Tunneling Proceeds from Seasoned Equity Offering: The China Experience

Tunneling Proceeds from Seasoned Equity Offering: The China Experience E. Han Kim, Heuijung Kim, Yuan Li, Yao Lu, and Xinzheng Shi Abstract Investors are nervous about participating in Chinese securities offerings because of their opaqueness. We identify another reason they should worry controlling shareholders may tunnel the proceeds. Using regulatory changes on SEOs as exogenous shocks, we find cash outflows through related party transactions (CO_RPT) substantially increase following SEOs. We attribute the increases to tunneling; when CO_RPT increases, investments do not increase, outstanding debt does not decrease, and cash holdings increase less. Investors react negatively when they become aware of CO_RPT, and react more negatively at the time of an SEO announcement when it is later followed by greater increase in CO_RPT. In addition, balance sheet items show greater traces of tunneling following SEOs. Business group firms are more subject to tunneling, while firms audited by Big-4 international accounting firms and SEOs with a specific verifiable stated purpose are less subject to tunneling. These findings imply that the availability of SEO proceeds at managerial discretion increases agency problems manifested in China in a particularly brazen form tunneling. First and Preliminary Draft: November 25, 2015 Keywords: Equity Issuance, Tunneling, Related Party Transactions, Corporate Investments, Cash Holdings. JEL Classifications: G32 G34 E. Han Kim is Everett E. Berg Professor of Finance at the University of Michigan, Ross School of Business, Ann Arbor, Michigan 48109: ehkim@umich.edu. Heuijung Kim is an instructor at Sungkyunkwan University, SKK Business School, Seoul, Korea: heuikim@skku.edu. Yuan Li is a doctoral student at University of Southern California: yuan.li.2019@marshall.use.edu. Yao Lu is Associate Professor of Finance at Tsinghua University School of Economics and Management, Beijing, China: luyao@sem.tsinghua.edu.cn. Xinzheng Shi is Associate Professor of Economics at Tsinghua University School of Economics and Management, Beijing, China: shixzh@sem.tsinghua.edu. We are grateful for helpful comments and suggestions by Guodong Chen, Jun-Koo Kang, Vic Khanna, Hongbin Li, Chen Lin, Gordon Philips, Amiyatosh Purnanandam, Martin Schmalz, and finance seminar participants at Nanyang Technological University, SMU, and University of Michigan, This project received generous financial support from Mitsui Life Financial Research Center at the University of Michigan. Yao Lu acknowledges support from Project 71202020 of National Natural Science Foundation of China. 1

1. INTRODUCTION Media reports abound of investors concern about Chinese equity offerings because of opaqueness on growth data and financial results. 1 There is another concern what the Chinese firms will do with the proceeds? On September 19, 2014, Alibaba made an equity offering in America. An article in Financial Times reports, Mr. Ma (the founder-ceo) says his priorities are customers first, employees second, and shareholders third. What will this mean in practice? 2 It may mean a sound strategic vision that could lead to practices good for the long-run. But on the day of the offering an article on the same newspaper appearing points out, backing Mr. Ma and his team brings with it a hefty degree of governance risk. The split-off of payment arm Alipay in 2011 was a graphic reminder of the risks. 3 Implied in the risks is tunneling, which Johnson et al. (2000) define transfer of assets and profits out of firms for the benefit of those who control them. We doubt tunneling is the main purpose of Chinese equity offerings. However, it could be a byproduct having a new infusion of cash at the discretion of those in control. According to Jensen s (1986) free cash flow hypothesis, agency problems increase when more funds become available at managers discretion. In comparison to other externally raised capital such as bank loans and bonds, managers have more discretion on how equity capital is deployed. Jung, Kim, and Stulz (1996) argue SEOs by low growth firms are associated with value-destroying use of funds. Kim and Purnanandam (2014) argue, with supporting evidence that the misuse of newly raised equity capital is due to weak corporate governance. If a country has sufficiently weak corporate governance systems, some proceeds from equity offerings could be subject to tunneling. 1 For example, a Wall Street Journal article reports, there are plenty of reasons American investors aren t loving Chinese IPOs. There has been skepticism over the veracity of growth data and financial results. Lynn Cowan, Taomee gets set for debut. (June 6, 2011: C2) 2 Lucy Colback and Robert Armstrong, Lex in depth: Alibaba Financial Times, London Edition (September 10, 2014) P.11. 3 Richard Waters, Alibaba investors have to reckon with the Jack Ma factor, Financial Times, USA Edition (September 19, 2014) P.16. 2

China has weak corporate governance and legal systems to protect investors (Aharony, Lee and Wong, 2000; Allen, Qian and Qian, 2005; Fan, Wong and Zhang, 2007; Jiang, Lee and Yue, 2010; Berkowitz, Lin and Ma, 2015) allowing more latitude for managers and controlling shareholders to derive private benefits of control. Jiang et al. (2010) argue Chinese corporate governance structure is highly conducive for tunneling and provide evidence of tunneling through intercorporate loans. Aharony, Lee, and Yuan (2010) also provide similar evidence. 4 Thus, the possibility of tunneling proceeds from Chinese equity offerings could be of real concern. In this paper we investigate whether tunneling increases following SEOs in China. We also examine how investors react when they become aware of (our proxies for) tunneling transactions; whether at the time of SEO announcements the market anticipates which SEO proceeds will be tunneled more ex-post; which firm and SEO characteristics are helpful in predicting the likelihood of tunneling; and how SEOs affect top executives official pay and perks. These issues apply to any equity offerings. We choose SEOs because China made two regulatory changes on the eligibility to issue SEOs, which allow us to construct an instrument to study causal effects of SEOs on tunneling and how SEO proceeds are deployed. 5 In China, SEOs also represent a more important source of external capital than in the US. Over the period 2010 through 2012, for example, the ratio of capital raised through SEOs by non-financial Chinese firms to their market capitalization was about 2.18%, more than three times the ratio for US counterparts, which was only about 0.6%. (Source: http://data.worldbank.org and SDC) 4 According to Johnson et al. (2000), tunneling happens everywhere, in both developed and emerging economies. In the governance literature, most of the evidence of tunneling is focused on emerging markets. See Bertrand, Mehta, and Mullainathan (2002) on Indian business groups; Bae, Kang, and Kim (2002), Joh (2003), and Baek, Kang, and Lee (2006) on Korean firms; Cheung, Rau, and Stouraitis (2006) on Hong Kong firms; and Atanasov et al. (2010) on Bulgarian firms. For alternative interpretations of group firms behavior, see Gopalan, Nanda and Seru (2007); Siegel and Choudhury (2012); and Buchuk et al. (2014). For other related articles on emerging markets, see Morck, Wolfenzon, and Yeung (2005); Lemmon and Lins (2003); Bertrand et al. (2008); and Lin et al. (2011). 5 One cannot simply relate SEOs to tunneling to study causal effects because the decision to issue SEOs is endogenous, i.e., it is associated with a number of firm level factors such as internal funds, leverage, the market-to-book ratio, stock price, stages in firm lifecycle, and firm size, as well as other unaccounted time-varying factors. See Jung et al. (1996); Hovakimian, Opler, and Titman (2001); Baker and Wurgler (2002); Kim and Weisbach (2008); and DeAngelo, DeAngelo, and Stulz (2010). 3

We find significant and large increases in tunneling during the year of SEO and the year after. Consistent with previous studies on tunneling, firms belonging to a business group are more likely to engage in tunneling SEO proceeds than standalone firms. Our primary measure of tunneling is cash outflows through related party transactions (CO_RPT). Previous studies suggest related party transactions (RPTs) are popular means of tunneling (e.g., Bae et al., 2002; Joh, 2003; Baek et al., 2006; Cheung et al., 2006, 2010; Jiang et al., 2010). Firms tend to increase investments following SEOs (Kim and Weisbach, 2008); hence, some CO_RPT may represent payments for purchases of goods and services from related parties. If the RPTs are conducted at fair price, the cash outflows do not constitute tunneling. But we find when SEOs are followed by increases in CO_RPT, capital expenditures do not increase. Only when firms do not increase CO_RPT following SEOs do capital expenditures significantly increase following SEOs. We find a similar pattern for changes in cash holdings: Increases in cash holdings are smaller and less significant when CO_RPT increases. If increases in CO_RPT are a result of nontunneling business activities, it should have no negative impact on cash holdings. For example, if CO_RPT occurs to buy raw materials and services from related parties at fair prices to generate sales, the incremental sales revenue should more than offset the cash payment. And there is no evidence that SEO proceeds are used to pay down outstanding debt. Moreover, investors react significantly negative to reports containing RPTs. Three day announcement returns around annual and semi-annual reports (similar to 10-K reports in the U.S.) containing RPTs is -0.87%. This is in spite of the reports containing information on numerous other matters (e.g., earnings, business operations, and future prospects). The average market reaction to the announcement of SEOs is comparable to that in the U.S. The mean announcement return over a three-day window is significant -0.73% with a median of -1.18%. Chinese SEOs are primary shares with no secondary offerings. Kim and Purnanandam (2014) report an average investor reaction of -1.04% (with a median of -0.81%) to pure primary offerings in the US over the period 1994-2003. 4

The market seems to anticipate at the time of SEO announcements which SEOs are more likely to be used for CO_RPT. The market reaction to the announcement of SEOs is more negative, the greater the increase in CO_RPT following SEOs. 6 The anticipation is subject to uncertainty, enabling SEOs to succeed. If the market can anticipate tunneling with no uncertainty, the SEOs will fail and will not be in our sample. 7 Increases in CO_RPT following SEOs are concentrated among transactions between sibling firms belonging to a business group and among goods- and capital- related transactions (e.g., purchases of goods and payment for related parties employees, and inter-corporate loans and equity-related transactions). One may argue increases in these CO_RPT are strategic outcomes of co-insuring firms within a business group (Fisman and Wang, 2010) or manifestation of internal capital market (Gopalan et al. 2007; Buchuk et al., 2014). According to the coinsurance hypothesis, business groups have a listed firm most appealing to investors raise funds to help other firms in the group. Although such co-insuring may be good for the group s controlling shareholder, siphoning off funds raised through SEOs will hurt the SEO firm s shareholders with no stake in other firms. Even shareholders with stakes in other firms are likely to be hurt if they are non-controlling shareholders. Controlling shareholders tend to tunnel money from firms with less cashflow rights to co-insure firms with more cashflow rights (Bertrand et al., 2002). If controlling shareholders have relatively small cash flow rights of the SEO firm, the coinsurance premium the SEO firm has to pay to insure against future financial troubles is likely to be too high. Some tunneling activities escape the legal definition of RPT. Lending company money interest free to someone with a legally undefined relationship will not be classified as an RPT in 6 This finding is in contrast to Cheung et al. (2006) who find that the stock market in Hong Kong does not ex ante discount share price more for firms expropriating minority shareholders and it revalues firms only when expropriation actually occurs, i.e., investors are unable to predict expropriation. The difference could be due to difference in sample and empirical design. 7 Our sample includes only completed SEOs, which by definition survived initial screenings by regulators and investors. SEOs are unlikely to be approved by regulators or attract investors if they believe with certainty the proceeds will be tunneled. 5

