Industrial Policy’s Effect on Cross-Border Mergers’ Decisions—Theoretical and Empirical Analysis

Abstract

To explore the relationship between industrial policy and cross-border M&As in Chinese enterprises, the PSM method and a two-way fixed model were used. Using a sample of A-share listed companies during the period 2005–2019, we theoretically and empirically analyzed the mediating role of financial constraints and the moderating role of political connections between the companies. It was found that industrial policy has a significant positive effect on cross-border M&As and that firms’ financial constraints mediate the relationship between industrial policy and M&As. Political connections moderate the relationship between industrial policy and M&As. This study enriches the research on the relationship between industrial policy and firms’ M&A decisions from a micro perspective, and provides evidence of industrial policy promoting cross-border M&As through alleviating the financial constraints of the firms.

1. Introduction

The continued growth of the Chinese economy has resulted in difficulties in industrial upgrading, the insufficient utilization of resources, and serious environmental pollution. The traditional mode of economic development has encountered bottlenecks, such as a decline in investment and reduced domestic consumption [1]. Therefore, enhancing the level of enterprise innovation and unleashing social innovation have become important bases for the transformation of China’s economic development from factor-driven to innovation-driven, in the new era. Due to the complexity and high risk of innovation, most enterprises cannot provide all the resources required for innovation themselves [2].

To achieve this goal, the Chinese government has issued relevant plans to promote the sustainable development of the Chinese economy [3]. For example, in 2015, the Chinese government introduced “Made in China, 2025”, which proposes the goal of becoming a powerful nation in the future. Moreover, the implementation of the “going out” strategy, and the opinions on promoting enterprise mergers and reorganizations provide sufficient policy conditions for Chinese enterprises to directly acquire advanced foreign technologies in a relatively low-cost way, through cross-border M&A, to realize innovation and break innovation bottlenecks [3]. M&A is an important way for enterprises to achieve extensibility expansion and leapfrog growth [4].

According to the statistics of the Ministry of Commerce of China, since 2010, Chinese enterprises’ overseas M&A have generally maintained a sharp rise in both investment quantity and number of transactions [5]. In particular, the cross-border M&A value of Chinese enterprises made rapid progress in 2016 and 2017. The number of M&A cases reached a record high of 738 in 2016. The number of overseas M&As reached a record high of USD 180.4 billion in 2017, a five-fold increase compared with 2010. In 2018, influenced by the sluggishness and instability of the international situation, the number and value of overseas M&As performed by Chinese enterprises declined year-on-year, but 488 M&A projects were still completed, and the value of M&A remained at a high level, at USD 89.8 billion [6].

According to a previous study, there are many factors that can affect overseas M&As, such as the production scale, productivity, financing ability, and innovation ability of the enterprise; the economic and institutional advantages of the host country and home country; and the bilateral trade relations and bilateral exchange rates [7]. Unlike companies in developed countries, the decision making of Chinese companies is largely influenced by the government. The government’s industrial policies have an impact on the technological innovation of Chinese companies. The Chinese government usually introduces new industrial policies every five years. Examples are “The 13th Five-Year Plan for the National Economic and Social Development of the People’s Republic of China”, “The National Strategic Emerging Industries Development Plan of the 13th Five-Year Plan”, and “The National Science and Technology Innovation Plan of the 13th Five-Year Plan”. Thus, in this study we focused on the impact of industrial policy on enterprises’ decision making regarding cross-border M&A [8].

Furthermore, in recent years, some researchers have indicated that there should be cross-border M&As [9]. The corresponding literature has focused on the antitrust review of cross-border M&As. Researchers put forward the point that the relevant norms should be formulated by international organizations, so as to establish a transnational or supranational executive organ to review the transnational M&A cases [10]. Therefore, it is possible to establish a set of legal norms for transnational corporations’ cross-border M&As in the world. In addition to the traditional, common bilateral agreements, the influential world organizations, such the OECD, the WTO, and the UN, are still dedicated to formulating some regulatory measures and to seeking international cooperation [10,11]. For example, the “recommendations on cooperation among member states on restrictive business practices affecting international trade” were put forward by the OECD. As one of the international organizations that have long been keen on developing cooperation in the field of international antitrust laws, the United Nations has also been actively providing technical assistance to developing countries and countries in transition that plan to enact, or are drafting, anti-monopoly laws. The United Nations also adopted a set of fair principles for multilateral agreements on the control of restrictive business practices. These legal documents provide guidance for the M&A regulation of different countries and regional organizations [11].

However, first, whether industrial policies can promote investment by Chinese companies is still a matter of controversy. Feito-Ruiz et al. [12] found that China’s industrial policies have a significant positive impact on the decision making and profit growth of micro entities. Chen et al. also pointed out that, with the support of industrial policy, enterprises can obtain more cheap sources of funding, thereby strengthening the motivation for cross-border M&As. While some scholars hold the opposite opinion, they found that the role of industrial policy in promoting enterprise investment is not obvious, and even triggers excessive investment in industries supported by policies, making companies’ investments inefficient [13].

Previous studies on the impacts industrial policy have mainly focused on corporate finance, productivity, investment efficiency, and enterprise innovation. Recently, various studies have been conducted on the effect of industrial policy on the decision making in cross-border mergers, especially in China. Buckley [14] pointed out that China’s “going out” policy provides guidance and support for Chinese enterprises in relation to cross-border M&As. The Chinese enterprises supported by the “Five-Year Plan” on industrial policy can obtain more low-cost funds, and they can thus supply higher M&A premiums for cross-border M&As. Some scholars have conducted further research to explore the mechanisms of impact of industrial policy on cross-border M&A. Buckley et al. [15] believe that industrial policies stimulate enterprises’ cross-border M&As by promoting technological innovation. Some scholars have also found that industrial policies strengthen the motivation of enterprises’ cross-border M&As by alleviating financing constraints.

Second, the rapid development of China’s overseas mergers and acquisitions has inevitably caused suspicion, restriction, and even resistance from target countries, as they believe that China’s overseas M&As are driven by non-economic factors. They believe that it is non-market-oriented behavior, and that the enterprises are trying to take over the market and increase China’s political influence in the target country through mergers and acquisitions. Due to this, they focus on investigating the acquisitions of crucial infrastructure and technologies [16]. Moreover, since Chinese state-owned enterprises have close ties with the government, stricter standards are imposed for mergers and acquisitions performed by state-owned enterprises.

Third, due to China’s unique fiscal decentralization system, local governments will adopt a competitive strategy around resources in order to develop the local economy. The increasing intensity of government competition has also led to market segmentation, regional blockading, property rights regulation, and other local protectionist acts. In order to prevent the outflow of high-quality resources, local governments blockade resources in the region through these means [17]. This has also led to the serious industrial isomorphism in most regions of China, and thus, the factor market is relatively closed and non-circulating. In addition to affecting the level of regional specialization, local protection may also interfere with local development by setting administrative barriers [18]. The investment decisions of state-owned enterprises affect the healthy development of cross-regional M&As. Therefore, local governments will obstruct the cross-regional M&A transactions of enterprises through market segmentation, policies, and regulations. The normal development of dynamic enterprises increases the barriers to cross-regional M&As, and increases the costs and difficulties of the cross-regional M&As of enterprises. It will guide local enterprises to carry out localized M&As and to develop the local economy. These are the specific impacts of local protection on cross-regional M&As [19].

