Ultimate ownership, crash risk, and split share structure reform in China

Ultimate Ownership, Crash Risk, and Split Share Structure Reform in China

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Ultimate Ownership, Crash Risk, and Split Share Structure Reform in China Quanxi Liang, Donghui Li, Wenlian Gao PII: DOI: Reference:

S0378-4266(20)30018-2 https://doi.org/10.1016/j.jbankfin.2020.105751 JBF 105751

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Journal of Banking and Finance

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28 July 2015 14 January 2020

Please cite this article as: Quanxi Liang, Donghui Li, Wenlian Gao, Ultimate Ownership, Crash Risk, and Split Share Structure Reform in China, Journal of Banking and Finance (2020), doi: https://doi.org/10.1016/j.jbankfin.2020.105751

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Ultimate Ownership, Crash Risk, and Split Share Structure Reform in China Quanxi Liang, Donghui Li, and Wenlian Gao∗

Current Version: Jan. 2020

∗

Li and Gao are the corresponding authors. The three co-authors have equally contributed to this paper. Liang is from the School of Business, Guangxi University, Nanning 530004, PR China; Li is from the College of Economics, Shenzhen University, Shenzhen 518061, PR China; Gao is from the College of Business at Northern Illinois University, DeKalb, IL 60305; Authors’ contact information: Liang, [email protected], +86 (0771) 3232880; Li, [email protected], +86 (755) 26536120; Gao, [email protected], (815) 7536396; Liang would like to thank the National Natural Science Foundation of China for financial support (Grant Nos. 71762005 and 71362013), and the Natural Science Foundation of Guangxi Province for financial support (Grant No. 2013GXNSFBA019011); Li would like to thank the National Natural Science Foundation of China for financial support (Grant No. 71873058).

Ultimate Ownership, Crash Risk, and Split Share Structure Reform in China

Abstract This study investigates the relationship between ultimate ownership and stock price crash risk for Chinese firms and the impact on this relationship by the implementation of the split share structure reform, which rendered previously non-tradable shares freely tradable. We find that governmentcontrolled firms, especially local ones, have a significantly higher crash risk than privately controlled firms. After the reform, crash risk of all firms decreases significantly, with a greater risk reduction for privately controlled firms than for government-controlled firms. Further evidence demonstrates that government-controlled firms with stronger political incentives tend to have a higher crash risk. Keywords: Crash risk, Government control, Split share structure reform, Political connections JEL classification: F23, F30, G15, G32, O32

1.

Introduction

Stock price crashes occur when share price falls precipitously following the sudden release to the market of adverse news. As the largest transition economy in the world, China has sustained an increasing number of stock price crashes of late. For example, on June 1, 2016, an investigation by the China Securities Regulatory Commission (CSRC) revealed that Xintai Electric, an electrical equipment manufacturer, provided fraudulent financial information during its IPO application. Within a month of CSRC’s official announcement of Xintai’s administrative punishment, the company’s shares plunged by 83%. Given the importance of crash risk in both portfolio management and risk management contexts, recent theoretical and empirical research has focused on the underlying causes of firm-level stock price crashes. Extensive literature suggests that conflict of interest between managers and outside investors is a factor for stock price crash risk because firm managers have an incentive to strategically withhold negative information due to concerns over compensation, career development, or reputation. Specifically, incomplete transparency regarding firm performance, which enables insiders to capture additional cash flows, affects the division of risk-bearing between insiders and public investors. Consequently, opaque firms are more prone to stock price crashes (Jin and Myers, 2006; Hutton et al., 2009). Conflict of interest has also been captured in crash risk literature through studies on such topics as corporate tax avoidance (Kim et al., 2011a); CFO option sensitivity (Kim et al., 2011b); frequency of management earnings guidance (Hamm et al., 2012); internal control weaknesses (Zhou et al., 2013); and conservatism in financial reporting (Kim and Zhang, 2016). Our study focuses on the agency conflict between controlling shareholders and minority investors and investigates the relation between the characteristics of ultimate controlling shareholders and stock price crash risk in a Chinese setting.1 Owing to the highly concentrated ownership structure in China, there is a severe conflict of interest between controlling shareholders and minority investors. Controlling shareholders have an incentive to maintain their private benefit of control - divert 1

See Boubaker et al. (2014) and Chauhan et al. (2015) for international evidence on ownership concentration and stock price crash risk. Boubaker et al. (2014) examine French firms and show that firms with substantial excess control are more likely to experience stock price crashes, suggesting that controlling shareholders are more likely to hoard bad information when their control rights exceed their cash flow rights. Chauhan et al. (2015) look at Indian firms and find that block ownership is positively and significantly related to one-quarter-ahead crash risk.

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corporate wealth to themselves rather than share it with other investors (Johnson et al., 2000a; Johnson et al., 2000b; Djankov et al., 2008). Conversely, when listed companies are financially distressed, controlling shareholders might choose to prop up the firms’ earnings through favorable asset-related transfers to meet key performance targets stipulated by market regulators (Jian and Wong, 2010). Widespread tunneling and propping by controlling shareholders have resulted in rampant earnings management, which has been posited as a driving force of stock price crash risk (Jin and Myers, 2006; Hutton et al., 2009). The literature also suggests that firms with controlling state ownership tend to follow less transparent, less timely, and/or lower-quality reporting and auditing practices to render financial statements less informative (Bushman et al., 2004; Wang et al., 2008; Guedhami et al., 2009; Chaney et al., 2011). Given the intent to extract private control benefits and create an opaque firm environment, controlling shareholders have an incentive to withhold unfavorable or negative information from outside investors (Watts and Zimmerman, 1986; Bushman and Piotroski, 2006; Piotroski et al., 2015). However, once the stockpiled negative information reaches a tipping point, it will all be released at once, thereby leading to stock price crashes. We categorize our sample firms into different groups according to their ultimate ownership. We initially divide them into privately controlled and government-controlled firms, with the former referring to firms that are ultimately owned by non-government entities, such as entrepreneurs, townships, villages, and foreign companies and the latter further classified into central and local government-controlled firms. The central and local governments, respectively, serve as the first and second layers of agents to represent the nation. Empirical results show that government-controlled firms, particularly local government-controlled firms, exhibit a higher crash risk than their privately controlled counterparts. This finding implies that the lengthy chain of agency representation in local government-controlled firms, as well as the severe separation of control and cash flow rights, may give managers additional collusion incentives. To alleviate potential endogeneity concerns, we perform a battery of robustness tests, such as ultimate ownership changes, firm fixed-effects models, and lagged ultimate ownership variables. Our main results remain valid. We then explore the channels through which ultimate ownership influences crash risk. Three 2

