Public Governance and Tunneling: Evidence from a Quasi-Experiment in China

Guowei Xu PhD Candidate Department of Finance Renmin University of China Beijing, China and Department of Accountancy and Law Hong Kong Baptist University Hong Kong, China [email protected]

Wenming Wang Department of Accountancy and Law Hong Kong Baptist University Hong Kong, China [email protected]

Danhua Zhou MBA student Andersen School of Management University of California, Riverside California, U.S.A [email protected]

Gaoguang Zhou Department of Accountancy and Law Hong Kong Baptist University Hong Kong, China [email protected]

This version: 2016 Oct

Public Governance and Tunneling: Evidence from a Quasi-Experiment in China

Abstract: In this study, we take advantage of the unprecedented anti-corruption campaign launched in December 2012 to examine the effect of improved public governance on tunneling in China. Using a sample of Shanghai and Shenzhen Stock Exchange listed companies from 2010 to 2014, we find that the scale of tunneling decreases significantly after the campaign, supporting the notion that increased public governance effectively curbs tunneling. Cross-sectional results show that such effect is more pronounced in firms controlled by private entrepreneurs, in private firms having political connections, in firms audited by non-Big 8 auditors, in firms with large divergence between control right and ownership right, and in firms located in areas with low marketization. Our results highlight the importance of anti-corruption initiatives in improving public governance and in turn reducing expropriation. Our study provides implications for many emerging economies with similar institutional background.

Keywords: Anti-corruption, Public governance, Tunneling, China.

JEL classification: D73 G34

I. INTRODUCTION Tunneling is the process of transferring assets or profit out from a company to the controlling shareholders (Johnson et al. 2000).1 Such expropriation behaviors are prevalent especially in emerging economies where legal protection is under-developed (e.g. Johnson et al. 2000;

Shleifer and Vishny 1997; La Porta et al. 1997, 1998; Shleifer and Wolfenzon 2002). Although the governments in many emerging economies have launched initiatives to combat corruption in order to improve public governance, scant studies examine whether such initiatives are effective in deterring misconducts. In this study, we take advantage of China’s unprecedented anti- corruption campaign initiated in December 2012 as an exogenous event to examine whether the campaign could curb tunneling.

We use China as a setting to examine aforementioned research question for the following three major reasons. First, the anti-corruption campaign is an exogenous event that is believed to improve public governance in China. In December 2012, Mr. Xi Jinping launched this campaign shortly after his assumption of General Secretary of Communist Party of China (CPC). He demonstrates his determination to tackle corruption and vow to target all the party members regardless of their ranks in the government. The campaign is unexpected and shocks even the most seasoned political observers (Griffin et al. 2016). Our study using this exogenous event as a setting could alleviate endogeneity problems that are major concerns in prior cross-country studies (e.g. Nenova 2003; Dyck and Zingales 2004; Lemmon and Lins 2003).2 Second, China’s listed firms are prone to engage in tunneling because of concentrated ownership, weak legal protection, and the limited authority of the security market regulators (Jiang et al. 2010). Several

1 Tunneling is also called self-dealing (e.g. Djankov et al. 2008). In this study, self-dealing and tunneling are interchangeable. 2 The studies based on cross-country data are subject to endogeneity problems, which may weaken the inference (Fan, et al. 2008).

3 studies show that the controlling shareholders in China extract private benefits from the listed companies using various methods, including inter-corporate loans (Jiang et al. 2010), loan guarantees (Beckman et al. 2009), and related party transactions (Jian and Wong 2010). As such,

China is an ideal lab to examine tunneling. Finally, the wide variations of institution background in different areas in China allow us to examine how local institutions moderate the relation between public governance and tunneling within one country. By doing so, we are able to extend prior tunneling literature and deepen our understanding of tunneling.

We argue that the anti-corruption campaign provides profound effects on the governance environment and thus reduce tunneling. The anti-corruption campaign significantly improves public governance (Shleifer and Vishny 1997) and thus reduce tunneling (e.g. Johnson et al.

2000; Nenova 2003; Dyck and Zingales 2004; Lemmon and Lins 2003). In particular, the measures taken by the CPC to pose restrictions on government officials’ ties with firms and punishments for detected misconduct force the regulators to monitor the firms closely. The anti- corruption campaign also improves corporate governance at firm level and thus reduce tunneling

(Jiang et al. 2010; Gao and Kling 2008). Based on the above arguments, we hypothesize that the level of tunneling decreases following the anti-corruption campaign in China.

We further examine whether the mitigating effect of the anti-corruption campaign on tunneling varies with firm characteristics, including ownership structure, political connection, quality of audits, and marketization at province level. One feature of China’s economy is that the government is the controlling shareholder in many large listed companies (or SOEs). As the controlling shareholders in SOEs, the governments appoint high ranked officials as executives in the firms. The promotion prospects coupled with high costs of misconducts discourage them to

4 engage in tunneling.3 In contrast, private companies have strong incentive to extract private benefits through tunneling. As a result, we expect that non-SOEs are more affected by the campaign. Another feature of China’s corporate sector is the wide use of political ties to obtain favorable treatment in regulatory process (Wu et al. 2016). However, such strategy is no longer effective after the carrying out of the campaign because the regulators are receiving greater scrutiny from the party and the public than ever before. We thus hypothesize that the deterrence of tunneling after the campaign is more significant for companies with more political connections. In China, large auditors are more effective in curbing tunneling compared to small auditors (Gao and Kling 2008; Jiang et al. 2010). In response to increased audit risk caused by the campaign, small auditors take more efforts to detect and report tunneling which is conductive to fraud and regulatory sanctions (Firth et al. 2005). As such, we expect that the effect of the anti-corruption campaign on tunneling is more pronounced for firms hiring small auditors. Like firms in other emerging economies, the firms in China exhibit large divergence between control right and ownership right (C/O ratio), providing the controlling shareholders strong incentive to expropriate the minority shareholders (e.g. Lemmon and Lins 2003; Claessens, et al. 2002; Jiang et al. 2010). The anti-corruption campaign enhances corporate governance and public enforcement and deters these firms to commit tunneling. Hence, we expect that the effect of the anti-corruption campaign in curbing tunneling is more pronounced in firms with high C/O compared to those with low C/O. Finally, we expect that the effect of the anti-corruption campaign is more effective in low marketization regions as tunneling is pervasive in these regions before the campaign.

3 Indeed, according to China’s laws and regulations, the executives of SOEs engaging in tunneling bear rather high litigation and administrative costs.

Using a sample of China’s listed firms during a four-year period around the launch of the anti-corruption campaign in 2012, we find that the tunneling activities undertaken by Chinese listed firms are significantly reduced in the post-campaign period relative to the prior-campaign period. These results suggest that the anti-corruption campaign materially improve public governance via strengthened regulation enforcement and increased investor protection, which in turn effectively deters the controlling shareholders from tunneling resources from their listed firms. Further analyses reveal that the mitigating effect of the anti-corruption campaign on tunneling is more pronounced for firms that are controlled by private, audited by non-Big 8 auditors, controlled by the largest owner via the pyramidal structure, located in the regions with low marketization, and non-SOE firms with political connections. All those firms are believed to have a higher propensity to conduct tunneling in the pre-campaign period. Our results are robust to several sensitivity tests including alternative measure of tunneling and alternative sampling.

