Political Relations and Overseas Stock Exchange Listing: Evidence from Chinese Stateowned

Transcription

1 Political Relations and Overseas Stock Exchange Listing: Evidence from Chinese Stateowned Enterprises Mingyi Hung Leventhal School of Accounting Marshall School of Business University of Southern California Los Angeles, CA T.J. Wong School of Accountancy The Chinese University of Hong Kong Shatin, NT, Hong Kong Tianyu Zhang Department of Accountancy City University of Hong Kong Kowloon Tong, Hong Kong September 2007 First Draft We acknowledge the financial support of the HKSAR Research Grant Council and USC-China Institute, as well as the Hong Kong Exchanges and Clearing Ltd. for providing data for the Hong Kong listed firms.

2 Political Relations and Overseas Stock Exchange Listing: Evidence from Chinese Stateowned Enterprises Abstract Using a sample of Chinese listed firms that emerge in the global equity markets, this paper examines the decision to cross-list and its consequences for the state-owned enterprises (SOEs). Contrary to prior research that focuses primarily on non-soes and finds that their cross-listing is motivated by capital raising and foreign sales expansion needs, we find that the Chinese SOEs cross-list on overseas stock exchanges to fulfill political objectives rather than to respond to market incentives. Despite the government involvement, there is evidence of bonding effects on the SOEs, which is typically found in cross-listing of non-soes in prior studies. We observe bonding through the adoption of international accounting and governance standards, and importantly, through increased analysts following, while we fail to find stronger law enforcement by the overseas regulators. Finally, we find that cross-listed SOEs have less earnings management, more professional board of directors, higher investment efficiency, and greater firm valuation than their domestically listed counterparts.

3 Political Relations and Overseas Stock Exchange Listing: Evidence from Chinese Stateowned Enterprises 1. Introduction A growing literature has documented many factors affect firms decisions to cross-list on overseas stock exchanges. For example, firms with high growth prospects or foreign sales are likely to cross-list on overseas stock exchanges to increase the ability to raise capital, improve global visibility, and enhance firm value (Pagano et al., 2002; Doidge et al., 2004). While these benefits may motivate firms to cross-list, research also shows that there are significant costs that deter firms from listing overseas. For example, firms with relatively strong political connections or high private benefits of control are less likely to cross-list because of greater disclosure requirements, stronger public scrutiny, and better law enforcement associated with such listings (Doidge et al., 2006; Leuz and Oberholzer-Gee, 2006). However, prior studies mainly focus on non-state firms and often exclude transitional economies. Thus, they provide little insights on the cross-listing decisions for state firms in transitional economies. The purpose of this study is to investigate the influence of political factors on cross-listing decisions for Chinese state-owned enterprises (SOEs) and the consequences of cross-listing. China provides a unique setting to explore the relation between state ownership and political factors in firms cross-listing decisions for the following reasons: (1) the prevalence of state ownership and politically-connected Chief Executive Officers (CEOs) (Fan et al., 2007), (2) the heavy government control over the cross-listing process, and (3) the significant capital raising activities of her firms in global financial markets, which trigger fierce competition among global stock exchanges to seek new listings from Chinese companies (Kissel and Santini, 2004). For example, China has been dominating the worldwide initial public offerings (IPO) in recent years, 1

4 notably for the world s biggest IPO by Industrial & Commercial Bank of China Ltd. (ICBC) s $19 billion share issuance. We conduct our investigation using a sample of all Chinese SOEs listed on worldwide stock exchanges during the period 1991 to Our empirical investigation is comprised of three sets of analyses. Our first set of analysis examines the influence of political factors on Chinese SOEs decisions to cross-list on overseas stock exchanges. We conjecture that Chinese politicians are motivated to cross-list in order to pursue national or private agendas (e.g., showcasing China s economic power or fulfilling politicians self-interests). Consequently, we hypothesize that Chinese cross-listed firms are likely to be protected or politically connected. We define protected firms as firms controlled by the central government or belonging to regulated industries (finance, natural resources, or public utilities). These protected SOEs are typically state monopolies that are directly under the supervision of the central ministries or the State Council. We define politically connected firms as firms with CEOs or Chairmen who are current or former government bureaucrats. To achieve the national agenda of promoting Chinese SOEs global reach, we argue that politicians are likely to prefer firms with political ties or government protection because government support such as granting and protection of monopoly rights, direct subsidies, and bailout in times of financial distress, increases the likelihood of success for the international listing. Firms with greater government protection or political ties can also be chosen by politicians out of self-interest, because such listings will enable these politicians to circumvent government regulations such as pay restrictions to top executives or enhance their political capital by expanding overseas operations. Consistent with both of these arguments, the analysis finds that the decision of Chinese SOEs to cross-list on overseas exchanges is politically 2