our database. A CEO may borrow company money interest free without reporting it as an RPT. These are tunneling, but will not be recorded as CO_RPT. However, money has to change hands, and the funds siphoned off have to be debited to other accounts in the balance sheet. Our private conversations with informed sources suggest that the missing funds usually end up as debits to account receivables or prepaid expenses. If they are debited to accounts receivable, the accounts receivable are soon classified as unlikely to be collected. We find account receivables, prepaid expenses, and the fraction of account receivables unlikely to be collected all increase significantly following SEOs. Particularly revealing, the percentage of unlikely to be collected accounts receivables increases more than twice the increase in accounts receivable. These findings buttress our evidence of tunneling based on CO_RPT. Not all SEO firms engage in tunneling. Many use the proceeds for good purposes. Which firm and SEO characteristics are helpful in picking SEOs with lower likelihoods of tunneling? Firms audited by a Big-4 international accounting firm show little evidence of tunneling. 8 We cannot tell whether it is causal or selection (i.e., more effective audits preventing tunneling or firms with stronger governance choosing Big-4 accounting firms as auditors). 9 However, so far as investors are concerned, the correlation is useful in deciding whether to participate in an SEO. SEO proceeds are also safe from tunneling when they have a verifiable stated purpose such as acquisition of a specific firm or tangible assets, which reduces controlling shareholders discretions over the use of SEO proceeds. 10 In spite of the robust evidence of tunneling, we find no significant increase in top executives pay following an SEO. We attribute this to the communist ideology imposing an upper limit on top executives official pay. Instead, we find managerial perks, as measured by the 8 This is consistent with Mitton (2002) and Fan and Wong (2005) who find that firms in emerging markets with Big-6 and Big-5 auditors, respectively, are associated with stronger governance, and with Pittman and Fortin (2004) who find that US firms with Big-6 auditors enjoy lower borrowing costs. 9 Guedhami, Pittman and Saffar ( 2014) find that politically connected firms are more likely to choose Big-4 auditors, presumably because insiders want to convince outside investors that they will refrain from tunneling by exploiting their connections. 10 Consistent with this finding, Walker and Yost s (2008) find, using US data, that market responses are more favorable when firms provide specific plans for the use of SEO proceeds. 6

ratio of administrative expenses to sales, increase by 19%, implying 19% reduction in operating profits! In China, the agency problem associated with free cash flows manifests as tunneling and perks, but not as higher official pay. This paper contributes to the literature in several ways. Much of the SEO literature focuses on explaining negative investor reaction to SEO announcements 11 or use the announcement returns to infer misuse of SEO proceeds (e.g.,, Jung, et al., 1996, Walker and Yost, 2008; Kim and Purnanandam, 2014). The exceptions include DeAngelo et al. (2010) and McLean (2011), who relate SEOs to cash holdings, and Kim and Weisbach (2008), who relate SEOs to investments. We add to these studies by providing evidence on the causal relation between SEOs and possible deployment of the proceeds, which includes tunneling, investments, higher cash holdings, and paying down outstanding debt. Our findings provide direct evidence on how SEOs exacerbate the severity of agency problems when corporate governance is weak, as manifested in China in a particularly brazen form tunneling. The evidence is based on exogenous shocks, allowing us to draw causal inferences Our study also adds to the literature on tunneling (Johnson et al, 2000; Bae et al., 2002; Bertrand et al. 2002; Baek et al., 2006; Urzúa I., 2009; Atanasov et al., 2010; Jiang et al., 2010; Siegel and Choudhury, 2012; Buchuk et al., 2014, and Piotroski and Zhang, 2014). While most previous studies rely on the verification of implications of tunneling as evidence, we present more direct evidence of tunneling by focusing on specific channels through which managers and controlling shareholders can help themselves to the SEO proceeds.. Finally, SEOs have been a major source of external financing for Chinese firms. How the financing source in the second largest stock market affects tunneling should be of interest on its own right. What happens in the Chinese market, such as the 43% drop in Shanghai Stock Exchange Composite Index from June 12 to August 26, 2015, often spills over to other parts of 11 The explanations include information asymmetry, adverse selection, and market timing. See Leland and Pyle (1977); Myers and Majluf (1984); Choe, Masulis, and Nanda (1993); Baker and Wurgler (2000); and Butler, Grullon, and Weston (2005). 7

the world. Our findings highlight the dire need for more effective governance and securities regulation for more productive use of capital in the world s second largest economy. The next section provides general backgrounds and regulatory changes on Chinese SEOs. Section 3 describes our empirical strategy and data. Section 4 presents our main findings on tunneling. Section 5 examines cross-sectional differences; Section 6 provides further evidence of tunneling and managerial perks; Section 7 conducts robustness tests; and Section 8 concludes. 2. SEASONED EQUITY OFFERINGS IN CHINA 2.1 General Background on Chinese Financial Markets and SEOs The Chinese stock market is well-suited to study causal effects of SEOs on tunneling. Most Chinese listed firms have a dominant/controlling shareholder (Jiang et al., 2010). Combined with a relatively weak corporate governance system (Allen et al., 2005; Aharony et al., 2000), their presence makes tunneling SEO proceeds more plausible than in other countries with a stronger governance system. The legal system also suffers from weak public enforcement of securities laws due to the limited authority of securities market regulators and few options available to minority shareholders to take private enforcement actions against misconduct. Since the opening of the Shanghai and Shenzhen Stock Exchanges in 1990 and 1991, equity markets have been more important source of external financing than bond markets, as corporate bond markets have been growing at a much slower pace. 12 Over the period 2010 through 2012, for example, Chinese listed firms raised 2,147.5 billion RMB through equity markets (including IPOs), while bond markets helped raise only 429.5 billion RMB. Over the same period, non-financial Chinese firms issued SEOs more than three 12 A regulated bond market for enterprises began in 1996; however, the strict approval process required for issuing bonds has led to a situation where only very large and stable companies can issue bonds. 8

times those issued by US counterparts, adjusted for difference in stock market capitalization. 13 The types of SEOs available and the underwriting procedures in China are similar to those in the US. There are three types of SEO: rights offerings, underwritten offerings, and private placement in which new shares can be purchased by no more than ten qualified and investors. We include only rights and underwritten offerings, excluding private offerings because the regulatory shocks used to construct our instrument apply only to public offerings. As in the US, two types of underwriting contracts, best efforts and firm commitment, are practiced in China. One major difference from US SEOs is that virtually all Chinese SEOs are primary shares, 14 which provide a cleaner sample to study agency/governance problems associated with SEO proceeds. US SEOs often include secondary offerings, sale of shares held by insiders and block holders. Proceeds of secondary offerings do not go to the firm and hence cannot be subject to misuse by the management. Absence of such offerings helps minimize confounding effects associated with negative signals transmitted from better informed insiders and block holders (Leland and Pyle, 1977). 2.2 Regulatory Changes on the Eligibility to Issue SEOs China has experienced two regulatory regime changes on the eligibility to issue SEOs. These changes allow us to construct an instrument to address endogeneity issues associated with the decision to issue SEOs. Prior to 2006, a listed firm could issue equities as long as it issued a 13 Over the period 2010 through 2012, the average total Chinese stock market capitalization is 3,949.77 billion USD and non-financial Chinese listed firms raised 86.09 billion USD through SEOs, 2.18% of total market cap. This is more than three times the ratio for US counterparts. During the same period, the average market capitalization of US stock market is 17,149.34 billion USD and non-financial US listed firms raised 102.75 billion USD through SEOs, 0.6% of total market cap. Total stock market capitalization excludes financial firms. Capital raised through SEOs is taken from SDC Platinum. The market capitalization data are taken from data on the World Bank website (http://data.worldbank.org/). Capital raised through SEOs only includes proceeds from primary offerings. 14 There were three mixed offerings containing secondary offerings of state-owned shares during June 2001 and October 2001 when China Securities Regulatory Commission (CSRC) required that if a firm plans to issue N new shares through an underwritten offering and the firm has state-owned shares (which were non-tradable at the time), then the offering must contain 10% of N state-owned shares. This means the firm will issue 1.1N shares in total, with 0.1N state-owned shares. The regulation was effective for only four months, and only three SEO cases were completed during that time. 9

dividend in the past three years. On May 6, 2006, China's Securities Regulatory Commission (CSRC, equivalent to the US SEC) issued Decree No.30, which requires that if a firm wants to conduct a public SEO, the cumulative distributed profits of the firm in cash or stocks during the past three years shall be no less than 20% of the average annual distributable profits realized over the same period. The requirement was further strengthened by the CSRC on October 9, 2008, when it issued Decree 57, which tightened the eligibility to issue SEOs. It states the cumulative distributed profit in cash in the past three years shall be no less than 30% of the average annual distributable profits realized in the same period. The 2008 regulation not only raises the dividend requirement, but also counts only cash dividends. The catalyst for the 2006 regulation was the Split Share Structure Reform of 2005, which made non-tradable shares tradable. 15 The reform led to a big increase in the supply of stocks traded in the stock market. (See Liao, Liu and Wang (2014) for a detailed description.) The CSRC became concerned with oversupply of tradeable stocks and its potential impact on stock price, which led to the 2006 regulation. Raising the bar for new share issuers, it hoped, would limit the supply of new shares and stabilize stock prices. 16 After two years in force, the mainstream opinion was that a higher bar would benefit long term investors through higher dividends and help stabilize stock prices. 17 The stock market was on a free fall after it reached its peak on October 16, 2007. By June, 2008, the Shanghai Stock Exchange Composite Index fell by more than 50%. The CSRC issued a draft of the 2008 regulation on August 22, 2008, followed by an official announcement on October 9, 2008. 3.1 Empirical Design 3. EMPIRICAL DESIGN AND DATA 15 Until 2006, trading controlling shares in China was highly restricted, which limited the ownership benefits of price appreciation to the controlling shareholder, increasing the incentive to obtain benefits through other channels, such as tunneling. The 2005 Reform may have reduced the undesirable incentive. 16 Regulation for Issuing Stocks, 2006, China s Securities Regulator Commission. 17 A news report by First Financial Daily on October 9, 2008. 10