Our study provides several contributions to the literature. First, this study enriches our understanding of the effect of institutions on expropriation. (e.g. Johnson et al. 2000; Nenova

2003; Dyck and Zingales 2004; Lemmon and Lins 2003). Prior studies examining the effect of public enforcement on tunneling are predominantly based on cross-country data, which is subject to omitted-correlated variable problems. Our study using a quasi-experiment approach alleviates endogeneity problems and thus provides strong evidence. Our study also adds to prior studies by showing the role of anti-corruption campaign in strengthening public enforcement in emerging economies. In addition, our cross-sectional results deepen our understanding on how public enforcement deters tunneling.

Second, our study complements the emerging literature examining the economic consequences of the anti-corruption campaign (Fan et al. 2008; Lin et al. 2016; Ke et al. 2016;

Griffin et al. 2016). A central question of the literature is whether and how the anti-corruption campaign affects firm value (e.g. Lin et al. 2016; Ke et al. 2016). Extending the extant studies, our study shows reduced tunneling as another economic consequence. In addition, as the expropriation of minority shareholders reduces firm value (e.g. Berkman et al. 2009), our study suggests that curbing tunneling could be one channel that increases firm valuation after the campaign (e.g. Lin et al. 2016; Ke et al. 2016).

Finally, our study provides important implications for regulators in China and beyond.

Tunneling prevails in many emerging economies where legal protection is weak (e.g. Johnson et al. 2000; Dyck and Zingales 2004; Lemmon and Lins 2003). In order to curb tunneling, the governments in many emerging economies have taken various measures to improve public enforcement. Our study showing the effectiveness of the anti-corruption campaign in reducing tunneling in China, the largest emerging economy around the world, suggests that anti-corruption campaign is a viable way to curb tunneling in emerging economies. Our study thus provides implications for other emerging economies with similar institutional background with China.

The remaining of our paper proceeds as follows. Section 2 reviews the literature and develops our hypotheses. Section 3 describes the research design, sample selection, the descriptive statistics of our sample, and the correlation matrix of key variables. Section 4 presents the empirical results. Section 5 concludes the paper.

II. INSTITUTIONAL BACKGROUND AND HYPOTHESES DEVELOPMENT 2.1 The Anti-Corruption Campaign in China China has serious problems with corruption. The Corruption Perception Index (CPI) developed by Transparency International ranked China 78 of 175 in the world in 2010, worse

7 than many emerging economies such as Brazil (69) and Malaysia (56).4 Despite that the Chinese governments took various measures to tackle corruption in past years, corruption is still a serious concern. On November 14th 2012, Mr. Xi Jinping was elected as General Secretary of CPC in the 18th CPC National Congress. After his assumption of General Secretary, he stressed that corruption has threatened the survival of the CPC and he would commit to tackle corruption seriously. Shortly after he took office, he embarked on the anti-corruption campaign in China.

On December 4th, 2012, the party promulgated Eight-point Regulation and Correct for “Four winds” to regulate the party members’ conducts, representing official start of the anti-corruption campaign. The target of campaign is “tigers and flies”, suggesting that the campaign covers all the party members regardless of their ranks. The Central Commission for Discipline Inspection

(CCDI), an internal-control institution of the CPC, takes the responsibility to supervise all party members in the anti-corruption campaign by eliminating party members who have corruption activities and who break the party line of CPC. CCDI also encourages scrutiny and whistleblowing from the public by providing various channels including email and telephones.

Another important initiative is to regulate government officials’ corruption by severing the ties between the government officials and firms and posing more severe punishment for misconducts.

In October 2013, the Organization Department of the CPC promulgated its 18th Decree to govern government officials’ employment in enterprises. The decree poses limitations on government officials’ employment in enterprises and payment from the firms for approved cases. The decree also specifies that any breaches of the regulations must be rectified shortly.

According to Chinanews, there were about 336,000 officials receiving administrative punishment in 2015 and about 14,000 of them were suspected of committing crimes and referred

4 For details, see the link: http://www.transparency.org/cpi2010/results.

8 to judicial authority5. The unprecedented anti-corruption campaign is perceived to improve the public governance. A number of studies examine the economic consequence of this campaign.

At the firm level, Lin, et al. (2016) show that almost all SOEs gain more shareholder valuations after the anti-corruption campaign because the campaign force the cadres to forgo perks. For non-SOEs, only those headquartered in the areas with better developed market institutions, higher productivity, greater growth potential, and larger external finance dependence attain more shareholder valuations. Different from Lin et al. (2016), Ke et al. (2016) concentrate their analyses on firms selling luxury goods and services. They show that such firms experienced profit decrease as a result of the campaign, but the campaign has no effect on their shareholder value. While they find that SOEs reduce their excessive perk consumption by luxury goods and services, the effect of anti-corruption campaign on reducing excessive perk consumption is not significant. Griffin et al. (2016) observe that firms with more related party sales, lower performance, and suspicion of corrupted executives are more likely to be investigated. However, they show no signs of improvement in corruption indicators and suggest that more comprehensive reforms are needed in order to make such campaign more effective. Zhang (2016) reveals that firms’ likelihood to commit fraud is lower after the campaign and such effect is more pronounced in private companies, firms with younger CEOs, and firms located in weak legal protection and under-developed areas.

2.2 Tunneling: Theory and China’s Experience

5 Chinanews reported, in 2015, 1023 officials fleeing to other countries were recaptured; about 82,000 officials were given severe administrative party discipline measure; 2,479 discipline supervisors were given administrative punishment.

The conflict of interest between managers and shareholders is the central agency problem in developed economies, such as the U.S. and U.K., where ownership is dispersed (Berle and

Means 1932; Jensen and Meckling 1976). In many other economies where ownership is concentrated (La Porta et al. 1999; Claessens et al. 2000; Claessens et al. 2002; Faccio and Lang

2002), another major type of agency problem is the tendency that the controlling shareholders expropriate the minority shareholders. Such behaviors are called as tunneling (Johnson et al.

2000) or self-dealing (Djankov et al. 2008). The controlling shareholders could use two main forms of tunneling: outright transferring resources by self-dealing and increasing the controlling shareholders’ shares in the firm without asset outflows through financial transactions that discriminate against minority shareholders. Such expropriation behaviors are prevalent especially in emerging economies where legal protection is under-developed (e.g. Johnson et al. 2000,

Shleifer and Vishny, 1997; La Porta, et al. 1997, 1998; Shleifer and Wolfenzon, 2002).

Although there are ample anecdotes of tunneling around the world, direct measure of tunneling is challenging until recently. 6 Jiang et al. (2010) provide a novel and direct measure of tunneling in China. Because suspected tunneling is likely to be through inter-party loans and these transactions are reported as part of other receivables (OREC) in China, they use OREC as proxy for tunneling. They show that firms with small size, high leverage, less profit, and non- state controlling shareholder are more likely to commit tunneling. They further find that firms with large OREC have poor financial performance and tunneling could be mitigated by high quality auditors, institutional investors, and regulations. Jian and Wong (2010) show that the group firms in China are more likely to use related party transactions as means to tunnel.

