Do Political Connections Help Privately Owned ...

Do Political Connections Help Privately Owned Chinese Enterprises Go Global?** Denis Schweizer*, Thomas Walker†, and Aoran Zhang‡

ABSTRACT This paper explores how political connections can influence the likelihood of “going global” by assisting privately owned enterprises (POEs) in China to complete crossborder acquisitions. Using a sample of Chinese POEs from 2007 to 2016, we show that politically connected POEs are more likely to acquire foreign companies and to have outsize financial performance than domestic POEs in the three years after beginning the globalization process. We also find that multinational POEs in China exhibit better corporate governance.

Keywords: China, Emerging Markets, Multinational Enterprise, Political Connections JEL Classification: G15, G18, G34

*

Concordia University, Associate Professor of Finance, John Molson School of Business Building, 1450 Rue Guy, Montreal, Quebec, Canada H3G 1M8, Phone: +1 514-848-2424, ext. 2926, Fax: +1-514-848-4500, e-mail: [email protected]. † Concordia University, Professor of Finance, John Molson School of Business Building, 1450 Rue Guy, Montreal, Quebec, Canada H3G 1M8, Phone: +1 514-848-2424, ext. 2387, Fax: +1-514-848-4500, e-mail: [email protected]. ‡ PhD Candidate in Finance, John Molson School of Business Building, 1450 Rue Guy, Montreal, Quebec, Canada H3G 1M8, e-mail: [email protected]. ** Acknowledgments: We are grateful to Douglas Cumming, Wenxuan Hou, Juliane Proelss, and Zhenyu Wu for helpful comments and suggestions, and to Max Cote for excellent research assistance. This research was funded by Concordia’s Office of the Vice President, Research & Graduate Studies (OVPRGS). Denis Schweizer gratefully acknowledges the financial support provided through the Manulife Professorship.

Do Political Connections Help Privately Owned Chinese Enterprises Go Global?

ABSTRACT This paper explores how political connections can influence the likelihood of “going global” by assisting privately owned enterprises (POEs) in China to complete crossborder acquisitions. Using a sample of Chinese POEs from 2007 to 2016, we show that politically connected POEs are more likely to acquire foreign companies and to have outsize financial performance than domestic POEs in the three years after beginning the globalization process. We also find that multinational POEs in China exhibit better corporate governance.

Keywords: China, Emerging Markets, Multinational Enterprise, Political Connections JEL Classification: G15, G18, G34

1.

Introduction Outbound foreign direct investment (FDI) by multinational corporations (MNEs) plays a vital

role in the world economy. According to the 2016 World Investment Report (UNCTAD, 2016), a strong FDI rally occurred in 2015. Global FDI by MNEs increased by 38% to $1.8 trillion, a record high since the 2008 financial crisis. The surge in cross-border mergers and acquisitions (M&A) is the principal contributor to the recovery of FDI. The value of cross-border deals by MNEs in 2015 soared to $721 billion, almost double the amount in 2014 ($432 billion). MNEs in China, the largest emerging economy in the world, have likewise been increasingly active. From 2009 to 2015, Chinese MNEs’ outbound FDI increased more than threefold to $128 billion. This put China in third place after the U.S. and Japan. Moreover, as UNCTAD (2016) reports, China is the only developing economy among the top ten foreign investors. In contrast to other major developing economies, whose outbound FDIs typically take the form of reinvested earnings, China’s FDI consists mainly of new equity investments. This reflects the exponential growth in cross-border M&A by Chinese MNEs. As is commonly known, state-owned enterprises (SOEs) have traditionally been the dominant players in China’s economic and financial activities, including with respect to outbound FDI. However, this has been changing since the Chinese government’s introduction of its “go global” policy. In fact, privately owned enterprises (POEs) are becoming increasingly active in outbound FDI, and, specifically, in cross-border M&A. According to the Financial Times (2016), investment banks are recognizing the necessity of working closely with this new group of entrepreneurs. More surprisingly, as the China Daily (2016) 1

reports, in September 2016, China’s POEs overtook SOEs for the first time in outbound FDI. Specifically, POEs now lead in terms of both value and number of cross-border M&A, accounting for 65.3% of aggregate deals. According to Sina Finance (2016), of the sixty-five listed Chinese companies with cross-border deals, forty-five are POEs. Nevertheless, the increasingly active role of Chinese privately owned MNEs has not yet drawn tremendous attention from academics. The limited number of Chinese MNE studies all consider state-owned MNEs. In addition, existing MNE studies are overwhelmingly from a management perspective; few studies have been conducted from a corporate finance viewpoint yet. To the best of our knowledge, no empirical study has examined the factors that affect Chinese POEs’ decisions to go global, an issue of particular interest given China’s unique institutional environment as well as the increasing importance of POEs. The fact that SOEs have, until recently, been the major player in China’s outbound FDI is largely because their government ownership significantly facilitates their financing activities. The Chinese government has traditionally considered SOEs to be the pillars of their economy, and therefore have acted accordingly to maximize their prosperity. In contrast, POEs tended to face discrimination under China’s institutional environment, and their business activities were severely restricted. Cultivating political connections has become a way for POEs to somewhat overcome market discrimination and gain access to state-controlled economic resources (see Li et al., 2008; Luo and Liu, 2009; Wu et al., 2012; Piotroski and Zhang, 2014; Li and Zhou, 2015; and Schweizer, Walker, and Zhang, 2016). In this vein, our research aims to explore whether and how political ties to the Chinese government affect POEs’ “going global” 2

strategy. Anecdotal evidence supports our conjecture. In recent years, two Chinese POEs completed two mega cross-border deals. The chairmen of both have political connections. According to UNCTAD (2016), Haier, China’s largest home appliance maker, acquired GE Appliances in the U.S. Haier’s chairman and CEO is a member of the central committee of the China Communist Party (CCP). Wanda Group, a privately owned conglomerate whose chairman was a member of the Chinese National People’s Congress (CNPC) and the Chinese People’s Political Consultative Conference (CPPCC), has made a series of record deals in the American entertainment industry since 2012. In addition to studying the effect of political connections on Chinese POEs’ going global strategy, we also analyze the impact of corporate governance. Chinese POEs have developed alongside market-oriented economic reforms. However, it is unclear whether the growth in outbound FDI by Chinese POEs can also be explained by market-related factors. Existing literature has documented that Chinese SOEs’ outbound FDI is connected not only with domestic political strategies, but also with international forces, such as political and security concerns. In contrast, the vast majority of Chinese POEs are market seekers, and their motivation to engage in outbound FDI stems mainly from technological developments and a desire to acquire strategic assets. In addition, the host countries of Chinese POEs’ outbound FDI are mainly developed economies with sound institutional environments (see Cheng and Ma, 2010; Ramasamy, Yeung, and Laforet, 2012; Meyer et al., 2014). Given these characteristics, we investigate whether firm-level corporate governance has an impact on Chinese POEs’ going global strategy. We also aim to examine empirically whether the 3

transition to operating as an MNE results in increased financial performance for Chinese POEs, because e.g. Pan et al. (2014) find that political connections reduce for instance transaction costs of Chinese MNEs. Therefore, we also aim to determine whether this ultimately results in higher profitability measured by Return on Equity. Using a sample of 1,782 Chinese POEs listed on either the Shanghai or Shenzhen stock exchanges, we provide empirical evidence that political connections are a significant determinant of whether POEs in China conduct outbound FDI. 4 Politically connected POEs have a greater likelihood of operating as MNEs by successfully completing cross-border M&A. Our results remain robust after accounting for any potential endogeneity between political connections and POEs’ going global decisions. Note that we find no link between corporate governance (measured by corporate governance index) and the likelihood of operating as an MNE for Chinese POEs in general. Nevertheless, our empirical evidence reveals a positive relationship between corporate governance and the going global decision for politically connected Chinese POEs. In other words, better corporate governance increases the likelihood of becoming an MNE, but only for those with political connections. Furthermore, our study sheds some light on the post-MNE performance of Chinese POEs that enter the overseas market. Our empirical results suggest that Chinese POEs who become MNEs are more profitable than other domestic POEs within the first three years of operation. We also demonstrate that politically connected Chinese POEs exhibit much higher post-MNE performance. 4

In our study, we follow the approach of Chinese POEs, and define outbound FDI as cross-border mergers and acquisitions (see UNCTAD, 2016).