When considering the ownership of enterprises, local governments, as behind-the- scenes agents of local state-owned enterprises, can be used for the public. Corporate boards of directors’ and the executives’ appointments directly affect the production and operation activities of local state-owned enterprises, especially when it comes to M&As. Since the local government is more inclined to develop the local economy, it will exert influence on local state-owned enterprises to make them more advanced [20]; an example could be local M&As of banks. In order to realize the interests of the country and the people, the central government is committed to improving the comprehensive strength of the country. As well as the expansion of the core influence of industry, relevant preferential policies will be formulated across regions, and laws and regulations will be promulgated to promote the development of key industries. Enterprises in industry carry out cross-regional M&As, and through cross-regional M&As, they can quickly become stronger and bigger, and improve their core competitiveness [21]. Therefore, the central government will provide incentives to promote central SOEs to carry out cross-regional M&A transactions, so as to give full play to the role of the central government and to the leading role of state-owned enterprises.

After entering the new stage of the new century, Chinese enterprises began actively practicing the enterprise “going out” strategy with unprecedented scale and intensity. The “going out” behavior of enterprises will certainly influence their own development [22,23] and China’s industrial policy. Therefore, the purposes of this study were as follows. The first was to explore how to scientifically evaluate the impact of enterprises on their own development, under the influence of industrial policy. The second was to explore whether industrial policy influences the decision making in cross-border M&As. The third was to explore the following question: If a company is involved in a cross-border merger, will there be a cross-border merger and acquisition premium? It is problems such as that which Chinese enterprises need to solve through large-scale “going out” strategies, and they are also the problems that academic circles need to study in depth and systematically.

The marginal contributions of this paper mainly include the following: First, from the perspective of micro-enterprises, we explored the impact of industrial policy on M&As and their mechanisms—previous studies have mainly explored this at the macro-level and meso-level. Second, an empirical model was used to accurately verify the impact mechanism; most of the previous studies have been verified by introducing the interaction term and establishing a difference-in-difference model. In this paper, based on the literature on the impact of industrial policy on investment, we used a fixed-effects model and tested the mechanism using step-by-step regression. Third, the heterogeneity of the impact of industrial policies on enterprises of different natures was examined. Fourth, on the basis of previous research, we further explored cross-border merger and acquisition premiums.

2. Literature Review

2.1. Industrial Policy

The industrial policy is the sum of the various policies formulated by the government to intervene in industrial development and to improve the economic development of industries [24,25]. In other words, there are two definitions of industrial policy. On the one hand, industrial policy refers to government policy targeting industrial development. On the other hand, it refers to market-related policies that affect industry. In China, industrial policies refer to the policy regarding the direction of national industrial development. Industrial policy can guide and promote the industrial structure alterations which are beneficial to economic development [26].

The industrial policy’s role in the economy’s development will be enhanced when the economy is developed [27,28]. In other words, the effect of the government interventions on economic development will be enhanced [29,30]. Industrial policy includes the government subsidies, tax incentives, policy loans, and other forms of implementation. In other words, industrial policy means that the government adjusts the industrial structure and organization by many methods, according to economic development [31,32]. Therefore, the industrial policy will increase the total supply growth rate and could alter the economic structure [33]. Therefore, alteration to industrial policy means that the industrial structure and organization are altered [34]. The industrial structure/organization’s form cannot be measured from the macro perspective.

Liu et al. [35] studied the relationship among industrial policy, corporate strategic differences, and debt financing costs. The results indicated that strategic differences increase the debt financing cost of enterprises, and that the quality of accounting information, internal controls, and financing constraints plays a mediating role in it. In addition, the results indicated that the industrial policy weakened the impact of strategic differences on the debt financing cost of enterprises. In addition, the industrial policy had an effect on innovation. Wang et al. [36] studied the industrial policy’s effect on innovation, and the research was based on the wind-power enterprises. The results indicated that industrial policy would be beneficial to enterprise innovation. In addition, the industrial policy could have a synergistic effect with the enterprise innovation.

2.2. The Industrial Policy’s Effect on Enterprise Micro Actions

Industrial policy influenced enterprises’ microscopic behavior, and the corresponding impact was positive in one study [37]. Industrial policy promoted effective competition. The effective competition increased the innovation, which was used to promote enterprises’ development. The government formulated industrial policy, and these policies influenced enterprises’ behavior. The driving force of industrial policy was strong according to some studies [38,39]. However, some researchers hold the view that the industrial policy has not brought obvious benefits to enterprises. Industrial policy improved the elective support and government intervention, restricted different enterprises’ competition, and reduced enterprises’ production efficiency [40,41]. Industrial policy promoted enterprises’ competition, which increased the enterprises’ value. The industrial policy effected enterprises’ microscopic behavior, including capital, financing, innovation, and mergers and acquisitions.

2.3. Cross-Border M&As

The essence of M&A decisions was found to be increasing enterprise profits in some studies [42,43]. There were many factors which can influence the M&A decision, and the main reasons for M&A decisions are as follows [44]. Whether the decision-making direction is right will influence an M&A decision. The choice of development direction will play a decisive role in the process of M&A decision making [45]. The decision makers’ attitudes will influence an M&A decision. The more decisive frame of mind will make the M&A more likely [44]. In addition, the key information will influence the M&A decision, and if the information is promising, the M&A decision will be more likely [46].

Eduardo Pablo [47] studied the determinants of cross-border M&As in Latin America. The results indicated that the business environments in the target and bidder countries were important. In addition, when property rights decrease, the cross-border deals will decrease.

The M&A decision-influencing factors could be divided into macroscopic aspects and microcosmic aspects [48]. The macroscopic aspects include the corresponding background, for instance, the financial crisis, marketization, national risk, and national integration. The microscopic aspects include physical productivity level, policy opportunities, the marketization process, regional openness, qualitative considerations, bilateral political relations and political connections, bilateral frontier investment agreement, and public security [49]. Enterprise type will influence the M&A decision, and a state-owned enterprise with strong external policy control will have low willingness to carry out M&As. In addition, the cultural differences will also influence the M&A decisions. If there exist the obvious cultural differences, the M&A decision will be difficult [50,51].

An M&A decision is influenced by the enterprises’ strategic considerations, learning experiences, innovation, and characteristics [52]. Due to the fact that a cross-border M&A is a strategic development decision, enterprises will consider it based on their current strategies. As is known to us all, cross-border M&As could be influenced by filling the technique gap [12]. The corresponding price increase would have an effect on the cross-border M&A decision. The property and resource increases will also impact cross-border mergers and acquisitions of enterprises. In addition, the transportation infrastructure will have an effect on M&As [49,53]. However, the conclusions on cross-border M&As are different. Some studies indicate that M&As could improve productivity and increase the shareholders’ wealth. However, some researchers stated that M&As provided no business performance increase, and there was no obvious improvement for enterprise performance [54].

For Chinese enterprises, seeking resources and funds is the motivation of cross-border M&As. In addition, the corporate executives’ behavior will have an effect on the M&A.

2.4. Mechanism Analysis

According to the theoretical framework of the figure, if we want to examine the impacts of industrial policy on corporate investment and the mechanism of its use, we must find an angle to better verify the relationship between industrial policy and the impact mechanism—political relevance and funding and the nature of property rights are exactly such perspectives. Based on whether the government holds shares in enterprises, state-owned enterprises and private enterprises have great differences in terms of enterprise objectives, political associations, government intervention, access to political resources, and policy burdens, which have led to obvious differences between them in terms of corporate investment, bank credit, resources, and investment administrative control, providing us with a unique perspective on the impact of industrial policy on corporate investment and its mechanisms [23]. Before analyzing the nature of property rights, according to the theoretical framework, the following logic can be easily deduced: when enterprises are in industries incentivized by industrial policies, political relevance and increases in the supply of bank credit brought about by the implementation of policies are more inclined to increase investment.