5 motivated. Specifically, SOEs with stronger political relations, either from government protection or top executives political connections, are more likely to list on overseas stock exchanges. In contrast, economic considerations such as the need to raise capital or strengthen the company s overseas product market do not seem to explain the cross-listing decisions. Our second set of analysis investigates the primary mechanisms in overseas markets that mitigate the propensity for cross-listed Chinese SOEs to take excessive private benefits. Specifically, we analyze the existence of the following three bonding mechanisms suggested in prior studies (e.g., Coffee, 2002; Karolyi, 2006).: (1) increased legal requirements of financial disclosure and corporate governance, such as the use of internationally-accepted accounting standards and independent board members, (2) strong enforcement powers of overseas regulators against corporate corruption, and (3) improved scrutiny by reputational intermediaries, such as analysts and the news media. Our analysis finds that compared to their domestically listed counterparts, cross-listed Chinese SOEs use more internationally-accepted accounting standards, have more independent boards of directors, attract more analysts to follow the firm, but are not subject to increased law enforcement against corruption. Our result is consistent with legal requirements and reputational intermediaries in overseas markets, but not law enforcement by overseas regulators, being used as bonding mechanisms for cross-listed Chinese SOEs. Our third set of analysis examines whether the bonding mechanisms previously identified result in significant changes in Chinese SOEs corporate policies and valuation premium. On one hand, given that cross-listing of Chinese SOEs is mainly politically motivated, it is doubtful that these firms would manifest substantial bonding effects. For example, managers of crosslisted Chinese SOEs may care more about politicians private agendas than responding to minority shareholders demands. If so, they will be more interested in their political careers than 3

6 companies business development. On the other hand, politicians who have incentives to showcase Chinese SOEs in the global markets may be motivated to ensure that their firms follow higher international governance and performance standards. Our analysis finds evidence suggesting significant bonding effects for cross-listed Chinese SOEs. Specifically, we find that compared to their domestically listed counterparts, cross-listed Chinese SOEs engage in less earnings management (as captured by a decreased tendency to report small profits or an increased tendency to report small losses), have more professional board of directors (as captured by a higher percentage of board members serving industry associations, with banking experience, or with law/accounting experience), enjoy higher investment efficiency (as captured by increased sensitivity of capital expenditure to lagged Tobin s q), and command greater valuation premium (as captured by higher Tobin s q using domestic share prices). Our findings suggest that the bonding mechanisms of legal requirement and reputational intermediaries induce the changes in corporate policies and firm valuation for cross-listed Chinese SOEs. In an attempt to corroborate this conclusion, we perform additional analysis after replacing the cross-listing dummy variable in our regressions of bonding effects with the bonding mechanisms previously identified (i.e., the use of internationally-accepted accounting standards, board independence, and number of analysts following the firm). Our analysis finds that number of analysts following significantly explains the bonding effects manifested on decreased earnings management, increased board professionalism, improved investment efficiency, and enhanced valuation premium. It also finds that board independence significantly explains all the bonding effects except valuation premium. However, we obtain mixed results for the bonding effects from the adoption of internationally-accepted accounting standards. 4

7 Overall, the additional analysis finds further support suggesting bonding via greater scrutiny of financial analysts and board independence. Our study contributes to prior research in several ways. First, we document a relation between political relations and cross-listing choices for state-owned firms. Using a sample of Indonesian firms that have ties with the Soharto families, Leuz and Oberholzer-Gee (2006) document that firms likelihood of cross-listing decreases when they have political connections. Contrary to their finding, our evidence shows that Chinese SOEs with stronger political relations are more likely to cross-list, but financing decisions and other economic factors do not affect their global listing choices. Second, our study adds to the recent debate on the impact of the Sarbanes-Oxley Act on the flow of international listings in the U.S. Piotroski and Srinivasan (2007) find a shift of foreign listing between U.S. and U.K. stock exchanges subsequent to the Sarbanes-Oxley Act (SOX). More specifically, they find that after SOX the increase in likelihood of listing in the U.K. is greater among smaller and less profitable firms from developed economies, while the increase in likelihood of listing in the U.S. is mostly from profitable firms in emerging markets. However, we document a decrease in U.S. listing among Chinese SOEs. Compared with the profitable firms in emerging markets that increase their propensity to list in the U.S., Chinese SOEs probably find that the expected costs such as higher political risks outweigh the expected benefits from the new regulatory requirements of SOX, thereby decreasing their propensity to cross-list on the U.S. stock exchange. Third, the paper provides additional insights on the bonding mechanisms for SOEs. Our results suggests that reputational intermediaries such as analysts provide significant bonding effects for Chinese SOEs, consistent with Besley and Prat (2006) that independent media can 5

8 help curb the grabbing hand of the government and increase its accountability. In addition, consistent with Siegel (2005), we find that overseas regulators are not more effective enforcers for the cross-listed firms. Finally, the paper adds to our understanding on the causes and consequences of cross-listing. While prior studies suggest that cross-listing on foreign stock exchanges is a mechanism for controlling shareholders of companies in weak institutional environments to commit to consuming less private benefits in exchange for the ability to raise capital and increase share liquidity (Stulz, 1999; Doidge, 2004), these studies typically do not include Chinese firms. Our evidence suggests that while political factors rather than economic considerations dominate Chinese SOEs decisions to cross-list on overseas stock exchanges, cross-listing does induce better accounting disclosure, higher board professionalism, greater investment efficiency and a cross-listing premium for these firms by subjecting them to increased scrutiny of international investment communities. The remainder of the paper is structured as follows. Section 2 discusses institutional background and section 3 presents research methodology. Section 4 describes the sample and empirical results. Section 5 summarizes our investigation. 2. Institutional Background 2.1 POLITICAL RELATIONS AND OVERSEAS LISTING FOR CHINESE SOES Prior studies suggest that firms with strong political relations are less likely to tap into foreign capital markets because these ties facilitate access to domestic capital and exacerbate monitoring costs for accessing foreign capital (Leuz and Oberholzer-Gee, 2006). We argue that such a relation may not hold for Chinese SOEs, because the overseas listing process is tightly controlled in China and dominated by noncommercial factors. Specifically, an SOE seeking 6