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Our study provides the following contributions to the existing literature. To the best of our knowledge, we are the first to deliver an empirical analysis of how an emerging market’s institutional environment influences a POE’s decision to go global. Given China’s unique characteristics, an empirical study of how POEs can overcome institutional discrimination to expand their businesses overseas is highly meaningful. Thus, our investigation enriches the study of MNEs’ business activities in emerging markets (see Pan et al., 2014; Meyer et al., 2014; and Liang, Ren, and Sun, 2015). Second, our study contributes to the literature on how government influence in emerging markets can impact domestic firms’ decisions to expand internationally through cross-border M&A (see Xiao and Sun, 2005; Rui and Yip, 2008; Luo, Xue, and Han, 2010; Peng, Wang, and Jiang, 2008; and Du and Boateng, 2015). Third, we provide valuable evidence on the importance of corporate governance in cross-border M&A deals, especially in cases where a POE originates from an emerging market but wishes to expand its business to a developed country with solid institutional standards. Finally, our study contributes to research methodology. Sutherland and Anderson (2015) note the pitfalls of applying outbound FDI data from Chinese firms’ annual reports when analyzing Chinese MNEs’ activities. They argue that existing empirical studies are flawed because they are based exclusively on outbound FDI data. However, a non-trivial amount of Chinese MNEs use and route FDI through tax havens and offshore financial centers. Thus, these data are subject to severe biases and can yield misleading results. Unfortunately, the vast majority of published papers have not accounted for this problem. We believe that by using cross-border M&A to analyze Chinese 5

MNE activity, and hence excluding the acquisition targets located in tax havens and offshore financial centers, our study will produce more accurate empirical results. The remainder of this study is developed as follows. Section 2 contains the literature review and the institutional background on China’s go global policy. Section 3 develops our hypotheses, while section 4 describes the data collection process. Our research methodology is in section 5, and section 6 provides our empirical results. Section 7 concludes.

2.

Literature Review and Institutional Background

2.1

China’s Go Global Policy

Over the past three decades, China’s economy has experienced a rather dramatic transformation from a centrally planned economic system to one that is more market-oriented in nature. Since China joined the World Trade Organization (WTO) in 2001, its economy has become even more deeply integrated with the world economy. As a result, China’s policymakers began encouraging Chinese enterprises to “go global” in 2002. At the sixteenth national congress of the Chinese Communist Party (CCP) in 2002, the Chinese president formally announced the “go global” policy, with outbound foreign direct investment (FDI) being a prominent component. Since then, Chinese MNEs have grown at an exponential pace, largely by acquiring foreign subsidiaries. In January 2004, the Ministry of Commerce, the Ministry of Finance, and the State Administration of Foreign Exchange implemented a stream of reforms to promote Chinese FDI. The reforms included removing restrictions on overseas investment, accelerating and simplifying approval procedures, and establishing a special fund to support outbound FDI. In addition, a variety 6

of new services were introduced, including promoting state firms during official visits to host countries, incorporating FDI arrangements into bilateral cooperation frameworks with host countries, and offering a consulting service to assist Chinese companies in going global (Cheng and Ma, 2010). However, prior to 2006, policy assistance from the Chinese government was offered predominantly to SOEs, and therefore few POEs were able to go global. The Chinese government recognized this favoritism toward SOEs in 2006, and took steps to correct it by issuing a document that became vital to POEs’ going global strategy. The document specified that support for POEs should include tax rebates and favorable financing terms (Cheng and Ma, 2010). However, as Luo, Xue, and Han (2010) note, although POEs fared better after 2006, the approval process for POEs’ FDI remains more complex and restrictive than that for SOEs. The “go global” strategy has been further boosted by the “One Belt, One Road” initiative introduced in the current decade, which is the hallmark economic policy of President Xi Jinping. One purpose of this initiative is to stimulate China’s FDI in countries located on the Silk Road Economic Belt and the 21st Century Maritime Silk road, a region known as “The Belt and Road.” Most of the countries in Eurasia are within this region. UNCTAD statistics suggest that China has now reached the world’s top ten outward investors. By the end of August 2016, China’s outward FDI had hit a new high, exceeding U.S. $118 billion (China.org.cn, 2017).

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2.2

Political Connections and Chinese POEs

Political connections can be quite valuable to firms’ financing activities in both developed and developing countries, as many empirical studies have shown (see, for example, Roberts, 1990; Fisman, 2001; Khwaja and Mian, 2005; Adhikari, Derashid, and Zhang, 2006; Charumilind, Kali, and Wiwattanakantang, 2006; Faccio, 2006; Claessens, Feijen, and Laeven, 2008; Bunkanwanicha and Wiwattanakantang, 2009; and Schweizer, Walker, and Zhang, 2016). However, the benefits are generally more pronounced in emerging markets because of their relatively inferior institutional environments, more concentrated ownership structures, and less efficient legal systems (La Porta et al., 1998, 2000). In the case of China, the largest emerging economy in the world, we argue that the political connections of top management team members are more beneficial for POEs than SOEs simply because Chinese POEs face a different institutional environment. State-owned companies in China are the pillars of the national economy and, as such, enjoy highly favorable economic positions. Many of the government’s policies have been intentionally established to guarantee SOE prosperity (Luo, Xue, and Han, 2010). Thus, Chinese POEs must seek ways to overcome the discrimination they face in the capital market. One method is to build political ties with the government by hiring top managers with specific political backgrounds (Chen et al., 2011). One branch of empirical studies demonstrates the positive influence of political connections on Chinese POEs’ economic activities. Li et al. (2008) find that POE founders are more likely to obtain financing from state-controlled institutions if they have political party membership. Luo and Liu (2009) note that it is easier for politically connected POEs in China to enter industries with high 8

entry barriers, such as banking and telecommunications. Wu et al. (2012) show that political connections in China are only valuable to POEs, not to SOEs, a point made here earlier. Their empirical analysis also suggests that POEs with political connections enjoy more tax benefits. Li and Zhou (2015) demonstrate empirically that it is easier for politically connected POEs to get IPO requests approved in China, and that such POEs are also less likely to be subjected to on-site auditing from regulatory authorities. 2.3

Political Connections and Chinese MNEs

There has been some limited research on the link between political connections and Chinese MNEs. Pan et al. (2014) investigate how political connections can influence Chinese MNEs’ FDI ownership in their overseas subsidiaries. They measure political connections by governmental ownership and the number of politically connected top managers. They conduct cross-sectional analysis using data from MNEs’ 2010 annual reports, and find that political connections significantly increased Chinese firms’ ownership in their overseas subsidiaries. Nevertheless, we argue that their study has two important limitations. First, Pan et al.’s (2014) sample includes both SOEs and POEs, without any clear differentiation. As we have discussed here, China’s unique institutional environment explicitly favors SOEs, so we assume all Chinese SOEs are politically connected to some extent. Hu and Liu (2011) find a positive relationship between political connections and the cost of borrowing that is significant only for POEs. They note that including SOEs would undermine the robustness of their empirical results. Thus, we argue that Pan et al.’s (2014) empirical results have the same limitations as those of Hu and Liu (2011). Second, Pan et al.’s (2014) empirical analysis is cross-sectional, and cannot 9

capture the time dynamics in terms of the link between political connections and going global. Liang, Ren, and Sun (2015) examine Chinese SOEs’ decision to go global by means of a natural experiment involving their response to China’s split-share structure reform. They find that, prior to the reform, SOEs’ political connections significantly impacted their going global opportunities. That effect diminished somewhat after the reforms. This finding is consistent with our argument that political connections are endemic to SOEs. To the best of our knowledge, no empirical study has specifically examined the link between political connections and privately owned MNEs in China. Given the significant and increasing role of Chinese POEs in cross-border acquisitions, a comprehensive empirical analysis would shed important light on the factors affecting Chinese POEs’ decisions to go global. Our research aims to fill this gap. 3.