proxies for firm-level information environment are considered: (i) earnings smoothing; (ii) accounting conservatism; and (iii) stock price synchronicity. We find that, compared to privately controlled firms, government-controlled firms, especially local ones, tend to have higher earnings smoothing, lower accounting conservatism, and higher stock price synchronicity. These results corroborate our primary conclusion that ultimate government ownership may negatively affect the information environment of individual firms, consequently leading to a higher risk of stock price crash. We further examine the effect of split share structure reform (hereafter, the reform) on the association between ultimate ownership and crash risk. Initiated by the Chinese government in 2005, the reform repealed restrictions on the transferability of controlling shareholders’ shares. By removing significant market frictions, the reform entails an exogenous shock to corporate governance, thereby introducing an improved incentive alignment between controlling shareholders and minority shareholders (Lin, 2009; Chen et al., 2012; Huang et al., 2013; Liao et al., 2014). We find that after the reform, crash risk is significantly reduced for all firms, thus indicating that the reform substantially improved corporate governance and relieved conflicts of interest between insiders and outsiders. In particular, privately controlled firms sustain a more pronounced effect of crash risk reduction, which indicates that they have benefited largely from the reform. We also investigate the underlying factors of the relation between ultimate ownership and crash risk from the perspective of political incentives. Political incentives could motivate firm managers to temporarily withhold negative information, which can lead to stock price crashes (Piotroski et al., 2015). To quantify the strength of a firm’s political connections, we construct a political connection index based on the political background of the firm’s senior management. We find that government-controlled firms with strong political connections tend to have a higher crash risk than their privately controlled counterparts. We further show that the crash risk of firms controlled by corrupt local government officials declines substantially after the officials are dismissed. These findings suggest that the effect of government ownership on crash risk can be partially explained by the strong political incentives of the firm management. Our study makes several contributions to the extant literature. First, our work advances the finance and economics literature regarding the importance of the fundamental agency problem 3

between controlling shareholders and minority investors (Johnson et al., 2000a; Djankov et al., 2008). Specifically, we focus on different types of ultimate controlling shareholders in Chinese listed firms. We find that government-controlled firms, particularly local ones, are associated with a higher stock price crash risk than privately controlled firms. Moreover, the positive effect of government ownership on stock price crashes is more pronounced in local government-controlled firms with high political connections, an outcome that is consistent with the view about the collusion incentives of local government controllers (Wang et al., 2008). Second, this study contributes to the growing literature on stock price crash risk. Most studies have examined the effect of various accounting characteristics and determinants related to market structures and institutional infrastructures on stock price crashes.2 However, only a few have examined the possible effect of ownership structure (see Callen and Fang, 2013 for the effect of institutional investor stability; Andreou et al., 2016 for institutional ownership and ownership of directors; and Chauhan et al., 2015 for outside block ownership). We identify the ultimate controlling shareholders and examine their possible effect on crash risk to shed additional light on this issue. Third, our work complements the literature on split share structure reform, specifically the thread examining the real effects of the reform. These effects on firms include fewer party-related transactions (Lin, 2009); low cash holdings and high market valuations of cash holdings (Chen et al., 2012); increased corporate risk-taking (Huang et al., 2013); and improved output, profit, and employment (Liao et al., 2014). We extend the positive consequences of the reform by showing its effect on the reduction of stock price crash risk, especially for privately controlled firms. This finding is consistent with the view that the reform significantly lowered market frictions, thereby leading to an improved interest alignment between controlling and minority shareholders. The rest of this paper is organized as follows. Section 2 discusses the hypothesis development.

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The former includes firm opacity (Jin and Myers, 2006); earnings management (Hutton et al., 2009); corporate tax avoidance (Kim et al., 2011a); CFO option sensitivity (Kim et al., 2011b); frequency of management earnings guidance (Hamm et al., 2012); conservatism in financial reporting (Kim and Zhang, 2016); and internal control weaknesses (Kim et al., 2013). The latter includes trading volume and past returns (Chen et al., 2001); stock illiquidity (An et al., 2018); mandatory adoption of IFRS (DeFond et al., 2015); and political events (Piotroski et al., 2015).

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Section 3 describes the data and variables. Section 4 presents the empirical results. Finally, Section 5 concludes.

2.

Hypothesis development

2.1

Ultimate controlling shareholders and crash risk

Ownership structure is a fundamental issue of corporate governance. A well-dispersed ownership structure is mainly observed in the United States and Japan, whereas a structure comprised of one or more controlling owners is often observed in European and Asian companies.3 Therefore, the central agency problem has shifted from relieving the conflict of interest between managers and shareholders to protecting the minority shareholders from expropriation by controlling shareholders (Johnson et al., 2000a; Djankov et al., 2008). The diversion of firm resources to corporate controllers, a strategy called “private benefits of control,” has been empirically investigated in different contexts.4 Shleifer and Vishny (1997) posit that expropriation takes several forms, including excessive executive compensation; transfer pricing; self-serving financial transactions (e.g., directed equity issuance or personal loans to insiders); and outright theft of corporate assets. Chinese firms have a highly concentrated ownership structure, usually with one major owner holding a significant percentage of the shares,5 giving the controlling shareholders substantial discretionary power to use firm resources for their private gains at the expense of other shareholders. This is often observed among firms that are ultimately controlled by the government or governmentrelated entities. Politicians and governments may have an incentive to pursue social or political goals, such as infrastructure development and resolution of fiscal and unemployment challenges, which are inconsistent with the interests of shareholders (Lin et al., 1996). Moreover, the managers of government-controlled firms are typically government-appointed and do not own shares in these firms (Fan et al., 2007; Jian and Wong, 2010). Executive compensation in these cases is low 3

See La Porta et al. (1999); Claessens et al. (2000); Johnson et al. (2000a); and Faccio and Lang (2002). These contexts include the Mexican and Asian financial crises (La Porta et al., 2003; Johnson et al., 2000a); legal disputes over tunneling (Johnson et al., 2000b); and corporate governance during the transition from socialism (Glaeser et al., 2001). 5 Allen et al. (2005) find that 80% of their 1,100 sample Chinese firms are former state-owned enterprises with a considerable market share. Bai et al. (2004) also find that the largest owner of Chinese listed firms holds 44.8% of the total shares on average. 4