9 overseas listing must first gain approval from CSRC (China Securities Regulatory Commission), an executive branch of the State Council supervising the securities markets. It is commonly believed that the CSRC s selection process is subject to heavy political pressure. Anecdotal evidence suggests that key motivations behind politicians selection of cross-listed firms are: (1) pursuing national agenda at increasing Chinese firms international presence, and (2) fulfilling politicians private agenda at enhancing their political and economic capital. To promote the national agenda, politicians may use cross-listing on overseas stock exchanges as a way to showcase the country s economic power. 1 An analogy for securing a listing on New York Stock Exchange is winning Olympic medals because of the prestige and visibility such an achievement can bring to the politicians and the government. In addition, to fulfill the private agenda, politicians may use overseas listing to circumvent government regulations such as pay limits for top executives (Ke et al., 2006; Charitou et al., 2007). The politicians may also use the opportunity to expand the firms operations overseas as a way to enhance political capital. To fulfill these objectives, we conjecture that the politicians are more likely to select firms under their protection or with political connection to cross-list on overseas stock exchanges for two reasons. First, we argue that the government is more likely to select firms under tight government protection to cross-list because (1) the government can maintain control of the firms even after global listing, and perhaps more importantly (2) the government can ensure the success of the global listing of firms that enjoy stable monopoly profits and other privilege such as subsidies. Examples of firms that fit such a profile are those owned by the central government or in regulated industries (finance, natural resources, or public utilities) because these firms are under the direct supervision of State Council, constitute a major component to the central 1 For example, see Chinese industrial giant to launch HK share offer, Reuters News, July 2,

10 government s budget, and tend to be large state monopolies. Second, we argue that the government is more likely to select firms with strong political connection to cross-list to ensure a close cooperation with the government officials in global listing. Examples of firms with strong political connection are those with executives being ex- or current government bureaucrats (or, politically-connected executives, as in Fan et al., 2007). Thus, while firms with stronger political relations have lower incentives to cross-list due to easier access to domestic capital and greater monitoring costs, it is also possible that they are more likely to cross-list due to their strong political incentives either for national pride or politicians self interests. These political relations in the form of government protection and political connection will enhance the preferential treatment to the favored SOEs in the selection process. It is an empirical issue whether political relations will increase or decrease Chinese SOEs chances of listing in overseas market. 2.2 OVERSEAS LISTING, BONDING MECHANISMS, AND BONDING EFFECTS Prior studies such as Coffee (1999, 2002) propose that a foreign firm from a jurisdiction featuring weak investor protection can commit to limit expropriation of minority shareholders by bonding itself to a foreign securities regime through cross-listing (i.e., the bonding hypothesis). These studies suggest three main bonding mechanisms via which the bonding of cross-listed firms takes effect: (1) legal requirements mandated on overseas stock exchanges, (2) law enforcement by overseas regulators, and (3) monitoring by financial intermediaries. While the cross-listing literature generally supports the bonding hypothesis for U.S. exchange listing (Doidge et al., 2004), it provides little evidence on bonding mechanisms or bonding effects for state-owned firms or firms cross-listed on non-u.s. stock exchanges (Brockman and Chung, 2003; Guo and Tang, 2006). 8

11 It is an empirical issue whether cross-listed Chinese SOEs will exhibit any bonding effects. On the one hand, the bonding effect may be minimum for Chinese SOEs cross-listed on foreign stock exchanges because of ineffective bonding mechanisms or serious agency problems. The legal bonding mechanisms are generally viewed as ineffective for Chinese SOEs. 2 For example, it is often difficult for overseas regulators to take enforcement actions against Chinese SOEs due to bureaucratic burden and government intervention. 3 In addition, the state serving as the controlling shareholder is generally perceived to give rise to agency problems in several ways. First, the state is likely to soften the budget constraint of its SOE managers (Kornai, 1980, 1986), creating a moral hazard problem of over-investments in negative NPV projects. Second, government officials are likely to use state ownership as a vehicle to provide employment, subsidies and other benefits to supporters in exchange for brides or votes (e.g., Shleifer and Vishny, 1994; La Porta et al., 2002). Third, since the government officials who control the SOEs never have residual claims of the firms, there is poor alignment between their interests and those of the minority shareholders. Fourth, since the government retains key personnel and strategic decisions, managers of Chinese SOEs are likely to care more about government s political agenda than responding to the demand of minority shareholders. On the other hand, the bonding effect may be significant for Chinese SOEs due to increased monitoring of foreign financial intermediaries. This is because international cross-listing attracts 2 See Chinese cos cast shadow on HK corporate governance Dow Jones International News, September 13, In addition, cross-listed Chinese SOEs often do not voluntarily conform to corporate governance practices recommended by foreign stock exchange regulators. For example, the 2004 Annual Report of China Telecom states the company, as a foreign private issuer, is not required to comply with all of the corporate governance rules of Section 303A of the NYSE Listed Company Manual. the Company is not required to have a board with a majority of independent directors. 3 The Securities and Futures Commission (SFC) in Hong Kong is alleged to abandon one in five investigations for mainland firms because suspects disappear across the border (see Still tough catching crooks across the boarder, Business Times Singapore, April 21, 2007). In addition, the investigation by overseas regulators could stall if the Chinese government does not allow the overseas regulators to contact executives in her state-owned firms ( Beijing shields executive in Hong Kong probe, The Asian Wall Street Journal, December 8, 2003). 9