Hypothesis Development

3.1

Political Connections and Multinational POEs

The value of political connections for privately owned MNEs can be explained against the context of institutional theory, which suggests that a company’s environment is the primary driver of its behaviors and actions. Scott (1995) specifies three aspects that bring order and stability to an institution and its actors: 1) rules and laws, 2) social knowledge and perceptions, and 3) social values and norms. A great deal of existing literature has explored the indispensable role that institutional environment plays in the functioning of market transactions and in the transaction costs of firms’ business activities (see Meyer et al., 2009; Buckley et al., 2007; and North, 1990).

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In the case of China, the institutional environment is set up to favor SOEs because the financial resources are controlled by the state (Martin, 2012). With respect to operating as MNEs, state-controlled enterprises are capable of obtaining preferential treatment that significantly facilitates the process of making outward investments (see the previous section as well). However, in contrast, POEs tend to face severe financial constraints when attempting to conduct overseas investments (see Guariglia, Liu, and Song, 2011; and Poncet, Steingress, and Vandenbussche, 2010). The value of political connections can also be explained in relation to transaction cost theory, which sheds light on companies’ motivations for expanding their business activities. Coase (1937) showed that firms always weigh the external costs of exchanging resources on the open market against the internal costs of conducting those activities in-house. Thus, firms will opt for the open market only if the external transaction costs are lower than the internal bureaucratic costs. Williamson (1981) elaborates further on the factors that can affect transaction costs, including two that are particularly relevant here: environmental uncertainty and risk. The financial and policy assistance provided by the Chinese government can significantly alleviate the transaction costs of firms operating as MNEs and, in turn, can increase their risk tolerance when making investments in the global market. Again, given that such assistance is available exclusively to SOEs, POEs are not able to reduce their transaction costs in a similar way when they enter the global market. As we noted earlier in section 2, in 2006, the Chinese government made some effort to support POEs’ going global strategy. However, anecdotal evidence suggests that POEs continue to face severe limitations when conducting overseas investments today. According to Cheng and Ma 11

(2010), the Chinese government may have technically lifted many of the restrictions for POEs, but adequate assistance remains out of reach, for several reasons. For example, some POEs find the approval procedure for going global to be tedious and overly time-consuming. They suggest that a “one-stop-shop” approach to obtaining approvals would be more efficient. In addition, many POEs feel they are at a serious disadvantage when attempting to obtain credit for an international business because the quotas for long-term loans are allocated exclusively to state-owned companies.5 Furthermore, major Chinese commercial banks are owned and controlled by the Chinese government, and their primary function is thus to support SOEs’ economic activities (Morck, Yeung, and Zhao, 2008). These banks tend to screen out privately owned firms from their lending activities because they are considered high risk. Consequently, many Chinese POEs must turn to other sources, from employing their own capital (Liu and Tan, 2004) to raising capital overseas (Sutherland and Ning, 2011), or even allying with private equity (Financial Times, 2012). Given the restrictions POEs face, as well as the remarkable favoritism SOEs enjoy, it is clear that hiring top managers with political ties to the Chinese government could be an effective way for POEs to overcome financing discrimination and obtain stronger support for their cross-border M&A deals. This, in turn, could attenuate the transaction costs of starting an international business. Our argument above is supported by the existing literature, which shows that, among other things, politically connected Chinese firms are more likely to obtain loans with longer terms and

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There are three policy banks in China: The Agricultural Development Bank of China, the China Development Bank, and the Export-Import Bank of China. They were established in 1994 and are responsible for financing economic and trade development as well as state-controlled projects.

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lower interest rates when borrowing from state-owned banks (see Luo and Zhen, 2008; Yu and Pan, 2008; and Yuan, Jing, and Liao, 2010). Li et al. (2008) find that POEs are more likely to obtain financing from state-controlled institutions and to be treated as favorably in the legal system as SOEs when private entrepreneurs have political party membership in China. Furthermore, as elaborated upon in the previous section, the Chinese government aims to encourage POEs to “go global.” Therefore, it is likely that POEs whose top managers have ties to the Chinese government are more willing to follow the government’s strategic policy than POEs without such connections. Based on the arguments above, we develop Hypothesis 1 as follows: Hypothesis 1: Politically connected POEs are more likely to transform into MNEs than unconnected POEs. 3.2

Corporate Governance and Multinational POEs

In the previous section, our argument for the value of political connections is based on the institutional environment of an MNE’s home country. In other words, the value for POEs stems from the fact that China’s system is geared to favor SOEs. Nevertheless, when MNEs enter the global market, they are also affected by the institutional environment of the host countries (see Lu et al., 2014; Regner and Edman, 2014; and Kostova, 1999). Meyer and Rowan (1977) argue that when MNEs enter a foreign market, they are more likely to adapt the prevalent organizational practices and structures in the host country with the goal of enhancing their overall sense of legitimacy.

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This issue is more prominent when MNEs from emerging economies, with relatively poorer institutional environments, enter more advanced economies that typically feature higher-level institutional environments. As an example, Antkiewicz and Whalley (2006) discuss why most of the cross-border M&A transactions attempted by Chinese MNEs are unsuccessful in Organization of Economic Cooperation and Development (OECD) countries. A crucial concern of OECD countries is the transparency and financial security of those MNEs. They note that most acquiring Chinese firms do not comply with the high corporate governance standards OECD countries require. We also argue that sound corporate governance is even more vital to privately owned MNEs than to their state-owned counterparts. Cui and Jiang (2012) show that different types of foreign investors face different institutional pressures from host countries. Specifically, the pressure on state-owned MNEs results mainly from the political resistance of host countries. On the other hand, concerns regarding private foreign investors are corporate governance-based. We know (from Table 3) that the investing targets of China’s privately owned MNEs come primarily from advanced economies, rather than from developing countries that have good political relationships with China. Therefore, we believe Chinese POEs should be dedicated to ameliorating their corporate governance in order to successfully enter advanced markets as MNEs. Schweizer, Walker, and Zhang (2016) explore the interplay of political connections, corporate governance, and corporate bond issuance for POEs in China. Their empirical results suggest that politically connected POEs that issue corporate bonds have poorer corporate governance than

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unconnected POEs. This implies that the approval of security issuance in China is not purely market-based, but incorporates political factors as well. Nevertheless, our conjecture regarding the corporate governance of politically connected POEs operating as MNEs is different from the conclusion Schweizer, Walker, and Zhang (2016) reach for purely domestic companies. We argue that political connections do not imply poor corporate governance for privately owned MNEs because such POEs need to make investments outside China and hence will tend to face rigorous governance requirements from their host countries. To successfully complete cross-border deals, Chinese POEs will need to exhibit higher governance standards. For privately owned MNEs, political connections are likely to be valuable in helping them obtain the resources and assistance required to operate as an MNE in a host country. In other words, political connection increases the likelihood that the firm’s decision to go global will be approved, while simultaneously decreasing the associated transaction costs. As a result, these firms will be more motivated to adopt higher corporate governance standards in order to ensure they meet local governance standards before attempting to conduct their first cross-border deals. In Hypothesis 2, we posit the following relationship between corporate governance and privately owned MNEs: Hypothesis 2: Politically connected POEs with better corporate governance are more likely to transform into MNEs through cross-border M&A.