5

and insensitive to firm performance (Firth et al., 2006). These factors increase the separation of management utility and firm profit maximization, thereby leading to widespread expropriation in China. In particular, Chinese firms are controlled by different levels of government, and the government owners behave differently in capital markets due to diverse allocation of power and responsibilities. Legally, the ultimate owners of government-controlled firms are the people. The central government acts as the first-agent layer representing the people, and local governments act as agents of the central government. Thus, local governments serve as lower-agent layers representing the people. Higher-level governments protect their reputation more rigorously than lower-level governments. Therefore, lower-level governments are more likely to expropriate minority shareholders severely. In addition, local governments have a strong incentive to collude with local firms in earnings management in order to circumvent central government regulations (Chen et al., 2008). By contrast, the conflict of interest between controlling and minority shareholders is less severe in privately controlled firms: The controlling shareholders have control and cash flow rights, as well as greater concern for their firm’s long-term performance. Given the severe agency problem and the resulting expropriation in government-controlled firms, corporate insiders might have strong incentives to manipulate financial statements to obscure the information on actual firm performance (La Porta et al., 2000). The literature on financial reporting and auditing illustrates that the availability of firm-specific information to outside investors is negatively related to the extent of state ownership. Liu and Lu (2007) and Chen et al. (2008) provide evidence that agency conflicts between controlling government shareholders and minority investors account for a large proportion of earnings management in Chinese listed companies. Chaney et al. (2011) find that politically connected firms report low-quality earnings than other firms. In country-level regressions, Bushman et al. (2004) document that high government ownership undermines financial transparency, including the intensity and timeliness of financial disclosures, analyst following, and media penetration. The cross-country analysis of Guedhami et al. (2009) provides robust evidence that state owners are less likely to choose a Big 4 auditor whose client assurance services are presumed superior to those of non-Big 4 auditors. Wang et al. (2008) examine auditor 6

choice decisions in Chinese listed firms and find that the tendency of state-owned firms to hire low-quality auditors to facilitate collusion is greater than that of non-state-owned firms. Lin and Liu (2009) focus on IPO firms in China and show that firms with large controlling shareholders are less likely to hire a high-quality auditor. Corporate insiders in government-controlled firms may also develop strong incentives to directly manipulate firms’ information environments. Studies indicate that politicians and governments tend to suppress negative news about their activities (Watts and Zimmerman, 1986). More recently, Bushman and Piotroski (2006) find that, firms hasten the recognition of good news and delay bad news in the reported earnings in countries characterized by high state involvement in the economy than those in countries with less state involvement. Piotroski et al. (2015) document that political incentives lead local politicians and their affiliated firms to temporarily restrict the flow of negative information regarding the companies. In sum, the market performance of Chinese firms is influenced by governments and politicians as a result of their prominent role in influencing firm-specific information environments. Stock price crashes are observed when negative information accumulates beyond a certain level (Jin and Myers, 2006; Hutton et al., 2009; Kim et al., 2011a, 2011b). Therefore, we argue that if a firm is owned by the government, at either the central or local level, then its information environment will be less transparent than that of a privately controlled firm. Consequently, that firm would have a higher likelihood of a crash risk. Local government-controlled firms have a longer chain of agency problems, which can lead to more frequent and severe crashes. Based on these arguments, we propose the following hypothesis:

H1:

2.2

Ceteris paribus, government-controlled firms, especially local ones, have a higher crash risk than privately controlled firms.

Split share structure reform and crash risk

Property rights research identifies three essential aspects of complete corporate share ownership. Specifically, corporate shareholders are entitled to (i) control or voting rights; (ii) cash flow rights; 7

and (iii) the rights to transfer shares and associated control and cash flow rights to another party. Share transferability ensures future profit allocation through the capital markets (Alchian, 1969), enhances the alignment of interests between controlling and minority shareholders, and improves control over managerial shirking and incompetence by promoting market discipline through takeovers and effective incentive contracts (Karpoff and Rice, 1989). Before the reform, trade of controlling shares was strictly prohibited for all publicly listed firms. This lack of share transferability causes the interests of controlling shareholders to become incongruent with share performance because they cannot benefit from stock price appreciation. Instead, controlling shareholders only realize the gains and obtain cash from cash distributions and possibly from related-party transactions and tunneling. Liu et al. (2010) argue that tunneling (other than cash dividend) is the best choice for controlling shareholders to earn investment profit when ownership is not tradable. To remove trading restrictions on the shares of controlling shareholders, the Chinese government initiated the reform in 2005, which allowed non-tradable shares owned by controlling shareholders to be freely traded in the stock market following a one- or two-year lock-in period. The reform provides a quasi-natural experiment enabling us to more accurately identify the effect of ultimate controlling shareholders on firm-specific crash risk. It has removed a substantial amount of market friction and strengthened controlling shareholders’ cash-flow rights relative to voting rights. As a result, agency conflicts have been reduced due to controlling shareholders’ increased motivation to take value-maximizing actions and reduce their expropriating behaviors. For example, Lin (2009) finds that controlling shareholders reduce expropriation through related party transactions after the reform eliminates the discrepancy between control rights and cash flow rights. Liao et al. (2014) document that the percentage of firms engaged in related party transactions with, and lending to, controlling shareholders dropped significantly after the reform. More important, better incentive alignment between controlling shareholders and minority investors has also been reflected in the improved information environment. Chen et al. (2016) find that the reform encouraged more analysts to follow Chinese firms and forecasting accuracy improved as the proportion of tradable shares in firms increased. Green et al. (2010) find that companies showed higher mandatory and voluntary disclosures in their post-reform annual reports than their pre-

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reform annual reports. Hou et al. (2012) provide evidence of increased share price informativeness among firms with greater sensitivity to the reform’s impact, i.e. those with more restricted shares. Given the enhanced alignment of interests between controlling and minority shareholders and the resulting improvement in corporate transparency, we expect that the reform could lead to decreased stock price crash risk in publicly listed Chinese firms. The effect of the reform on incentive alignment might differ across firms with diverse types of controlling shareholders. After the reform, controlling shareholders of privately controlled firms have both control and cash flow rights and are expected to focus on maximizing returns. Their interests have become more aligned with those of minority shareholders. By contrast, the controlling shareholders of government-controlled firms are government agencies whose objective function entails substantial non-price considerations, such as meeting specific political and social welfare objectives (Shleifer, 1998). Consequently, they still do not benefit directly from stock price appreciation due to the divergence of control and cash flow rights. Consistent with this, Huang et al. (2013) find that the controlling shareholders of family firms have been more likely to sell their shares after the reform, whereas those of government-controlled firms have largely retained their holdings because they primarily assume the political role of a government agent and prefer the private benefits of control. Therefore, the improvement in interest alignment in government-controlled firms is less pronounced than in privately controlled firms. Given that local government-controlled firms have a longer chain of agency problems, they would benefit less than central government-controlled firms from the reform. Based on these arguments, we propose the following hypothesis:

H2a:

Ceteris paribus, the crash risk of firms with diverse types of controlling shareholders declines after the reform.