We begin investigating how the availability of SEO proceeds affects tunneling by relating SEO to proxies for tunneling with two-stage least square regressions. 18 3. 1.1. The Instrument The dependent variable in the first stage regression is an indicator for when SEO proceeds are available for use by the firm, SEO. It is equal to one in the year of and the year after an SEO. We focus on these two years because the impact on tunneling, if any, should be most noticeable during those years. The coefficient on the predicted SEO in the second stage reflects the two-year average effect of the availability of SEO proceeds. The two-year average reduces noise arising from conducting SEOs at different points in a year (e.g., February vs. November). The instrument for the availability of SEO proceeds, IV_SEO, is constructed using the 2006 and 2008 regulations with a two-year lag. IV_SEO takes a value equal to one in 2010 for all firms affected (i.e., became ineligible to issue SEOs) by the 2008 regulation. Firms are affected by the regulation if the ratio of total dividends over the past three years to the average annual distributable profits over the past three years is less than 30%. 19 The two-year lag between 2008 and 2010 allows for time elapsed from obtaining an approval to issue SEO to the use of the proceeds. In our sample, the average time from the initial announcement of an SEO to the receipt of the proceeds is 242 days. We use a two-year lag to allow an extra year because we examine the use of SEO proceeds over two years. The instrument does not turn on in 2011, because firms affected by the 2008 regulation can become eligible to issue an SEO in 2011 by increasing their payout ratio in 2009. For firms affected by the 2006 regulation, we follow the same procedure and set IV_SEO equal to one in 2008. 18 We cannot estimate differences-in-difference using the regulatory shocks because the shocks affect only the eligibility to issue SEOs and some eligible firms may not issue SEOs. Another identification strategy one may consider is the regression discontinuity; i.e., comparing firms having dividend payout ratios just above 20% (30%) before 2006 (2008) with those having dividend ratios just below 20% (30%) to identify the potential effects of SEOs. However, this approach is problematic because the dividend payout ratio is not completely random, which undermines its validity. 19 The ratio in year t, Ratio t = (D t-1 + D t-2 + D t-3 ) / [(I t-1 + I t-2 + I t-3 ) / 3] 0.30, where D is the amount of dividends paid and I is the amount of distributable profits. 11

The validity of instruments requires two conditions. The relevancy condition requires that the instrument must be correlated with the endogenous variable (SEO). This condition will not be satisfied if low dividend-paying firms can circumvent the regulations without costs. For example, some firms may anticipate the regulatory changes, increase cash dividends prior to the regulation, and then gross up the size of SEO to make up for the cash used to pay the higher dividends prior to the SEO. However, such maneuvers are not frictionless; they impose costs. For one, firms wishing to issue SEOs tend to be cash constrained (DeAngelo et al., 2010). Paying out extra cash dividends may lead to foregoing value-enhancing investments. If the firm takes on more borrowing to meet the cash needs, the financial leverage will be higher than optimal. Perhaps more important, anticipation is subject to uncertainty, making the benefits from dividend maneuvers subject to uncertainty, which reduces the present value of the benefits. The uncertainty is not just about the future regulations. There is also approval uncertainty. SEOs in China and the amount that can be raised require the CSRC s approval, which adds further uncertainty over whether and how much capital can be raised through an SEO. The 2006 regulation counts stock dividends towards meeting the dividend requirement. If low dividend-paying firms anticipated this aspect of the forthcoming regulation, they could have satisfied the dividend requirement by issuing sufficient stock dividends during 2003-2005. Data show otherwise. Stock dividends are relatively rare in China during that period. Among 600 dividend cases in 2005, for example, only 41 included stock dividends. Over the period 2003-2005, 94% of all the dividend cases did not issue any stock dividends. Furthermore, the 2008 regulation excludes stock dividends in defining the dividend requirement. It seems safe to assume that the relevancy condition is satisfied. The second condition is the exclusion restriction; the instrument should not be correlated with the error term in the second-stage regression. So the instrument should not be correlated with the dependent variable after controlling for relevant variables. One concern is that higher dividends may reduce free cash flows, discouraging firms from misusing their capital (Jensen, 12

1986). However, the regulation is based on past dividend payout ratios, not current or future dividend payout ratios. Even if some firms are able to anticipate the regulation and temporarily increase dividends prior to the regulation to satisfy the future regulatory requirement, such a maneuver is unlikely to reduce free cash flows after the SEO, because such a firm will gross up the size of SEO by the amount of additional dividends paid prior to the regulation. Thus, the regulation is unlikely to directly affect how the proceeds of SEOs are used. Another concern is that dividend payout ratios may be serially correlated due to financial policy persistency (Lemmon, Roberts, and Zender, 2008) and the current dividend payout ratio may be correlated with current corporate behaviors including tunneling activities. To control for persistency in corporate policies, we control for firm fixed effects. Additionally, we include the current dividend ratio as an additional control variable in all regressions to ensure that the instrument is unrelated to the error term in the second-stage regression. We also check the correlation between past dividend payout ratios and our proxies for tunneling activities. We find no significant correlation. Our final concern is that the instrument may be indirectly related to tunneling activities through its relation with the strength of corporate governance. That is, firms with higher past dividend payout ratios may practice better governance, leading to less tunneling. For this reason, we control for a number of proxies for the strength of corporate governance in all regressions. 3.1.2 Tunneling Variables Accurate documentation of tunneling is difficult because those who engage in tunneling have every incentive to hide it. Most previous studies rely on either value implications of tunneling or on RPTs. Our primary proxy for tunneling is cash outflows through RPTs (CO_RPT). We also use more direct tunneling proxies based on changes in balance sheet items that are likely to contain traces of tunneling through both RPTs and non-rpts. Evidence of an increase in RPTs following SEOs does not necessarily imply an increase in tunneling. Cheung et al. (2006, 2010) classify RPTs into 1) transactions likely to result in 13

expropriation of minority shareholder wealth, i.e., tunneling; 2) transactions likely to benefit minority shareholders; and 3) strategic transactions unrelated to tunneling. The first type is likely to be cash outflows, while the second type is likely to be cash inflows. So our tunneling proxy is based on the gross cash outflows through RPT per firm-year, CO_RPT. To infer tunneling from CO_RPT, we focus on changes, not levels, in CO_RPT following SEOs because some CO_RPT could arise from non-tunneling RPTs at fair prices. Non-tunneling RPTs may increase when capital expenditures increase. Thus, we infer tunneling when CO_RPT increases without concomitant increases in capital expenditures. The data source for RPTs is the GTA database, a part of the CSMAR database. 20 GTA provides deal-level information on RPTs for all A-share listed firms in China since 1997. For each transaction, GTA provides information about the type of transaction, the relationship between the listed firm and its counter party, and the amount of money involved in the transaction. GTA also provides an indicator variable showing the direction of cash flow. We include only RPTs in which a listed firm pays out cash and the information on the amount is provided. These screens yield 54,802 RPTs over 2000-2012. Using these observations, we construct total cash outflows from listed firm i through all RPTs in year t, CO_RPT_All it. We then separate them by the type of relationships a listed firm has with the counter party and by the type of transactions. We define three types of counter parties: the parent firm; a sibling firm belonging to the same business group; and others, which include subsidiaries, blockholders, and firms controlled (or highly influenced) by the firm s top executives or their family members. We also separate by the type of transactions: capital transactions (e.g., inter-corporate loans and equity-related transactions); goods transactions (e.g., purchases from, and paying for employees of the related party); and all other transactions (e.g., fixed assets transactions, leasing transactions, and joint project investments). 3.2 Data 20 Website: http://www.gtadata.com/ 14

3.2.1. Sample Construction Our sample is constructed with all A-share firms 21 listed on the Shanghai and Shenzhen Stock Exchanges. 22 Our data are taken from several sources. Financial accounting and corporate governance data are from Resset. 23 SEO and RPT data are from CSMAR. 24 The dividend ratios required by the 2006 and 2008 regulations are from Wind Information. 25 Financial firms as defined by the CSRC (e.g., banks, insurance firms, and brokerage firms) are excluded. We also exclude ST (special treatment) and *ST companies. Firms are classified as such if they have two (ST) and three (*ST) consecutive years of negative net profit. These companies are not allowed to issue SEOs and hence are unaffected by the 2006 and 2008 regulations. We also exclude observations where relevant tunneling and control variables are missing. These screens yield 18,343 firm-year observations associated with 2,342 unique firms over the period 2000-2012. The sample period starts in 2000 because underwritten offerings were first allowed in 2000. Board information also is available only after 2000. All accounting variables are winsorized at the 1% level. All monetary variables are normalized to 2000. Table 1 lists the sample distribution by year. Column (1) reports the number of firms in the full sample. Column (2) shows the number of public SEOs by the offering year. In total, there are 557 SEOs during 2000 to 2012. The table shows a steady decline in the number of SEOs until 2007. There were very few SEOs in 2005 and 2006 because during the Split Share Structure Reform in April 2005 the CSRC stopped approving any IPO or SEO proposals until May 2006. 21 In mainland China there are two types of stocks: A-share and B-share. Originally, the A-share market was designed for domestic investors to trade with RMB, and the B-share market was designed for foreign investors to trade with US dollars. The B-share market was opened to domestic investors in 2001, and qualified foreign institutional investors (QFII) were also allowed to trade in the A-share market beginning in 2006. A firm can issue both A-shares and B-shares, and these shares have identical rights. We restrict our sample to the A-share market because the total market capitalization of the A-share market is about 122 times that of the B-share market as of the end of 2013. In addition, most of firms listed in the B- share market are also listed in the A-share market. 22 The Shenzhen Stock Exchange has the main board, established in 1991; the small and medium enterprise board, established in 2004; and the growth enterprises board, established in 2009. The sample includes firms listed on all three boards. The Shanghai Stock Exchange has only the main board. 23 Resset is the Chinese equivalent to Compustat in the US. Website: http://www.resset.cn/en/ 24 CSMAR database for seasoned equity offerings is more detailed than Resset s. 25 Website: http://www.wind.com.cn/en/default.aspx. 15

The sharp increase in SEOs in 2007 and 2008 reflects the release of pent-up demand to issue SEOs during 2005 and 2006. The Chinese stock market also reached its peak in 2007. 26 3.2.2. Descriptive Statistics Table 2 provides summary statistics for all key variables. Variable definitions and data sources are provided in Appendix 1. The instrument, IV_SEO, has a mean of 0.05, indicating 5% of firm-year observations are treated by the regulatory shocks. The low percentage reflects the IV indicator turning on only in 2008 and 2010, which contains about 18% of all observations. The mean CO_RPT_ALL, the average cash outflow through RPTs per firm-year is 177 million RMB. When we separate it by the type of counter-parties, roughly half of the cash outflows goes to the parent (82M), with the rest going to sibling firms (28M) and to other entities (33M). The numbers do not add up to 177 million because of insufficient information for some RPTs to classify the type of related parties. When we separate cash outflows by the type of transactions, we find most cash outflows occur through trading goods and paying for related party s employees (53M) or through other means (66M), and cash outflows through capitalrelated transactions (24M) are less than half of those. Account receivables and prepaid expenses are, on average, 9% and 4% of total assets; the average percentage of account receivables classified as unlikely to be collected is 13%. These percentages are not too different from their US counterparts, which are 14%, 2%, and 14% for firms covered by Compustat over the same periods. 4. TUNNELING In this section we investigate how CO_RPT changes when SEO proceeds become available. We then separate SEOs followed by increases in CO_RPT from those with no increase in CO_RPT, and compare changes in capital expenditures, cash holdings, debt outstanding. 26 The Shanghai Stock Exchange Composite Index reached its peak of 6,124.04 on October 16, 2007 and remains below that level as of November 2015. 16