Berkman et al. (2009) find that the controlling shareholders expropriate the minority

6 See Jiang et al. (2010) for detailed discussion on two indirect approaches to estimate tunneling (P3-4).

10 shareholders by issuing related guarantees. They show that the issuance of related guarantees is less pervasive in firms with smaller size, more profits, and high growth opportunities. Using

Hong Kong data, Cheung et al. (2006) provide evidence that the controlling shareholders undertake connected transactions to tunnel and firms announcing these transactions experience significant stock return loss. Peng et al. (2011) provide evidence showing that the controlling shareholders are more likely to tunnel when firms are financially distressed and such transactions have significant economic impacts on listed firms. Using IPO as a setting, Aharony et al. (2010) shows that IPO firms inflate their earnings using related party sales before IPO and tunnel to their parent companies after IPO. The widespread use of tunneling in China has attracted both regulators’ and researchers’ attention. Starting from 2003, the China Securities Regulatory

Commission (CSRC), the SEC like regulator in China, launched a series of initiates to curb tunneling (see Jiang et al. 2010 for details).

Several studies examine factors that could deter the controlling shareholders from engaging in tunneling. For example, Gao and Kling (2008) hypothesize and find that better corporate governance is associated with less tunneling in China. Tu and Yu (2015) show that the severity of post-privatization tunneling behaviors by acquirers declined after regulatory enforcement against tunneling strengthened after 2003, highlighting the importance of public enforcement in curbing expropriations. Chen et al. (2016) find that firms donating more are less likely to engage in tunneling, supporting that preserving reputation dis-incentivize the controlling shareholders to expropriate the minority shareholders.

2.3 Hypotheses Development

We argue that the anti-corruption campaign deters tunneling for at least two major reasons.

First, the anti-corruption campaign increases the effectiveness of public governance and thus reduces the controlling shareholders’ incentives to expropriate the minority shareholders. Prior literature shows that tunneling is more prevalent in countries where legal enforcement is under- developed (e.g. Johnson et al. 2000; Nenova 2003; Shleifer and Vishny 1997; La Porta et al.

1997, 1998; Shleifer and Wolfenzon 2002). Corruption is among one of the important factors that negatively affect legal enforcement and thus weakens investor protection (Shleifer and

Vishny 1997). Like many emerging economies, China has limited legal protection against insider abuse because of corruption. The anti-corruption campaign improves public governance by deterring regulators to provide favorable treatments to firms and thus curb tunneling. As China’s government could exert influence on regulatory agencies (Naughton 2007), many firms seek to lower their regulation risk by corrupting the regulators in pre-campaign period (Wu et al. 2016).

During the campaign, CPC takes various initiatives to restrict such rent-seeking behaviors. For example, in October 2013, the organization Department of the Communist Party (DCP) of China promulgated its 18th Decree to govern government officials’ employment in enterprises. The decree poses limitations on government officials’ employment in enterprises and payment from the firms for approved cases. In addition, the regulators are more closely monitored by CCDI.

CCDI encourages greater scrutiny from the public and provides many channels to encourage individuals to be whistleblowers. The initiatives not only effectively lower the regulators’ incentive to corrupt but also deter them from providing favorable treatments to their connected enterprisers. We thus expect that regulators increase their monitoring efforts and in turn tunneling incentive decreases.

Second, the anti-corruption campaign increases corporate governance at firm level and facilitates the minority shareholders to curb tunneling behaviors. Doidge et al. (2004, 2007) contend that a country’s institution affects firm-level governance quality. More specifically,

Shleifer and Vishny (1994) theoretically demonstrate that managers have strong incentive to extract private benefits from the firm by lowering corporate governance in highly corrupted environment. Ng et al.(2008) theoretically and empirically show that corruption is negatively related to firm level corporate governance quality. After reviewing several corporate governance studies in emerging economies, Claessens and Yurtoglu (2013) posit that firms in countries with high level of corruption have lower corporate governance quality. To the extent that the controlling shareholders are more likely to expropriate the minority shareholders in firms with weak corporate governance (e.g. Gao and Kling 2008; Jiang et al. 2010), the anti-corruption campaign could promote corporate governance quality and thus reduce tunneling. Based on the above arguments, we formally state our first hypothesis as:

H1: The level of tunneling decreases after the anti-corruption campaign in China.

China’s government retains great influence in corporate sector through holding controlling ownership in a large amount of large listed companies (or SOEs). As the controlling shareholders in SOEs, the governments from different levels appoint high ranked officials as executives in the firms (Chen et al. 2009). These executives have incentive to pursue political advancement. In

China, the executives in SOEs are subject to greater scrutiny and more severe punishment if their tunneling activities are detected compared to those in private companies (Tu and Yu 2015). As a result, these executives have strong motivation to seek political promotion rather than engage in

13 tunneling in order to gain short-term benefits. 7 In contrast, the controlling shareholders in private companies do not have incentive to seek political promotion but have strong incentive to extract private benefits by transferring out the assets from the firm. Consistent with this argument, Jiang et al. (2010) observe that private companies are more likely to engage in tunneling compared to the SOEs. As the anti-corruption campaign increases public enforcement and corporate governance and poses large costs for firms to engage in tunneling, non-SOEs who are more actively engage in tunneling in pre-campaign period (Jiang et al. 2010) are affected more by the campaign. As a result, we argue that the mitigating effect of the anti-corruption on tunneling is more significant for non-SOEs. Our second hypothesis is thus as follow:

H2: The effect of the anti-corruption campaign in reducing tunneling is more pronounced in non-

SOEs compared to that in SOEs.

China’s economy is characterized by weak legal protection and instable institution (Nee

1992), putting the private companies in a disadvantageous position compared to SOEs. In response to the environment, the private companies seek to obtain political connections to gain political legitimacy and resources in favorable terms. As China’s government could exert great influence on the regulatory agencies (Naughton 2007), political connections enable the firms to obtain favorable treatments in regulation process. Wu et al. (2016) hypothesize and find that firms with political connections have lower odds of enforcement action against fraud. They further show that such effect is more pronounced in non-SOEs compared to SOEs because non-

7 The executives in SOEs normally have agency problem in the form of perk consumption. Consistent with this view, Lin et al. (2016) show that the managers in SOEs reduce their perk consumption after the anti-corruption campaign. However, due to the focus of our study, we do not examine how the campaign affect perk consumption.

SOEs devote more resources to obtain favorable treatments from the regulators. Their findings suggest that political connections help to circumvent regulatory actions and facilitate non-SOEs to engage in tunneling with low detection and punishment risk before the campaign. However, the anti-corruption campaign changes the whole landscape of the regulatory environment. The party curbs the collusion between the regulators and firms by severing their ties and encourages whistleblowing from the public. As a result, non-SOEs with political connections are more affected by the campaign. Based on the reasoning, we argue that the effect of anti-corruption campaign in reducing tunneling is more effective for non SOEs with political connections. Our third hypothesis is stated as follow:

H3: The effect of the anti-corruption campaign in reducing tunneling is more pronounced in non-

SOEs with political connections compared to those without political connections.