H2b:

The reduction in crash risk is more pronounced for privately controlled firms than for government-controlled firms, particularly local ones.

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3.

Data and variable description

3.1

Sample construction

Our primary data source is the China Stock Market and Accounting Research database, from which we obtain information on ultimate ownership, stock returns, and financial status of firms. Our study spans from 2003 to 2014. We choose 2003 as the starting year because the disclosure of ownership structure has been complete since then. Our sample starts with all publicly traded firms listed on the Shanghai and Shenzhen stock exchanges. We subsequently exclude financial firms and firm-years with less than 26 weeks of stock-returns data. We further exclude firms in which the ultimate shareholders are unidentifiable. Finally, we eliminate firms that have missing observations during our study period to obtain a balanced panel data set. We impose this requirement because the majority of Chinese listed firms were government-controlled during the early part of our sample period, whereas a large fraction of privately controlled firms has been listed since 2009. Therefore, it is difficult to contrast government-controlled firms with their privately controlled counterparts due to the large variations in China’s economic and policy environments over the past decade, such as the financial crisis of 2008 and the 4 trillion RMB stimulus plan. Therefore, we employ a balanced panel sample to maintain a consistent comparison over time and cross-sectionally. We ultimately obtain 9,012 firm-year observations for 751 unique firms, in which 1,948 (21.62%) are for central governmentcontrolled firms; 4,530 (50.27%) are for local government-controlled firms; and 2,534 (28.11%) are for privately controlled firms.

3.2

Measures of crash risk

We use three measures of firm-specific stock price crash risk, namely, COLLAR, N CSKEW , and CRASH. To construct these measures, we first estimate the abnormal weekly returns for each firm-year using the following expanded market model (Jin and Myers, 2006): 10

RETi,τ =αi + βi,1 M KRETi,τ −2 + βi,2 IN DRETi,τ −2 + βi,3 M KRETi,τ −1 +βi,4 IN DRETi,τ −1 + βi,5 M KRETi,τ + βi,6 IN DRETi,τ

(1)

+βi,7 M KRETi,τ +1 + βi,8 IN DRETi,τ +1 + βi,9 M KRETi,τ +2 +βi,10 IN DRETi,τ +2 + ei,τ , where RETi,τ is the stock return for firm i in week τ . M KRETi,τ is the average return of the value-weighted Shanghai and Shenzhen composite indices, excluding firm i in week τ . IN DRETi,τ is the value-weighted industry return excluding firm i in week τ . Two lead and two lag terms are included to correct for non-synchronicity for market and industry returns (Dimson, 1979). Firmspecific abnormal weekly return is defined as the natural logarithm of 1 plus the residual term from the estimation of Eq. (1). Following Jin and Myers (2006), we use COLLAR to account for the frequency and severity of crashes. It is defined as the mean profit or loss from buying a put option and shorting a call option on the abnormal weekly returns multiplied by 1,000. Strike prices are set to the mean abnormal weekly returns minus 3.09 standard deviations for the put and the mean abnormal weekly returns plus 3.09 standard deviations for the call, with 3.09 chosen such that the put will be in the money with a frequency of 0.1% in normal distribution. The put-call strategy has zero expected value under normal distribution. Specifically, COLLAR is calculated as: n

COLLARi,t = 1000 ×

1X [max(0, w ¯i,t − 3.09σi,t − wi,t,τ ) − max(0, wi,t,τ − w ¯i,t − 3.09σi,t )] , (2) n τ =1

where n is the number of trading weeks in year t, and wi,t,τ is firm i’s abnormal weekly returns in week τ of year t. w ¯i,t and σi,t are the mean and standard deviation of abnormal weekly returns for firm i in year t, respectively. Following Chen et al. (2001) and Kim et al. (2011a, 2011b), we set our second measure of crash risk as the negative skewness of abnormal weekly returns (N CSKEW ). A high N CSKEW implies that a crash is more likely to occur. Specifically, for firm i in year t, N CSKEW is defined as follows: "

3/2

N CSKEWi,t = − n(n − 1)

n X τ =1

3 wi,t,τ

#,"

11

# n X 2 3/2 (n − 1)(n − 2)( wi,t,τ ) , τ =1

(3)

where n is the number of trading weeks, and wi,t,τ is firm i’s abnormal weekly returns in week τ of year t. Our third measure, CRASH, measures the likelihood of a crash (Hutton et al., 2009; Kim et al., 2011a, 2011b). For any given firm-year, we identify the number of crash weeks when the abnormal weekly returns is 3.09 standard deviations below the mean. CRASH is defined as an indicator variable that equals 1 for a firm-year if at least one crash week is observed during the fiscal year period, and 0 otherwise.

3.3

Classification of ultimate controlling shareholders

We classify our sample firms into different categories according to their ultimate ownership and denote each category using a dummy variable.6 We separate the firms into privately controlled firms (P rivate) and government-controlled firms (Gov), with the former referring to firms that are owned by non-government entities, such as entrepreneurs, townships, villages, and foreign companies, and the latter referring to firms that are owned by government entities. We further group the government-controlled firms into central government-controlled (Gov centr) and local government-controlled (Gov local). The former are owned by the central government (e.g., the Ministry of Finance and the Central Industrial Enterprises Administration Committee) and the latter are owned by local governments (e.g., province, city, or county governments, and the Bureau of State Assets Management at the province, city, or county level).

3.4

Control variables

Following the literature, we also consider a list of control variables that have been shown to affect stock price crash risk. The variable DT U RN refers to the detrended stock trading volume, a proxy 6

According to the “Measures for the Administration of the Takeover of Listed Companies” by the CSRC, “Under any of the following circumstances, it may constitute the holding of the controlling right of a listed company: (1) The investor is the controlling shareholder that holds more than 50% of the shares of the listed company; (2) The investor can actually control more than 30% of the voting right of shares of the listed company; (3) The investor can decide the election of more than half of the directors of the board of directors of the company by actually controlling the voting right of shares of the listed company; (4) The voting right of shares of a listed company under the actual control of the investor is sufficient to produce significant effects on the resolutions of the general assembly of shareholders of the company; or (5) Any other circumstance as recognized by the CSRC.” (http://www.fdi.gov.cn/1800000121 39 4237 0 7.html).

