0.041 **	0.041 **
Size		−0.140 ***	−0.140 ***	−0.138 ***	−0.138 ***
Total income		−0.017	−0.017	−0.017	−0.017
Employee		0.020 **	0.021 **	0.020 **	0.020 **
TATOR		0.045	0.045	0.047	0.047
Growth asset		−0.068 ***	−0.069 ***	−0.067 ***	−0.068 ***
MI			−0.004		−0.005
FCI × MI			−0.002 **		−0.002 **
Ownership				−0.028 *	−0.029 *
FCI × Ownership				−0.009 ***	−0.009 ***
Cons	0.486 ***	3.692 ***	3.696 ***	3.658 ***	3.661 ***
R2	0.409	0.430	0.430	0.432	0.432
Year and Indus	Included	Included	Included	Included	Included
N	9296	9296	9296	9296	9296

Note: Regression results of Equations (5)–(9). The sample comprises 15,500 firm-year observations from 2011 to 2020. Results are from two-way fixed effects regression with Tobin’s Q_t+1 as a dependent variable. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. See Appendix A for definitions of variables.

is the estimated result of Equation (5), which is the regression result of the univariate forecast model. The FCI coefficient β₁ in column (1) was −0.023, which is significant at the 1% level. Therefore, the results are consistent with the previous conclusion that financing constraints have negative effects on corporate value. Column (2) is the estimated result of Equation (6). Columns (3) and (4) are the estimation results of Equations (7) and (8), which are regression results when only MI or only Ownership is discretely added into the model as a moderator variable. Column (5) is the estimation result of Equation (9), which is the regression result when both moderating variables MI and Ownership are added into the model. The FCI × MI coefficient β₂ was −0.002 and the FCI × Ownership coefficient β₃ was −0.009. Only the latter was significant at the 1% level. Compared with the benchmark regression results, the dynamic regression results better support Hypotheses 1 and 3.

5.3. Results of Heterogeneity Analysis

Next, we studied differences in corporate value between SOEs and non-SOEs under financing constraints. In Panel A of

Table 6. Results of Heterogeneity Analysis.

Panel A. Sorted by Enterprise Ownership Type and Firm Size
	(1)	(2)	(3)	(4)
	SOEs	Non-SOEs	Large-Scale Enterprise	Small and Medium-Sized Enterprises
FCI	−0.022 ***	−0.016 ***	−0.023 ***	−0.010 **
ROE	0.041 ***	0.059 ***	0.025 ***	0.080 ***
Age	0.006	0.081 ***	−0.045 *	0.081 ***
Size	−0.138 ***	−0.149 ***	−0.095 ***	−0.208 ***
Total income	−0.021	−0.045	−0.031	−0.001
Employee	0.002	0.042 ***	0.029 ***	0.018
TATOR	0.040	0.176 ***	0.051 *	0.092 *
Growth asset	−0.066 **	−0.081 ***	−0.018	−0.230 ***
Cons	4.144 ***	4.196 ***	3.288 ***	4.868 ***
R2	0.430	0.518	0.403	0.613
Year and Indus	Included	Included	Included	Included
N	3694	5735	4379	5198
Panel B. Sorted by Financing Constraints and Multinationality Index
	(1)	(2)	(3)	(4)
	High-FCI	Low-FCI	High-MI	Low-MI
FCI	−0.018 ***	−0.009 *	−0.022 ***	−0.015 **
ROE	0.015	0.105 ***	0.015	0.068 ***
Age	−0.037	0.015	−0.010	0.081 ***
Size	−0.129 ***	−0.114 ***	−0.157 ***	−0.159 ***
Total income	−0.028	−0.035	0.006	−0.010
Employee	0.013	0.009	0.008	0.023 ***
TATOR	0.074 *	0.066	0.088	0.082 ***
Growth asset	−0.050	−0.097 **	−0.060	−0.144 ***
Cons	4.046 ***	3.960 ***	3.339 ***	4.017 ***
R2	0.526	0.435	0.465	0.467
Year and Indus	Included	Included	Included	Included
N	1404	3221	3225	6352

Note: Regression results of Equation (6) specifically for different levels of firms. In Panel A, firms are classified as SOEs (non-SOEs) based on ownership type, Large-Scale Enterprise (Small and Medium-Sized Enterprises) based on firm size. In Panel B, firms are classified as High-FCI (Low-FCI) ranked by interest coverage ratio, High-MI (Low-MI) based on the mean of the multinationality index. The sample comprises 15,500 firm-year observations from 2011 to 2020. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. See Appendix A for definitions of variables.