Relating changes in CO_RPT to the alternative uses of SEO proceeds helps shed light on whether increases in CO_RPT are attributable to tunneling. 4.1. Cash Outflows through RPTs during SEO Years Our 2SLS regressions control for firm- and year fixed effects and time-varying firm characteristics that might be related to SEOs and CO_RPT. Control variables include firm age as measured by log of the number of years a firm has been listed, ln(nyear_listed); firm size as measured by log of sales, ln(sales); firm performance as measured by return on equity, ROE; financial leverage as measured as the sum of short- and long-term debt over total assets, Leverage; asset tangibility as measured by property, plants, and equipment over total assets, PPE/TA; firm growth rate as measured by sales growth rate, SALES_GR. In addition, we control for variables related to the strength of governance: the percentage of independent directors on the board, %_IND_DIR; the percentage of shares held by the local or central government, %_STATE_OWN; the percentage of shares held by the largest shareholder, %_LARGEST_SH; and the percentage of non-tradable shares, NONTRDPCT. The last variable controls for the possibility that when controlling shareholders own more tradable shares, they may have less incentive to tunnel because they can realize benefits through higher share prices. 27 Including these governance characteristics as controls helps satisfy the exclusion restriction for our instrument. For the same reason we control for concurrent dividend payout ratio, DIVPRT. Some firms may issue SEOs through private equity offerings, proceeds of which can be used to finance CO_RPT. We include an indicator for private equity offerings, D_PRIVATE_PLACE. Standard errors of the first-stage regression are clustered at the firm level and standard errors of the second-stage regression are corrected by bootstrapping 200 times. For the first-stage estimation, we use conditional logit regression instead of OLS because the endogenous variable is an indicator. Under the assumption that IV has predictive power over 27 Li et al. (2011) and Chen et al. (2012) show that the Split Share Structure Reform in 2005 has significantly affected financial behaviors of Chinese publicly listed firms. 17

the endogenous variable, IV estimators using logit model in the first stage are asymptotically efficient, meaning that coefficients of the model can be more precisely estimated (Wooldridge, 2010, p.939). The regression result is reported in Appendix 2, Panel A. The coefficient on the instrumental variable IV_SEO is negative and significant at the one percent level, implying that the regulations significantly reduced the likelihood of SEOs. It also suggests that the IV has strong predictive power over the endogenous variable. F-statistics are not reported because the first-stage regression is conditional logit, which is a non-linear estimation. When the first stage is reestimated using OLS, F-statistic is 19. Table 3 reports second-stage estimation results. Column (1) shows CO_RPT significantly increases when SEO proceeds become available. The magnitude of the coefficient on SEO shows that CO_RPT during the year of SEO and the year after increases by 86.7%. Other columns show estimation results by type of related parties and transactions. They all show positive coefficients on SEO, but the coefficients are significant only for sibling firms and for capital and goods transactions; the rest are insignificant. Most of CO_RPT go to sibling firms in the same business group through capital and goods transactions. The control variables reveal interesting correlations. CO_RPT shows a highly significant negative correlation to firm age for all CO_RPTs, suggesting that newly-listed firms are more likely to provide cash to their related parties. The significant positive correlation with financial leverage could be due to business groups using firms with high debt capacity to raise debt capital for other firms in the group. The correlation with the largest shareholder s share ownership is also positive and highly significant for all RPTs. The more shares the largest shareholder owns, the greater the influence to dictate the movement of cash to related parties. In addition, firms with greater sales and state ownership show more CO_RPTs. Sales generate cash that can be sent to related parties and state ownership tends to exacerbate agency problems. In addition, the concurrent dividend payout ratio shows no significant correlation to any CO_RPT. Since our instrument is related to the dividend payout ratio over the past three years, 18

the insignificant correlation further buttresses our assertion that the instrument satisfies the exclusion restriction. Finally, the indicator for privately placed equity offerings shows positive correlations to cash outflows to the parent through capital and other RPTs, consistent with our conclusion that SEOs are followed by increases in CO_RPTs. 4.2 Are Increases in Cash Outflows through RPTs Attributable to Tunneling? The increase in CO_RPT per se is not evidence of tunneling. The increase may simply reflect greater RPTs associated with more investments following SEOs. If an RPT is conducted at a fair price, the cash outflow is not tunneling. Thus, we examine how changes in CO_RPT are related to investment activities, as measured by capital expenditures. We also examine how changes in CO_RPT are related to changes in cash holdings and debt outstanding after SEOs. 4.2.1 Changes in Capital Expenditures In a study covering 38 countries around the world, Kim and Weisbach (2008) find that financing investments is a major reason for issuing equities. If this is also the case for Chinese SEOs, we would expect a significant increase in capital expenditures, CAPEX, measured as cash paid to acquire fixed, intangible, and other long-term assets. In the first three columns of Table 4, we relate SEO to CAPEX, using the same control variables in Table 3, including firm- and year fixed effects. The first column shows that for the full sample, CAPEX increases significantly following SEOs. This increase in CAPEX masks important differences between firms increasing and not increasing CO_RPT following SEOs. We separate SEO firms into those increasing and not increasing CO_RPT by calculating ΔCO_RPT_ALL = CO_RPT_ALL t, t+1 - CO_RPT_ALL t-1,t-2. The year of SEO is t, so CO_RPT_ALL t, t+1 and CO_RPT_ALL t-1,t-2 are total CO_RPT, respectively, during the two SEO years and during the two-year period preceding the SEO. Then we combine each subsample with firms which never issued SEOs. This yields two samples: One sample includes SEO firms with positive ΔCO_RPT_ALL and firms which never issued SEOs; the other sample includes SEO firms with non-positive ΔCO_RPT_ALL and firms which never issued SEOs. For each sample, 19

we separately estimate how SEO affects capital expenditures. First-stage estimation results are reported in Appendix 2, Panel B. Columns (2) and (3) in Table 4 report the second-stage regression results. Coefficients on SEO are positive and significant only when firms do not increase CO_RPT. When SEO firms increase CO_RPT, they do not increase capital expenditures. The increase in CAPEX during the SEO years for the total sample is driven by firms with no increases in CO_RPT. Increases in CO_RPT during SEO years cannot be explained by increases in investments. 4.2.2 Changes in Cash Holdings If SEO proceeds are not used to finance investments, they could be used to increase cash holding, which DeAngelo et al. (2010) and McLean (2011) show is a major purpose of issuing SEOs in the US. To examine whether Chinese SEO proceeds also are used to increase cash holdings, we follow Chen et al. (2012) and calculate changes in the ratio of cash holdings to noncash assets, Δ(CASH/NON-CASH ASSET) from year t-1 to year t. Column (4) of Table 4 shows cash holdings increase significantly following SEOs, as in the US. The increase in cash holdings will be smaller if an important portion of CO_RPT represents tunneling. If CO_RPT increases arise from non-tunneling business activities, they should not reduce cash holdings. For example, if a firm pays related parties to buy raw materials and services at fair prices to generate sales, the incremental sales revenue should more than offset the cash payment. To test this conjecture, we estimate separate regressions for subsamples containing firms with increases, and no increase in CO_RPT. Columns (5) and (6) report the second-stage estimation results. 28 Both samples show positive and significant coefficients, but with noticeable differences in both the magnitude and the significance level. When CO_RPT does not increase, the magnitude of the coefficient is more 28 Although cash can be viewed as negative debt, we keep financial leverage as a control variable because some SEO proceeds can be used to pay down debt or to support more borrowing. In either case, the concurrent financial leverage will be lower or higher, depending on how SEO proceeds are used. 20

than twice that when CO_RPT increases. Cash holdings increase substantially less when CO_RPT increases. This evidence points increases in CO_RPT to tunneling. 4.2.3 Changes in Debt Outstanding If SEO proceeds are not used to finance investments, lead to smaller increases in cash holdings, and not tunneled away, they could be used to pay down outstanding debt. We relate SEO to changes in debt outstanding. Debt is short- and long term loans plus bonds outstanding in millions of 2000 RMB. Change in debt outstanding is the difference from year t-1 to year t. The estimation result reported in Column (7) indicates an insignificant increase in debt level following SEOs. When we break the sample into those with vs. without increases in CO_RPT in Columns (8) and (9), we observe the level of debt increasing significantly for firms with no increase in CO_RPT, suggesting these firms are raising equity to support a higher level of debt, which, in turn, helps pay for the higher level of capital expenditures shown in Column (3). In contrast, for firms with an increase in CO_RPT, the level of debt remains unchanged. These firms did not use SEO proceeds to pay down debt, nor to support higher level of debt. These results indicate that firms with increases in CO_RPT do not use SEO proceeds in the normal, expected ways observed among firms with no increase in CO_RPT. This evidence implies that the increases in CO_RPT are not the result of conducting more RPTs at a fair price. Taken together, these findings suggest much of increases in CO_RPT following SEOs is tunneling to related parties. 4.3 Market Reactions to announcements of RPTs and SEOs If some CO_RPTs indeed contain tunneling, as we suspect, investors will react negatively when they become aware of CO_RPT. If investors do react negatively when they become aware, can they also anticipate at the time of SEO announcements which SEOs will be subject to more CO_RPT? In this section, we estimate the market s ex-post reaction to reports containing RPTs and the ex-ante reaction to SEOs. 4.3.1 Investor reaction to RPT announcements 21