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for investor heterogeneity or the difference of opinions among investors. Chen et al. (2001) document that firms with high stock turnovers are more likely to crash. The 1-year lagged N CSKEW captures the potential persistence of the third moment of stock returns. F SRET and SIGM A denote the average abnormal weekly returns and standard deviation of abnormal stock returns over a fiscal year, respectively. Chen et al. (2001) argue that past returns and past return volatility are positively associated with future crashes. ABACC denotes the previous 3-year moving sum of the absolute value of discretionary accruals, estimated using the modified Jones model (Dechow et al., 1995). Opaque firms are more prone to future stock price crashes (Hutton et al., 2009). We also control for firm characteristics, including firm size (SIZE), measured by the logarithm of total assets; return on assets (ROA); market-to-book ratio (M B); leverage ratio (LEV ); and B/H-share (BH), an indicator variable for issuing both A- and B- or H-shares.

3.5

Descriptive statistics

Table 1 reports the summary statistics for crash risk measures and other variables used in our study. As shown, the entire sample is classified according to the ultimate ownership of firms. A t-test is performed on the differences between privately controlled firms and various categories of government-controlled firms. In contrast to privately controlled firms, government-controlled firms, especially local ones, sustain higher levels of crash risk. This relationship holds for all three crash risk measures. For example, the differences in COLLAR are 0.035, 0.030, and 0.037 between government-controlled firms and privately controlled firms, between central government-controlled firms and privately controlled firms, and between local government-controlled firms and privately controlled firms, respectively. These differences are statistically significant at the 1% level. Most of the control variables also exhibit a significant difference between privately controlled firms and their counterparts. For example, privately controlled firms show lower average abnormal returns and higher return volatility than government-controlled firms. They also tend to be smaller and have a higher market-to-book ratio, higher levels of discretionary accruals, and lower levels of political connections. Table 2 presents the correlation matrix. The three measures of crash risk are shown to be highly 13

correlated with one another but not highly correlated with other firm characteristics. In general, the correlations among the variables in our sample are moderately low, which indicates the absence of multicollinearity.

4.

Empirical results

This section investigates the effect of ultimate ownership structure of a firm on its stock price crash risk. First, we assess the effect of different types of controlling shareholders on the three crash risk measures using multivariate analysis and identify the channels through which ownership structures influence crash risk. Second, we address endogeneity issues and perform a battery of sensitivity tests to check the robustness of our results. Third, we examine the impact of the split share structure reform on crash risk and if it influences the association between ultimate ownership and crash risk. Fourth, we examine the political incentives of firm managers and the exogenous shock of dismissal of local government officials in order to explore the factors of the relation between ultimate ownership and stock price crash risk. 4.1

Main regression results

We use the following model frameworks to examine the effect of different types of controlling shareholders on stock price crash risk: CRi,t = β0 + β1 Govi,t +

P

βk × ControlV arsi,t−1 + εi,t

CRi,t = γ0 + γ1 Gov centri,t + γ2 Gov locali,t +

P

γk × ControlV arsi,t−1 + εi,t

(4) (5)

where CR denotes crash risk measures: COLLAR, N CSKEW , and CRASH. In Eq. (4), Gov is a dummy variable that represents government-controlled firms. In Eq. (5), governmentcontrolled firms are further classified into central government- and local government-controlled firms. Gov centr equals 1 if a firm is ultimately controlled by the central government, and 0 otherwise. Gov local equals 1 if a firm is ultimately controlled by local governments, and 0 otherwise. A set of control variables are considered, such as firm size (SIZE), return on assets (ROA), marketto-book ratio (M B), leverage ratio (LEV ), detrended stock trading volume (DT U RN ), negative 14

skewness of abnormal weekly returns (N CSKEW ), average abnormal weekly returns (F SRET ), standard deviation of abnormal weekly returns (SIGM A), discretionary accruals (ABACC), and B/H-share (BH). All control variables are 1-year lagged, except for ROA and BH, both of which are contemporaneous with crash risk measures. Logit regression is used when crash risk is measured by CRASH; otherwise, ordinary least squares regression is used. Year and industry fixed effects are included. We estimate Equations (4) and (5) for each crash risk measure and present the results in Table 3 which reveal two interesting results. First, government-controlled firms have higher levels of crash risk than privately controlled firms. Models 1, 3, and 5 show that Gov has significantly positive coefficients of 0.024 (t-statistic=2.81), 0.024 (t-statistic=2.24), and 0.142 (t-statistic=1.68) for COLLAR, N CSKEW , and CRASH, respectively. This indicates that government-controlled firms are more likely to sustain stock price crashes and, on average, have higher crash risk than their privately controlled counterparts. Second, the high crash risk associated with governmentcontrolled firms is mainly driven by local government-controlled firms. In Models 2, 4, and 6, the loadings of Gov local are all positive and significant at the 1% or 5% level. Meanwhile, the loadings of Gov centr are either not statistically significant or they are marginally significant at the 10% level, despite being positive. In terms of economic magnitude, Gov local has a coefficient of 0.026 in Model 2 when COLLAR is used as crash risk measure, indicating that the crash risk of local government-controlled firms exceeds that of privately controlled firms by 0.078 (=0.026/0.334, where 0.334 is the sample standard deviation of COLLAR) standard deviations. When CRASH serves as the measure of crash risk, we compute the marginal effect of Gov local as 0.0109, holding all other independent variables at their mean value (Kim and Zhang, 2016). The outcome implies that crashes in local government-controlled firms are 1.09% more likely than in privately controlled firms, equivalent to 15.58% (=0.0109/0.0700, where 0.0700 is the sample mean of CRASH) of the unconditional average crash probability. These findings indicate that the insiders of local government-controlled firms, which are characterized by a lengthy chain of agency problems, may have a strong incentive to hide negative information from outside investors, leading to a high crash risk. These results support H1. 15

4.2

The channels through which ultimate ownership affects crash risk

In Section 2.1, we posit that the ultimate ownership by governments could affect a firm’s information environment and, consequently, stock price crash risk. To empirically test the effect of government ownership on stock price crash risk by possibly influencing the firm-level information environment, we consider three channels: earnings smoothing, accounting conservatism, and stock price synchronicity. The literature suggests that earnings smoothing can be used to obfuscate a firm’s real performance, and specifically to conceal bad performance (Leuz et al., 2003). Chen et al. (2017) find that a high degree of earnings smoothing is associated with a high crash risk. Accounting conservatism refers to accountants’ tendency to require a higher verification threshold for recognizing good news as gains than recognizing bad news as losses (Basu, 1997). Watts (2003) and LaFond and Watts (2008) argue that conservatism is a governance mechanism that curbs managerial incentives and the ability to overstate accounting numbers and, thus, reduces information asymmetry. Kim and Zhang (2016) find that accounting conservatism is associated with a lower stock price crash risk. Many studies suggest a negative relation between stock price synchronicity and a given firm’s information environment. Specifically, a limited supply of firm-specific information to outside investors is expected to produce firm-level stock returns that are highly synchronized with general market movements (Roll, 1988). We follow Tucker and Zarowin (2006) to measure earnings smoothing. First, we estimate abnormal accruals based on the modified Jones model and calculate pre-managed earnings as reported earnings minus abnormal accruals.7 We then define earnings smoothing, SM OOT H, as the correlation between changes in abnormal accruals and changes in pre-managed earnings over a rolling window of four years, multiplied by -1. A high SM OOT H indicates a high level of earnings smoothing. 7