Auditors’ role is to verify the financial statements prepared by the company and express their opinions on the compliance of GAAPs. Larger auditors provide better audit quality and thus their audited financial statements are of better quality (e.g. DeAngelo 1981; Teoh and Wong 1993;

Lennox 1999; Francis et al. 1999; Francis and Wang 2008). In China, the audit market is rather segmented, with the international Big 4 auditors and a handful of local large auditors (or Big 88) auditing 52.68% of all listed companies at the end of our period 2014. Prior studies show that the

Big 8 auditors provide more effective audit services and firms audited by these auditors have less tunneling before the anti-corruption campaign (e.g. Gao and Kling 2008; Jiang et al. 2010). After

8 Big 8 auditors are the top 8 ones ranked by CICPA (The Chinese Institute of Certified Public Accountants) every year according to their business income, penalty and other indexes. The Big 8 auditors in 2014 are Price Waterhouse Coopers China, Deloitte China, Ruihua CPAs, BDO Shu Lun Pan CPAs, Ernst & Young, KPMG, Pan-China CPAs and Da Hua CAPs.

15 the campaign, all the auditors are exposed to increased risk associated with enhanced public enforcement and make more efforts (e.g. Geiger, Raghunandan, and Rama 2005; Ghosh and

Pawlewicz 2009). As the regulators in China are more likely to sanction auditors for failure to detect and report material misstatement frauds (Firth et al.2005), the auditors should take more efforts in detecting and reporting tunneling that is conductive to material misstatement frauds after the campaign. To the extent that tunneling is more prevalent in firms audited by non-Big 8 auditors before the campaign (e.g. Gao and Kling 2008; Jiang et al. 2010), their auditors are more sensitive to this type of misconduct after the campaign. As such, we expect that firms audited by non-Big 8 auditors experience a greater decrease in tunneling after the campaign. We thus hypothesize:

H4: The effect of the anti-corruption campaign in reducing tunneling is more pronounced in the non-Big 8 auditors’ clients compared to Big 8 auditors’ clients.

Prior studies show that large divergence between controlling right and ownership right (C/O ratio) provides the controlling shareholders strong incentive to expropriate the minority shareholders (e.g. Lemmon and Lins 2003; Claessens, et al. 2002). Consistent with prior studies,

Jiang et al. (2010) show a significantly positive relation between C/O ratio and tunneling based on a sample from China before the anti-corruption campaign. As we argued earlier, the anti- corruption enhances corporate governance and public enforcement, the costs of tunneling become larger after the campaign. As a result, one could expect that the net benefits of tunneling diminish, leading to weak incentive to commit tunneling. Hence, we expect that the effect of the

16 anti-corruption campaign in curbing tunneling is more pronounced in firms with high C/O compared to those without C/O. We thus hypothesize:

H5: The effect of the anti-corruption campaign in reducing tunneling is more pronounced in the firms with high C/O compared to those without C/O.

Our final hypothesis concerns the moderating role of marketization. In China, the institution development is largely uneven (Fan, Wang, and Zhu 2011). Consistent with the notion that the severity of tunneling varies with institutions (e.g. Nenova 2003; Dyck and Zingales 2004;

Lemmon and Lins 2003), Jiang et al. (2010) show that tunneling problems attenuate in firms located in provinces with more developed marketization. One reason is that self-dealing is easier and detection of such activities is difficult in low marketization areas. The slogan “cover all” in the anti-corruption campaign suggests that financial market regulators must take initiatives to investigate and detect all the misconducts regardless of locations. We thus argue that the scale of tunneling decreases more significantly in provinces with less developed marketization compared to those in better developed marketization provinces. As a result, we formally state our final hypothesis as follow:

H6: The effect of the anti-corruption campaign in reducing tunneling is more pronounced in the firms located in provinces with less developed marketization.

III SAMPLE AND RESEARCH DESIGN 3.1 Sample and Data Sources

Our initial sample consists of all publicly-traded A-share firms on Shanghai and Shenzhen stock exchanges from 2010 to 2014.We define year 2010 and 2011 (2013 and 2014) as pre- campaign (post-campaign) period and exclude the event year 2012 from our sample. We use the

China Stock Market and Accounting Research (CSMAR) database to obtain the firm ownership, financial and other related data. Based on the initial sample, we delete firms in finance industry, observations who have not been listed for 1 year yet and who have not at least one observation both in pre and post-anti-corruption period (before and after 2012). Collectively, our final sample contains 8,127 firm-year observations coming from 2,285 unique firms. Our sample firms represent 86.16% of all domestic listed firms at the end of our sample period 2014. To mitigate the effect of outliers, all continuous variables are winsorized at top and bottom 2.5%.

3.2 Regression Specification To examine the effect of the anti-corruption campaign on tunneling, we specify the following regression model:

푂푅퐸퐶푇퐴푖,푡 = 훼 + 훽 ∗ 푃표푠푡푖,푡 + 훾1 ∗ 퐵푙표푐푘푖,푡 + 훾2 ∗ 푀푎푟푘푒푡푖푧푎푡푖표푛푖,푡 + 훾3 ∗ 푅푂퐴푖,푡−1 + 훾4 ∗ 푆푖푧푒푖,푡 + 훾5 ∗ 푆푂퐸푖,푡 + 퐼푛푑푢푠푡푟푦 푑푢푚푚푖푒푠 + 푌푒푎푟 푑푢푚푚푖푒푠 + 휀푖,푡 (1)

where i indexes firms, t indexes years, and 휀푖,푡 is the error term.

We use the percentage of the net other receivables to total assets for firm i in year t

(ORECTAi,t) as main proxy for tunneling following prior literature (e.g., Jiang et al. 2010; Chen et al. 2016; among others). Our variable of interest is POST, a dummy variable which is equal to

1 if the observations fall in post-campaign period (2013 and 2014) and 0 otherwise.

We control for a set of variables that are found to affect tunneling in previous literature. We include BLOCK to control for the percentage of shares held by the largest shareholders which

18 might be negatively related to a firm’s tunneling (Jiang et al. 2010; Lemon and Lins 2003;

Claessens et al. 2002). The institution development in China is largely uneven and the severity of tunneling varies with institutions. Firms located in more developed regions tend to engage in less tunneling (Jiang et al. 2010; Wang and Xiao 2011). We thus include MARKETIZATION, measured as a comprehensive index about the development of the regional market constructed in

Fan, Wang and Zhu (2011) to control for the effect of local market development. We also control for firm performance (ROA in prior fiscal year) and firm size (SIZE), which are found to be related with tunneling in previous literature (Jiang et al. 2010; Tu and Yu 2015; Chen et al. 2016;

Haß et al. 2016). Specially, we define ROA as net profit divided by total assets, and SIZE as the natural logarithm of total assets. In China, most of the SOE managers are appointed by government, their motivations for political advancement and the severity of punishment for

SOEs’ executives caused by detected misconducts prevent them from tunneling. We thus include a dummy variable, SOE, which is equal to 1 if a firm is controlled by the government, and 0 otherwise and expect a significantly negative coefficient of SOE (e.g. Jiang et al. 2010). We finally include industry and year fixed effect dummies.