To estimate investor reaction to RPTs, we calculate cumulative abnormal returns over the three-day window (-1, 1) surrounding the filing date of a report containing RPTs with a combined transaction value greater than five percent of total assets. Both cash inflows and outflows through RPTs are included in calculating the threshold for the combined transaction value. CARs are calculated using the market model with the A-share value-weighted index. The estimation window for the market model is 270 trading days prior to the event window. RPTs are disclosed through two types of filings. When an RPT is a part of a firm s normal operations, disclosure is required in annual or semi-annual reports, similar to 10-K reports in the US. Firms must disclose RPTs that do not occur on a regular basis in a non-periodic report when the board approves it. The regular reports contain information about earnings, future prospects, operations, and so on. As such, our measure of announcement returns to RPT reports contained in the regular reports suffers from severe confounding effects. In spite of the confounding effects, however, Table 5, Panel A shows -0.67% average announcement return for the full sample, significant at the one percent level. Most reports containing RPTs contain those with both cash outflows and inflows. But a few reports contain RPTs with only cash outflows or inflows. For these, we recalculate announcement returns separately for those containing only cash outflows and only cash inflows. In spite of the small sample size, the market reaction is significantly negative to RPTs with cash outflows, while it is insignificant to RPTs with cash inflows. The full sample is then divided by whether RPTs are mentioned in a regular or a nonperiodic report. Although there is a guideline on which RPTs should be included in regular vs. non-periodic reports, what is considered a part of normal operations is subject to interpretation, allowing some leeway for firms to choose where to report an RPT. Firms can also choose the type of RPT so it can be reported in their preferred outlet. We suspect if a firm engages in an RPT for tunneling purposes, it is likely to include the RPT in a regular report that contains numerous other matters to divert attention from it. Consistent with this conjecture, Panel A of Table 5 shows that 22

the negative reaction to announcements containing RPTs is driven completely by those contained in regular reports. 4.3.2 Investor Reaction to SEO Announcements We calculate SEO announcement returns over the three-day window (-1, 1) surrounding the announcement date of SEO, following the same procedure as in RPT announcement returns. As in previous studies (e.g., Jegadeesh, Weinstein, and Welch, 1993; Denis, 1994; and Datta, Iskandar-Datta, and Raman, 2005; and Kim and Purnanandam, 2014), we use the filing date as the announcement date. Table 5, Panel B shows a significant average announcement return of -0.73%. The median is -1.18%. These numbers are less negative than the -2 to -3% average announcement returns documented for SEOs in the US (Eckbo, Masulis, and Norli, 2007, Table 13 (a)). The difference can be explained by the fact that Chinese SEOs are all primary shares without any secondary offerings. Secondary offerings are met with more negative investor reaction because they transmit negative signals from insiders and blockholders (Leland and Pyle, 1977; Kim and Purnanandam, 2014). For pure primary offerings in the US, Kim and Purnanandam find -1.04% average announcement returns with a median of -0.81% over the period 1994-2003. Can Chinese investors distinguish at the time of SEO announcements which SEO proceeds will be used more for CO_RPT? We relate SEO announcement returns to changes in CO_RPT_ALL, ΔCO_RPT_ALL, over net SEO proceeds after all expenses, NET_SEO. The changes are differences between the two-year period preceding an SEO and the two SEO years. The estimation results are reported in Panel C of Table 5. Column (1) is estimated with only log of NET_SEO as a control variable to maximize the sample size. In Column (2), we add industry- 29 and year fixed effects and the same set of the control variables used in Table 3, lagged by one year to the SEO year. The estimation results in both columns show that investors are more negative at an SEO that is later followed by greater increase in CO_RPT. This significant 29 We use the CSRC s industry classification that is equivalent to one-digit US SIC code. 23

negative relation implies that not only do investors react negatively when they learn CO_RPT occurred; they also anticipate which SEO proceeds will be tunneled more through RPTs. 5. FIRM AND SEO CHARACTERISTICS To anticipate which SEO proceeds will be tunneled more, investors may rely on certain firm and SEO characteristics. In this section we attempt to identify those characteristics. 5.1 Firm Characteristics We focus on two firm characteristics, business groups and state owned enterprises (SOEs), which previous studies show are related to tunneling (e.g., Bertrand et al., 2002; Aharony, Wang and Yuan, 2010). To study business groups, we search company websites of all publicly listed firms to identify whether a firm has a parent firm. We then assign an indicator, D_PARENT, equal to one if it has a parent and zero otherwise. Our conjecture is that non-parent firms belonging to a business group are more susceptible to tunneling than standalone firms or parent firms in business groups. Because controlling shareholders tend to have more cash flow rights of parent firms than of listed subsidiaries, tunneling is less likely to take place from the parent to subsidiaries. We construct two samples: one includes SEO firms with a parent and all firms with no SEOs, and a second includes SEO firms without a parent and all firms with no SEOs. The firststage results are reported Appendix 2, Panel C. The second-stage results are reported in the first two columns in Table 6. Coefficients on SEO are positive and significant only for SEO firms with a parent. Proceeds of SEOs issued by non-parent firms belonging to business groups seem especially vulnerable to tunneling. To examine whether belonging to an SOE makes a difference, we conduct a similar exercise with two samples constructed by an indicator D_STATE_OWN, equal to one if a listed firm s controlling shareholder is either a local or the central government and zero otherwise. The first-stage results are reported in Appendix II, Panel C. The second-stage results are reported in Columns (3) and (4). The magnitude of the coefficient of SEO for SOEs is somewhat greater than 24

non-soes. However, both coefficients are significant, suggesting that as far as tunneling SEO proceeds is concerned, whether or not a firm belongs to an SEO does not make a big difference. 5.2 External Monitoring Another important governance factor that may affect tunneling SEO proceeds is the quality of monitoring by outside auditors. We separate SEO firms by an indicator, D_Big_4, equal to one if the outside auditor is (affiliated with) one of the Big-4 international accounting firms and zero otherwise. The second-stage results are reported in Columns (5) and (6) (the firststage, in Appendix 2, Panel C). The coefficient on SEO is insignificant when the outside auditor is one of the Big-4, but highly significant when the firm is not audited by one of the Big-4. The Big-4 effect could be either causal (more effective monitoring by Big-4 auditing firms) or selection (firms with stronger governance selecting more reputable auditors). Although we cannot distinguish the selection from the causal effect, investors should feel safer when SEOs are put forward by firms audited by one of Big-4 accounting firms. 30 5.3 Stated Purposes Finally, we check whether the stated purposes of issuing an SEO make a difference. Stated purposes include increasing liquidity, developing new products, possibly opening new plants, spending more on R&D, acquiring a specific firm or assets, and so on. Of these, we choose the most tangible and verifiable one: to acquire a specific firm or assets. We separate SEOs by an indicator, D_SEO_ACQUISITION, equal to one if the purpose is to acquire a specific firm or assets and zero otherwise. The results are reported in the last two columns of Table 6 (the first stage, in Appendix 2, Panel C). SEO proceeds are relatively safe from tunneling when the stated purpose is specific, tangible and verifiable. 6. Further Evidence of Tunneling and of Greater Private Benefits 30 We do not conduct a similar test using the fraction of independent directors because of the lack of variation in the fraction. In China, starting in 2003 all publicly listed firms are required to have one-third independent directors and since then the fraction of independent directors have been clustered just above one-third for most firms, providing insufficient variation to conduct a meaningful test. 25

Our investigation of tunneling SEO proceeds so far has relied on CO_RPT. In this section, we rely on alternative proxies traces of tunneling left on the balance sheet. We also examine how the availability of SEO proceeds affect managerial compensation and perks. 6.1 Traces of Tunneling on Balance Sheet Items Some tunneling activities may escape the legal definition of RPT. Giving company money to legally undefined partners and friends may not be classified as an RPT. A CEO may borrow company money interest free with no intention to return it. These are also tunneling, but will not be included in the GTA database as RPTs. However, the reduction in the cash balance due to tunneling has to leave a trace somewhere on the balance sheet. To obtain hints about which items on the balance sheet are likely to reflect tunneling-related cash outflows, we talked to current CFOs and former employees of major auditing firms in China. We were told a CEO or a controlling shareholder of a public firm A is unlikely to give money directly to B. To make it difficult to detect the tunneling, the money is likely to go through a third party, say C, which takes money from A and gives it to B. (To make it more difficult to trace the trail of money, the transfer may go through several entities.) Since Firm A has to cover up the missing funds with another form of assets on the balance sheet, our sources tell us that the missing funds are usually recorded as either accounts receivable or pre-paid expenses. If recorded as accounts receivable, they say, the accounts receivable will soon be classified as unlikely to be collected. We relate SEO to the three balance sheet items. The estimation procedure is the same as in Table 3, relying on the same first-stage estimation results reported in Appendix 2, Panel A. Dependent variables in the second-stage are accounts receivable over total assets, ACCV/TA; prepaid expenses over total assets, PREPAY/TA; and the percentage of account receivables classified as unlikely to be collected, %_ACCV_BAD. These variables may reflect both nontunneling business activities and traces of tunneling. Our focus is whether there are significant increases in these variables when SEO proceeds are at managers discretion. 26

The first three columns in Table 7 show the second-stage estimation results. Coefficients on SEO are all positive and highly significant. During SEO years, the ratio of accounts receivable to total assets increases by 0.016; the prepaid expense ratio by 0.009; and the fraction of uncollectible accounts receivable by 0.035. When compared to the sample medians (0.07, 0.02, and 0.07, respectively), these increases are material, especially uncollectable accounts receivables. Perhaps most revealing, the fraction of uncollectible accounts receivable increased by 0.035, more than twice the increase in the ratio of accounts receivable to total assets. These results suggest that a significant portion of SEO proceeds are siphoned off through tunneling using uncollectible accounts receivable as a channel. Some control variables show coefficients consistent with intuition. Higher sales are associated with more accounts receivable and prepaid expenses but less uncollectible accounts receivable. When the largest shareholder owns more shares, all the dependent variables show negative coefficients, suggesting firms with more concentrated share ownership tend to manage working capital more tightly. 6.2 Managerial Compensation and Perks The last two columns in Table 7 estimate the effects on managerial compensation and perks. In Column (4) we relate SEO to total compensation paid to the three highest-paid managers. Most of the control variables with significant coefficients show signs consistent with intuition, e.g., the compensation is positively related to sales and ROE. However, the coefficient on SEO, albeit positive, is insignificant; suggesting the availability of SEO proceeds at managers discretion has no significant effect on their pay. This could be due to the fact that the ideology in China is still communism, which discourages unequal pay, thereby imposing an upper limit on top executives official pay. Over the period 1999-2014, the average ratio of three highest paid executives annual compensation per person to the average annual compensation of all employees excluding the top-three is only 6.38. (Data source: Resset.) 27

Our next query is managerial perks. In China, many of the benefits of being a top executive come in the form of corporate perks, such as expense accounts, free travels, and hosting events for private enjoyment. These perks are typically recorded as administrative expenses. The dependent variable in the last column is the ratio of administrative expenses to sales. The coefficient on SEO is highly significant, and the magnitude implies the administrative expense to sales ratio increases by 19%. In China, the agency problem associated with free cash flows manifests mainly as tunneling and perks, but not as higher official pay. 7. ROBUSTNESS In this section, we re-estimate the baseline regressions with alternative definitions of key variables. Control variables are not reported. 7.1. Alternative Definition of SEO The SEO indicator in our baseline regression is turned on for all completed SEOs. As an alternative SEO indicator, we exclude small SEOs with proceeds in the bottom 10 th percentile. These small SEOs are often made by small firms with highly volatile performance. Table 8, Panel A reports second-stage re-estimation results for CO_RPT_ALL and %_ACCV_BAD, and subsample analyses for CAPEX and Δ(CASH/NON-CASH ASSET). 31 The results do not change our conclusion. 7.2. Alternative Instrument In constructing the instrument we assume a two-year elapsed time from the beginning of an SEO process to the beginning of proceeds use, defining the usage year as 2008 and 2010. Since the process for some SEOs may take less than two years, we re-estimate the IV regressions by re-defining the usage year as 2007 and 2009. The re-estimation results, reported in Panel B, are robust. 8. CONCLUSION 31 The number of observations in Columns (1) and (2) remain the same as in Tables 3 and 7 because we do not take out the small SEOs. We just set those SEOs equal to zero instead of one. 28