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3.3

Post-MNE Performance of Chinese POEs

We note that transaction cost theory can also be used to analyze the performance of Chinese POEs after entering the global market. As we discussed previously, POEs may receive a wide range of support from the Chinese government when conducting outward FDI. Such assistance at least partially mitigates the transaction costs of operating as a multinational company, which in turn can increase profitability. When outward FDI takes the form of a cross-border M&A, and the company in question is an SOE, the benefits of government support have been clearly illustrated by previous literature (see Child and Rodrigues, 2005; UNCTAD, 2005; and Scott, 2002). Xiao and Sun (2005) demonstrate that the most prominent benefits received from the Chinese government are value-added tax breaks and favorable financing terms. In addition, one branch of studies suggests these preferential treatments can be incorporated into stock prices, thus enhancing the acquiring entity’s abnormal returns. Du and Boateng (2015) study a sample of 468 Chinese firms with 1,063 cross-border deals from 1998 to 2011, and find that state-owned acquirers achieve higher abnormal returns over a ten-day event window than their privately owned counterparts. Their empirical evidence supports the notion that the value creation for state-owned acquirers stems from the Chinese government support in the form of lower lending rates. In a similar vein, we argue that politically connected POEs receive more government support than non-connected POEs when deciding to go global. They also tend to receive preferential treatment from the government that effectively reduces their transaction costs and can smooth the

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approval and completion of cross-border deals (see section 2). These benefits ultimately convert to better long-term profitability once the POEs become MNEs. In addition, we can analyze post-MNE performance by means of resource-based view theory (RBV), which has two fundamental assumptions: resource heterogeneity and resource immobility (Barney, Wright, and Ketchen, 2001). Resource heterogeneity specifies that different firms own different levels of resources, while resource immobility indicates that heterogeneous resources tend to be relatively “sticky,” and cannot be transferred among firms without incurring considerable costs. Brouthers and Hennart (2007) note that firm-specific resources are rare, precious, inimitable, and non-substitutable. These features form the comparative advantage of globalization. However, the hallmarks of these resources (cutting-edge technologies, brand names, advanced management skills, and advanced marketing strategies) are found mainly in developed country firms. Emerging market MNEs have strong incentives to try to acquire such resources from international markets (Wei et al., 2014). Huang and Renyong (2014) find that the primary motive for POEs to go global is to obtain strategic assets. Many Chinese POEs have conducted outbound FDI through cross-border M&A in order to “catch up” with their international competitors and integrate their value network more fully. The three most common methods used by POEs to conduct cross-border M&A deals are: 1) acquiring competitors, 2) purchasing strategic assets (technology and brand name), and 3) implementing vertical integration. We argue that the advanced resources acquired from global markets will eventually contribute to POEs’ post-MNE performance. 17

Furthermore, it is well recognized that the outbound FDI of SOEs and POEs generally belong to different industries, with that of POEs being primarily in more competitive industries (textile, electronics, and machinery) (see Cheng and Ma, 2010; Huang and Renyong, 2014; and Wei et al., 2014). According to industry-based view theory (IBV), industry conditions substantially shape firms’ strategic behavior, including globalization (see Porter, 1980; Boter and Holmquist, 1996; Yamakawa, Peng, and Deeds, 2008). Lu, Liu, and Wang (2011) find that this competitiveness can lead Chinese POEs to reduce their prices and in turn lower their profitability in the domestic market. Such pressure pushes firms to conduct direct investments in overseas companies as a way to seek new markets and boost profits. According to the above arguments, we develop Hypotheses 3 and 4 as follows: Hypothesis 3: Post-MNE financial performance of multinational POEs is stronger than the financial performance of domestic POEs. Hypothesis 4: Politically connected MNEs exhibit stronger financial performance than non-connected MNEs.

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4.

Data

4.1

Financial and Corporate Governance Data

We identify listed Chinese POEs by using the China Listed Private Enterprise Research Database, provided by China Stock Market and Accounting Research (CSMAR), which includes all Chinese POEs listed on the Shanghai or Shenzhen Stock Exchanges. In contrast to Chinese SOEs, POEs are defined as enterprises directly controlled by individuals, families, other non-state entities, or foreign enterprises. The financial data on the Chinese POEs in our sample comes from CSMAR’s China Stock Market Financial Statement Database, and the corporate governance data comes from CSMAR’s China Listed Firms’ Corporate Governance Research Database. We have found that some POEs are missing information on corporate governance-related data. We therefore manually collect the missing data from the Stockstar website (www.stockstar.com), which provides detailed top management information for firms traded on either exchange. Although the Chinese government first mandated that Chinese enterprises “go global” in 2001, as we have noted, the policy assistance predominantly supported SOEs (Liang, Ren, and Sun, 2015). The government recognized how this favoritism was limiting POEs in 2004, and issued a draft calling for stronger financing support for POEs wishing to go global in 2006 (Cheng and Ma, 2010). Thus, our sample begins in 2007 and ends in 2016. We winsorize all the financial data at the 1% and 99% levels to minimize the influence of potential outliers. Our final sample consists of 1,782 POEs and 9,946 firm-year observations.

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4.2

Identification of Multinational Companies

To identify a Chinese POE operating as a multinational company (MNE), we examine whether it has completed any cross-border M&A over the sample period. We then define POEs as MNEs from the year of completion of their first cross-border M&A. We obtain the cross-border deals of Chinese POEs from CSMAR’s China Listed Firms’ Merger & Acquisition, Asset Restructuring Research Database. We exclude any cases where the cross-border M&A occurred in tax havens or offshore financial centers.6 From 2007 to 2016, we find that 290 Chinese POEs completed 385 cross-border M&A deals. We exclude two POEs that engaged in cross-border M&A activities before that time period. We then define those 288 firms as MNEs from the time they acquired their first overseas target over our sample period. CSMAR’s China Listed Firms’ Merger & Acquisition, Asset Restructuring Research Database also provides the country of origin of each overseas target that is acquired. In total, the cross-border deals completed by Chinese POEs span forty countries (see Table 3 for an overview).

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Our sample excludes the following tax havens and offshore financial centers: American Samoa, the Bahamas, Bermuda, the British Virgin Islands, the Cayman Islands, Mauritius, Panama, and Samoa.

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4.3

Identification of Political Connections

We proxy for political connections by following recent literature from Faccio (2006), Fan, Wong, and Zhang (2007), Li and Zhou (2015), and Schweizer, Walker, and Zhang (2016). This study only considers the political background of a Chinese POE’s board chairman or CEO. To obtain that information for each company in our sample, we hand-collect the information from Stockstar, which provides past and current work experience in detail for each listed company’s top management. Next, we define a POE’s chairman or CEO as politically connected if he or she is or was a representative in the People’s Congress (PC), the Chinese People’s Political Consultative Conference (CPPCC), an officer in local or central government, or an officer in the military. We code the political connection dummy variable (Connection) as 1 if a Chinese POE’s chairman or CEO has political connections for each year, and 0 otherwise (see Li and Zhou, 2015; and Schweizer, Walker, and Zhang, 2016). In addition, we measure the political connections of each firm’s chairman or CEO by creating a political connection index (PC Index). The value of this index ranges from 1 to 3 depending on the strength of the political tie (where 3 represents the strongest political connection). Detailed information on how the index is created can be found in panel A of Table 1.