, Columns (1) and (2) are the results of the grouped regression based on ownership type. The coefficients of FCI are significant for both SOEs and non-SOEs, indicating that FCI indeed inhibits corporate value. For SOEs, the magnitude of the FCI coefficient was larger. This means that the disincentive is more pronounced in SOEs. Therefore, Hypothesis 3 is confirmed. We adopted the method of Xue et al. [54] to judge firm size by using the mean of the natural logarithm of assets. According to the results, financing constraints have a more significant effect on corporate value in large-scale enterprises, with a coefficient of −0.023. The results show that the financing constraint has a greater inhibiting effect on the corporate value of large-scale enterprises.

When we grouped the example by high and low financing constraints, we found similar results—that is, financing constraints remain negatively related to corporate value. In Panel B, the results of the grouped regression are in Columns (1) and (2) based on Equation (6), according to differing levels of financing constraints. In the high FCI group, it is negatively significant above the 1% level, with the FCI coefficient of −0.018. This indicates that the higher the level of financing constraint, the greater the negative impact on corporate value. Thus, Hypothesis 1 is strongly supported. To classify multinationality, we chose the mean of MI as the boundary. According to the results, the group of high-MI significantly affected the inhibiting effect of FCI on firm value. Therefore, the higher the level of multinationality, the more difficult it will be to alleviate the inhibiting effect. Hence, Hypothesis 2 is supported.

5.4. Instrumental Variables Analysis

To solve the endogeneity problem, we used instrumental variables to obtain asymptotically unbiased estimations. Our first instrumental variable is the distance from a firm’s headquarters to the nearest stock exchange market of Shanghai or Shenzhen. This instrumental variable is similar to the research approach of Wei and Wu [56] and Di Giuli and Laux [57], coinciding with concepts therein [96]. The closer the headquarters are to Shanghai or Shenzhen, the stronger the market vitality and economic development will be. Proximity also decreases risk exposure and reduces financing difficulties for firms.

Our second instrumental variable was an indicator variable as to whether the workplace of the independent directors was the same as the registered place of the listed firm. Our reasoning behind this technique is that firms rely on their local reputations among independent directors [97], and the presence of on-site independent directors increases opportunity for observation and reduces costs of information acquisition [98]. This can help firms expand their financing channels, which should reduce financing constraints for firms in capital markets. In contrast, if the independent directors hired by a firm are not near firm headquarters, then the role of the independent directors cannot be maximized. Independent directors may obtain asymmetric information and fail to optimize the financing of firms.

Using the 2SLS method of regression analysis, we obtained the estimated value of the model. In the tabular results reported in this section, we focused on financing constraints to corporate value. The results pertain to distance and independent directors as instrumental variables. Columns (1) and (4) show first-stage findings of the 2SLS method. We discovered that distance and workplace were negatively related to the financing constraints index, controlling for the broad set of other regressors. Columns (2) and (5) show the second-stage results of 2SLS by using two instrumental variables. Due to limitations in the instrumental variables, we performed underidentification and weak identification tests. Because the model has a heteroscedasticity problem, the generalized method of moments (GMM) model, which is more efficient than 2SLS, was selected for analysis. This method is similar to the endogeneity test of Meng et al. [90], Ge et al. [99], and Bae et al. [100]. Columns (3) and (6) are the estimation results of GMM, and the final results are shown to be consistent with the above hypotheses. The FCI coefficients were −0.205 and −0.372, and both significant at the 1% level. The FCI × MI coefficients β₂ were −0.125 and −0.115. The FCI × Ownership coefficients β₃ were −0.085 and −0.184. Because the cross-terms were significant at the 1% level, Hypotheses 2 and 3 are supported. All results reveal strong negative effects between financing constraints and corporate value; therefore, the benchmark results in

Table 7. Instrumental Variables Analysis.