We expect to find a significantly negative coefficient of POST to support our main hypothesis that the anti-corruption campaign improves public governance and in turn reduces tunneling. To examine the other hypotheses, we use partitioning approaches. More specifically, we partition the whole sample into two sub-samples based on the moderating variables and examine the differential effect of anti-corruption campaign between these two groups.

IV EMPIRICAL RESULTS 4.1 Descriptive Statistics

Table 1 Panel A reports descriptive statistics of the variables and the univariate comparisons of these variables before and after the anti-corruption campaign. The mean (median) ORECTA for the full sample is 1.681% (0.83%) with a standard deviation of 2.239%. The mean ORECTA decreases from 1.772% before the anti-corruption campaign to 1.608% after the campaign. This decrease in the mean ORECTA is significant at the 1% level as shown in Panel B, which suggests that our sample firms undertake less tunneling activities since the launch of anti-corruption campaign. On average, the largest shareholders (BLOCK) own 35.7% of shares in our sample firms, suggesting the prevalence of concentrated ownership structure in Chinese listed companies.

The mean of the marketization index (MARKETIZATION) is about 9.09 with a standard deviation of 1.98, indicating the wide variations of marketization levels in China. The mean

(median) ROA is 0.039 (0.037) and the mean (median) SIZE is 21.915 (21.774). We show that the state-owned firms (SOE) account for 46.83% observations in our full sample.

Table 1 Panel C shows the descriptive statistics for ORECTA across industries.

Manufacturing (education) industry contributes the most (least) amount of observations to our sample. There is a large variance of the mean ORECTA across different industries. Firms in the construction industry report the highest level of ORECTA (3.621%) while firms in scientific and technical services industry report the lowest level of ORECTA (1.006%).

[Insert Table 1 here]

Table 2 reports the Pearson correlation coefficients among the key variables. The correlation between ORECTA and POST is significantly negative, providing the preliminary evidence that the anti-corruption campaign reduces the tunneling activities in China. ORECTA is also significantly and negatively correlated with BLOCK, ROA, and SIZE, indicating that firms with

20 higher largest shareholding, better performance, and/or larger size engage less in tunneling activities (e.g. Jiang et al. 2010). The correlation results also reveal that firms located in areas with more developed marketization (MARKETIZATION) and/or controlled by the government

(SOE) also tend to tunnel less.

[Insert Table 2 here]

4.2 The Effect of the Anti-Corruption Campaign on Tunneling (H1)

Table 3 reports the regression results on the mitigating effect of the anti-corruption campaign on tunneling. The coefficient of POST is negative and statistically significant at the 1% significance level. This mitigating effect of the anti-corruption campaign on tunneling is economically significant as well. Holding other variables constant, the level of tunneling is reduced by around 10% (-0.176/1.772*100%) in the post-campaign period compared with the pre-campaign period. These results are consistent with our first hypothesis (H1) that the anti- corruption campaign improves public governance and reduces the level of tunneling in China.

[Insert Table 3 here]

The results on the control variables are generally consistent with prior literature (e.g., Jiang et al. 2010; Chen et al. 2016). The coefficient on BLOCK is significantly negative, suggesting that firms with more shares owned by the largest shareholder engage less in tunneling. The coefficients on MARKETIZATION and SOE are negative and statistically significant, implying that firms located in more developed regions and/or controlled by the government undertake less tunneling. Both SIZE and ROA display significantly negative coefficients, suggesting that firms with higher profitability and/or larger size conduct less tunneling.

4.3 The Impact of the Nature of the Ultimate Controller (H2)

To test our second hypothesis (H2), we divide the sample into SOE and non-SOE subsamples, and re-estimate the Equ. (1) for the two groups, separately. Table 4 reports the results. The coefficients of POST are negative for both subsamples but statistically significant only for non-

SOE firms, suggesting that the mitigating effect of the anti-corruption on tunneling is more pronounced for non-SOE firms than for SOE firms. These results are supportive of our second hypothesis (H2) that non-SOEs have stronger incentive to tunnel and thus are affected more by the campaign compared to SOEs.

[Insert Table 4 here]

4.4 The Impact of Political Connections (H3)

To test the impact of political connection on the relation between the anti-corruption campaign and tunneling (H3), we further split non-SOE subsamples into two groups: non-SOEs with political connections versus non-SOEs without political connections. We then re-estimate

Equ. (1) for the two groups, respectively. The results reported in Table 5 are consistent with the hypothesis H3. The coefficients of POST are negative for both groups but statistically significant only for non-SOE firms with political connections, suggesting that non-SOE firms with political connections reduce their tunneling activities to a greater extent than their counterparts without political connections do. This finding is in line with the notion that the facilitating role of political connections in tunneling in pre-campaign period is significantly weakened by the anti- corruption campaign.

[Insert Table 5 here]

4.5 The Impact of Auditor (H4)

To test H4, we divide our full sample into firms audited by Big 8 auditors and firms audited by non-Big 8 auditors, and re-estimate the regression in Table 3 for the two groups, separately.

The results are reported in Table 6. The coefficients of POST are negative for both groups but statistically significant only for firms audited by non-Big 8 auditors, indicating that non-Big 8 auditors’ clients reduce their tunneling activities to a greater extent than their counterparts audited by Big 8 auditors do. These results support our hypothesis H4. Our findings are consistent with prior studies that non-Big 8 auditors are more tolerate with tunneling activities conducted by their clients than Big 8 auditors before anti-corruption campaign (e.g. Gao and

Kling 2008; Jiang et al. 2010), and thus are more vulnerable to enhanced public governance accompanied with the anti-corruption campaign.

[Insert Table 6 here]

4.6 The Impact of the Divergence between Control Rights and Ownership (H5)

To examine whether the mitigating effect of the anti-corruption campaign on tunneling is affected by the deviation of control rights from ownership rights (H5), we divide our full sample into two groups based on whether the controlling shareholder’s control rights are larger than her ownership rights, and re-estimate the regression in Table 3 for the two groups, separately. The results reported in Table 7 shows that the coefficients of POST are negative for both groups but statistically significant only for firms with a deviation of control rights from ownership rights, indicating that the mitigating effect of the anti-corruption campaign on tunneling is more pronounced in the firms with a high divergence between controlling rights and ownership rights

23 than in those without divergence between controller’s controlling right and ownership right as predicted by the hypothesis H5.

[Insert Table 7 here]

4.7 The Impact of Regional Market Development (H6)

To examine whether the reduction in tunneling due to the anti-corruption campaign is more significant for firms located in less developed regions than those in more developed regions (H6), we split the full sample into two groups: developed subsamples consisting of the observations from the regions with a higher than sample median marketization index, and undeveloped subsamples consisting of all the other observations. The regional marketization index is obtained from Fan et al. 2011. We then re-estimate the regression in Table 3 for the two groups, separately, and report the results in Table 8. The coefficients of POST are negative for both groups but statistically significant only for firms located in undeveloped regions, suggesting that firms located in undeveloped regions reduce their tunneling activities to a greater extent than their counterparts located in developed regions. These findings are supportive of our last hypothesis

(H6).