We use the estimated residual from Eq. (6) as a proxy for abnormal accruals:   T Acci,t 1 ∆Salesi,t P P Ei,t = α1 + α2 + α3 + α4 ROAi,t + εi,t , Assetsi,t−1 Assetsi,t−1 Assetsi,t−1 Assetsi,t−1

(6)

where T Acc is total accruals; Assets is total assets; ∆Sales is the change in sales; P P E is property, plant, and equipment; and ROA is return on assets. Eq. (6) is estimated for each one-digit CSRC industry in each fiscal year.

16

We follow Khan and Watts (2009) and Kim and Zhang (2016) to construct our measure of accounting conservatism, CSCORE. Its estimation is based on Basu’s (1997) piecewise linear regression model: Xit = β1t + β2t Dit + β3it Rit + β4it Dit Rit + εit ,

(7)

Where, for firm i and year t, X is net income scaled by the lagged market value of equity, R is the annual stock returns, and D is a dummy variable that is equal to 1 when R < 0 and 0 otherwise. β3 measures the timeliness of good news, while β4 measures the incremental timeliness for bad news over good news - accounting conservatism.8 Firms with a higher CSCORE are considered more conservative. To estimate a firm’s stock price synchronicity, we follow Gul et al. (2010) to retrieve R2 from the estimation of the market model in Eq. (1) and then compute SY N CH as follows: SY N CHi,t =

2 Ri,t 2 1 − Ri,t

,

(11)

A high SY N CH indicates a high stock price synchronicity and a less transparent information environment. The summary statistics of the information environment measures in Table 1 show that government-controlled firms are likely to have a higher degree of earnings smoothing, lower accounting conservatism, and higher stock price synchronicity than privately controlled firms. To examine how ultimate controlling shareholders may affect stock price crash risk by influencing a firm’s information environment, we employ the intermediary factor test (Baron and Kenny, 1986) 8

Both β3 and β4 are specified as linear functions of firm-specific characteristics each year: β3it = µ1t + µ2t M KVit + µ3t M Bit + µ4t LEVit

(8)

β4it = CSCORE = λ1t + λ2t M KVit + λ3t M Bit + λ4t LEVit

(9)

where M KV is the natural log of market value, M B is market-to-book ratio, and LEV is financial leverage ratio. Then, the empirical model used to estimate CSCORE can be rewritten as Xit =β1t + β2t Dit + Rit (µ1t + µ2t M KVit + µ3t M Bit + µ4t LEVit ) +Dit Rit (λ1t + λ2t M KVit + λ3t M Bit + λ4t LEVit ) + (δ1t M KVit

(10)

+δ2t M Bit + δ3t LEVit + δ4t Dit M KVit + δ5t Dit M Bit + δ6t Dit LEVit ) + εit We estimate Eq. (10) using 5-year rolling panel regressions and calculate accounting conservatism, CSCORE, using Eq. (9) with the estimated coefficients λ1 , λ2 , λ3 , and λ4 from Eq. (10).

17

and estimate the following models (SM OOT H is used for illustration in all the models): CRi,t = α0 + α1 Govi,t +

P

αk × ControlV arsi,t−1 + εi,t

SM OOT Hi,t = β0 + β1 Govi,t +

P

βk × ControlV arsi,t−1 + ξi,t

CRi,t = ϕ0 + ϕ1 Govi,t + ϕ2 SM OOT Hi,t +

P

ϕk × ControlV arsi,t−1 + εi,t

(12) (13) (14)

where α1 in Eq. (12) measures the total effect of government ownership on crash risk; β1 in Eq. (13) measures the effect of government ownership on earnings smoothing; and ϕ2 in Eq. (14) measures the effect of the intermediate variable earnings smoothing on crash risk. Control variables in all three models are the same as those in our baseline regression model. The mediation effect of information variables is captured by the product of β1 and ϕ2 .9 To test the null hypothesis β1 ×ϕ2 = q 0, we compute the test statistic as Z = β1 ϕ2 /sβ1 ϕ2 (Sobel, 1982), where sβ1 ϕ2 = β12 s2ϕ2 + ϕ22 s2β1 is

the standard error of β1 ϕ2 and sβ1 and sϕ2 are the standard errors of β1 and ϕ2 , respectively. The

mediation effect holds if the null hypothesis is rejected. To perform the test, we exclude firms with ultimate ownership changes as we use rolling panel regressions to estimate earnings smoothing and accounting conservatism. We also use a linear probability model when crash risk is measured by the dummy variable, CRASH, to facilitate the comparison of coefficients across different models. Table 4 presents the results, with panels A and B showing the mediation effect of information environment for government-controlled firms and local government-controlled firms, respectively. In Panel A, Model 1 shows that government controlling ownership leads to a less transparent information environment. Gov has a coefficient of 0.052 (t-statistic=7.15), -0.006 (t-statistic=-3.44), and 0.037 (t-statistic=2.36) for SM OOT H, CSCORE, and SY N CH, respectively. Thus, compared to privately controlled firms, government-controlled firms have higher earnings smoothing, lower accounting conservatism, and greater stock price synchronicity. Models 2, 4, and 6 show that government-controlled firms have a higher crash risk than their privately controlled counterparts, which is consistent with our baseline regression results in Table 3. Models 3, 5, and 7 present the regression results of Eq. (14), with information variables as the intermediary factor. It shows that the effect of government ownership has been reduced after incorporating the direct effect of 9

To establish mediation, the following conditions must hold: (i) the independent variable Gov must be shown to affect the dependent variable CR in Eq. (12); (ii) the independent variable Gov must affect the mediator SM OOT H in Eq. (13); and (iii) the mediator SM OOT H must affect the dependent variable CR in Eq. (14).