Using a sample of Chinese listed firms, we find robust evidence that tunneling increases substantially during SEO years the year of an SEO and the year after. The free cash flow agency problem articulated in Jensen (1986) is particularly severe in the aftermath of Chinese SEOs, as manifested in the form of tunneling. These results are based on instruments constructed with exogenous regulatory shocks on the eligibility to issue SEOs, allowing us to make causal inferences. Our evidence of tunneling is robust whether our proxies for tunneling are based on cash outflows through related party transactions or balance sheet items containing traces of tunneling. The validity of our tunneling proxy CO_RPT is affirmed by a number of corroborating findings. When CO_RPT indicates greater tunneling, capital expenditures do not increase, outstanding debt does not decrease, and cash holdings increase much less. Other corroborating evidence includes negative investor reaction when annual and semi-annual reports contain RPTs, the negative relation between investor reaction at the announcement of SEOs and subsequent increases in CO_RPT, and higher frequency of tunneling when issuing firms belong to business groups. Tunneling is clearly harmful to new investors participating in SEOs with no stake in related parties where SEO proceeds are tunneled. The tunneling is also harmful to pre-existing minority shareholders of SEO firms, even if some of the tunneled money goes to related firms in the same business group as a part of co-insuring (Fisman and Wang, 2010) or as an internal capital market transaction (Gopalan et al., 2007; Buchuk et al., 2014). The minority shareholders get hurt because controlling shareholders have the incentive to tunnel money from firms with less cash flow rights to firms with more cash flow rights (Bertrand, Mehta, and Mullainathan, 2002), so the present value of reciprocation in the time of need has to be smaller than the money tunneled away. Considering all these, it seems safe to conclude that in China, controlling shareholders and managers engage in more self-dealing and steal more when SEO proceeds become available. 29

We hasten to point out, however, that not all SEOs in China are subject to tunneling; many companies put SEO proceeds into productive use. Some firm and SEO characteristics help separate these firms from those more prone to tunneling. We find firms audited by Big-4 international accounting firms and SEOs with specific, verifiable stated purposes such as acquisition of a specific firm or assets show no signs of tunneling SEO proceeds. Finally, our overall findings call for more effective corporate governance mechanisms to safeguard minority shareholders against violation of their property rights. More regulation may not be the solution, as it often leads to unintended consequences with worse outcomes. We suggest a market-based governance mechanism that provides greater transparency and protection for minority shareholders so they can better mitigate agency problems arising from the availability of new funds raised by SEOs at the discretion of those in control. 30

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Table 1: Sample Description. This table reports, by year, the number of firms in our sample and number of seasoned equity offerings. The sample includes Chinese firms listed on Shanghai and Shenzhen Stock Exchanges from 2000-2012. Financial firms, ST (special treatment), and *ST firms are excluded. Firms are classified as ST and *ST if they have two (ST) and three (*ST) consecutive years of negative net profit. We also exclude observations where key dependent variables and relevant control variables are missing. Column (1) shows the number of firms in the full sample by year. Column (2) shows the number of public offerings (underwritten offerings and rights offerings) by offering year. Year Full SEO (1) (2) 2000 897 154 2001 977 131 2002 1,038 44 2003 1,103 38 2004 1,200 32 2005 1,207 7 2006 1,237 7 2007 1,368 28 2008 1,442 43 2009 1,537 18 2010 1,883 20 2011 2,168 23 2012 2,286 12 Total 18,343 557 34

Table 2: Summary Statistics of Key Variables. This table reports summary statistics of key variables. Variable definitions are provided in Appendix I. Instrumental Variable Mean Median Std. Dev (1) (2) (3) IV_SEO 0.05 0.00 0.22 Tunneling Variables CO_RPT_ALL 177.31 0.00 736.25 CO_RPT_Parent 82.25 0.00 391.76 CO_RPT _Sibling 28.07 0.00 142.33 CO_RPT _Other 33.05 0.00 147.82 CO_RPT _Capital 23.64 0.00 134.15 CO_RPT _Good 53.30 0.00 264.11 CO_RPT _Other 66.17 0.00 293.31 ACCV/TA 0.09 0.07 0.08 PREPAY/TA 0.04 0.02 0.04 %_ACCV_BAD 0.13 0.07 0.16 Other Variables CAPEX 431.77 70.32 3197.54 Δ(CASH/NON-CASH ASSET) -0.03-0.01 0.26 DEBT 907.61 257.74 2189.33 Adm_Exp/Sales 0.12 0.07 0.41 TopEx_Pay 0.70 0.51 0.78 NET_SEO 781.29 353.00 1828.79 NYEAR_LISTED 7.12 7.00 5.03 SALES 2898.77 872.12 6986.32 ROE 0.06 0.07 0.14 LEVERAGE 0.25 0.24 0.18 SALES_GR 0.24 0.15 0.54 PPE/TA 0.32 0.29 0.20 %_IND_DIR 0.30 0.32 0.12 %_STATE_OWN 0.19 0.00 0.25 %_LARGEST_SH 0.39 0.37 0.16 NONTRDPCT 0.21 0.00 0.30 D_PRIVATE_PLACE 0.05 0.00 0.21 D_SEO_ACQUISITION 0.13 0 0.34 D_BIG_4 0.06 0 0.23 D_STATE_OWN 0.61 1.00 0.49 DIVPRT 0.26 0.18 0.31 35

Table 3: Changes in Cash Outflows through Related-party Transactions during SEO Years. This table estimates how cash outflows through related-party transactions (CO_RPT) change during SEO years after firms receive SEO proceeds. SEO years are the year SEO proceeds are received and the year after. Dependent variables are log of CO_RPT. Column (1) reports results for all types of relationships and transactions; Columns (2) (4), by relationship type; Columns (5) (7), by transaction type. All reported results are second-stage IV regression results. First-stage regression results are reported in Appendix II, Panel A. The sample period covers 2000 2012. All regressions include firm- and year fixed effects. Bootstrapped standard errors are reported in parentheses. Coefficients marked with *, **, and *** are significant at 10%, 5%, and 1%, respectively. Variable definitions are provided in Appendix I. Dependent Variable Ln(CO_RP T_ALL) Separated by Relationship Types Ln(CO_RPT _Parent) Ln(CO_RPT _Sibling) Ln(CO_RPT _Others) Separated by Transaction Types Ln(CO_RPT _Capital) Ln(CO_RPT _Goods) Ln(CO_RPT _OtherTr) (1) (2) (3) (4) (5) (6) (7) SEO 0.867*** 0.461 0.974*** 0.539 1.017*** 0.644** 0.386 (0.30) (0.33) (0.34) (0.38) (0.39) (0.30) (0.41) ln(nyear_listed) -0.878*** -0.692*** -0.918*** -0.800*** -1.113*** -0.653*** -0.960*** (0.13) (0.16) (0.16) (0.17) (0.17) (0.14) (0.17) ln(sales) 0.241*** 0.190** 0.273*** 0.335*** 0.092 0.551*** 0.200** (0.07) (0.08) (0.07) (0.07) (0.07) (0.06) (0.08) ROE -0.207-0.241-0.253-0.701** -0.341-0.171-0.850*** (0.22) (0.27) (0.25) (0.29) (0.27) (0.23) (0.27) LEVERAGE 1.063*** 1.870*** 1.569*** 1.157*** 2.869*** 0.061 1.931*** (0.30) (0.39) (0.32) (0.36) (0.37) (0.28) (0.34) PPE/TA -0.285 0.284-0.572-1.212*** -1.041** -0.480-0.043 (0.36) (0.43) (0.36) (0.43) (0.41) (0.31) (0.43) SALES_GR 0.004-0.116-0.051 0.028 0.083-0.114* -0.033 (0.07) (0.08) (0.07) (0.08) (0.07) (0.07) (0.08) %_IND_DIR 0.205-0.644 0.046 0.385 0.037 0.123-0.451 (0.38) (0.46) (0.37) (0.48) (0.47) (0.36) (0.44) %_STATE_OWN 0.417** 0.479* 0.702*** 0.035 0.302 0.470** 0.305 (0.16) (0.25) (0.21) (0.26) (0.24) (0.19) (0.22) %_LARGEST_SH 3.656*** 3.603*** 4.311*** 1.550*** 3.058*** 2.314*** 2.583*** (0.52) (0.56) (0.51) (0.56) (0.50) (0.45) (0.60) NONTRDPCT 0.010-0.306-0.011 0.144-0.597* -0.116-0.073 (0.30) (0.35) (0.31) (0.35) (0.31) (0.31) (0.32) D_PRIVATE_PLACE 0.178* 0.344** 0.063 0.070 0.319* -0.014 0.319** (0.11) (0.16) (0.15) (0.17) (0.18) (0.13) (0.16) DIVPRT 0.137-0.044 0.169-0.041-0.186 0.122 0.133 (0.09) (0.13) (0.11) (0.13) (0.13) (0.09) (0.12) Constant -9.567*** -11.395*** -12.282*** -10.690*** -10.720*** -13.119*** -9.776*** (0.58) (0.66) (0.71) (0.74) (0.70) (0.55) (0.71) Firm & Year FE Y Y Y Y Y Y Y Observations 18,343 18,343 18,343 18,343 18,343 18,343 18,343 Adjusted R-squared 0.852 0.651 0.652 0.653 0.374 0.796 0.686 36

Table 4: Changes in Capital Expenditures, Cash Holdings, and Debt Outstanding during SEO Years. This table estimates changes in capital expenditures, cash holdings, and debt outstanding during SEO years after firms receive SEO proceeds. SEO years are the year SEO proceeds are received and the year after. The dependent variable is log of capital expenditures in columns (1) (3), changes in CASH/NON-CASH ASSET from year t-1 to year t in columns (4) (6); and changes in debt outstanding from year t-1 to year t in columns (7) (9). For each SEO firm-year, we calculate the change in total cash outflows through related-party transactions, ΔCO_RPT_ALL, from the two-year period preceding an SEO to SEO years. We then separate the sample by whether ΔCO_RPT_ALL is positive or not. For each subsample, we add non-seo firms as control firms. All reported results are second-stage IV regression results. The first stage regression results are reported in Appendix II, Panels A and B. The sample period covers 2000 2012. All regressions include firm- and year-fixed effects. Bootstrapped standard errors are reported in parentheses. Coefficients marked with *, **, and *** are significant at 10%, 5%, and 1%, respectively. Variable definitions are provided in Appendix I. Dependent Variable ln(capex) Δ(CASH/NON-CASH ASSET) ΔDEBT All ΔCO_RPT _ALL>0 ΔCO_RPT _ALL<=0 All 37 ΔCO_RPT _ALL>0 ΔCO_RPT _ALL<=0 All ΔCO_RPT _ALL>0 ΔCO_RPT _ALL<=0 (1) (2) (3) (4) (5) (6) (7) (8) (9) SEO 0.521*** 0.023 0.554*** 0.092** 0.059** 0.110*** 428.105 229.306 610.700** (0.10) (0.12) (0.10) (0.04) (0.03) (0.04) (299.04) (170.73) (242.58) ln(nyear_listed) -0.479*** -0.382*** -0.518*** 0.304*** 0.373*** 0.323*** 38.911-133.294-98.575 (0.04) (0.04) (0.04) (0.02) (0.03) (0.03) (126.25) (129.90) (119.86) ln(sales) 0.803*** 0.822*** 0.797*** -0.017*** -0.023*** -0.021*** 196.250*** 214.836*** 164.361*** (0.03) (0.03) (0.03) 0.00 (0.01) (0.01) (30.56) (32.14) (35.47) ROE 0.863*** 0.836*** 0.980*** 0.048*** 0.056*** 0.060*** -110.038* -111.768-30.404 (0.09) (0.12) (0.09) (0.01) (0.02) (0.02) (60.72) (93.66) (88.85) LEVERAGE 0.667*** 0.494*** 0.766*** -0.016 0.01 0.005 - - - (0.12) (0.15) (0.12) (0.02) (0.03) (0.02) - - - PPE/TA 2.770*** 2.688*** 2.702*** -0.173*** -0.186*** -0.185*** -232.450* 166.349-85.744 (0.10) (0.13) (0.13) (0.03) (0.04) (0.04) (136.42) (175.95) (178.55) SALES_GR -0.067*** -0.076*** -0.062** 0.001-0.005 0 32.212 3.304 47.438* (0.02) (0.03) (0.03) (0.01) (0.01) (0.01) (23.69) (23.67) (25.22) %_IND_DIR 0.214* 0.245* 0.190* 0.025 0.043-0.001 137.617 374.161** 112.429 (0.12) (0.14) (0.11) (0.02) (0.04) (0.03) (165.08) (179.81) (198.16) %_STATE_OWN -0.036-0.012-0.013 0.027** 0.038** 0.029** -47.032 138.333* -21.425 (0.06) (0.07) (0.06) (0.01) (0.01) (0.01) (82.80) (83.38) (96.00)