Due to different institutional arrangements, there are many differences in the investment behavior of state-owned enterprises and private enterprises: The state-owned enterprises have to bear many non-marketized burdens, strategic policy burdens, social policy burdens, and so on [55,56]. Under the political system of economic decentralization and political centralization, local governments have sufficient motivation to achieve local growth, and it is easy for state-owned enterprises to have internal control over them, and state-owned enterprise executives can be motivated by personal promotion incentives and implicit salary incentives to invest [57]. They all tend to expand the scale of investment in enterprises, and it is easy to produce excessive investment behavior. The above analysis shows that compared with private enterprises, state-owned enterprises have more inefficient investment, are more likely to receive government support to increase investment, and may be more likely to over-invest.

2.5. The Current Research

Table 1. Studies on the relationship between IP and cbma.

Author	Variables	Method	Conclusion	Time
Li Wenjing [58]	IP; INVEST, LOAN, PRIVATE; TQ; IP CFOLOAN	Fixed effect regression model	Policy has not significantly increased the investment of state-owned enterprises, but has been able to increase the investment of private enterprises.	2014
Yu Guansheng [59]	VOFDI; PIV; WGI; NOFDI	Random effects model; intermediary effect model	Higher level of governance by the host government can effectively regulate the adverse effects of international investment protection	2020
Yang [60]	Div; NIP	PSM-DID	Enterprises that are not supported by IP receive less government policies and tax incentives	2018
Cai Qing-feng [61]	CROSS; TIP; TIP IP; TIP SOE; TIP IP SOE	Probit;	IP promote the firms to launch the M&As, especially for SOEs	2019
Zhong Ninghua [62]	SAI; Premium; Completion; lnLoan; Loan/Asset; (Loan+Bond)/Asset; Interest/Asset; LnSubsidy	OLS; logit; mechanism analysis	Firm that supported by IP with relatively high M&As premium rates but low completion rates, and obtain more bank loans and government subsidies.	2019
Qiu et al. [63]	IP; recession; soe, ip recession	OLS; PSM	Industrial policy has obvious resource effect on the recession enterprises, industrial policies can improve the recession enterprises M&A.	2020
Yu Guansheng [59]	VOFDI; PIV; WGI; NOFDI	Random effects model; intermediary effect model	Higher level of governance by the host government can effectively regulate the adverse effects of international investment protection	2020
Yan Bing, [64]	Policy; OFDI; fc; rd	Probit regression; DID model; intermediary effect model;	IP significantly promote enterprises’ FDI; The external financial dependence of market competition environment and the stage of enterprise life cycle will affect the implementation effect of industrial policy.	2021
Gao Jingzhong, [65]	Bpol, Premium, Ecopolunc,	Probit	Firm that supported by IP with relatively high M&As premium rates.	2021
Hou Yaoxin et al. [66]	CAR, CROSS, SOE, fc	OLS	State-owned enterprise, Cbma are more likely to be recognized by the market.	2021
This paper	Policy, cbma, long asset; soe; policy soe	Two-way fixed model; intermediary effect model; step-by-step regression method	Industrial policy could promote the cbma through financing restraint and political correlation.	2022

Note: NIP/TIP/IP/bpol (industrial policy); INVEST (Investment amount of enterprise); Ecopolunc (level of economic policy uncertainty); TQ (Tobin’s Q); FDI (foreign direct investment); OFDI; fc (funding constrains); rd; CROSS/cbma (cross-border mergers and acquisitions); CAR (cumulative abnormal return rate); VOFDI (China’s foreign direct investment volatility index); NOFDI (number of FDI); Div (diversification); PIV (international investment protection degree index); WGI (indicators of a government’s governance level); long asset; soe (state-owned enterprise); policy soe (interaction item of policy and soe).

shows a collection of studies on the relationship between IP and cbma. There are studies focused on the industrial policy’s effect on enterprises’ micro behavior, and the relationship between industrial policy and enterprise micro behavior [34,58]. However, the relationship between industrial policy and enterprises’ micro behavior is controversial. On the one hand, some researchers indicate that industrial policy will promote competition, investment, and employment, which will help increase the enterprises’ profits. In addition, the industrial policy could then be relaxed. Other research indicates that industrial policy could help improve enterprises’ innovation. However, still other researchers think that industrial policy is inefficient, and in other words, the industrial policy may reduce the investment efficiency. The reasons were based on ostensible problems with government subsidies and tax incentives.

Table 2. The relationship between industrial policy and financial constraints.

Author	Variables	Method	Conclusion
Zhangxinmin et al. (2017) [69]	Industrial policy, financing constraint, enterprise investment efficient	DID model	Local government’s industrial policy aggravate area listed firms’
Yuminggui and Fanrui (2016) [70]	Industrial policy, corporate technology innovation, bank loan, fiscal instruments	Cross-section difference estimation, DID model	Industrial policy improves technological innovation
Liwenjing and Liyao tao (2014) [58]	Industrial policy, firms’ investment	Regression	Industrial policy does not significantly improve firms’ investment
Caiqingfeng and Tianlin (2019)	Industrial policy, cross-industry M&As	Game theory model, Probit regression,	Industrial policy support firms are more likely to acquire the targets.
Yuejinlong et al. (2020) [63]	Industrial policy, M&A decision	A-share listed companies; regression, PSM Heckman two-stage method	Industrial policy M&A? Industrial policy influence the allocation of credit resources, industrial policies alleviate the financing constraints
Chen donghua et al. (2010) [69]	Industrial policy, corporate financing	DID estimation	Industrial policy supported firms get capital market financing.
Lianlishuai and Chenchao (2015) [71]	Industrial policy, allocation of credit resources	Regression, DID estimation	Industrial policy support high growth firms’ credit financing.
Zhang Li et al., (2017) [72]	Key industrial policy, resource allocation; industrial land transfer	Regression, DID estimation	Key industrial policy significantly promotes lands transferred to key industries.
Yang Xingquan et al. (2018) [60]	Industrial policy; diversification, support effect, force effect	DID estimation	Industrial policy supported industries receive more government subsidies and tax relief.
Zhong Ninghua et al. (2019) [62]	Cross-border M&As, industry policy, M&As premium, M&A completion	OLS model, logit model	Industrial policy-supported firms receive more cheap capital from the financial system and more subsidies from the government. Chinese firms tend to pay higher prices for M&As and lower completion rates.

shows the relationship between industrial policy and financial constraints.

Table 3. The relationship between financial constraints and M&As.

Author	Variables	Method	Conclusion
Li shan min and yang ruo ming (2022) [73]	Financial constraints, M&A	2009–2015 A-share listed company, M&A Events, OLS regression	Proposed signal transmission hypothesis to test the impact of financial constraints on M&A.
Hou Yaoxin, Liu Juntong, jing Qi (2021) [66]	Industrial policy, cross-border M&A	A-share listed companies from 2010–2019 Event study method	Cross-border M&A can gain market acceptance in the short term.
Weiping and mao xiao dan (2017) [74]	Cross-border merger and acquisition, director network, financial constraints	IV-Probit, SYS-GMM	Cross-border M&A capabilities are hindered and constrained by financing constraints. Executives’ financial connections alleviate financial constraints of cross-border M&A.
Yanbing and guoshao yu (2021) [64]	Industrial policy, outward foreign direct investment; investment mode	Panel data of Chinese listed companies from 2006–2017 Panel data Probit regression; DID model	Industrial policy promotes OFDI by easing financing constraints and increasing R&D, the easing of financing constraints will promote firms to choose M&A investment

shows the relationship between financial constraints and M&As. The primary study indicated that industrial policy would influence cross-border M&As [67]. The cross-border M&As have changed from resource-acquisition to technology-seeking M&As. The high-technology enterprise has strong motivation to obtain new technology. The main reasoning for the industrial policies of China is supporting enterprises’ technological upgrading. Then, the enterprises should respond to the industrial policy and achieve the corresponding enterprise mergers. The Chinese enterprises have high premiums after the cross-border mergers [15,68].