12 greater coverage by financial analyst and press (Baker et al., 2002; Lang et al., 2003), an important group with a significant monitoring function (Moyer et al., 1989; Miller, 2006). In addition, anecdotal evidence suggests that foreign investors and financial intermediaries are particularly concerned about government intervention when investing in Chinese SOEs because the Chinese government has been frequently alleged to divert resources of listed SOEs for social/political objectives (Moiseiwitsch, 2002). An example of the potential political risk associated with government intervention is the recent U.S. lawsuit against China life Insurance Co., which accuses the company for not adequately disclosing an audit report on accounting irregularities related to use of funds at its state-owned predecessor firm. 4 Thus, given the increased monitoring by foreign media and investment professionals on cross-listed Chinese SOEs, the government may constrain her intervention on cross-listed firms and promote greater transparency to prevent negative publicity and political backlash in international markets. This is likely to be the case when cross-listing is used politically for demonstrating China s rising economic power and her willingness to adhere to international standards. 3. Research Design 3.1 TESTS RELATED TO POLITICAL FACTORS AND CROSS-LISTING DECISIONS We test the relation between cross-listing decisions and political relations by regressing a dummy variable indicating whether a firm is cross-listed on foreign stock exchanges on the following two variables: (1) protected firm, a dummy variable capturing whether the firm is owned by the central government or in regulated industries, and (2) politically connected firm, a dummy variable capturing whether the Chairman or CEO is an ex- or current government bureaucrats. In addition, our regression analysis includes several economic factors that are expected to affect cross-listing decisions based on prior studies (Pagano et al., 2002; Sarkissian 4 See China Life stock slumps on accounting probe report, Reuters News, April 2,

13 and Schill, 2004; Doidge et al., 2006). Specifically, we include the following variables to capture the economic incentives for cross-listing: (1) firm size, to control for cross-listing economies of scale, (2) capital expenditure, leverage, and sales growth, to control for financing needs, (3) return on assets, to control for the possibility that higher quality firms may be more likely to cross-list to signal their quality, and (4) tradable industry, to control for the possibility that a firm with tradable goods is more likely to cross-list to strengthen the company s product market. Finally, we include a dummy variable indicating years following the passage of the Sarbanes-Oxley Act of 2002 because recent studies suggest a decrease in U.S. foreign listing subsequent to the Act (Piotroski and Srinivasan, 2007). Our formal regression model is: X-list = β 0 + β 1 (Protected firm)+ β 2 (Politically connected firm)+β 3 (Firm size)+ β 4 (Capital expenditure)+ β 5 (Leverage)+ β 6 (Sales growth)+ β 7 (Return on assets)+β 8 (Tradable industry)+β 9 (Post-SOX) +ε (1) where: X-list = A dummy variable equal to 1 if the firm is cross-listed on overseas stock exchanges, and 0 otherwise. Protected firm = A dummy variable equal to 1 if the firm is owned by the central government or in a regulated industry (finance, natural resources, or public utilities), and 0 otherwise. Politically connected firm = A dummy variable equal to 1 if the Chairman or CEO is an ex- or current officer of the central government, a local government, or the military; and 0 otherwise. Firm size = Natural logarithm of total assets in thousand US dollar. Capital expenditure = Expenditure to acquire fixed assets, measured as change in fixed assets and in-process projects, divided by total assets. Leverage = Total debt divided by total owners equity. Sales growth = Growth in sales. Return on assets = Net income divided by total assets. Tradable industry = A dummy variable equal to 1 if the firm s industry belongs to chemicals, consumer goods, electronics, manufacturing, healthcare, mining, oil and gas, and paper, and 0 otherwise (Sarkissian and Schill, 2004). Post_SOX = A dummy variable equal to 1 for years subsequent to the passage of the 2002 Sarbanes-Oxley Act, and 0 otherwise. 11

14 We use a logistic regression because our dependent variable is a binary variable. In addition, since each firm appears multiple times in our sample, we use robust standard errors clustered by firm to control for the dependence among error terms. 3.2 TESTS RELATED TO BONDING MECHANISMS OF CROSS-LISTING To test the mechanisms through which insiders of cross-listed Chinese SOEs may commit themselves not to take excessive private benefits, we examine three main bonding devices: (1) legal requirement on accounting standards and corporate governance imposed by overseas stock exchanges, measured as the adoption of internationally-accepted accounting standards (IFRS or U.S. GAAP) and appointment of independent directors, (2) law enforcement, measured as securities regulators enforcement action against a firm s top executives for corruption charges 5, and (3) monitoring by financial intermediaries, measured as number of analysts following the firm. In addition, we include the following control variables: (1) political factors controlling whether the firm is protected or politically connected, (2) firm characteristics controlling for size, leverage, and growth, and (3) year dummies controlling for year effects. Our formal regression model is as follows: Bonding mechanism =β 0 +β 1 (X-list)+β 2 (Protected firm)+β 3 (Politically connected firm)+ β 4 (Firm size)+β 5 (Leverage)+ β 6 (Sales growth)+β n (DYear)+ε (2) where: Bonding mechanism = A variable capturing legal requirement, enforcement action, and number of analysts following. DYear = Dummies for years. For ease of presentation, year dummy coefficients are not tabulated. The other variables are defined following equation (1). 5 We focus on corruption because of its economical significance corruption often causes severe damage for shareholders and results in harsh penalty for the convicted executives. For example, Liu Jinbao, the former chief executive of Hong Kong-listed Bank of China Hong Kong Ltd. was given a suspended death sentence by a Chinese court after being convicted of embezzling 14.3 million yuan ($1.8 million US dollar) with others and 7.5 million yuan ($0.9 million US dollar) for himself (see Bank of China ex-officer sentenced, The Wall Street Journal Europe, August 15, 2005). 12