[Insert Table 8 here]

4.8 Alternative proxy for tunneling using related party transactions

In the above analyses, we employ the other receivables (ORECTA) to proxy for tunneling.

However, the funds taken away from listed firms by their controlling shareholders that are reflected in the other receivables are merely one of various mechanisms of tunneling. To confirm the robustness of our main results, we introduce an alternative proxy for tunneling. Prior

24 literature shows that the controlling shareholders could conduct related party transactions to divert resources from listed firms (e.g. Cheung et al. 2006; Jian and Wong 2010). Following

Cheung et al. (2006), we identify five types of related party transactions, including asset acquisitions, asset sales, equity sales, trading relationships involving the trade of goods and services, and cash payments happening between the listed company and the private company majority-controlled by a connected person, and then construct an alternative measure of tunneling, RPT, by calculating the total related party transaction amount divided by total assets.

Using RPT in place of ORECTA as dependent variable, we repeat the regression in Table 3 and report the results in Table 9. The coefficient of POST remains significantly negative though the level of significance is weakened, indicating that the tunneling through the aggregated related party transactions is substantially reduced by the anti-corruption campaign as well. This finding supports that our main results are robust to alternative measure of tunneling.

[Insert Table 9 here]

4.9 Additional tests

Our primary sample includes all A-share listed companies on Shanghai and Shenzhen

Exchanges, excluding companies in finance industry and observations listed not more than 1 year or having no at least one observation before and after 2012. However, a small proportion of our sample firms also listed their shares on the exchanges outside Mainland China like Hong Kong,

Singapore and U.S. Those cross-listing firms9 are subject to different regulatory environments and disclosure requirements, and are found to undertake a lower level of tunneling. The anti-

9 Cross-listing firms refer to these companies who list their shares on Hong Kong Exchange, New York Exchange, Singapore Exchange or other oversea stock markets while on china mainland exchanges (Shanghai Exchange or Shenzhen Exchange), there are 93 cross-listing firms in our sample which account for 4.07% of our firm observations.

25 corruption campaign would have different impact on those cross-listing firms. We thus exclude those cross-listing firms from our sample and repeat the regression in Table 3. The results reported in Table 10 shows that the coefficient of POST remains significantly negative, indicating that our results are robust to different sampling.

[Insert Table 10 here]

Our sample period spans two years before and after the launch of the anti-corruption campaign. We further examine whether the mitigating effect of the campaign on tunneling manifests in both years after the launch of the campaign. Specifically, we introduce two indicator variables, YEAR2013 and YEAR2014, for each year post the campaign and repeat the regression in Table 3 by substituting the two variables for POST. The results are reported in Table 11. Both

YEAR2013 and YEAR2014 are negative and statistically significant, suggesting that the role of the anti-corruption campaign in reducing tunneling applies to the whole post-campaign period.

[Insert Table 11 here]

In order to see whether our results keep robust in a longer window period, we re-estimate the regression in table3 using a 3-year-window, which starts from 2009 to 2015 and excludes the event year 2012. We still find a negative and statistically significant coefficient of POST at 1% significance level in the untabulated result.

V CONCLUSIONS

Tunneling is a prevalent manifestation of agency conflicts between the controlling and minority shareholders in most emerging economies and public governance is believed to play an

26 important role in constraining tunneling. This study takes advantage of an exogenous shock on public governance, i.e. the unprecedented anti-corruption campaign initiated in December 2012 by CPC, to identify the mitigating effect of improved public governance on tunneling in China.

We find that Chinese listed firms engage in less tunneling in the post-campaign period than in the pre-campaign period, and the reduction in tunneling in the post-campaign period is more pronounced among non-SOEs, particularly those non-SOEs with political connections, firms audited by non-Big 8 auditors, firms with a separation of control between ownership, and firms located in less developed regions. Our findings support that this unprecedented anti-corruption campaign materially improves public governance and in turn significantly reduces tunneling activities conducted by Chinese listed firms, especially those that were highly likely to tunnel in the pre-campaign period.

This study enhances our understanding of the economic consequences of China’s anti- corruption campaign in the tunneling context. Since tunneling is merely one of various misconducts by the controlling shareholders that undermine the interests of minority shareholders, it is likely that the expropriation of minority shareholders other than tunneling will also be mitigated by the improved public governance as a result of the anti-corruption campaign.

We leave this possibility to future research.

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Appendix I: Variable Definitions

Variable Definition Dependent variable Tunneling proxy, calculated as the percentage of the net other receivables scaled by the ORECTA total assets. Variable of interest Dummy variable, coded as 1 if the observations fall in post-campaign period (2013 and POST 2014), and 0 (2010 and 2011) otherwise. Control and grouping variables

Dummy variable, defined as 1 if a company hires a top 8 auditors listed in ranking list BIG8 published by CICPA every year, and 0 otherwise. Block shareholders’ ownership, calculated as the percentage of shares held by the BLOCK largest shareholder to total shares outstanding. LEVERAGE Leverage, measured as the ratio of total debt over total assets. Marketization index, a comprehensive index measuring the development of the MARKETIZATION regional market in which the company is registered (Fan, Wang and Zhu, 2011). The market-to-book value of the firm, defined as the total market value of a firm’s MBRATIO equity divided by total assets. Dummy variable defined as 1 if the company is politically connected, whose CEO or chairman used to work for or are currently working for government at the level of local PC or central government, 0 otherwise. (Fan et al. 2007; Chen et al. 2011; Francis et al. 2009) Return on assets, calculated as net income scaled by total assets in the previous fiscal ROA year Related party transaction, following Cheung et al. (2006), we identify five types of related party transactions, asset acquisitions, asset sales, equity sales, trading relationships, which involve the trade of goods and services, and cash payments, RPT happened between the listed company and the private company majority-controlled by a connected person. We calculate the total related party transaction amount divided by total assets as variable RPT. SIZE Firm size, measured as the natural logarithm of total assets. Dummy variable which takes 1 if the ultimate controller is any government-owned SOE institutions and 0 otherwise. YEAR2013 Dummy variable defined as 1 if the year is 2013, 0 otherwise. YEAR2014 Dummy variable defined as 1 if the year is 2014, 0 otherwise.