The literature indicates that improving financing availability and reducing enterprise financing costs would promote cross-border enterprise M&As. The merger details, the industry relevance of both parties, the payment method, and the institutional environment will influence the cross-border M&As [69,70].

As is shown in the table, in previous study, government support affects enterprises’ cross-border M&As. The literature on industrial policy’s effects on enterprise investment, financing, and innovation is abundant. However, there are few studies on industrial policy’s effect on enterprises’ cross-border M&As [61,66]. Therefore, there is lack of systematic analysis on industrial policy and enterprise cross-border M&As, including the impact, impact mechanism, and the effects of the characteristics of the firms.

Therefore, according to previous studies on the industrial policy’s impact on FDI, we used a two-way fixed-effects model and an intermediary-effects model to systematically analyze the industrial policy’s impact on cross-border M&As, to fill the key gaps in research. Thus, the main content of this research was as follows: analyzing industrial policy’s effects on the enterprise cross-border M&As; analyzing the mechanisms in detail.

3. Theoretical Hypotheses

3.1. Industrial Policy and Firms’ Cross-Border M&A Decisions

Industrial policy, as a policy instrument, aims at promoting a country’s economic development through adjustments to industrial structure, and it has been widely used all over the world [29,75].

The impact of industrial policy on the economy has been broadly discussed. Recently, researchers focused on the micro-perspective of industrial policy at the firm level. According to the most recent study [76], which evaluated the impact of the Korean government policy on firms’ performances, there is causal evidence for the impact of industrial policy on firms’ performances in the long term, and the research suggests that the majority of total improvements come from the long-term benefits of learning by doing rather than the short-term benefits of relaxing financial constraints.

Existing studies have mainly explored industrial policies affecting corporate M&A decisions from two perspectives: resource effects and signaling [25,29].

Resource-based theory considers that a firm is an integration of a series of resources, so acquiring resources can help the firm to improve its competitiveness. One of the purposes of national industrial policy is to implement resource allocation tilting for supportive industries to help enterprises develop better [77]. When the government supports an industry, it generally adopts one of two means—direct intervention or indirect induction. Direct intervention instruments include market entry control, price control, and environmental protection control [78]. Indirect inducement means include fiscal policy and monetary policy, where fiscal policy involves government investment, government subsidies, etc., and monetary policy refers to the financial policy implemented in conjunction with industrial policy, such as bank credit resource rationing. Industrial policy has obvious resource effects and can allocate financial resources to fit the development needs of enterprises. Wang et al. [36] found that enterprises supported by industry have a higher level of investment compared to those not supported by industrial policy. In terms of fiscal policy, industrial policy has a distinctive “supporting hand” role, and enterprises supported by industrial policy are able to receive more government subsidies. Enterprises supported by industrial policy can obtain more cheap funds and more government subsidies from the financial system. The Chinese government gives policy preferences such as tax relief, government subsidies, land allocation, and even financial assistance to industry-supported enterprises.

As for financial policies, since banks’ credit rationing is subject to stronger government intervention, industrial policies can adjust the allocation of credit resources, and highly developed firms supported by industrial policies can obtain more credit resources, which in turn improves the efficiency of credit resource use [79]. Industrial policy can attract mergers and acquisitions to supported industries by influencing the allocation of credit resources and alleviating corporate financing constraints.

Based on industrial policy signaling theory, in the capital market, industrial policy transmits a signal to the outside world about the future development direction and adjustments of industry. Enterprises supported by industrial policies often have good development prospects [80]. The support of industrial policy can alleviate the financing constraints of enterprises, and in the equity financing market, industrial policy has a leading role, and the scale of equity refinancing of enterprises supported by industrial policy is significantly higher than that of other enterprises. In addition, industrial policy support provides implicit endorsement for enterprise development [54], which can attract resource input from stakeholders other than the government, enhance the resource control of enterprises in recession, and alleviate the financing constraint problem of enterprises in recession.

Therefore, this paper proposes hypothesis H1: Industrial policy has a positive impact on corporate cross-border M&As.

3.2. Intermediation of Financing Constraints

At the macro level, industrial upgrading can be achieved in the short term through cross-border mergers and acquisitions of firms, “learning by doing”, and alleviation of financing constraints. The government often uses its control of state-owned companies to help struggling firms finance themselves through cross-industry M&As and other means [81]. Stimulated by the national industrial policy, more and more Chinese firms are participating in cross-border M&As. Incentivized by the “policy mandate”, firms supported by industrial policy are more likely to pay higher M&A premiums.

At the micro level, companies will also consider M&A for their own growth strategies in order to achieve the faster growth external M&As allow [82]. According to the growth pressure theory, when the growth rate of a company does not meet the requirements of shareholders and executives face high growth pressure, they tend to choose M&As, which is a fast growth method. Compared to domestic M&As, for cross-border M&As, companies face greater challenges [83]. The factors affecting the outcome of an M&A include institutional factors, cultural gaps, unfamiliarity with laws, political relations, etc., especially when the acquiring company faces the dilemma of insufficient funds, which makes it more difficult to finance international projects.

From the current research on financing constraints and cross-border M&As, scholars agree that financing constraints may hinder and restrict firms’ cross-border M&As, and the alleviation of financing constraints will promote cross-border M&As [84]. Firms with strong financing capacities and firms with low financing constraints are more inclined to choose cross-border M&As. Li et al. [58] used 91 cross-border M&As carried out by multinational U.S. retail enterprises during 2002–2014 to evaluate the effect of financial constraints on cross-border M&As. They found that when firms face medium to high internal constraints, they prefer to adopt debt financing than equity financing. Industrial policy can alleviate the financing constraints of firms, and its core mechanisms lie in information benefits and resource effects. Compared with state-owned enterprises, the incentives of industrial policy have more pronounced effects on private enterprises’ financing.

In summary, we propose the following hypothesis, H2: there is a mediating effect of financing constraints on the influence of industrial policy on cross-border M&As.

3.3. The Moderating Role of State-Owned Enterprises

State-owned enterprises and private enterprises have different sensitivities to industrial policy. Due to the differences in the social responsibilities of SOEs and private firms, there are also significant differences in the selection of industrial policy support targets in M&As [85]. SOEs bear more policy burdens than non-SOEs. As a result of the policy burdens, SOEs’ business decisions often need to cooperate with government actions. When making cross-border M&A decisions, SOEs have to consider their functional positions, and their M&A motives need to be in line with the “national will”, and if there is a post-merger national security threat, SOEs will be reluctant to undertake overseas M&As [86].

State-owned enterprises (SOEs) are the mainstay of national economic development, and their every move has a profound impact on the market [87]. As the backbone of the national economy, SOEs are responsible for economic development and protecting people’s livelihoods, and China’s overseas M&As are normally initiated by large enterprises (especially large SOEs) [88]. Large SOEs have a significant advantage in China’s financial system. Due to the existence of soft budget constraints, they are able to obtain substantial policy subsidies and other resource support from the government to sustain their survival and development, even when they are in recession. Therefore, the influence that SOEs have on how industrial policy affects corporate M&As cannot be ignored.

In the context of the Chinese economy in particular, M&As have been used as a tool for restricting SOEs and the state sectors by introducing mixed ownership to increase SOE efficiency and innovation activities.

Political connections and/or the vicinity of firms to public actors have strong mediating roles in which policy activities impact M&A success and performance. This is because political connections are believed to affect access to credit, preferential fiscal measures, and in general, the overall gains obtained from public-policy actions. These aspects have been largely explored in Chinese studies.