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4.4

Measuring Corporate Governance

To examine the impact of corporate governance on Chinese POEs operating as MNEs, we construct a comprehensive index that measures the overall corporate governance level of Chinese POEs instead of using single governance factors. Our index thus reflects a company’s overall governance quality more accurately. It also eliminates multicollinearity in multivariate regressions when using single governance factors (Brown, Beekes, and Verhoeven, 2011). The advantages of a corporate governance index have already been elaborated on quite extensively by the extant literature (see Gompers, Ishii, and Metrick, 2003; Brown and Caylor, 2006; Dutordoir, Strong, and Ziegan, 2014, and Shan, 2015). Shan (2015) constructs an equally weighted corporate governance index for Chinese listed firms according to China’s two-tier board system. We follow this methodology to construct our index for Chinese POEs. However, ours differs slightly from that of Shan (2015). For example, Shan’s (2015) sample covers both SOEs and POEs in China. Given that we focus solely on POEs here, we exclude the factor differentiating SOEs and POEs. Moreover, because few of the POEs in our sample have foreign shares or are cross-listed, we also do not include these factors. We obtain the corporate governance data from CSMAR’s China Listed Firms’ Corporate Governance Research Database and construct the corporate governance index (Gov Index) by using nine equally weighted corporate governance factors. Detailed information on the construction and description is in panel B of Table 1.

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4.5

Control Variables

We include an array of control variables that could potentially affect the likelihood of a Chinese POE becoming an MNE: profitability (ROA), leverage (Leverage), firm size (Firm size), growth opportunities (Tobin’s q), and tangible asset ratio (Tangibility). We also use those variables to conduct a propensity score matching (PSM) technique. Detailed information on the control variables is in panel A of Table 1. 5.

Methodology

5.1

Political Connections and Multinational Companies

To examine how political connections can affect the likelihood of a Chinese POE becoming an MNE, we apply logistic regressions. However, we must first control for a self-selection bias that posits top managements’ political connections cannot randomly be found across POEs. For example, larger and more profitable POEs may be more capable of building political connections by hiring a chairman or CEO with a political background. To overcome such a bias, we use the PSM technique to study the clean effect of political connections on MNEs. The treatment variable is political connection (Connection). We use the nearest neighbor matching method to match each firm-year observation in our sample that has political connections with one that does not on the vector of control variables mentioned previously in subsection 4.5. Specifically, we first employ a probit regression by using the entire sample. The dependent variable is a dummy variable that equals 1 if a firm-year observation has political connections, and 0 otherwise. The independent variables are profitability (ROA), leverage (Leverage), firm size (Firm size), growth opportunities (Tobin’s q), and tangible asset ratio (Tangibility). In addition, to 23

combat the unobserved heterogeneity across industries and over time, industry and year fixed effects are included in the probit model.7 Second, after the PSM, we run a post-matching logistic regression as follows to examine whether having political connections helps Chinese POEs enter the global market: 𝑀𝑁𝐸(1/0) = 𝛼 + 𝛽1𝑎 ∙ 𝐶𝑜𝑛𝑛𝑒𝑐𝑡𝑖𝑜𝑛𝑡 + 𝛽1𝑏 ∙ 𝑃𝐶 𝐼𝑛𝑑𝑒𝑥𝑡 + 𝛽2 ∙ 𝐺𝑜𝑣 𝐼𝑛𝑑𝑒𝑥𝑡 + 𝛽3𝑎 ∙ 𝐶𝑜𝑛𝑛𝑒𝑐𝑡𝑖𝑜𝑛𝑡 ×𝐺𝑜𝑣 𝐼𝑛𝑑𝑒𝑥𝑡 + 𝛽3𝑏 ∙ 𝑃𝐶 𝐼𝑛𝑑𝑒𝑥𝑡 ×𝐺𝑜𝑣 𝐼𝑛𝑑𝑒𝑥𝑡 + 𝝂𝒏 ∙ 𝜨𝒏 + 𝜑𝑘 + 𝜋𝑡 + 𝜀,

(1)

where 𝑀𝑁𝐸 is a binary variable that equals 1 in the year a Chinese POE begins operating as a multinational firm and the following years within our sample period. In this study, we use the year a Chinese POE makes its first cross-border M&A to establish when it began operating as a multinational company. Political connections are measured by the independent variable of either Connection or PC Index, Gov Index captures the potential influence of overall corporate governance quality on the likelihood of becoming an MNE, 𝚴𝒏 is a vector of firm-specific characteristics (ROA, Leverage, Firm size, Tobin’s q, Tangibility), 𝜑𝑘 are industry fixed effects, and 𝜋𝑡 are year fixed effects. If political connections increase the likelihood of becoming an MNE for Chinese POEs, we expect the coefficients on Connection (𝛽1𝑎 ) and PC Index (𝛽1𝑏 ) to be positive and statistically significant. Moreover, if Chinese POEs with better corporate governance have greater chances of entering the global market, we expect the coefficient on Gov Index (𝛽2 ) to be positive and statistically significant. And if politically connected POEs operating as MNEs also have better corporate

7

We use fifteen industry dummy variables based on CSMAR’s industry classifications.

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governance, we expect the coefficients on the interaction terms (𝛽3𝑎 and 𝛽3𝑏 ) to be positive and statistically significant. 5.2

Endogeneity Concerns

We note that the potentially endogenous nature of political connections could impede the robustness of the proposed causal relationship between political connections and the possibility of going global for Chinese POEs. Despite using PSM, other unobservable differences between multinational and domestic POEs could affect the accuracy of our analysis, and in turn bias our results. We employ a quasi-natural experiment to cope with this problem. We examine whether the chairman/CEO turnover resulting in the increase in PC Index (more politically connected) increases the likelihood of Chinese POEs becoming MNEs. Our first step is to apply a similar PSM routine as in our previous analysis. We include PC Index as an additional matching variable and match in the year the POE completed its first cross-border M&A deal (288 firm-year observations) with domestic POEs that did not acquire any overseas companies during the observation period. This ensures that privately owned MNEs and domestic POEs have “identical” company characteristics at the time of their first cross-border M&A. If better political connections foster the “go global” strategy, we expect that companies replacing their top management with more politically connected successors will be more likely to engage in cross-border acquisitions.

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To measure this effect, we create a dummy variable (Political Turnover) that equals 1 if the CEO or chairman is replaced in the five years before the firm began operating globally by someone with a higher PC Index (stronger political connection ties), and 0 otherwise.8 Our model is as follows: 𝑀𝑁𝐸(1/0) = 𝛼 + 𝛽1 ∙ 𝑃𝑜𝑙𝑖𝑡𝑖𝑐𝑎𝑙 𝑇𝑢𝑟𝑛𝑜𝑣𝑒𝑟 + 𝛽2 ∙ 𝐶𝑜𝑛𝑛𝑒𝑐𝑡𝑖𝑜𝑛𝑡 + 𝛽3 ∙ 𝐺𝑜𝑣 𝐼𝑛𝑑𝑒𝑥𝑡 + 𝝂𝒏 ∙ 𝚴𝒏 + 𝜑𝑘 + 𝜋𝑡 + 𝜀,

(2)

Detailed information on the dependent variable and other independent variables are in Equation (1). The variable of interest in Equation (2) is the coefficient on Political Turnover (𝛽1). If political connections indeed increase the likelihood of starting a business in the global market for Chinese POEs, we expect that coefficient (𝛽1) to be positive and statistically significant. 5.3

Financial Performance of Chinese POEs after Becoming Multinational

We next explore whether privately owned Chinese companies become more profitable after “going global,” particularly for firms with political ties. We use a difference-in-differences (DID) approach to examine Chinese POEs’ profitability within three years of entering the international market, specified as follows: We first construct a dummy variable to identify the treatment group, which in our study is multinational POEs. The dummy variable (MNE) equals 1 for POEs going global from the year they acquire their first overseas company and all subsequent years within the sample period, and 0 otherwise. To assess an MNE’s financial performance (measured by the return on equity (ROE) during the three-year period after completion of the first cross-border M&A deal), we compare it with the performance of domestic POEs. The model is as follows:

First, we retrieve the chairman/CEO turnover information from CSMAR’s China Listed Firms Corporate Governance Research Database. Next, we manually check the background information in top managers’ profiles provided by Stockstar to identify whether the turnover is considered political (if the PC Index value increases). 8