15 We use OLS regression when the dependent variable is a continuous variable and logistic regression when the dependent variable is a binary variable. In addition, we use robust standard errors clustered by firm to control for the dependence in the error terms. 3.3 TESTS RELATED TO BONDING EFFECTS OF CROSS-LISTING To test the bonding effect of cross-listing, we regress corporate policies and firm valuation on a dummy variable indicating whether the firm is cross-listed on foreign stock exchanges and several control variables. We explore potential bonding effects in the following three types of corporate choices: accounting, governance, and investment. First, we examine accounting choices as reflected in the propensity of earnings management. We use three variables to capture the propensity of earnings management: small profit, small losses, and the magnitude of accrual scaled by operating cash flows. All else equal, a lower frequency of reporting small profits, higher tendency of reporting small losses, and lower magnitude of accruals are consistent with less earnings management. Following Lang et al. (2003), we define small profit as net income scaled by total assets between 0 and 0.01 and small loss as net income scaled by total assets between and 0. Second, we examine corporate governance choices as reflected in board professionalism. Following Fan et al. (2007), we use three measures for the professionalism of corporate board: the percentages of board members serving industry associations, with banking experience, or with law/accounting experience. Third, we examine the choice of investment policies as reflected in investment efficiency. Following prior studies (Lang et al., 1996; Fan et al., 2005), we measure investment efficiency as the sensitivity of capital expenditure on lagged Tobin s q. Finally, we examine whether the bonding effect would reflect in firm valuation, using Tobin s q as our valuation measure (Doidge et al., 2004). The challenge for this test is that price differences between cross-listed SOEs and their domestically listed counterparts are merely 13

16 driven by reduced agency costs as a result of bonding, but also by different market structure, ownership composition, and investor recognition. For example, prior studies suggest that lack of alternative investment opportunities drives much domestic Chinese savings into stock markets, causing domestic shares (or, A-shares) of cross-listed Chinese firms to trade at a premium than their foreign shares (Bailey et al., 1999). 6 In addition, Baker et al. (2002) suggest that increased investor recognition associated with cross-listing also affect firm valuation. Thus, to avoid the confounding factors other than reduced agency costs on firm valuation, we restrict the sample to domestically listed firms and cross-listed firms that have also issued shares domestically and test the cross-listing valuation premium using only the A-share prices. As in the tests for bonding mechanisms, we include the following control variables: (1) political factors controlling whether the firm is protected or politically connected, (2) firm characteristics controlling for size, leverage, and growth, 7 and (3) year dummies controlling for year effects. We use OLS regression when the dependent variable is a continuous variable and logistic regression when the dependent variable is a binary variable. In addition, we use robust standard errors clustered by firm. Our formal regression model related to accounting policies, corporate governance, and firm valuation is as follows: Bonding effect =β 0 +β 1 (X-list)+β 2 (Protected firm)+β 3 (Politically connected firm)+ β 4 (Firm size)+β 5 (Leverage)+β 6 (Sales growth)+β n (DYear)+ε (3) Our formal regression model related to investment efficiency is as follows: Capital expenditure =β 0 +β 1 (X-list*lTobin s q)+β 2 (ltobin s q)+β 3 (X-list)+β 4 (Protected firm)+ β 5 (Politically connected firm)+β 6 (Firm size)+β 7 (Leverage)+β n (DYear)+ε (4) where: 6 Only mainlanders and selected foreign institutional investors are allowed to trade on the Renminbi-quoted A- shares of Chinese companies. 7 In the regression analyzing investment efficiency, we exclude sales growth as a control because the model includes lagged Tobin s q, which also captures the growth prospect. 14