Table 1 Descriptive Statistics of Sample Firms

POST N Mean Median Std. Dev. Q1 Q3 Panel A: Key variables

ORECTA 0 3,615 1.7719 0.8647 2.3597 0.3558 2.0091

1 4,512 1.6080 0.8066 2.1356 0.3536 1.8451

Total 8,127 1.6809 0.8303 2.2394 0.3545 1.9185

BLOCK 0 3,615 35.9098 33.7704 15.2340 23.4297 47.4950

1 4,512 35.5306 33.6724 14.9539 23.4216 46.0644

Total 8,127 35.6993 33.7474 15.0794 23.4297 46.7559

MARKETIZATION 0 3,615 9.0039 9.0200 1.9940 7.5600 10.4200

1 4,512 9.1513 9.0200 1.9665 7.6500 10.9600

Total 8,127 9.0857 9.0200 1.9800 7.5600 10.9600

ROA 0 3,615 0.0425 0.0409 0.0513 0.0165 0.0680

1 4,512 0.0369 0.0325 0.0480 0.0118 0.0622

Total 8,127 0.0394 0.0367 0.0496 0.0137 0.0650

SIZE 0 3,615 21.7815 21.6309 1.2503 20.8916 22.5482

1 4,512 22.0224 21.8697 1.2312 21.1474 22.7724

Total 8,127 21.9153 21.7735 1.2454 21.0113 22.6735

SOE 0 3,615 0.5234 1.0000 0.4995 0.0000 1.0000

1 4,512 0.4242 0.0000 0.4943 0.0000 1.0000

Total 8,127 0.4683 0.0000 0.4990 0.0000 1.0000

Panel B: Mean value of ORECTA: before and after the anti-corruption campaign

ORECTA 0 1.7719

1 1.6080

Diff. 3.2817***

Panel C: ORECTA by industry

Agriculture, Forestry 131 2.2654 0.9972 2.6734 0.4388 3.2063 and Fishing

Mining 260 1.7558 0.7676 2.5285 0.3243 1.9241

Table 1 Continued

Manufacturing 4,969 1.3437 0.7046 1.8517 0.3245 1.5207

Electricity, Steam, Gas 328 1.2160 0.4744 1.9737 0.1941 1.2613 and Water Supply

Construction 228 3.6205 2.7681 2.9706 1.3043 5.5015

Wholesale and Retail 529 2.6809 1.6579 2.7550 0.6865 3.6929

Transportation, Storage 292 1.5732 0.7886 2.3953 0.2027 1.5690 and Post

Accommodation and 44 2.0572 0.7799 2.7265 0.4512 2.3386 Catering

Information and 369 2.1868 1.3872 2.4005 0.6603 2.5533 Communication

Real Estate Activities 527 2.3286 1.2658 2.8071 0.3804 2.9344

Leasing and Commercial 100 2.8006 1.8181 2.7386 1.0226 3.7666 Services

Scientific and Technical 37 1.0061 0.5985 0.8862 0.3968 1.3839 Services

Irrigation, Environment and Public 86 1.8872 1.0558 2.2873 0.4852 2.2554 Administration

Education 4 1.6430 1.1532 1.1889 0.9014 2.3847

Health and Social Work 14 1.2048 0.6058 1.1770 0.5589 1.2430 Activities

Culture, Sports and 109 2.2811 1.1810 2.8522 0.5040 2.3654 Entertainment

Comprehension 100 2.5226 1.1603 2.8813 0.5584 3.7595

This table presents descriptive statistics for key variables in our sample. In Panel A, we report descriptive statistics for key variables in both pre- and post-campaign periods. In panel B, we present the difference of tunneling proxy (ORECTA) before and after the campaign. In Panel C, we report descriptive statistics for ORECTA by industry, according to CSRC (China Securities Regulatory Commission) Industrial Classification Guidelines (2012). All the variables are defined in Appendix 1. ***, ** and * signify statistical significance at the 1%, 5% and 10% levels, respectively.

Table 2 Correlation Matrix

ORECTA POST BLOCK MARKETIZATION ROA SIZE

POST -0.036***

BLOCK -0.126*** -0.012

MARKETIZATION -0.064*** 0.037*** 0.039***

ROA -0.141*** -0.057*** 0.099*** 0.115***

SIZE -0.062*** 0.096*** 0.314*** -0.011 0.055***

SOE -0.028** -0.099*** 0.213*** -0.196*** -0.117*** 0.363***

This table presents the Pearson’s correlation coefficients of key variables in our multivariate analysis. ***, ** and * signify statistical significance at the 1%, 5% and 10% levels, respectively. All the variables are defined in Appendix 1.

Table 3 The Effect of the Anti-Corruption Campaign on Tunneling

Dep. Var.=ORECTA Coef. t-value INTERCEPT 5.1232*** 5.92 POST -0.1755*** -2.80 BLOCK -0.0147*** -6.23 MARKETIZATION -0.0749*** -3.38 ROA -5.4981*** -8.13 SIZE -0.0640* -1.77 SOE -0.1984** -2.23

Industry fixed effect Yes

Year fixed effect Yes

Obs 8,127

Adj-R2 0.1058

This table report the regression result of the effect of the anti-corruption campaign on tunneling (ORECTA). We control for industry and year fixed effects in the regression. We calculate t -statistics based on clustering standard errors at the firm level. All the variables are defined in Appendix 1. ***, ** and * signify statistical significance at the 1%, 5% and 10% levels, respectively.

Table 4 The Differential Effect of the Anti-Corruption Campaign on Tunneling in SOEs and Non-SOEs

(1) (2) Dep. Var.=ORECTA SOEs Non-SOEs Coef. t-value Coef. t-value

INTERCEPT 5.8087*** 5.07 4.5623*** 3.50

POST -0.0518 -0.67 -0.3082*** -3.02

BLOCK -0.0145*** -4.08 -0.0134*** -4.28

MARKETIZATION 0.0026 0.08 -0.1272*** -4.24

ROA -4.1881*** -4.32 -6.3840*** -6.96

SIZE -0.0740* -1.65 -0.0622 -1.06

Industry fixed effect Yes Yes

Year fixed effect Yes Yes Obs 3,806 4,321

Adj-R2 0.1294 0.1234

The table presents the results of the effect of anti-corruption campaign on tunneling in SOEs and Non-SOEs. The dependent variable is tunneling proxy (ORECTA). We control for industry and year fixed effects in the regression. We calculate t -statistics based on clustering standard errors at the firm level. All the variables are defined in Appendix 1. ***, ** and * signify statistical significance at the 1%, 5% and 10% levels, respectively.

Table 5 Effect of Anti-Corruption on Tunneling in non-SOEs with and without Political Connections

(1) (2) Dep. Var. Non-SOEs with Political Non-SOEs without Political =ORECTA connections connections Coef. t-value Coef. t-value

INTERCEPT 1.5254 0.95 6.7711*** 3.58

POST -0.5671*** -3.82 -0.0925 -0.66

BLOCK -0.0048 -1.19 -0.0208*** -4.57

MARKETIZATION -0.0942*** -2.73 -0.1444*** -3.18

ROA -7.9911*** -6.11 -5.1094*** -4.12

SIZE 0.0772 1.07 -0.1828** -2.07

Industry fixed effect Yes Yes

Year fixed effect Yes Yes Obs 1,965 2,355

Adj-R2 0.1555 0.1239

This table presents the results of the effect of anti-corruption campaign on tunneling in non-SOEs with political connections and non-SOEs without political connections. The dependent variable is tunneling proxy (ORECTA). We control for industry and year fixed effects in the regression. We calculate t -statistics based on clustering standard errors at the firm level. All the variables are defined in Appendix 1. ***, ** and * signify statistical significance at the 1%, 5% and 10% levels, respectively.