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𝑅𝑂𝐸𝑖,𝑡 = 𝛼 + 𝛽1 ∙ 𝐶𝑜𝑛𝑛𝑒𝑐𝑡𝑖𝑜𝑛 + 𝛽2 ∙ 𝐺𝑜𝑣 𝐼𝑛𝑑𝑒𝑥𝑖,𝑡 + 𝛽3 ∙ 𝑀𝑁𝐸𝑖,𝑡 ×𝑃𝑜𝑠𝑡 𝐹𝐷𝐼 + 𝛽4 ∙ 𝑀𝑁𝐸𝑖,𝑡 ×𝐶𝑜𝑛𝑛𝑒𝑐𝑡𝑖𝑜𝑛 + 𝝂𝒊,𝒕 ∙ 𝚴𝒊,𝒕 + 𝜑𝑖 + 𝜋𝑘 + 𝜀𝑖,𝑡 ,

(3)

where the dependent variable is the ROE of firm i, and Post FDI is a dummy variable that equals 1 for the first three years after a POE completes its first cross-border M&A, and 0 otherwise. Connection is a dummy variable that equals 1 for each firm-year observation with political connections, and 0 otherwise. Gov Index represents the overall corporate governance of each firm in our sample, Ν𝑖,𝑡 is a vector of firm-specific characteristics (ROA, Leverage, Firm size, Tobin’s q, Tangibility), 𝜑𝑘 are industry fixed effects, and 𝜋𝑡 are year fixed effects. Our main coefficients of interest are 𝛽1, and the interaction terms 𝛽3 and 𝛽4 . If those POEs were able to increase profitability after they began operating multinationally, we expect 𝛽3 to be positive and statistically significant. If POEs that have political ties to the Chinese government exhibit better financial performance, we expect 𝛽1 to be positive and statistically significant. If those political connections are particularly valuable for MNEs, we expect 𝛽4 to also be positive and statistically significant.

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6.

Empirical Results

6.1

Descriptive Statistics

Table 2 shows the annual number of Chinese POEs entering the global market for the first time from 2007 through 2016. There are 288 multinational Chinese POEs in total during the sample period. Note that, following the Chinese government’s “go global” policy, there has been an upward trend in the number of POEs operating as multinational firms. The annual percentage of new private MNEs during our sample period increased dramatically in 2011, reaching double digits for the first time. The percentage peaks at 21.53% in 2015, with sixty-two POEs entering the international market. However, 2016 saw a significant drop in that number, presumably because of the economic slowdown in China last year. —Please insert Tables 1 and 2 about here— Table 3 specifies the countries of origin of the target companies acquired by Chinese POEs. The targets are spread throughout the world. However, it also shows that most of the cross-border deals are from major world economies, such as Australia, Canada, Japan, Germany, the U.K., and the U.S. Interestingly, most POEs in China seem to extend their business to countries that are not political “close” to the Chinese government. This is consistent with findings in Ramasamy, Yeung, and Laforet (2012), who find that SOEs are attracted more to countries that have closer bilateral political relationships with China, and those that are natural resources-based. On the other hand, POEs tend to be market seekers. Their motivations for going global are based on technology and strategy. This finding is supported by our sample. —Please insert Table 3 about here— 28

Table 4 gives the descriptive statistics for the company characteristics of domestic and multinational POEs (see Table 1 for variable definitions). Our sample consists of 1,494 domestic (8,776 firm-year observations) and 288 multinational (1,170 firm-year observations) POEs. We note that, in China, only a small percentage of POEs have entered international markets. For POEs operating as multinational firms, we can clearly see that those firms are politically connected to the government, regardless of Connection or PC Index. The mean of Connection (0.393) implies that almost 40% of multinational POEs have political ties. The differences in Connection and PC Index between domestic and multinational POEs are statistically significant at a 1% level, providing univariate support for Hypothesis 1. However, we find no univariate evidence that the corporate governance index (Gov Index) of multinational POEs is higher for international than for domestic POEs. We will explore this factor further in our multivariate analyses. For the control variables, we find that multinational POEs on average have more debt (Leverage), are larger (Firm size), have lower growth opportunities (Tobin’s q), and hold more tangible assets (Tangibility). There is no significant difference in profitability (ROA) between domestic and multinational POEs (see Table 4, panel C). The correlation matrix in Table 5 shows that the pairwise correlations are not greater than 0.5. For the multivariate regressions, we calculate the Variance Inflation Factors (VIF) to discover any potential multicollinearity issues. —Please insert Tables 3, 4 and 5 about here—

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6.2

Political Connections and Multinational POEs

To investigate the link between political connections and the probability of becoming a multinational POE, we first use a PSM method. Specifically, we apply a balanced one-to-one nearest neighbor matching for each firm-year observation without replacement based on political connections (Connection). The balanced post-matching sample has 6,034 firm-year observations (3,017 matched firm-year pairs). We next run a probit regression for the entire sample of 9,946 firm-year observations to estimate the propensity scores. The dependent variable in the probit model is Connection, and the explanatory variables are ROA, Leverage, Firm size, Tobin’s q, and Tangibility. Moreover, we control for industry and year fixed effects. The results in Table 6 (panels A and B) after the PSM indicate that the firm characteristics of domestic POEs are not statistically different from those of multinational POEs. Thus, the sample is well-balanced. —Please insert Table 6 about here— Using the balanced sample, we then run multivariate logistic regressions to find the determinants for POEs becoming MNEs. Our baseline results in column 1 indicate that politically connected POEs are more likely to acquire overseas companies. The coefficient of Connection is 0.398, which is statistically significant at a 1% level. The related marginal effect reveals that the predicated probability of becoming a multinational POE increases from 11.1% by 4.2 percentage points or 37.8% by hiring a politically connected top manager. Column 2 shows the results of measuring political connections by PC Index. The results are similar to our first measure. The coefficient of PC Index is 0.152, also statistically significant at 1% and similarly shows an increase the 30

probability of going global for POEs with higher levels of the PC Index. Specifically, the predicated probability of becoming a multinational POE is 11.4% when PC Index is 0. The predicated probability increases by 1.5 percentage points or 13.2% when the PC Index increases from 0 to 1. A much higher increase in the predicated probability is found when the PC Index is 2 (14.6%) or 3 (16.4%). These results support Hypothesis 1, namely, that politically connected POEs have a higher likelihood of entering the global market, which is statistically and economically significant. Our results are in line with the intuition that political connections of POEs’ top managers assist their companies in smoothing the “going global” procedure. Such POEs are likely more motivated to expand their businesses to other countries.9 To investigate the interplay between the corporate governance of politically connected POEs and the likelihood of becoming an MNE, we include an interaction term between political connections and corporate governance. We first interact Connection (and second PC Index) with our corporate governance index (Gov Index) (see Table 7, columns (3) and (4)). We can clearly see that the coefficients of the interaction terms are positive and statistically significant at a 5% level for both measures. These results indicate that politically connected POEs with higher corporate governance indices, which we interpret as higher corporate governance standards, are more likely to acquire companies outside China. Interestingly, the base effects of political connection (measured either by Connection or PC Index) and corporate governance are no longer statistically significant when including the interaction with corporate governance. This is empirical support for 9

To test our empirical results for robustness, we run the logit regression by using the entire sample without performing PSM. The results remain qualitatively the same and are available from the authors upon request.