In conclusion, this paper puts forward the following hypothesis, H3: state-owned enterprises play a positive moderating role between industrial policies and cross-border mergers and acquisitions.

4. Empirical Method

4.1. Model Settings

4.1.1. Benchmark Model

The theoretical mechanism of industrial policy’s influence on enterprise decision making regarding cross-border mergers and acquisitions is discussed above. In this part, we analyze the relationship between industrial policy and the cross-border merger and acquisition decision making of enterprises from an empirical perspective. We used the panel data model to estimate the relationship between industrial policy and cross-border merger and acquisition decision making. Compared with general time series or cross-sectional data, panel data have greater sample capacity, which can significantly improve estimation efficiency and accuracy, and it can solve the problem of missing variables that do not change over time by controlling the fixed effects of individuals to alleviate the non-observable individual differences or “heterogeneity”.

The baseline regression model (Equation (1)) was as follows:

$$\mathrm{cbm}{a}_{it}={\alpha }_0+{\alpha }_{1}polic{y}_{it}+{\alpha }_2{X}_{it}+{\mu }_i+{\gamma }_t+{\epsilon }_{it}$$

(1)

where i indicates the enterprise and t indicates time. The interpreted variable cbma is a dichotomous variable equal to one when a business i has performed a cross-border acquisition at time t and zero otherwise. Policy is an industrial policy dummy variable; if the listed company belongs to an industry with the support industry, the value is equal to 1, otherwise, it is 0. X represents the control variables for low-carbon economic transformation. μ and γ represent individual fixed effects and time-fixation effects, respectively. ε represents random error terms affected by time changes. In this paper, we focus on the explanatory variables’ positive, negative, and significant coefficients, which indicate that industrial policy can greatly boost or restrain cross-border mergers and acquisitions.

4.1.2. Impact Mechanism Inspection Model

In the above, we hold that industrial policy can promote cross-border mergers and acquisitions by alleviating the financing constraints of enterprises, so financing constraints are the intermediary variables. At the same time, due to the nature of state-owned enterprises, they are more connected to the country, so they may have more incentives for certain behaviors, including carrying out cross-border mergers and acquisitions, so we propose that the nature of enterprise ownership and industrial policy affect the cross-border mergers and acquisitions of enterprises. In order to verify this hypothesis, we examined the intermediary effect of financing constraints by establishing the following intermediary effect model (Equations (2) and (3)).

$${\mathrm{long}\_\mathrm{asset}}_{it}={\alpha }_0+{\beta }_{1}polic{y}_{it}+{\beta }_2{X}_{it}+{\mu }_i+{\gamma }_t+{\epsilon }_{it}$$

(2)

$$\mathrm{cbm}{a}_{it}={\alpha }_0+{\lambda }_{1}polic{y}_{it}+{\lambda }_{2}long\_asse{t}_{it}+{\lambda }_3{X}_{it}+{\mu }_i+{\gamma }_t+{\epsilon }_{it}$$

(3)

Among them, the outcome variables in Equation (2) are long assets for the financing capacity of each enterprise. Equation (2) adds the long assets on the basis of Equation (1). In order to identify the intermediate effects, we used the step-by-step regression method to carry out the intermediate effect test. The general steps were as follows: (1) we first performed the regression with Equation; (2) if alpha 1 is not significant, this indicates that the causal relationship between industrial policy and the decision making on cross-border mergers and acquisitions of enterprises is weak, and then the intermediate effect test must stop—if alpha 1 is significant, then one should continue the regression equation of the structure; (3) next, we tested whether the industrial policy affects the financing ability of the enterprises—If beta 1 is not significant, then the causal relationship between the industrial policy and the enterprises’ financing constraints is weak, and the intermediate effect test should be stopped. If beta 1 is significant, the regression equation of the continuing structure tests whether the intermediary effect of industrial policy exists, and if it does exist, the ratio of 1 to 1 will be close to 0; then, we could conclude that the financing constraint appears to be an intermediary variable between industrial policy and the decision making of Chinese enterprises regarding cross-border mergers and acquisitions. If beta 1 is not significant, but beta 2 is significant, it indicates that there is a full mediation effect.

Next, this paper sets up the influence mechanism model for political relevance. As mentioned before, we assume that industrial policy will be more stimulating for enterprises with high political relevance to carry out transnational reporting. In order to verify our hypothesis, we chose the nature of enterprises as a proxy index of political relevance. The specific model settings were as follows:

$$\mathrm{cbm}{a}_{it}={\alpha }_0+{\alpha }_{1}polic{y}_{it}+{\alpha }_{2}so{e}_{it}+{\alpha }_{3}polic{y}_{it}\times so{e}_{it}+{\alpha }_4{X}_{it}+{\mu }_i+{\gamma }_t+{\epsilon }_{it}$$

(4)

On the basis of Equation (4), we added a dummy variable soe; it equals 0 when the enterprise i is a state-owned enterprise and equals 1 otherwise. We also introduced an interaction item, policy*soe, which is used to identify the effect of policy; if its coefficient is significantly positive, the implication is that more industrial policy will promote cross-border mergers and acquisitions by political relevance—that is, the role of industrial policy in supporting the decision making of cross-border mergers and acquisitions of enterprises is significantly related to the ownership of enterprises.

4.2. Definitions of Variables

4.2.1. The Interpreted Variable

The interpreted variable, cbma, is the central interpreted variable in this paper. It is a dummy variable and depends on whether the enterprise decided to go through with an M&A or not. If the listed company has performed a cross-border merger and acquisition this year, its value is 1; otherwise, it is 0.

Explanatory variable: industrial policy. The main explanatory variable in this paper is industrial policy, which is a virtual variable that is assigned a value of 0 or 1. If the listed company belongs to an industry supported by industrial policy, it is 1. We identified industries supported by industrial policy by learning the industries supported and encouraged in China’s five-year national economic development plan, and the revised “Guidelines on Industry Classification of Listed Companies” of the CSRC, 2012.

Financing constraints: we used the ability of external financing to express the extent of financing constraints faced by enterprises.

Political relevance: it is a virtual variable, and this paper represents political relevance by the nature of property. If an enterprise is a state-owned enterprise, the value is 1; otherwise, it is 0.

Control variables: in order to avoid the interference of other factors, we selected enterprise size, return on net assets, asset–liability ratio, return on assets, size of the board, two jobs in one, main business income, growth rate of main income, proportion of executive compensation, age of enterprise, return on equity, growth rate of enterprise cost, and enterprise price–earnings ratio as control variables. Descriptions of all the variables are given in

Table 4. Variable descriptions.

Variables	Variables Symbol	Definition	Source of the Data	Calculation Method
Explained variable	cbma	Cross-border acquisition of virtual variables	SDC platinum database	According to SDC platinum after finishing
Explanatory variable	policy	Industrial policy	Compiled according to the government’s work report	Compiled according to the government’s work report
Mediator variable	Long_asset	Financial constraints	-	Use the ability of external financing to present the extent of financing constraints
Moderator variable	soe	The properties of listed companies	CSMAR	Dummy variable, state-owned enterprise = 1, non state-owned enterprise = 0
Control variables	size	Total assets of the listed company	CSMAR	/
	roe	Return on net assets	CSMAR	Net profit/total assets
	assetdebt	Financial leverage	CSMAR	Total liabilities/total assets
	roa	Return on assets	CSMAR	Net profit/total assets
	boardsize	The size of the board of directors of a listed company	CSMAR	Number of boards of directors of listed companies
	dual	Whether the director and CEO are the same person	CSMAR	Dummy variable, the director and CEO are the same person = 1, otherwise = 0
	lnsales	Income of the company	CSMAR	/
	salegrowth	The growth rate of the main income of listed companies	CSMAR	/
	salaryratio	The proportion of management compensation of listed companies	CSMAR	The proportion of management compensation of listed companies
	firmage	The number of years a listed company has been established	CSMAR	/
	roe	Return on equity of listed companies	CSMAR	Net profit/net assets
	costgrowth	Cost growth rate of listed companies	CSMAR	The value of the difference between the main cost of the listed company
	pe	Price/earnings ratio	CSMAR	Share price/net assets per share

.