%_LARGEST_SH 0.406** 0.592*** 0.356** -0.014-0.023 0.007 29.893 16.035 203.058 (0.16) (0.18) (0.17) (0.03) (0.04) (0.03) (192.75) (200.05) (190.94) NONTRDPCT -0.625*** -0.574*** -0.541*** 0.036** 0.035 0.025 75.864 212.497** -21.761 (0.10) (0.12) (0.11) (0.02) (0.02) (0.02) (113.36) (107.27) (118.60) D_PRIVATE_PLAC E 0.415*** 0.370*** 0.411*** 0.055*** 0.055*** 0.060*** 218.956*** 160.587** 221.076** (0.04) (0.05) (0.04) (0.01) (0.01) (0.01) (83.28) (79.86) (91.25) DIVPRT 0.102*** 0.113*** 0.106*** -0.006-0.002-0.011-31.621-26.147-64.787 (0.03) (0.04) (0.04) (0.01) (0.01) (0.01) (45.68) (50.48) (47.52) Constant -1.671*** -1.992*** -1.568*** -0.646*** -0.771*** -0.657*** -1,331.836*** -1,176.019*** -872.472** (0.21) (0.26) (0.23) (0.07) (0.08) (0.08) (370.49) (380.08) (401.08) Firm & Year FE Y Y Y Y Y Y Y Y Y Observations 18,253 12,931 14,734 11,664 7,718 9,049 11,664 7,718 9,049 Adjusted R-squared 0.721 0.717 0.727 0.573 0.62 0.606 0.316 0.349 0.295 38

Table 5: Market Reactions to Reports Containing RPTs and to the Announcement of SEOs. Panel A reports the average three-day cumulative abnormal return surrounding reports containing related-party transactions, RPT_CAR(-1, 1). The sample includes reports of RPTs larger than 5% of total assets in that year. RPT_CAR(-1, 1) is reported for the full sample, a subsample of RPT reports contained in regular annual and semi-annual periodic reports, a subsample of those reported in non-periodic reports. We also identify reports containing only pure cash outflows and pure cash inflows, and report RPT_CAR(-1, 1) for each group. Panel B reports the mean and median three-day cumulative abnormal return surrounding the announcement of SEOs, SEO_CAR(-1, 1). Panel C reports regression estimation results relating SEO_CAR(-1, 1) to ex-post changes in total cash outflows through related party transactions, Δ CO_RPT_ALL, normalized by net SEO proceeds after all expenses, NET_SEO, Δ CO_RPT_ALL/NET_SEO. Column (1) contains no controls other than ln(net_seo). Column (2) includes the same set of control variables as in Table III and industry- and year fixed effects. Robust standard errors are reported in parentheses. Coefficients marked with *, **, and *** are significant at 10%, 5%, and 1%, respectively. Variable definitions are provided in Appendix I. Panel A: Market Reaction to Reports Containing RPTs N RPT_CAR(-1, 1) Full Sample: All 3,086-0.67%*** Pure Cash Outflow 336-0.44%* Pure Cash Inflow 414 0.31% Periodic Reports: All 2,485-0.87%*** Pure Cash Outflow 180-0.72%* Pure Cash Inflow 86-0.02% Non-Periodical Reports: All 601 0.17% Pure Cash Outflow 156-0.12% Pure Cash Inflow 328 0.40% Panel B: Market Reactions to the Announcement of SEOs SEO_CAR(-1, 1) N 557 Mean -0.73%*** Median -1.18% Panel C: Market Reaction to the Announcement of SEOs and Ex-post Total Cash Outflows through RPTs Dependent Variable SEO_CAR(-1, 1) (1) (2) ΔCO_RPT_ALL/NET_SEO -0.723** -0.689** (0.34) (0.32) ln(net_seo) -0.031-0.949 (0.34) (0.61) Constant -1.150 4.018 (2.10) (3.50) Controls N Y Industry & Year FE N Y Observations 253 247 Adjusted R-squared 0.011 0.051 39

Table 6: Tunneling by Firm Characteristics and SEO Stated Purposes. This table relates firm characteristics and stated purposes to how cash outflows through related-party transactions (CO_RPT) change during SEO years after firms receive SEO proceeds. SEO years are the year SEO proceeds are received and the year after. The dependent variable is log of CO_RPT_ALL, the total CO_RPT. Column (1) includes SEO firms with a parent and non-seo firms. Column (2) includes firms without a parent and non-seo firms. Columns (3) and (4) separate the sample the same way by whether the firm is state-owned. Columns (5) and (6) separate the sample the same way by whether the firm is audited by a Big-4 international accounting firm. Columns (7) and (8) separate the sample the same way by whether the stated-purpose of SEO contains acquisition of a specific firm or assets. Second-stage IV regression results are reported. First-stage regression results are reported in Appendix II, panel C. The sample period covers 2000-2012. All regressions include firm- and year-fixed effects. Bootstrapped standard errors are reported in parentheses. Coefficients marked with *, **, and *** are significant at 10%, 5%, and 1%, respectively. Variable definitions are provided in Appendix I. Dependent Variable ln(co_rpt_all) VAR D_PARENT D_STATE_OWN D_Big_4 D_SEO_ACQUISITION =1 =0 =1 =0 =1 =0 =1 =0 (1) (2) (3) (4) (5) (6) (7) (8) SEO 0.732*** 0.680 0.880*** 0.619*** 0.316 0.770*** 0.192 0.746** (0.27) (0.43) (0.32) (0.24) (0.24) (0.28) (0.37) (0.29) ln(nyear_listed) -0.846*** -0.772*** -0.879*** -0.774*** -0.633*** -0.855*** -0.630*** -0.854*** (0.14) (0.16) (0.14) (0.14) (0.11) (0.15) (0.13) (0.14) ln(sales) 0.253*** 0.229*** 0.255*** 0.219*** 0.267*** 0.229*** 0.302*** 0.216*** (0.07) (0.08) (0.07) (0.08) (0.09) (0.07) (0.09) (0.07) ROE -0.149-0.274-0.157-0.277-0.056-0.184-0.276-0.213 (0.24) (0.27) (0.25) (0.28) (0.36) (0.24) (0.30) (0.24) LEVERAGE 1.153*** 1.004*** 1.185*** 1.060*** 1.060*** 1.246*** 1.006*** 1.082*** (0.30) (0.36) (0.30) (0.34) (0.33) (0.29) (0.36) (0.30) PPE/TA -0.287 0.047-0.239-0.064 0.100-0.234-0.049-0.219 (0.38) (0.42) (0.35) (0.38) (0.41) (0.33) (0.41) (0.37) SALES_GR -0.011-0.026 0.011-0.041-0.064-0.001-0.047 0.004 (0.07) (0.09) (0.08) (0.08) (0.09) (0.07) (0.09) (0.07) %_IND_DIR 0.176-0.115 0.091-0.087-0.349 0.252-0.103 0.160 (0.36) (0.48) (0.39) (0.44) (0.50) (0.35) (0.46) (0.35) %_STATE_OWN 0.326* 0.154 0.366** 0.209 0.079 0.349** 0.137 0.320** (0.18) (0.18) (0.18) (0.20) (0.22) (0.18) (0.23) (0.16) %_LARGEST_SH 3.514*** 3.961*** 3.764*** 3.653*** 3.564*** 3.800*** 4.085*** 3.367*** (0.49) (0.64) (0.52) (0.57) (0.61) (0.49) (0.61) (0.43) NONTRDPCT 0.155-0.419-0.030-0.172-0.343-0.063-0.398 0.098 (0.31) (0.33) (0.27) (0.32) (0.33) (0.26) (0.36) (0.29) D_PRIVATE_PLACE 0.130 0.215 0.142 0.207 0.111 0.172 0.095 0.182* (0.11) (0.14) (0.11) (0.13) (0.13) (0.11) (0.14) (0.10) DIVPRT 0.159 0.065 0.116 0.162 0.180 0.130 0.155 0.123 (0.11) (0.11) (0.09) (0.11) (0.12) (0.09) (0.13) (0.09) Constant -9.676*** -9.837*** -9.668*** -9.740*** -10.130*** -9.710*** -10.464*** -9.441*** (0.62) (0.68) (0.62) (0.66) (0.70) (0.61) (0.72) (0.58) Firm & Year FE Y Y Y Y Y Y Y Y Observations 17,152 13,785 17,132 13,830 12,841 17,695 13,273 17,689 Adjusted R-squared 0.849 0.858 0.850 0.858 0.855 0.854 0.853 0.854 40