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Hypothesis 2: For a POE planning to go global, political connections are important, but proper corporate governance must also be in place. —Please insert Table 7 about here— To address the potential endogeneity issues associated with the decision to become a multinational POE, we use a quasi-experiment. We focus only on top management turnovers (CEO or chairman) for which the degree of political connection to the Chinese government increases, meaning a turnover with an increase in PC Index. In other words, we focus solely on turnovers where the chairman or CEO who was replaced was not politically connected (PC Index of zero), and the successor has political ties (PC Index greater than zero). or the successor has stronger political ties than his or her predecessor. We code these top management turnovers as Political Turnover. If political connections result in a higher probability of acquiring a company outside China, we expect to find a higher likelihood of POEs entering global markets after such turnovers. As explained in the previous section, we use a PSM routine that is similar to our prior one, but requires the 288 firm-year observations with the first cross-border deals to have the same PC Index as those in the control group. The diagnostic tests from Table 8 show that the PSM successfully balances the sample. We again run a logit regression with the dependent variable of becoming an MNE by including the Political Turnover dummy variable. The results from Table 9 show that the coefficient on Political Turnover is positive and statistically significant, indicating that the likelihood of going global for POEs significantly increased after the political turnovers. This finding provides strong

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support for our proposed causal relationship between political connections and Chinese POEs beginning to operate as multinationals. —Please insert Tables 8 and 9 about here— 6.3

Financial Performance of Multinational POEs After Cross-border Acquisitions

The empirical results of the DID estimates for Chinese POEs’ performance after becoming MNEs are in Table 10. To examine a POE’s financial performance during the three-year period after it has completed its first outbound FDI (Post FDI), we calculate the return on equity (ROE) for domestic and multinational POEs. Our main variables of interest are the interaction terms 𝑀𝑁𝐸×𝑃𝑜𝑠𝑡 𝐹𝐷𝐼 and 𝑀𝑁𝐸𝑖,𝑡 ×𝐶𝑜𝑛𝑛𝑒𝑐𝑡𝑖𝑜𝑛 . The coefficient of the interaction term of 𝑀𝑁𝐸× 𝑃𝑜𝑠𝑡 𝐹𝐷𝐼 is 0.052, which is statistically significant at a 10% level. This indicates an outperformance, measured by ROE, of MNEs over domestic POEs after the completion of the first cross-border M&A deal. This 5.2% outperformance over the three-year period by MNEs over their peers supports our Hypothesis 3. In contrast, the coefficient of the interaction term 𝑀𝑁𝐸×𝐶𝑜𝑛𝑛𝑒𝑐𝑡𝑖𝑜𝑛 is not statistically different from zero and thus does not support Hypothesis 4, that politically connected MNEs outperform non-politically connected ones. It seems the dominant factor for financial performance is political connection itself, regardless of whether the POE operates globally. The related coefficient is positive and statistically significant at a 1% level. The results support the notion that political connections are a catalyst for financial performance. Overall, our empirical evidence supports Hypothesis 3, that Chinese POEs can improve their financial performance by acquiring an overseas company. However, we find no empirical evidence 33

that political connections are continuously valuable for going global POEs in their immediate postMNE stage. It seems that, in China, political connections are a valuable means for POEs to overcome institutional discrimination and smooth their going global strategy. However, they seem unrelated to stronger MNE financial performance.

7.

Conclusion This study investigates the factors that affect the likelihood of Chinese POEs’ being able to

smoothly enter the global market as MNEs. Using a sample of 1,782 privately owned and publicly listed firms in China over the 2007-2016 period, we find strong empirical evidence that Chinese POEs’ decisions to go global are affected by political factors. That is, politically connected POEs have a greater chance of expanding their operations internationally through cross-border M&A than POEs without such connections. Our results hold after accounting for the potentially endogenous relationship between political connection and Chinese POEs’ global entry. We demonstrate further that corporate governance has a significantly positive effect on Chinese POEs’ outbound FDI, although this effect is only observed for POEs with political connections. Finally, with respect to Chinese privately owned MNEs’ financial performance after global entry, we trace their return on equityfor the three years after they begin operating as MNEs. We find that POEs with cross-border deals are more profitable than other domestic POEs. Overall, our empirical analysis incorporates an emerging market’s institutional environment into MNEs’ business activities. Our empirical results highlight the crucial role political connections play in China in facilitating POEs’ outbound FDI. When operating in an institutional environment that features excessive favoritism toward state-owned companies, building political ties can be an 34

effective way to overcome market discrimination and obtain state-controlled financial resources. Moreover, establishing political connections allows Chinese POEs to receive preferential treatment from the Chinese government in completing the financial and bureaucratic activities necessary for successful cross-border M&A (for example, obtaining credit from state-owned banks, obtaining tax rebates, and simplifying the tedious and complex “going global” approval process). By anticipating a greater chance of successfully entering global markets, we believe politically connected POEs will be more motivated to improve their corporate governance strategies to the level required by host countries, thereby ensuring the success of their cross-border deals.

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Table 1: Variable Definitions Panel A: Independent Variables Variable MNE

Definition Dummy variable that equals 1 when a POE is an MNE, and 0 otherwise. Specifically, the dummy variable equals 1 in the year a POE conducts its first cross-border M&A and in following years, and 0 otherwise.

Source CSMAR: China Listed Firms’ Merger & Acquisition, Asset Restructuring Research Database

Connection

Dummy variable that equals 1 if the chairman or CEO is currently working or has worked in a central or local government department, the military, the People's Congress (PC), the People's Court and Procuratorate, or the Chinese People's Political Consultative Conference (CPPCC), and 0 otherwise.

http://www.stockstar.com/

PC Index

Political connection measure that equals 3 if the politically connected chairman or CEO is the head of a government department or the head or the standing member of the PC or CPPCC, 2 if a member of the PC or CPPCC, 1 if an officer of a local government department or a military officer, and 0 otherwise.

http://www.stockstar.com/

ROA

Net income over the value of total assets.

CSMAR: China Stock Market Financial Statements Database

ROE

Net income over sum of the book value of total shareholder’s equity.

CSMAR: China Stock Market Financial Statements Database

Leverage

Book value of total liabilities over the book value of total assets.

CSMAR: China Stock Market Financial Statements Database

Firm size

Logarithm of the book value of total assets.

CSMAR: China Stock Market Financial Statements Database

Tobin’s q

Sum of the market value of equity and the book value of debt over the sum of the book value of equity and the book value of debt.

CSMAR: China Stock Market Financial Statements Database

Tangibility

Net fixed assets over the value of total assets.

CSMAR: China Stock Market Financial Statements Database (continued)

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Table 1: Variable Definitions—continued Panel B: Corporate Governance Index The Corporate Governance Index is constructed as in Schweizer, Walker, and Zhang (2016). Gov Indexi,t = ∑𝟏𝟎 𝐣=𝟏 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐌𝐞𝐜𝐡𝐚𝐧𝐢𝐬𝐦 j Governance Mechanism Chairman age

Definition

Measurement and Supporting Literature

AGEi,t – age of the company’s chairman

Equals 1 if AGEi,t of firm i in fiscal year t is less than the mean value of the sample in fiscal year t, and 0 otherwise (Waelchli and Zeller, 2013; Jiang and Kim, 2015).10

Chairman tenure

TENUREi,t – number of years the company’s chairman has been in office

Equals 1 if TENUREi,t of firm i in fiscal year t is less than the mean value of the sample in fiscal year t, and 0 otherwise (Berger, Ofek, and Yermack, 1997; Jiang and Kim, 2015).

Board size

BSIZEi,t – number of directors on the board of directors

Equals 1 if BSIZEi,t of firm i in fiscal year t is less than the mean value of the sample in fiscal year t, and 0 otherwise (Yermack, 1996; Conyon and Peck, 1998; Core, Holthausen, and Larcker, 1999).

Board independence

INDi,t – number of independent directors on the board of directors

Equals 1 if INDi,t of firm i in fiscal year t is greater than the mean value of the sample in fiscal year t, and 0 otherwise (Agrawal and Knoeber, 1996; Kim, Kitsabunnarat-Chatjuthamard, and Nofsinger, 2007).