According to the guidance of 11th–13th Five-Year Plans for the National Economic and Social Development of the People’s Republic of China, if the selected companies belong to an industry that is clearly encouraged or supported, the corresponding value of IP is 1, and it is 0 otherwise.

Table 5. The 11th Five-Year Plan (2006–2010), 12th Five-Year Plan (2021–2015), and 13th Five-Year Plan (2016–2020) industrial-policy-supported industries. (✓ was existent, ✕ was non-existent, V was descriptive).

Code	Industries	11th Five-Year Plan (2006–2010)	12th Five-Year Plan (2011–2015)	13th Five-Year Plan (2016–2020)
(A) Agriculture, forestry, animal husbandry and fishery
A01	Agriculture	✓	✓	✓
A02	Forestry	✓	✓	✓
A03	Livestock	✓	✓	✓
A04	Fisheries	✓	✓	✓
A05	Agriculture, Forestry, Livestock, Fisheries Services	✓	✓	✓
(B) Mining industry
B06	Coal Mining and Washing	✓	✕	✕
B07	Oil and Gas Extraction	✓	✕	✕
B09	Nonferrous Metals Mining	✓	✕	✕
(C) Manufacturing industry
C14	Food Manufacturing	✓	✕	✕
C26	Manufacturing of chemical raw materials and chemicals manufacturing	✓	✓	✕
C27	Pharmaceutical manufacturing	✓	✓	✓
C32	Industry of non-ferrous metal smelting and rolling processing	✓	✕	✕
C34	General equipment manufacturing	✕	✓	✓
C36	Automotive manufacturing	✕	✓	✓
C39	Computer, communications, and other electronic equipment manufacturing	✓	✓	✓
(D) Industry of electric power, heat, gas and water production and supply
D44	Electricity, thermal production and supply	✓	✓	✓
D45	Gas production and supply,	✓	✕	V
D46	Water production and supply,	✕	V	V
(G) Transport, storage and postal service industry
G53	Rail transport/railway transportation industry	✓	V	✕
G54	Road transport road transport industry	✓	✓	✕
G55	Water transport waterway transport industry	✓	✓	✕
G56	Air transport, air transport industry	✓	✓	✕
G57	Pipeline transport, pipeline transport industry	✓	✓	✕
(I) Industry of information transmission, software and information technology services
I63	Telecommunications, radio and television and satellite transmission services	✕	✓	✕
I64	Internet and related services	✕	✓	✓
I65	Industry of software and information technology services	✓	✓	✕
(K) Real estate industry
K70	Real estate	✓	✓	✕
(L) Leasing and commercial service industry
L72	Commercial service industry? business services	✓	✓	✓
(P) Education
P82	Education	✕	✕	✓
(Q) Health and social work
Q83	Health	✕	✕	✓
(R) Industry of culture, sports, and entertainment
R85	Press and publishing industry, news and publishing	✕	✓	✓
R86	Radio, television, film, and film recording	✕	✓	✓
R87	Industry of culture and arts	✕	✓	✓
Source: authors’ compilation, Guidelines for the industry classification of listed companies (2012 revision) 11th 5-year plan, 12th 5-year plan, 13th 5-year plan

shows the 11th Five-Year Plan (2006–2010), 12th Five-Year Plan (2011–2015), and 13th Five-Year Plan (2016–2020) industrial-policy-supported industries.

4.2.2. Data Sources

In this paper, the corresponding data were from 2005 to 2019, via the Shanghai Stock Exchange (SHSE) and Shenzhen Stock Exchange (SZSE). The detailed data-treatment procedure was as follows. The M&A data were from the SDC platinum merger and acquisition transaction database. The financial indicators were from the CSMAR database. The data processing and regression analysis in this paper were carried out using STATA 12.0. The descriptive statistics for the main variables in this article are shown in

Table 6. Descriptive statistics of main variables.

Variable	Mean	sd	min	max	N
cbma	0.1246	0.3421	0.0000	1.0000	31,400.0000
policy	0.3418	0.5463	0.0000	1.0000	31,400.0000
size	26.5213	1.8413	17.2316	33.2648	31,400.0000
assetdebt	0.3126	0.5123	0.01823	54.5495	31,400.0000
roa	0.1201	0.1263	−7.3264	3.8426	31,400.0000
dual	0.9612	0.5613	0.0000	1.0000	28,600.0000
lnsales	24.1562	1.8623	19.6324	31.216	27,968.0000
salegrowth	1.3458	98.1234	−1.3628	168.324	29,840.0000
salaryratio	0.3126	0.1864	0.1632	1.0000	29,840.0000
firmage	19.3624	7.1264	4.0000	65.0000	29,840.0000
roe	0.3652	0.6812	−48.2368	3126	29,840.0000
costgrowth	0.4628	8458	−11.0264	586.126	29,840.0000
pe	116.2456	3354.268	0.4528	580,000.0000	29,840.0000

below.

5. Results and Discussion

5.1. Baseline Analysis

The advantages and disadvantages of the two-fixed, two-way fixed model were as follows. Advantages: (1). A fixed-effects model requires weaker assumptions, and explanatory variables can be correlated with individual characteristics; (2). Factors that do not vary over time or between individuals are difficult to observe. These factors may be related to explanatory variables, creating endogenous problems if included in a random perturbed variable. The two-way fixed-effects model can solve the endogeneity problem caused by unobservable factors. Disadvantages: As there are many parameters to be estimated, the two-way fixed-effects model has few degrees of freedom.

Table 7. Industrial policy’s effect on M&A decision making.

	(1) cbma	(2) cbma	(3) cbma	(4) cbma
policy	0.128 ***	0.074 ***	0.089 ***	0.037 ***
	(8.134)	(6.529)	(3.846)	(4.126)
size		0.026 ***		0.138 ***
		(4.562)		(4.018)
assetdebt		−0.023 *		0.129 ***
		(−1.386)		(0.527)
roa		0.315 ***		0.456 **
		(162)		(284)
dual		−0.064 ***		−0.362 **
		(−6.146)		(−3.234)
lnsales		−0.016 **		−0.214 ***
		(−2.168)		(−4.542)
salegrowth		−0.014 **		−0.126 ***
		(−2.365)		(−2.136)
salaryratio		−0.245 **		0.268 **
		(−248)		(0.426)
firmage		−0.034 *		0.038 ***
		(−2.154)		(0.654)
roe		−0.064 ***		−0.126 **
		(−3.328)		(−2.346)
costgrowth		0.016 ***		0.042 ***
		(2.546)		(3.176)
pe		0.000		0.000
		(0.126)		(−0.114)
Individual fixation effect	NO	NO	YES	YES
Time fixation effect	NO	YES	NO	YES
_cons	0.226 ***	−0.064 *	0.127 **	−0.238 **
	(22.138)	(−1.349)	(1.268)	(−1.268)
R2	0.011	0.007	0.169	0.154
N	31,855	28,456	32,126	27,584

z statistics in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

shows the industrial policy’s effect on M&As. Table 7 shows the two-way fixed-effects regression’s empirical results, and the results show that the industrial policy had an effect on the M&A decision making. Results in (1) cbma and (3) cbma do not account for the control variables, but those in (2) cbma and (4) cbma accounted for the control variables. (1) cbma and (2) cbma results were not obtained by the two-way fixed-effects model, but the (3) cbma and (4) cbma results were obtained by the two-way fixed-effects model. The results indicate that the industrial policy coefficient is positive, which indicates that the industrial policy promotes enterprises to make cross-border merger and acquisition decisions, regardless of the control variables and two-way fixed effects.