18

information environment measures. For example, when the information environment is proxied by earnings smoothing, the coefficient of Gov decreases from 0.025 in Model 2 to 0.024 in Model 3. The Sobel Z-statistic is 2.445, which is significant at the 5% level, indicating that the null hypothesis is rejected and there is a partial mediation effect of earnings smoothing. The mediation effect of earnings smoothing is 3.47% (=0.016×0.052/0.024) of the effect of government ownership. The results are similar when crash risk is measured by N CSKEW and CRASH and when the information environment is proxied by CSCORE and SY N CH. Panel B shows that the mediation effect of all three information variables is statistically significant for local government-controlled firms, but not for central government-controlled. This indicates that the information environment can partially explain the effect of local government ownership on crash risk.10 In summary, the intermediary factor test suggests that government ownership appears to be associated with a less transparent firm-level information environment, which may facilitate bad news hoarding by firm management, and consequently lead to stock price crash risk. 4.3 4.3.1

Endogeneity Event study

To mitigate potential endogeneity, we employ an event study to identify the causality between ultimate ownership and crash risk. The sample is selected based on the following procedures. First, we identify the ultimate ownership switches between government and private owners and verify that there were no ultimate ownership changes either in the prior or subsequent year. We thus obtain 148 cases of ultimate ownership changes (Gov−P rivate), 30 of which occur between the central government and private owners (Gov centr − P rivate) and 118 between local governments and private owners (Gov local − P rivate). Second, we identify a matching firm for each event firm in the year prior to the ownership change. The matching firm must have no ownership change in the sample period, maintain the same ownership type, operate in the same industry as the event 10

In addition, we follow Deng et al. (2018) and employ a two-stage analysis to test the information channels. We model information variables as a function of ultimate controlling ownership and obtain their fitted value. Then, we estimate the relation between the fitted value of information variables and stock price crash risk. The (untabulated) results show that the fitted value of SM OOT H and SY N CH (CSCORE) is significantly positively (negatively) associated with crash risk, suggesting that the information environment could be an important channel through which government controlling ownership can increase stock price crash risk.

19

firm, and be closest in total assets to the event firm. Next, we compute the changes in crash risk surrounding the event year (D COLLAR, D N CSKEW , and D CRASH) for event and matching firms, respectively. The changes in crash risk are computed as year(t+1) - year(t−1) for switches from government to private and as year(t−1) - year(t+1) for switches from private to government. We further compute the differences in crash risk changes between event firms and matching firms. The univariate results are reported in Table 5 Panel A. For event firms, crash risk declines for the Gov − P rivate group and the decline is even greater for the Gov local − P rivate group. In contrast, matching firms do not exhibit a similar pattern in their crash risk measures. The t-test results further illustrate that the differences in crash risk changes between event firms and matching firms are statistically significant for the Gov − P rivate and Gov local − P rivate groups. We also perform multivariate analysis to control for possible changes in other firm characteristics owing to changes in ultimate ownership. Specifically, we control for changes in all control variables in Eq. (4), excluding BH. The year and industry fixed effects are also included. The results are shown in Table 5 Panel B. The three dummy variables, Gov − P rivate, Gov centr − P rivate, and Gov local − P rivate, equal 1 for event firms that have undergone ultimate ownership changes between government and private, between central government and private, and between local government and private, respectively, and 0 for matching firms. Consistent with the univariate results, the coefficient estimates of Gov − P rivate and Gov local − P rivate are significantly negative across all models. This suggests that in comparison with the matching sample, crash risk declines more for firms with ownership switching from government to private, particularly for local government to private.

4.3.2

Firm fixed effects

We then mitigate potential endogeneity by using the firm fixed-effects model to control for unobserved sources of firm heterogeneity. As shown in Table 1, the privately controlled firms differ significantly from their government-controlled counterparts. The fixed-effects model assists in controlling for time-invariant unobserved heterogeneity and mitigating the endogeneity problems 20

caused by firm heterogeneity. Following Kim and Zhang (2016), we use the conditional fixed-effects Logit model for CRASH, an indicator variable, and the linear fixed-effects model for other crash risk variables. The same set of control variables as in Eq. (4) are included except BH. The regression results are presented in Table 6 Panel A. Gov local has positive coefficients in Models 2, 4, and 6. It is statistically significant in Model 4 and marginally significant in Model 2. However, Gov centr is insignificant across Models 2, 4, and 6. This within-firm test confirms that the association between ultimate ownership and crash risk is unlikely to be driven by differences across firms.

4.3.3

Lagged ultimate ownership

We use contemporaneous ownership variables in our primary model framework because ultimate ownership is characteristically long-term in China, which minimizes the concern about reverse causality. However, studies suggest that hoarding bad news over an extended period leads to crash risk. We thus substitute contemporaneous ownership variables with one-year lagged variables to make a stronger causality inference. The results are reported in Table 6 Panel B. Gov local is significantly positive for COLLAR and N CSKEW and marginally significant for CRASH. Meanwhile, Gov centr is insignificant or only marginally significant for the three crash risk measures. These findings confirm our argument that local government-controlled firms tend to have a higher crash risk than privately controlled firms, whereas central government-controlled firms do not.

4.4

Other robustness checks

In this section, we conduct several other robustness checks, including alternative model specification, alternative sample selection procedures, alternative computation methods for crash risk measures, and alternative crash risk measures. The empirical results, unreported and available on request, are qualitatively similar to those reported earlier. The findings reinforce the empirical evidence on the high crash risk associated with local government-controlled firms. 21

4.4.1

Including a proxy for world market

Given China’s recent internationalization, we follow the literature and consider a model with both local and global benchmarks (see Bekaert et al., 2014). We add the change in the RMB exchange rate and the return of the MSCI World Index (Morgan Stanley Capital International Index) as a proxy for world market to Eq. (1). We then recalculate crash risk measures and redo the estimation of Table 3. Our main results remain robust. 4.4.2

Alternative computation methods for crash risk measures

We estimate the abnormal weekly returns for each stock using the value-weighted market indices and industry returns to compute for the crash risk measures. The weight for each stock in the market indices or industry returns is based on the market value of its overall outstanding shares, including tradable and non-tradable shares. To eliminate a possible bias resulting from the significant proportion of non-tradable shares, we consider only tradable shares as an alternative measure of weight in computing the returns for market indices and industries. The regression results are consistent with our main findings. 4.4.3

Excluding firms with fewer than 50 weeks of trading data

If a firm has only a few weeks of trading information, then the market regression model of Eq. (1) might yield biased estimates. We exclude firms with fewer than 50 weeks of trading data and repeat our main analysis to avoid potential bias in computing crash risk measures. The regression results show our main conclusions to be robust to this alternative sample screening procedure. 4.4.4

Alternative measures of crash risk

Following the literature, we consider two alternative crash risk measures: COU N T and DU V OL. COU N T is based on the number of abnormal weekly returns exceeding 3.09 standard deviations above and below the mean and is computed as the downside frequencies minus the upside frequencies (Jin and Myers, 2006). A high COU N T indicates a high frequency of crashes. DU V OL is a measure of return asymmetries, which does not involve third moments and is thus less likely to be excessively 22

influenced by extreme returns (Chen et al., 2001). For each firm-year, we separate all the weeks with abnormal returns below the annual mean (“down” weeks) from those with returns above the annual mean (“up” weeks) and compute the standard deviation for each subsample. DU V OL is defined as the natural logarithm of the ratio of the standard deviation on the down weeks to the standard deviation on the up weeks.11 A high DU V OL implies a high crash risk. The regression estimates based on these two alternative measures are consistent with our main results.