Variable	(1)	(2)	(3)	(4)	(5)	(6)
	IV1: Distancemin			IV2: InDWP
	2SLS_First	2SLS_Second	GMM	2SLS_First	2SLS_Second	GMM
Distancemin	−0.106 *
InDWP				−0.119 ***
FCI		−0.258 **	−0.205 ***		−0.456 **	−0.372 ***
ROE	−1.920 ***	0.535 **	−0.351 ***	−1.918 ***	0.917 ***	−0.362 ***
Age	0.532 ***	0.335 ***	0.188 ***	0.694 *	0.148	−0.951 ***
Size	0.183 *	−0.248 ***	−0.175 ***	0.124	−0.258 ***	−0.107 ***
Total income	0.454 ***	−0.071	0.175 ***	0.496 ***	−0.158*	−0.41 **
Employee	−0.014	−0.008	−0.020	0.019	−0.041	−0.501
TATOR	−0.315 *	0.096	−0.180 ***	−0.375 **	0.181	−0.299 **
Growth asset	−0.058	−0.006	−0.161 *	−0.106	−0.017	−0.261 *
MI	1.579 ***	−0.437 *	−0.349 ***	1.501 ***	−0.753 **	−0.239 *
FCI × MI	0.624 ***	−0.169 **	−0.125 ***	−0.597 ***	−0.274 ***	−0.115 **
Ownership	1.426 ***	0.000	0.252 ***	1.316 ***	0.000	−0.353 **
FCI × Ownership	0.482 ***	−0.134 ***	−0.085 ***	0.479 ***	−0.222 ***	−0.184 ***
Cons	−22.479 ***		−1.174	−23.173 ***		−1.218
R2	0.679	0.430	0.503	0.680	0.451	0.510
F		13.102	117.063		8.159	12.352
Underidentification test		9.083	20.795		8.339	20.835
Weak identification test		9.101	20.816		8.36	21.012
N	7960	7960	7960	7636	7636	7636

Note: Regression results of Equation (9) by 2SLS regression and the GMM. The sample comprises 15,500 firm-year observations from 2011 to 2020. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. See Appendix A for definitions of variables.

are supported.

6. Discussion, Implications, and Conclusions

6.1. Discussion

This study draws on institutional theory [21,22] and agency theory [23,24] to study the effect of financing constraints on corporate value in China and the moderating role of multinationality and ownership type. We analyzed 1550 Chinese-listed local firms from 2011 to 2020. Applying effective and rigorous empirical methods of measuring financing constraints [15,18,19,91], corporate value [11], and multinationality [38,101,102]; and analyzing the data through the dynamic regression method [103], heterogeneity analysis [53,54,55], and 2SLS regression analysis [56,57], we demonstrate our theory. This study’s main findings and their theoretical implications are threefold as follows.

First, as is explored by Deng and Zhao [11], we empirically verify earlier findings that financing constraints negatively affect the corporate value of Chinese firms. To provide explicit theoretical mechanisms of the relationship, we depend on the institutional perspective [21,22]. Our main argument is that when Chinese firms suffer from poorly developed financial institutions, they tend to use informal institutions such as bribery and networks that are highly costly [29,30] to gain legitimacy from external stakeholders and survive. In this situation, Chinese firms are less likely to secure enough slack resources for building their sustainable competitive advantages and corporate value enhancement. We believe that we advance the literature on the relationship between financing constraints and corporate value in the Chinese context as we take into account weak Chinese financial institutions and Chinese local firms’ efforts to address the financing constraints under this situation as a basis for our discussion.

Second, although prior studies draw on various finance and management theories and find that investors tend to value the multinationality of firms [104,105], our findings suggest that multinationality can exercise a negative effect on corporate value when it is combined with a firm’s financing constraints, at least under the Chinese underdeveloped institutional setting. For instance, taking account of transaction cost theory [106] and real option theory [107], Brouthers et al. [108] found that advanced country firms from Greece and The Netherlands that entered emerging markets leveraged their firm-specific resources and reduced transaction costs, achieving high corporate value. From internalization theory, Buckley and Casson [109] and Mishra and Gobeli [110] showed that the multinationality of US firms leads to high corporate value when firms’ technology investment and managerial incentives are high. We think these studies may not fully capture the effect of the multinationality of Chinese firms as they do not consider that the negative effect of financing constraints on corporate value is conditional on the level of multinationality of firms. In this study, we provide the institutional perspective explanation that multinationality can be helpful in increasing corporate value, but unplanned overseas market expansion is bound to bring irreparable losses to enterprises. When Chinese firms are limited in terms of financing due to weak financial institutions and large spending on informal institutions, an inflexible commitment to multinational strategies may cause capital chain tension and exacerbate the restraining effects of financing constraints on corporate value. We propose that when Chinese firms under financing constraints reduce their multinationality and focus on their core domestic business, however, the restraining effects of financing constraints on corporate value can be alleviated.