Table 6 The Effect of the Anti-Corruption Campaign on Tunneling in Firms Having Big 8 and Non-Big 8 Auditors

Dep. Var. (1) (2)

=ORECTA Big8 Clients Non-Big8 Clients Coef. t-value Coef. t-value INTERCEPT 3.1119** 2.58 7.1022*** 6.08 POST -0.0406 -0.39 -0.2059** -2.42 BLOCK -.0113*** -3.60 -0.0173*** -5.53 MARKETIZATION -0.0207 -0.74 -0.1210*** -3.99 ROA -4.8687*** -5.15 -5.7157*** -6.57 SIZE 0.0306 0.71 -0.1528*** -2.98 SOE -0.1006 -0.82 -0.2508** -2.28 Industry fixed effect Yes Yes Year fixed effects Yes Yes Obs 3,498 4,629 Adj-R2 0.1030 0.1221

This table presents the results of the effect of anti-corruption campaign on tunneling in firms hiring Big 8 auditors and those hiring non-Big 8 auditors. The dependent variable is tunneling proxy (ORECTA). We control for industry and year fixed effects in the regression. We calculate t -statistics based on clustering standard errors at the firm level. All the variables are defined in Appendix 1. ***, ** and * signify statistical significance at the 1%, 5% and 10% levels, respectively.

Table 7 The Effect of Anti-Corruption Campaign on Tunneling in Firms with and without Divergence between Control Rights and Cash Flow Rights

Dep. Var.= (1) (2) C/O=0 C/O>0 ORECTA Coef. t-value Coef. t-value INTERCEPT 3.5856*** 2.87 6.5438*** 5.87 POST -0.0511 -0.56 -0.2398*** -2.69 BLOCK -0.0126*** -3.88 -0.0168*** -5.28 MARKETIZATION -0.0716** -2.22 -0.0731** -2.57 ROA -4.8852*** -4.85 -5.7037*** -6.31 SIZE -0.0002 0.00 -0.1328*** -2.70 SOE 0.0256 0.19 -0.3343*** -2.84 Industry fixed effect Yes Yes Year fixed effect Yes Yes Obs 3,863 4,264 Adj-R2 0.1214 0.1101

The table presents the regression results of the effect of anti-corruption on tunneling in firms with and without divergence between the controlling shareholders’ controll rights and their ownership rights (C/O). Group (1) consists of firms whose controller has no divergence between their controlling right and ownership right, C/O is 0 while group (2) includes firms whose controller has larger controlling right than their ownership right, C/O is greater than 0. The dependent variable is tunneling proxy (ORECTA). We control for industry and year fixed effects in the regression. We calculate t -statistics based on clustering standard errors at the firm level. All the variables are defined in Appendix 1. ***, ** and * signify statistical significance at the 1%, 5% and 10% levels, respectively.

Table 8 The Effect of Anti-Corruption Campaign on Tunneling in Firms Located in High and Low Marketization Areas

Dep. Var.= (1) (2) ORECTA Developed Areas Undeveloped Areas Coef. t-value Coef. t-value INTERCEPT 5.8134*** 2.65 5.9152*** 5.28 POST -0.0846 -0.98 -0.2461*** -2.73 BLOCK -0.0138*** -3.98 -0.0155*** -4.92 MARKETIZATION 0.0084 0.11 -0.1914*** -4.04 ROA -6.4128*** -6.21 -4.8724*** -5.67 SIZE -0.0577 -1.08 -0.0689 -1.41 SOE 0.0963 0.72 -0.4457*** -3.90 Industry fixed effect Yes Yes Year fixed effect Yes Yes Obs 3,947 4,180 Adj-R2 0.1033 0.1332

This table presents the results of the effect of anti-corruption campaign on tunneling in firms located in developed and under-developed areas (MARKETIZATION). The dependent variable is tunneling proxy (ORECTA). We control for industry and year fixed effects in the regression. We calculate t -statistics based on clustering standard errors at the firm level. All the variables are defined in Appendix 1. ***, ** and * signify statistical significance at the 1%, 5% and 10% levels, respectively.

Table 9 The Effect of the Anti-Corruption Campaign on Tunneling: Using Related Party Transaction as Tunneling Measure

Dep. Var.=RPT Coef. t-value INTERCEPT -0.1787* -1.79 POST -0.0095* -1.72 BIG8 0.0033 0.47 LEVERAGE 0.1509*** 6.49 MBRATIO 0.0063* 1.82 ROA -0.1271 -1.36 SIZE 0.0088* 1.91 Industry fixed effect Yes Year fixed effect Yes Obs 6,030 Adj-R2 0.0566

This table presents the effect of anti-corruption campaign on tunneling using related party transactions (RPT) as tunneling measure following Cheung et al. (2006). We control for industry and year fixed effects in the regression. We calculate t -statistics based on clustering standard errors at the firm level. All the variables are defined in Appendix 1. ***, ** and * signify statistical significance at the 1%, 5% and 10% levels, respectively.

Table 10 The Effect of Anti-Corruption Campaign on Tunneling in a Reduced Sample Excluding Cross- listing Firms

Dep. Var.=ORECTA Coef. t-value INTERCEPT 5.4887*** 6.05

POST -0.1830*** -2.82

BLOCK -0.0151*** -6.37

MARKETIZATION -0.0779*** -3.48

ROA -5.5431*** -8.04

SIZE -0.0785** -2.05

SOE -0.2190** -2.44

Industry fixed effect Yes

Year fixed effect Yes

Obs 7,879

Adj-R2 0.1058

This table presents the results of the effect of anti-corruption campaign on tunneling in sample firms excluding the cross-listing firms. The dependent variable is tunneling proxy (ORECTA). We control for industry and year fixed effects in the regression. We calculate t -statistics based on clustering standard errors at the firm level. All the variables are defined in Appendix 1. ***, ** and * signify statistical significance at the 1%, 5% and 10% levels, respectively.

Table 11 Time Trend of the Effect of the Anti-Corruption Campaign on Tunneling

Dep. Var.=ORECTA Coef. t-value INTERCEPT 5.1232*** 5.92 YEAR2013 -0.2770*** -4.90 YEAR2014 -0.1755*** -2.80 BLOCK -0.0147*** -6.23 MARKETIZATION -0.0749*** -3.38 ROA -5.4981*** -8.13 SIZE -0.0640* -1.77 SOE -0.1984** -2.23

Industry fixed effect Yes Year fixed effect Yes Obs 8,127 Adj-R2 0.1058

This table reports the time trend of anti-corruption campaign’s effect on tunneling. We regress ORECTA on two year dummies after the campaign: YEAR2013, a dummy variable which takes 1 if the year is 2013 and 0 otherwise; YEAR2014, dummy variable defined as 1 if year is 2014 and 0 otherwise. We control for industry and year fixed effects in the regression. We calculate t -statistics based on clustering standard errors at the firm level. All the variables are defined in Appendix 1. ***, ** and * signify statistical significance at the 1%, 5% and 10% levels, respectively.