5.2. Robustness Test

We used the propensity score matching (PSM) method to explore the impact of industrial policy on cross-border M&A decision making by the selected listed firms. In order to increase the reliability and robustness of the results, the following robustness tests were done:

The selected sample of this study included firms listed on the Shanghai Stock Exchange and Shenzhen Stock Exchange. Firstly, ST, ST*, and PT firms were excluded. In addition, the firms that were not listed on one of the stock exchanges are excluded. Due to it not being random sample selection, there was sample selection bias.

There is potential reverse causality between financial constraints and M&As. In order to eliminate the selection bias and endogenous problems, we needed to determine whether there is a significant difference between the firms supported by the government’s industrial policy and those that are not in terms of M&As. A significant difference would mean that the firms supported by the industrial policy are more or less likely to participate in M&As. Therefore, in reality, there is only one possible status for each firm—supported by the industrial policy or not supported. We cannot know the industry-supported firms’ M&A decision making without know which firms are in this category. In addition, we cannot know the non-supported firms’ M&A decision making without the same. Hence, the Propensity Score Matching (PSM) method was used to deal with this problem. Propensity Score Matching (PSM) is a statistical method used to process data from observational studies. In observational studies, data biases (biases) and confounding variables are numerous for a variety of reasons, and the tendency to score match is intended to reduce the effects of these deviations and hybrid variables in order to make a more reasonable comparison between the experimental and control groups. Specifically, the firms supported by industrial policy were treated as the experimental group, and the firms not supported by industrial policy were treated as the control group. Based on samples of the experimental group and the control group having similar characteristics, the counterfactual status of the firms of the experimental group can be presumed, and the actual status and counterfactual status of the firms of the experimental group can be presumed. The difference is the average effect of the treatment on the treated group (ATT).

There are four steps in the PSM method. The first step is to choose the covariate; the second step is to estimate the propensity score (P-score); the third step is to match the samples; and the last step is to calculate the treatment effect. In this paper, whether the firm was supported by industrial policy can be seen as the treatment variable. The firms’ own characteristics will also affect their M&As; hence, we needed to introduce covariates to meet the assumption of ignorability, to make sure the treatment group’s M&As are not affected by the covariates. After finishing the above four steps, we combined the two parts of the sample into a new sample, and then performed two-way fixed-effects regression. Then, we adopted the method of matching the regression results yearly after the baseline match, and the year-on-year matches are shown in

Table 8. Regression results after base period matching.

	(1) cmba	(2) cmba	(3) cmba	(4) cmba
policy	0.068 **	0.057 ***	0.109 **	0.134 ***
	(8.143)	(5.846)	(1.546)	(2.018)
size		0.018 ***		0.054 ***
		(3.168)		(3.204)
assetdebt		−0.321 **		0.214 ***
		(−0.128)		(5.018)
roa		0.513 ***		0.324 **
		(6.246)		(2.35)
dual		−0.109 ***		−0.086 ***
		(−13.264)		(−2.984)
lnsales		−0.028 ***		−0.038 ***
		(−1.564)		(−3.542)
salegrowth		−0.018 **		0.026
		(−0.518)		(0.458)
salaryratio		0.168 ***		0.048 *
		(1.568)		(1.264)
firmage		−0.017 **		0.059 *
		(−0.832)		(1.384)
roe		−0.248 **		−0.212 *
		(−4.365)		(−1.638)
costgrowth		0.016 **		0.016 *
		(1.843)		(0.154)
pe		0		0
		(1.126)		(1.642)
Individual fixation effect	NO	YES	YES	YES
Time fixation effect	NO	YES	YES	YES
_cons	0.126 **	0.058	0.024 **	−0.546 *
	(3.18)	(0.846)	(12.462)	(−1.126)
R2	0.016	0.014	0.164	0.168
N	19,624	17,286	21,328	19,238

z statistics in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

and

Table 9. Regression results after year-by-year matching.

	(1) cmba	(2) cmba	(3) cmba	(4) cmba
policy	0.138 **	0.086 *	0.116	0.064 ***
	(2.462)	(3.126)	(1.524)	(3.625)
size		0.058 **		0.128 ***
		(4.168)		(3.146)
assetdebt		0.342 **		0.364 **
		(3.548)		(1.946)
roa		−3.116 **		−1.726 **
		(−1984)		(−362)
dual		0.658 ***		0.227 **
		(35.426)		(12.318)
lnsales		−0.126 **		−0.126 **
		(−7.135)		(−2.364)
salegrowth		−0.016 **		−0.033
		(−1.834)		(−0.517)
salaryratio		2.124 ***		2.018 ***
		(31.246)		(10.326)
firmage		−0.012 **		0.246 *
		(−3.824)		(0.842)
roe		−0.236 **		−0.541 *
		(−3.264)		(−1.038)
costgrowth		0.004 *		0.012 *
		(2.136)		(0.242)
pe		0.000		−0.000
		(0.384)		(−0.146)
Individual fixation effect	NO	NO	YES	YES
Time fixation effect	NO	NO	YES	YES
_cons	0.322 ***	−0.428 ***	0.328 ***	−3.168 *
	(33.426)	(−5.136)	(2.184)	(−2.142)
R2	0.026	0.342	0.846	0.652
N	4328	3964	3218	3012

z statistics in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

.

As can be seen in the results of Table 8 and Table 9, whether or not two-way fixed effects and control variables were used, the coefficient of policy was significantly positive, indicating that industrial policy has a significant positive impact on firms’ cross-border merger and acquisition decision making; that is, industrial policy can encourage Chinese listed firms to carry out cross-border mergers and acquisitions.

5.3. Impact Mechanism Test

5.3.1. Financial Constraint Mechanism Test

We used the gradual regression method to analyze the mechanism of industrial policy’s impact on firms’ cross-border M&As. Firstly, the direct effects were tested through Equation (1). Then, the mediating effects were tested through Equation (3) and Equation (4) to test the hypothesis of industrial policy promoting cross-border M&As by alleviating firms’ financial constraints. The results are shown in Table 6 below. We used long assets as the mediator to represent financial constraints. To test the effect of industrial policy on financial constraints, we ran four regressions. The first model and third model did not include any control variables. The only difference between these models was that the third model included individual fixed effects and a time-fixed effect. The second and fourth models included control variables. The difference between the second and fourth models was two-way fixed-effects regression.

The results show that the coefficient of policy is significantly positive regardless of whether two-way fixed effects and control variables are used, which indicates that the industrial policy improves the financing ability of enterprises; that is, the financing constraints of enterprises have been alleviated to a certain extent. To test the mediation of financial constraints on cross-border M&As after matching, both model 5 and model 6 used two-way fixed-effects regression. The difference between these two models was that, in the sixth model, control variables were included.

Table 10. Enterprise financing capacity mechanism.