Third, the second way through which financing constraints can inhibit corporate value we consider is ownership type of Chinese enterprises. Many prior studies suggest that prevalence and existence of inefficient SOEs is one of the unique features of the Chinese economy [111]. We point out that such Chinese SOEs tend to have agency problems attributed to the pursuit of sociopolitical objectives [85] and multiple principals of SOEs [84]. Indeed, the Chinese government tends to intervene to SOEs to achieve their political purposes and SOE executives’ discretions are highly restricted. Given this situation, based on agency theory [23,24], we discuss how managers of SOEs are less likely to be motivated to make an effort to overcome the financing difficulties in China and improve firms’ efficiency. We further argue that multiple monitoring authorities such as the SASAC, CPC, and governments tend to be ineffective and less responsible for ensuring SOEs managers’ effort [44] to address financing constraints and improving corporate value. Finally, we conclude that, under these circumstances, potential investors are likely to second-guess investment decisions in SOEs, thus further contributing to their financing difficulties and effectively reducing their corporate value over time. Our empirical findings clearly support the agency problem of Chinese SOEs, which can exacerbate the negative effects of financing constraints on corporate value of Chinese firms.

6.2. Implications and Future Research

The findings of this paper offer implications for policy makers and practitioners. First, due to the nature of weak institutions in China, it is necessary for regulators to focus on improving the financing constraints of firms. We suggest that to fundamentally remove the Chinese firms’ financing obstacles, Chinese regulators need to provide transparent financial information, enforce investor protection and governmental control, and strengthen accounting standards and legal systems. In other words, developing formal financial institutions is one of the important tasks for the central and regional government to reduce Chinese firms’ financing transaction costs. Second, Chinese firm managers should realistically assess financing constraints to determine appropriate strategies for overseas expansion and ownership structure. Although the internationalization of Chinese firms can lead to firm growth and performance, practitioners must keep in mind that overseas expansion beyond a firm’s financial capability can jeopardize firms’ financing. In addition, Chinese SOEs can consider increasing private ownership to reduce the state’s intervention on their management. Lastly, we propose that Chinese firms can pay more attention to R&D and corporate social responsibility, which can strengthen a firm’s competitive advantages and reputation [112,113]. In the long term, it can lead to a firm’s higher performance, improving cash flow and easing their financial constraints.

Financing constraints have extensive effects on corporate value in the context of incomplete markets. There is still much room for future research on the relationship between financing constraints and corporate value. For instance, we suggest that our results should be theoretically tested to see whether they still hold in other emerging economies with weak financial institutions such as Indonesia, Mexico, and Mongolia. Although we take into account two of the most particular firm-level characteristics of Chinese firms, multinationality and ownership type, as our moderators, we encourage research on other possible industrial- or regional-level moderators in need of attention by scholars. For instance, the industrial growth rate or regional institutional development level can be good candidates for analysis. Empirically, scholars can use several different time periods instead of 2011–2020 and examine whether our findings are confirmed using such sampling periods. In addition, there are several methods of evaluation for financing constraints, and the formulation of standards remains to be explored. We also suggest future studies consider using the copula method to analyze the dependence structure of financial data to replace the linear correlation [114,115]. Research to realize these ideas is a planned focus of future work.

6.3. Conclusions

This paper analyzes non-financial firms listed on the Chinese securities markets from 2011 to 2020. Based on institutional theory and agency theory, we investigate the changes in firm value when firms are constrained by financing in China. In general, the results presented in this paper provide supporting evidence of a negative impact of financing constraints on firm value in China and the underlying mechanism of it. Further, we show that one of the ways Chinese firms can overcome financing constraints is by easing their multinationality. In addition, improving the ownership structure of Chines firms can help them address the investors’ low evaluation on their financial constraints. Finally, we discuss the theoretical implications of the paper and suggest several practical implications for Chinese firm managers and policymakers.