17 Bonding effect = A variable capturing a firm s accounting properties, board professionalism, and valuation premium. ltobin s q = Lagged value of Tobin s q, which equals the sum of market value of equity and book value of liabilities divided by total assets. DYear = Dummies for years. For ease of presentation, year dummy coefficients are not tabulated. The other variables are defined following equation (1). 4. Sample and Empirical Results 4.1 SAMPLE AND DESCRIPTIVE STATISTICS Our sample includes Chinese SOEs cross-listed on overseas stock exchanges worldwide, as well as those listed on domestic Shanghai and Shenzhen stock exchanges. To identify overseas stock exchanges with cross-listed Chinese firms, we use the information based on CSRC s website. 8 Consistent with Yang and Lau (2006), we find four such markets -- Hong Kong, Singapore, the U.K., and the U.S. Table 1 presents a comparison of markets with listed Chinese SOEs in terms of country characteristics, stock market characteristics, legal requirement, law enforcement, and financial intermediaries. The table reports three main patterns. First, it shows that the Hong Kong market has the highest similarity with domestic Chinese markets based on language and distance. Since prior studies suggest that market proximity and familiarity play the dominant role in the choice of overseas listing (Sarkissan and Schill, 2004), we expect Hong Kong to be the most popular destination for Chinese firms cross-listing destinations. Second, the U.S. market has the largest number of listed companies and biggest market capitalization, as well as the strongest rule of law tradition and the greatest monitoring capacity by financial intermediaries. Thus, we expect the U.S. to be another popular destination for Chinese firms cross-listing if they would like to tap into the largest pool of foreign capital or achieve the highest visibility and recognition in worldwide capital markets. Third, compared to domestic Chinese markets, overseas markets (i.e., 8 See 15

18 Hong Kong, Singapore, the U.K., and the U.S.) tend to require higher quality accounting standards (as reflected in the use of internationally-accepted accounting standards) and greater corporate governance (as reflected in the mandate of independent directors). In addition, the overseas markets all offer higher investor protection, as measured by the rule of law tradition and corruption index, and greater monitoring by financial intermediaries, as captured by press freedom and average number of analysts following the firm. After identifying the key overseas stock exchanges where Chinese SOEs cross-listed, we collect initial company list from each stock exchange and supplement it with JP Morgan ADR list for U.S.-listed companies. We then obtain necessary financial and stock price data for firms listed in overseas markets from the Worldscope database and for those listed in domestic markets from the China Security Market and Accounting Research (CSMAR) database. To mitigate the influence of outliers, we winsorize all scaled variables at the top and bottom 1% of each distribution. In addition, we hand collect data on ultimate ownership (i.e., whether the ultimate owner is central government, local government, or private enterprise), as well as background information on executives (i.e., whether the chairman or CEO is politically connected) and boards (e.g., the percentage of directors that are independent or have law/accounting experience) based on available annual reports closest to the year in which the company was cross-listed on the overseas stock exchange. We obtain comparable information on executives and boards for domestically listed firms from Fan et al. (2007). Finally, we gather data on number of analysts following from the I/B/E/S International database and hand collect data on enforcement actions against a firm s top executives for corruption from the cnnewspaper database. 9 9 The website address is The database provides more than 300 major journals and other sources dating as far back as 1991, including information sources from outside of Mainland China covering all subject areas related to business. We search for enforcement actions related to top executives including Chairman, CEO, vice CEO, and CFO. 16

19 We note that while we measure financial data each year, we are unable to do so for the data on share ownership and management profile because it is prohibitively costly to manually collect these data from annual reports every year. To the extent that these data are fairly stable over time and it is unlikely that a firm will change its ownership from a non-soe to a SOE after cross-listing, this does not pose a significant problem in our empirical tests. Another drawback of the data is that for the majority of our cross-listed firms, we only have data after the firm is cross-listed. While certain variables such as industry membership is unlikely to change after the firm is cross-listed, several financial characteristics such as firm size, leverage, and returns on assets are likely to be affected as a result of listing on overseas stock exchanges. Although we do not expect the changes in financial characteristics to affect our inference on political relations, we note this limitation in interpreting certain financial characteristics in testing the determinants of cross-listing decisions. Our sample consists of 1,063 firms listed on domestic Chinese stock exchanges (Shenzhen and Shanghai stock exchanges) and 134 firms listed on overseas stock exchanges (Hong Kong, Singapore, U.K., or U.S. stock exchanges) from 1991 to We start our investigation period in 1991 because Shenzhen and Shanghai stock exchanges are set up in 1990 and 1991, respectively. Table 2 reports the sample distribution of Chinese SOEs listed on stock exchanges worldwide. Panel A of Table 2 reports the number and percentage of firms listed on each stock exchange by year, as well as total market value of listed firms as of It shows that there is a decrease in local exchange listing in recent years, which is likely due to unfavorable market conditions after The table also shows that Hong Kong is the most popular destination, which constitutes 98% (=131/134) of the Chinese SOEs overseas listing, consistent with 17

20 proximity and familiarity being the dominant factors in cross-listing decisions. In addition, the U.S. is the second most popular destination next to Hong Kong. Consistent with the U.S. being the most prestigious cross-listing choice for Chinese SOEs, we find that while there are only 19 firms listed in the U.S. (versus 131 firms listed in Hong Kong), the U.S.-listed firms are much larger and have an aggregate market value of US$ 415 billion (versus US$ 635 billion for Hong Kong-listed firms). Interestingly, we note that the 19 U.S. listed Chinese SOEs are all listed on NYSE and also simultaneously listed on Hong Kong stock exchanges (not reported in the table). Panel B of Table 2 reports the number and percentage of firms listed on each stock exchange by industry sector, where the firms are classified by the industry categories as in Fan et al. (2007). The results in this panel show that, while domestically listed firms concentrate on the manufacturing sector and not on the public utilities sector, there are relatively fewer cross-listed firms in the manufacturing sector and more in the public utilities sector. Specifically, while 73% of the domestically listed firms are in the manufacturing sector and 7% are in the public utilities sector, 42% of the cross-listed firms are in the manufacturing sector and 22% are in the public utilities sector. Table 3 presents descriptive statistics and correlation analysis for our firm-level variables used in the test of cross-listing decisions. Panel A of Table 3 reports descriptive statistics partitioned by whether a firm-year observation is cross-listed. The far right column shows p- values for t-tests of the mean differences and for Wilcoxon two-sample tests of the median differences between local versus cross-listed firms. The panel reports that on average, 30% of the local firms and 52% of the cross-listed firms are protected, with the difference significant at p<1% (two-tailed). In addition, it shows that on average, 27% of the local firms and 50% of the cross-listed firms are politically connected, with the difference significant at p<1% (two-tailed). 18