	(1) long_asset	(2) long_asset	(3) long_asset	(4) long_asset	(5) cmba	(6) cmba
policy	0.012 ***	0.011 *	0.013 ***	0.013 ***		0.036 *
	(7.428)	(1.264)	(4.018)	(284)		(1.564)
long_asset					0.038 **	0.016 **
					(2.114)	(1.936)
size		0.084 ***		0.058 ***	0.064 **	0.076 **
		(63.452)		(29.384)	(5.124)	(4.635)
assetdebt		0.126 ***		0.324 **	0.128 *	0.089 *
		(54.264)		(44.265)	(3.174)	(1.864)
roa		0.076 ***		0.044 **	0.514 **	0.318 *
		(648)		(2.146)	(6.258)	(2.524)
dual		0.018 **		0.014	−0.027 *	−0.024 *
		(7.162)		(2.625)	(−3.246)	(−3.15)
lnsales		−0.124 **		−0.038 ***	−0.124 *	−0.022 *
		(−54.326)		(−18.246)	(−3.126)	(−3.09)
salegrowth		0.000		0.000	−0.000 *	−0.000 *
		(0.426)		(2.468)	(−2.541)	(−2.64)
salaryratio		0.028 ***		0.014 **	0.136 **	0.024 *
		(3.214)		(0.526)	(0.328)	(0.526)
firmage		−0.014 ***		−0.146 ***	0.076	0.018
		(−6.234)		(−5.264)	(1.564)	(0.836)
roe		−0.134 **		−0.134 **	−0.134	−0.134
		(−1.846)		(−2.016)	(−0.364)	(−0.24)
costgrowth		−0.012 *		−0.013 *	0.012 **	0.012 *
		(−0.664)		(−2.364)	(1.546)	(1.374)
pe		−0.000		0.000	−0.000	−0.000
		(−0.364)		(0.426)	(−0.038)	(−0.18)
Individual fixation effect	NO	NO	YES	YES	YES	YES
Time fixation effect	NO	NO	YES	YES	YES	YES
_cons	0.061 ***	−0.226 ***	0.060 ***	−0.099 *	−0.490 *	−0.468 *
	(82.421)	(−23.456)	(51.236)	(−3.148)	(−2.034)	(−2.37)
R2	0.017	0.264	0.542	0.682	0.162	0.318
N	25,624	22,126	26,384	22,154	19,864	20,126

z statistics in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

shows that the coefficient of financial constraints, which is represented by financial capability, had a significant positive effect on M&As, with or without control variables. Furthermore, industrial policy had a significant positive effect on M&As, which meets the requirements of the mediation effect test; that is to say, the financial capability is the mediator through which industrial policy promotes cross-border M&As. Improving firm’s financing capacities plays an important part in promoting cross-border M&As by Chinese firms.

5.3.2. The Political Relevance Mechanism

Further, to test the moderating influence of political connection on the relationship between industrial policy and M&As, an interaction item called c.policy#c.soe was tested in the regressions models. The first question was whether political connections moderate the relationship between industrial policy and M&As. The second question was, what is direction of the moderation effect—positive or negative? SOEs were used to represent the moderator variable of political connection.

Table 11. Political relevance mechanism.

	(1) cmba	(2) cmba	(3) cmba	(4) cmba
policy	0.108 ***	0.048 ***	0.012 ***	0.126 ***
	(9.018)	(6.236)	(3.126)	(2.118)
soe	−0.038 **	−0.158 ***
	(−158)	(−1124)
c.policy c.soe	0.342 ***	0.154 ***	0.138 ***	0.264 ***
	(4.526)	(2.426)	(3.246)	(2.342)
size		0.038 ***		0.054 ***
		(124)		(2.146)
assetdebt		−0.108		0.124 **
		(−0.648)		(1.24)
roa		0.364 **		0.256 **
		(4.522)		(3.64)
dual		−0.026 **		−0.024 ***
		(−4.625)		(−3.176)
lnsales		−0.014		−0.108 **
		(−0.764)		(−3.624)
salegrowth		−0.000 *		−0.000 **
		(−3.624)		(−2.548)
salaryratio		−0.029 **		0.026
		(−5.24)		(0.426)
firmage		−0.014 ***		0.013 **
		(−5.184)		(0.764)
roe		−0.108 ***		−0.241 *
		(−3.164)		(−0.845)
costgrowth		0.036 ***		0.024 **
		(3.642)		(3.642)
pe		−0.000		−0.000
		(−0.068)		(−0.058)
Individual fixation effect	NO	NO	YES	YES
Time fixation effect	NO	NO	YES	YES
_cons	0.078 ***	−0.138 **	0.123 ***	−0.462
	(24.651)	(−1.462)	(2.46)	(−2.364)
R2	0.011	0.024	0.146	0.456
N	28,469	23,465	26,846	25,346

z statistics in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

shows the political relevance mechanism results. The results show that the interaction item was significantly positive when tested in all the regression models, which supports the moderation of political connection over the relationship in industrial policy and M&As. The effect is positive, which means political connection enhances the effect of industrial policy on M&As. This was the case whether or not we used two-way fixed effects or control variables, which reinforces the conclusion. Of course, political ties are strong for state-owned enterprises. SOEs, with the support of industrial policies, are taking on more political tasks, so they will take the initiative to carry out cross-border M&As.

The empirical results indicate that the industrial policy has promoted cross-border mergers and acquisitions and enhanced the financing ability of enterprises by easing their financing constraints. The results provide evidence that industrial policy has a significantly positive effect on relieving a firm’s financial constraints, which is consistent with previous studies. Firms with strong financing, meaning without financial constraints, are more likely to participate in M&As. Industrial policy has a significantly positive effect on firms’ M&As decisions.

This research is important because we discussed the mechanisms of industrial policy’s effect on cross-border mergers, and we showed the obvious significance.

6. Conclusions

In order to study the relationship between the industrial policy and the decision-making regarding cross-border mergers and acquisitions, we conducted this research. The detailed research methods were as follows: firstly, by constructing a two-way fixed-effects model, we empirically examined the impact of industrial policy on the decision making regarding cross-border mergers and acquisitions; secondly, the relationship between the two countries after excluding the sample selection bias was tested by using the method of propensity score matching; finally, we examined two mechanisms of industrial policy promoting cross-border mergers and acquisitions through an impact mechanism effect model. The detailed results were as follows:

(1)

Industrial policy can significantly promote cross-border mergers and acquisitions, and the mechanisms include financing and political connection.

(2)

Industrial policy can ease the financing constraints of enterprises, and then promote cross-border mergers and acquisitions.

(3)

State-owned enterprises with strong political relevance or connections play an important role in the process of promoting cross-border mergers and acquisitions.

There are still some limitations to our research, as follows:

(1)

The cross-border mergers and acquisitions’ effect on the industrial policy should be studied in the future.

(2)

As was known to us all, there exist different types of enterprises. Therefore, the next study should be focused on industrial policy’s effects on the different categories of enterprises’ M&A decisions.

(3)

For the next paper, the technology innovation effect in relation to the industrial policy’s effect on cross-border mergers and acquisitions, and the corresponding mechanism, should be analyzed.

The contributions of this study are as follows. The study filled up the research gap of the relationship between industrial policy and M&As. We explored the mediation and moderation mechanisms of the relationship. The empirical findings also expand the signaling transmission effects of industrial policy.

Government departments should formulate the corresponding industrial policy, and the detailed policy recommendations are as follows:

(1)

The heavy industry, light industry, new industries, and high-tech industries should be taken into consideration, and the industrial policy should be suitable for these industries. For different types of industry, the industrial policy’s effect on M&A decision making should be analyzed.

(2)

The cross-border mergers and acquisitions should be thought through carefully, as the M&As we observed were profitable in the short term, but their effects were unclear in the long run.

(3)

Scientific industrial policy should be modified based on the economic development. The goal should be to increase M&As.

(4)

The industrial policy should have a promotional effect on M&As, and the industrial policy should exert more influence. In addition, the market environment influences the industrial policy and M&A decision making, so the industrial policy should be suited to the market environment.