Board meeting

BMEETINGi,t – number of annual meetings of the board of directors

Equals 1 if BMEETINGi,t of firm i in fiscal year t is less than the mean value of the sample in fiscal year t, and 0 otherwise (Vafeas, 1999; Yi, Yu, and Jiang, 2011).

Supervisory board size

SBSIZEi,t – number of supervisors on the supervisory board

Equals 1 if SBSIZEi,t of firm i in fiscal year t is greater than the mean value of the sample in fiscal year t, and 0 otherwise (Firth et al., 2007; Ding et al., 2010; Jia et al., 2009).

Ownership concentration

OWNi,t – percentage of shares held by the company’s largest shareholder

Equals 1 if OWNi,t of firm i in fiscal year t is greater than the mean value of the sample in fiscal year t, and 0 otherwise (Stiglitz, 1985; Rediker and Seth, 1995; Voulgaris, Stathopoulos, and Walker, 2010; Huang et al., 2011).

Foreign auditor

FAUDITORi,t –hiring of a foreign auditor

Equals 1 if firm i in fiscal year t hires a foreign auditor, and 0 otherwise (Gao and Kling, 2008; Peng, Wei, and Yang, 2011).

State control

STATEi,t –ultimate controlling shareholder of the company is the state

Equals 1 if the ultimate controlling shareholder of firm i in fiscal year t is the state, and 0 otherwise (Bloom et al., 2012; Jiang, Huang, and Kim, 2013).

10

As Jiang and Kim (2015) point out, using chairman age and tenure for constructing the corporate governance index for Chinese companies is done because “the actual person who is actively in charge of the business is not the CEO. It is the board chairperson who actively controls and runs the firm. In China, this is common knowledge. However, based on the academic literature, it seems that many scholars are unaware of this.”

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Table 2: Chinese POEs Operating as Multinational Companies This table reports the number of Chinese privately owned enterprises (POEs) start operating as multinational companies (MNEs) through acquiring overseas targets for the first time by year, along with percentages, between 2007 and 2016. Chinese MNE data comes from the CSMAR database.

Year 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Total

Number of MNEs 12 12 13 22 32 42 32 28 62 33 288

Percentage (%) 4.17 4.17 4.51 7.64 11.11 14.58 11.11 9.72 21.53 11.46 100.00

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Table 3: Locations of the Targets Acquired by POEs This table reports the country and number of the acquired targets by Chinese POEs operating as MNEs between 2007 and 2016. Country of Acquired Targets Argentina Australia Belgium Brunei Bulgaria Cambodia Canada Cyprus Czech Denmark France Germany India Indonesia Israel Italy Japan Kazakhstan Liechtenstein Luxembourg Malaysia Mongolia Netherlands New Zealand North Korea Norway Pakistan Poland Portugal Singapore South Africa South Korea Spain Sweden Switzerland Thailand United Kingdom United States Uruguay Vietnam Total

Number 2 25 1 2 1 1 14 1 1 4 7 45 1 4 1 14 39 4 1 3 9 1 13 1 1 1 1 1 2 30 1 24 2 3 6 1 22 92 2 1 385

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Table 4: Summary Statistics for Domestic and Multinational POEs This table reports the summary statistics (i.e., mean, median, standard deviation, 25% and 75% quantiles, and number of firm-year observations) for all sample variables for Chinese domestic POEs (panel A) and Chinese multinational POEs (panel B) between 2007 and 2016. Variable definitions are as in panel A of Table 1. Panel C reports the pairwise differences in means (t-test) and medians (Wilcoxon test) of the variables between domestic and multinational POEs. Related p-values are shown to the right in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Mean Panel A: Domestic POEs Connection PC Index Gov Index ROA Leverage Firm size Tobin’s q Tangibility Panel B: Multinational POEs Connection PC Index Gov Index ROA Leverage Firm size Tobin’s q Tangibility Panel C: Equality Tests Connection PC Index Gov Index ROA Leverage Firm size Tobin’s q Tangibility

0.291 0.638 3.328 0.045 0.393 21.346 2.915 0.204

Median 0.000 0.000 3.000 0.044 0.382 21.234 2.372 0.181

0.393 0.000 0.868 0.000 3.344 3.000 0.046 0.044 0.409 0.419 21.880 21.801 2.664 2.184 0.218 0.197 Differences in Means -0.102*** (0.000) -0.230*** (0.000) -0.014 (0.698) -0.002 (0.315) ** -0.016 (0.013) -0.534*** (0.000) 0.151*** (0.000) -0.013*** (0.003)

Std. Dev.

25%

75%

0.454 1.057 1.201 0.052 0.207 0.917 1.703 0.141

0.000 0.000 3.000 0.017 0.222 20.680 1.691 0.095

1.000 1.000 4.000 0.073 0.548 21.919 3.646 0.291

8,776 8,776 8,776 8,776 8,776 8,776 8,776 8,776

0.000 1.000 0.000 2.000 2.000 4.000 0.019 0.069 0.269 0.546 21.243 22.415 1.548 3.133 0.117 0.304 Differences in Medians *** 0.000 (0.000) 0.000*** (0.000) 0.000 (0.759) -0.001 (0.996) *** -0.037 (0.001) -0.567*** (0.000) 0.188*** (0.000) -0.016*** (0.000)

1,170 1,170 1,170 1,170 1,170 1,170 1,170 1,170

0.487 1.154 1.244 0.051 0.187 0.929 1.832 0.135

N

46

Table 5: Correlation Matrix This table shows the correlation matrix. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

(1) MNE (2) Connection (3) PC Index (4) Gov Index (5) ROA (6) Leverage (7) Firm size (8) Tobin’s q (9) Tangibility

(1) 1.000 0.071*** 0.069*** 0.004 0.010 0.025* 0.184*** -0.047*** 0.030**

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

1.000 0.939*** -0.058*** 0.036*** 0.054*** 0.145*** -0.070*** 0.041***

1.000 -0.051*** 0.052*** 0.040*** 0.142*** -0.064*** 0.021*

1.000 -0.005 -0.034*** -0.038*** 0.053*** -0.076***

1.000 -0.355*** 0.055*** 0.251*** -0.173***

1.000 0.404*** -0.305*** 0.115***

1.000 -0.335*** -0.035***

1.000 -0.129***

1.000

47

Table 6: Propensity Score Matching This table reports the result of propensity score matching (PSM) for domestic and multinational Chinese POEs from 2007 to 2016. Chinese multinational POEs are defined as those with at least one cross-border M&A within the sample period. We match firms using the nearest neighbor propensity score matching on an array of firm-specific characteristics. Panel A reports the univariate balance test results for the pairs of treatment and control firms after matching. Panel B reports parameter estimates from the probit model used in estimating the propensity scores for the treatment and control groups (treatment is Connection). The dependent variable in the probit model is political connections dummy variable. The “Pre-Match” column contains the parameter estimates of the probit model estimated using the sample prior to matching. These estimates are then used to generate the propensity scores for matching domestic and multinational POEs. The “Post-Match” column contains the parameter estimates of the probit model estimated using the subsample of matched treatment-control pairs after matching. We match firms using a one-to-one nearest neighbor propensity score matching every year for each respective industry, without replacement. Definitions of all variables are in panel A of Table 1. Industry and year fixed effects are included. Coefficient estimates are reported and standard errors clustered within firm-level are in parentheses below. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Panel A ROA Leverage Firm size Tobin’s q Tangibility Panel B ROA Leverage Firm size q Tangibility Constant Industry FE Year FE Observations Pseudo R-square P-value of χ2

Control 0.048 0.413 21.624 2.724 0.215

Treatment 0.048 0.412 21.614 2.702 0.215 Pre-Match 0.861*** (0.006) -0.112 (0.207) 0.231*** (0.000) -0.004 (0.724) 0.230** (0.036) -4.856*** (0.000) Yes Yes 9,946 0.034