Table 7: Changes in Accounts Receivable, Prepaid Expenses, Uncollectable Accounts Receivable, Top Executive Compensation, and Administrative Expenses during SEO Years. This table estimates changes in accounts receivable, prepaid expenses, percentage of uncollectable accounts receivable, top executive compensation, and administrative expenses during SEO years after firms receive SEO proceeds. SEO years are the year SEO proceeds are received and the year after. The dependent variable is ACCV/TA, accounts receivables over total assets in column (1); PREPAY/TA, prepaid expenses over total assets in column (2); %_ACCV_BAD, percentage of accounts receivable classified as uncollectable in column (3); ln(topex_pay), log of three highest paid executives total compensation in column (4); Adm_Exp/Sales, administrative expenses over sales in column (5). Columns (1) (5) report second-stage IV regression results. First-stage regression results are reported in Appendix II, panel A. The sample period covers 2000-2012 for columns (1), (2), (4) and (5); 2004-2012, for column (3). All regressions include firm- and year-fixed effects. Bootstrapped standard errors are reported in parentheses. Coefficients marked with *, **, and *** are significant at 10%, 5%, and 1%, respectively. Variable definitions are provided in Appendix I. Dependent Variable ACCV/TA PREPAY/TA %_ACCV_BAD ln(topex_pay) Adm_Exp/Sales (1) (2) (3) (4) (5) SEO 0.016*** 0.009*** 0.035*** 0.019 0.188*** (0.00) (0.00) (0.01) (0.06) (0.06) ln(nyear_listed) 0.011*** 0.001 0.008-0.054** -0.008 (0.00) (0.00) (0.00) (0.03) (0.01) ln(sales) 0.012*** 0.004*** -0.049*** 0.202*** -0.157*** (0.00) (0.00) (0.00) (0.01) (0.03) ROE -0.003 0.006*** -0.025** 0.309*** -0.257*** (0.00) (0.00) (0.01) (0.04) (0.07) LEVERAGE -0.000 0.019*** -0.007-0.155*** 0.370*** (0.00) (0.00) (0.02) (0.06) (0.10) PPE/TA -0.047*** -0.039*** 0.046*** -0.173*** -0.044 (0.00) (0.00) (0.02) (0.06) (0.05) SALES_GR -0.001 0.001** -0.001-0.027** -0.036*** (0.00) (0.00) (0.00) (0.01) (0.01) %_IND_DIR 0.002-0.003-0.023 0.047 0.002 (0.00) (0.00) (0.02) (0.07) (0.05) %_STATE_OWN -0.003-0.003 0.007-0.002 0.027 (0.00) (0.00) (0.01) (0.03) (0.02) %_LARGEST_SH -0.017*** -0.012** -0.068*** 0.056 0.030 (0.01) (0.00) (0.02) (0.09) (0.06) NONTRDPCT 0.012*** -0.013*** 0.052*** -0.178*** 0.065* (0.00) (0.00) (0.01) (0.04) (0.04) D_PRIVATE_PLACE -0.006*** 0.002-0.011*** 0.019 0.066*** (0.00) (0.00) (0.00) (0.02) (0.01) DIVPRT -0.005*** -0.000-0.006 0.039** -0.015** (0.00) (0.00) (0.00) (0.02) (0.01) Constant -0.007 0.009 0.483*** -1.352*** 1.166*** (0.01) (0.01) (0.03) (0.13) (0.16) Firm & Year FE Y Y Y Y Y Observations 18,340 18,340 13,800 12,852 18,033 Adjusted R-squared 0.745 0.437 0.610 0.781 0.096 41

Table 8: Robustness Tests This table reports re-estimation results for key dependent variables with an alternative SEO definition and an alternative instrument. All reported results are second-stage IV regression results. Panel A shows re-estimation results while excluding small SEOs with proceeds in the bottom 10 percentile. Panel B shows re-estimation results with an instrument based on one year lag. Bootstrapped standard errors are reported in parentheses. Coefficients marked with *, **, and *** are significant at 10%, 5%, and 1%, respectively. Variable definitions are provided in Appendix I. Panel A: Alternative SEO definition; excluding small SEOs Dependent Variable Δ(CASH/NON-CASH Ln(CAPEX) Ln(CO_RPT_ %_ACCV ASSET) ALL) _BAD ΔCO_RPT_ ALL > 0 ΔCO_RPT_ ALL <= 0 ΔCO_RPT_ ALL > 0 ΔCO_RPT_ ALL <= 0 (1) (2) (3) (4) (5) (6) SEO 0.903*** 0.036*** -0.061 0.592*** 0.056** 0.110*** (0.31) (0.01) (0.11) (0.11) (0.02) (0.04) Controls Y Y Y Y Y Y Firm and Year FE Y Y Y Y Y Y Observations 18,343 13,800 12,931 14,734 7,718 9,049 Adjusted R-squared 0.852 0.610 0.717 0.727 0.620 0.606 Panel B: Alternative Instrument based on one-year lag Dependent Variable Δ(CASH/NON-CASH Ln(CAPEX) Ln(CO_RPT_ %_ACCV ASSET) ALL) _BAD ΔCO_RPT_ ALL > 0 ΔCO_RPT_ ALL <= 0 ΔCO_RPT_ ALL > 0 ΔCO_RPT_ ALL <= 0 (1) (2) (3) (4) (5) (6) SEO 0.811*** 0.035*** 0.029 0.556*** 0.033 0.101** (0.28) (0.01) (0.11) (0.10) (0.02) (0.04) Controls Y Y Y Y Y Y Firm and Year FE Y Y Y Y Y Y Observations 18,343 13,800 12,931 14,734 7,718 9,049 Adjusted R-squared 0.852 0.610 0.717 0.727 0.620 0.606 42

Appendices Appendix 1: Variable definitions. Variables Definition Sources Instrument-related Variables SEO An indicator variable equal to one in SEO years (the year when SEO CSMAR proceeds are received and the year after), and zero otherwise. AFFECT_REG An indicator variable equal to one if the firm does not satisfy the dividend Wind requirement of the 2006 and 2008 regulations. A firm is affected by the 2006 (2008) regulation if its two-year lagged dividend ratio, defined by cumulative dividend payout in the past three years over the average annual distributable profit over the past three years, is smaller than 20% (30%). The two-year lag allows for time elapsed from obtaining an approval to issue SEO to the use of the proceeds. IV_SEO Instrumental variable constructed based on the 2006 and 2008 regulation: IV_SEO = AFFECT_REG * POST_REG, where POST_REG is equal to one in year 2008 and 2010 for firms affected by the 2006 and 2008 regulation, respectively. Tunneling Variables CO_RPT_ALL CO_RPT _Parent CO_RPT _Sibling CO_RPT _Others CO_RPT _Capital CO_RPT _Good CO_RPT _OtherTr Total cash outflows through related-party transactions (RPT) by firm i in year t, measured in millions of 2000 price level RMB. Related parties includes parent firm; subsidiary firm; sibling firm (firms sharing the same parent firm); co-managed firm; large shareholders with material effects on the firm s operations; firms owned by major shareholders and their relatives; firms owned by key managers and their relatives; and others. Total cash outflows to the parent firm, measured in millions of 2000 price level RMB. Total cash outflows to sibling firms (firms sharing the same parent firm), measured in millions of 2000 price level RMB. Total cash outflows to related parties other than parent and sibling firms, measured in millions of 2000 price level RMB. These firms include subsidiaries; major shareholders with material effects on the firm s' operation; co-managed firms; firms owned by large shareholders or key managers; and other related entities. Total cash outflows to related parties through capital-related transactions and equity-related transactions, measured in millions of 2000 price level RMB. Total cash outflows to related parties through goods- and employee compensation-related transactions, measured in millions of 2000 price level RMB. Total cash outflows to related parties through fixed assets transactions; loan guarantees; leasing; non-pecuniary transactions; joint-project, and other transactions, measured in millions of 2000 price level RMB. CSMAR CSMAR CSMAR CSMAR CSMAR CSMAR CSMAR ACCV / TA Accounts receivable over total assets. Resset PREPAY / TA Prepaid expenses over total assets. Resset %_ACCV_BAD The fraction of accounts receivable classified as unlikely to be collected. Resset Other variables CAPEX Capital expenditure, measured in millions of 2000 price level RMB. Resset CASH/NON-CASH Ratio of Cash to Total non-cash assets. Resset ASSET NYEAR_LISTED Number of years since being listed. Resset DEBT Short- and long-term loans and bonds outstanding, measured in millions of Resset 43

2000 price level RMB. Adm_Exp/Sales Administrative expenses over the sales. Resset TopEx_Pay Total compensation of three highest-paid executives, measured in millions Resset of 2000 price level RMB. NET_SEO Gross proceeds from SEOs net of offering expenses, measured in millions CSMAR of 2000 price level RMB. SALES Total sales, measured in millions of 2000 price level RMB. Resset ROE Return on equity: the ratio of net profit to owner's equity. Resset LEVERAGE Ratio of total debts (short term debt + long term debt) to total assets. Resset PPE/TA Ratio of tangible asset (properties, plants, and equipment) to total assets. Resset SALES_GR Sales growth rate from year t-1 to year t. Resset %_IND_DIR Percentage of independent directors on the board. Resset %_STATE_OWN Percentage of shares held by the government through a designated Resset government agency. %_LARGEST_SH Percentage of shares held by the largest shareholder. Resset NONTRDPCT Percentage of non-tradable shares. Resset D_PRIVATE_PLACE An indicator variable equal to one if the firm has conducted private CSMAR placement in year t. D_PARENT An indicator variable equal to one if the firm has a parent firm. Resset D_STATE_OWN An indicator variable equal to one if the firm belongs to a state-owned Resset enterprise. D_Big_4 An indicator variable equal to one if the firm is audited by a Big-4 accounting Resset firm. D_SEO_ACQUISITION An indicator variable equal to one if the stated purpose of SEO is acquisition CSMAR of a specific firm or assets. DIVPRT Dividend payout ratio, equal total dividend paid over net income in year t. Resset 44

Appendix 2: First-stage Regression Results. Panel A. First-stage Regression Results for Tables 3, 4 (Columns 1, 4, and 7), 7, and 8 Dependent Variable SEO (1) IV_SEO -1.85*** (0.56) Controls Y Firm & Year FE Y Observations 18,343 Pseudo R2 0.361 Panel B. First-stage Regression Results for Tables 4 (Columns 2, 3, 5, 6, 8, and 9) and 8 (Columns 3-6) Dependent Variable SEO ΔCO_RPT_ALL > 0 <= 0 (1) (2) IV_SEO -14.24*** -1.15 (0.72) (0.72) Controls Y Y Firm & Year FE Y Y Observations 12,931 14,734 Pseudo R2 0.276 0.229 Panel C. First-stage Regression Results for Table 6 Dependent Variable SEO D_PARENT=1 D_PARENT=0 %_STATE_OWN>0 %_STATE_OWN=0 (1) (2) (3) (4) IV_SEO -1.93*** -1.63** -1.24** -2.38** (0.70) (0.84) (0.57) (1.04) Controls Y Y Y Y Firm & Year FE Y Y Y Y Observations 17,152 13,785 17,132 13,830 Pseudo R2 0.398 0.266 0.387 0.342 Dependent Variable SEO D_BIG_4 = 1 D_BIG_4 = 0 D_SEO_ACQUISITION D_SEO_ACQUISITION = 1 = 0 (5) (6) (7) (8) IV_SEO -12.16** -1.81** -15.27** -1.68** (1.50) (0.56) (0.60) (0.56) Controls Y Y Y Y Firm & Year FE Y Y Y Y Observations 12,841 17,695 13,273 17,689 Pseudo R2 0.279 0.370 0.379 0.379 45