21 Thus, the result from the univariate analysis is consistent with cross-listed Chinese SOEs having stronger political relations than domestically listed Chinese SOEs. Panel A of Table 3 also indicates that cross-listed SOEs tend to be bigger, consistent with prior studies that larger firms are more likely to cross-list. However, the table also shows that cross-listed Chinese SOEs make smaller capital expenditure, have lower leverage, and are less likely to have tradable goods. This result is inconsistent with prior studies that find cross-listing being driven by economic needs to fund growth and expand foreign sales. Thus, the result from our univariate analysis casts doubt on the applicability of economic factors documented in prior studies as drivers of the cross-listing decisions in the context of Chinese SOEs. Panel B of Table 3 presents Pearson correlation coefficients for the associations among firmspecific variables. It shows a significantly positive correlation between cross-listing and political relations. In addition, we find that firms with stronger political relations tend to be larger, have more capital expenditure, enjoy greater return on assets, have less leverage, and produce fewer tradable goods. 4.2 EMPIRICAL RESULTS Table 4 reports the results of the logistic regression that analyzes the determinants of crosslisting decisions. Model One regresses a dummy variable indicating whether a firm is crosslisted on any foreign stock exchange on our political factors and control variables. Model Two (Model Three) explores whether the cross-listing decisions vary across Hong Kong and U.S. listing, the two primary overseas listing destinations of Chinese SOEs, by changing the dependent variable to a dummy variable indicating whether a firm is cross-listed on Hong Kong stock exchanges (on U.S. stock exchanges). 19

22 Models One finds the coefficients on both political factors (i.e., protected firm and politically connected firm) to be significantly positive at p<1% (two-tailed). In addition, it finds that crosslisted Chinese SOEs are larger, make less capital expenditure, have lower leverage and return on assets, and are less likely to be in an industry with tradable goods. Thus, the multivariate analysis results confirm our univariate analysis results that cross-listing choices of Chinese SOEs are driven by political consideration and not economic needs. The result in Model Two is consistent with that in Model One. This is not surprising because almost all firms cross-listed on any overseas stock exchange also cross-list on Hong Kong stock exchanges. Finally, the result in Model Three indicates the following two differences between Hong Kong listing (Model Two) and U.S. listing (Model Three). First, the signs of the coefficients on capital expenditure, sales growth, and returns on assets turn into an opposite direction in Model Three, with the difference significant at p<10% (two-tailed). While the result in Model Three suggests that U.S.-listing is significantly associated with higher capital expenditure, it also shows that U.S.-listing is associated with lower sales growth. Thus, Model Three also yields mixed evidence on the influence of economic incentives for cross-listing decisions. Second, the coefficient on the dummy variable indicating years subsequent to the passage of the Sarbanes-Oxley Act is significantly more negative for U.S. listing at p<1% (twotailed). Thus, the evidence is consistent with that Chinese SOEs are less likely to cross-list on U.S. stock exchanges after 2002, possibly due to the increased regulatory requirements of the Sarbanes-Oxley Act on corporate governance and financial disclosure. Table 5 reports the results of the analysis on three types of bonding mechanisms: (1) legal requirement of accounting standards and corporate governance, (2) law enforcement against corruption of top executives, and (3) financial analysts following the firm. Panel A of Table 5 20

23 provides descriptive statistics on additional variables used in this analysis. It shows, on average, there is a low propensity of using internationally-accepted accounting standards and independent directors among Chinese SOEs. In addition, the enforcement actions against a cross-listed firm s top executives for corruption are rare and mostly initiated by local Chinese regulators, not overseas regulators. 10 The lack of enforcement by overseas regulators against cross-listed firms is consistent with Siegel (2005) that US SEC has not effectively enforced the law against crosslisted foreign firms. Finally, the panel shows that the average number of analysts following is less than one for Chinese SOEs. Panels B, C, and D of Table 5 test the bonding mechanism related to legal requirement, law enforcement, and monitoring by financial intermediaries, respectively. Panel B shows that crosslisted Chinese SOEs are more likely to use internationally-accepted accounting standards and have higher percentage of independent directors, with the coefficient on the cross-listing dummy variable being significant at p < 10% (two-tailed). 11 The result is not surprising because we would expect cross-listed Chinese SOEs to conform to mandatory reporting and corporate governance requirements in overseas markets, which are generally stricter than those in mainland China. Panel C indicates little bonding via law enforcement on cross-listed firms, with the coefficient on the cross-listing dummy variable being insignificant at conventional level. 12 The result is consistent with critics view that overseas regulators such as those in Hong Kong, fail to 10 Although rare, there are examples of anti-corruption enforcement of cross-listed firms by overseas regulators. For example, Hong Kong regulators sentenced a director of Hong Kong-listed Guangnan (Holdings) Ltd. to five years in prison for pilfering more than 53 million Hong Kong dollars ($6.8 million US dollars) in a credit scam (Associated Press Newswires, April 3, 2002). There is no enforcement action by overseas regulators against domestically listed firms in our sample. 11 The number of observation is smaller in our tests for legal requirement due to missing data on background information of board for domestically listed firms that are not covered in Fan et al. (2007). 12 For cross-listed firms, we include enforcement action by both local and overseas regulators. Additional analysis (not tabled) finds the coefficient on the cross-listing dummy variable to be significantly negative at p < 5% (twotailed) after including only enforcement action by overseas regulators for cross-listed firms. Thus, the additional evidence suggests that the enforcement by overseas regulators is significantly lower than that by local regulators, even though the rule of law tradition is higher in overseas markets. 21

24 uphold international standards against the Beijing government due to political pressure (Clifford, 2003). Finally, consistent with Yang and Lau (2006), Panel D shows a higher number of analysts following for cross-listed firms, with coefficient on the cross-listing dummy variable being significant at p < 1% (two-tailed). Overall, the analysis in Table 5 suggests that while Chinese SOEs may commit themselves to less expropriation of minority shareholders due to stricter legal requirement and greater monitoring of financial intermediaries in overseas markets, they are unlikely to do so due to law enforcement by overseas regulators. Table 6 reports the results of the analysis on bonding effects. Panel A of Table 6 provides descriptive statistics on additional variables used in this analysis. We note that the numbers of observations vary in this analysis because of additional data requirement for each test. 13 The panel shows an average of 14 percent of firm-years reporting small profits and an average of 0 percent of firm-years reporting small losses. In addition, it shows that the average percentage of board serving in industry associations or with law/accounting experience is 5%. Finally, the panel shows the average lagged Tobin s q is 2.46 for Chinese SOEs. Panels B, C, D, and E of Table 6 test the bonding effects related to earnings management, board professionalism, investment efficiency, and firm valuation, respectively. Panel B shows that, while cross-listed firms do not seem to have smaller magnitude of accruals, they are less likely to report small profits and more likely to report small losses, with the coefficient on the cross-listed dummy significant at p < 5% (two-tailed) in regressions with the dependent variable being small profits or small losses. Panel C shows that cross-listed Chinese SOEs have significantly more professional board of directors than their domestically listed counterparts, with the coefficient on the cross-listed dummy significant at p < 1% (two-tailed) across 13 For the test on valuation premium, the number of cross-listed firm-years is lower than the whole sample because not all cross-listed firms have corresponding domestic shares (recalled that the test is based on domestic shares of both cross-listed and domestically listed firms). 22

25 regressions where the dependent variables are percentage of boards serving industry associations, with banking experience, and with law/accounting experience. Panel D shows that capital expenditures of cross-listed firms are more sensitive to lagged Tobin s q, with the coefficient on the interaction term between the cross-listing dummy variable and lagged Tobin s q variable being significant at p < 5% (two-tailed). This result is consistent with greater investment efficiency of cross-listed Chinese SOEs due to decreased government intervention on investment policies. Finally, Panel E shows that cross-listed Chinese SOEs enjoy a higher valuation premium than their domestically listed counterparts, with the coefficient on the cross-listed dummy significant at p < 1% (two-tailed). 14 An underlying assumption for our analysis is that our cross-listing variable is a reasonable surrogate for mechanisms that bond firms to limit expropriation of minority shareholders. In an attempt to assess whether this is a reasonable assumption, we rerun the analysis on bonding effect in Table 6 after replacing the cross-listing dummy variable with the bonding mechanisms identified in the Table 5 analysis (i.e., the use of internationally-accepted accounting standards, board independence, and analysts following). Panels A, B, C, and D of Table 7 report the result related to the bonding effect on earnings management, board professionalism, investment 14 Since the cross-listed firms do not represent a random selection of Chinese SOEs, it is possible that our results on bonding effects are affected by self-selection bias. To assess the impact of self-selection on our results, we implement the two-step estimation procedure in the Heckman treatment effects model for the analysis in the Table 6 (Greene, 1993). In the first stage, we model the decision to cross-list as in the Table 4 analysis. Specifically, we use a probit model to analyze our sample firms decisions to cross-list, where the dependent variable equals one for cross-listed firms and zero for domestically listed firms. We include all independent variables in our cross-listing decision regression as in the Table 4 analysis, except for the variable serving as the dependent variable in the second stage regression. In the second stage we include the Inverse Mills Ratio from the first stage as an additional explanatory variable in the regression analysis in Table 6. This analysis (not tabled) finds results consistent with those reported in Table 6, with the following exception: the coefficient on the cross-listing dummy variable becomes significantly positive in the regression where the dependent variable is small profit. Since we continue to find that cross-listed SOEs report more small loss, have more professional boards, exhibit higher investment efficiency, and enjoy greater valuation premium, our overall conclusion on boding effects does not seem to be severely affected by potential self-selection bias. We note that we do not use the treatment effect model in the main analysis because prior studies show that attempts to control for potential self-selection are problematic and may produce estimates that are inferior to OLS regressions (Larcker and Rusticus, 2005). 23