4.5

Impact of the split share structure reform

As discussed in section 2.2, the reform of 2005 could have relieved the conflict of interest between controlling shareholders and minority investors, and thereby affected stock price crash risk. In this section, we empirically test how the reform shapes the relation between controlling ownership and crash risk. Given that firms have completed the reform at different times, with the majority having finished it between 2005 and 2007, we impose the following selection criteria to control for the potential influence of macroeconomic factors: (i) exclude firms that have not completed the reform by 2007 as they typically would have experienced some problems during the reform process; (ii) exclude the year during which a firm completed the reform and retain the same number of years before and after the reform for each firm;12 and (iii) exclude firms with ultimate ownership changes during the reform. Consequently, we obtain 2,488 firm-year observations for 459 unique firms, including 114 privately controlled firms, 83 central government-controlled firms, and 262 local governmentcontrolled firms. We create an indicator variable, Ref orm, to denote the firm-years during which the firm completed the reform. Ref orm equals 1 if a firm has completed the reform by the end of 2007, and 0 otherwise. We also construct interaction terms between Ref orm and various ownership variables 11

The formula of DU V OL is DU V OLi,t = log

(

(nu − 1)

X

2 wi,t,τ

down

,

(nd − 1)

where nd and nu are the number of down and up weeks, respectively. 12 We obtain similar results if we keep all sample years for each firm.

23

X up

2 wi,t,τ

)

,

(15)

to capture the impact of the reform on crash risk in different types of firms. Table 7 summarizes the analyses. First, the crash risk of our sample firms is lowered on average after the reform. Models 1, 4, and 7 show that Ref orm has a significantly negative coefficient for all three crash risk measures. For example, the coefficient of Ref orm in Model 1 is -0.094 (t-statistic =-12.61). In terms of economic magnitude, the sample standard deviation of pre-reform COLLAR is 0.341. Thus, the reform reduces crash risk by 0.276 (=0.094/0.341) standard deviations for our sample firms. Second, the effect of the reform on crash risk differs across firms with diverse ownership structures. For example, the coefficients for Ref orm and Ref orm×Gov in Model 2 are -0.124 (t-statistic =-9.40) and 0.039 (t-statistic=2.66), respectively. Given that the standard deviations of pre-reform COLLAR for private- and government-controlled firms are 0.365 and 0.333, respectively, the reform lowers crash risk by 0.339 (= 0.124/0.365) standard deviations for privately controlled firms and by 0.255 (= (0.124-0.039)/0.333) standard deviations for government-controlled firms. These results imply that the crash risk of privately controlled firms has been lowered by a greater extent than that of government-controlled firms. Further analyses of Models 3, 6, and 9 reveal that the effect of the reform differs between privately controlled and local government-controlled firms but not between privately controlled and central government-controlled firms. The coefficients for Ref orm×Gov centr are not statistically significant, whereas those for Ref orm×Gov local are significantly positive across all three models. The results from Table 7 support H2a and H2b. The reform helps relieve agency conflicts between controlling shareholders and minority investors, thus reducing stock price crash risk. The effect of the reform is more pronounced in privately controlled firms than in government-controlled firms.

4.6

Impact of political connections

We present robust evidence that government-controlled firms have a higher crash risk than privately controlled firms and that this difference is driven primarily by local rather than central 24

government-controlled firms. Next, we explore the driving force of the relationship between government ownership and crash risk. We specifically focus on firm managers’ political incentives, which are proxied by the political connections of the management. Political connections have a controversial effect on connected firms. They can boost firm performance through preferential treatment from the government. However, political connections can also generate an adverse impact on the firm’s information environment (see Chaney et al., 2011; Piotroski et al., 2015).13 For example, an international study by Chaney et al. (2011) suggests that the protection afforded by political ties results in greater opacity for politically connected firms; therefore, the information quality of connected firms will be lower than that of their non-connected counterparts. In addition, Piotroski et al. (2015) argue that political incentives could motivate firm managers to suppress negative information temporarily. They examine the stock price behavior of Chinese listed firms around two highly anticipated political events: the meetings of the National Congress of the Chinese Communist Party and the promotions of high-level provincial politicians and find that politically affiliated firms are significantly less (more) likely to experience stock price crashes before (after) the two political events. The effect of political connections may vary according to the type of ultimate controlling shareholders (Wang, 2015; Lee and Wang, 2017). Wang (2015) shows that having politicians as independent directors helps privately controlled firms outperform their non-connected counterparts but it does not add value to state-owned-enterprises (SOEs), especially local government-controlled ones. Lee and Wang (2017) find that politically connected directors help reduce stock price crash risk in listed privately controlled firms but exacerbate crash risk in listed SOEs. A connection with the state may grant privately controlled firms advantages over their non-connected counterparts. These advantages could reduce the possibility of extremely poor operating outcomes and alleviate stock price crash risk. Moreover, the management team of privately controlled firms may endure less political pressure and more market pressure for higher financial transparency and more intense scrutiny by outside investors. Conversely, political connections in government-controlled 13

The evidence from Indonesia, another Asian country with a highly concentrated ownership structure, supports this view. Leuz and Oberholzer-Gee (2006) and Habib et al. (2017) find that politically connected firms are less likely to cross-list their securities, adopt U.S. GAAP, and appoint reputable auditors.

25

firms enhance the control of the government as a dominant owner and increase the propensity to camouflage the opportunistic activities of controlling shareholders, such as tunneling and expropriation, which result in the accumulation of bad news and the high probability of a crash (Bushman et al., 2004; Boubaker et al., 2014). Managers of government-controlled firms have a strong incentive to seek political promotions and can be more motivated to engage in financial misconduct. Their opportunistic behavior is shaped by the intensity of outside monitoring. Central governmentcontrolled firms are subject to greater social media scrutiny, and the cost of their misconduct is substantially higher than that of local government-controlled firms. Hence, political connections are expected to have a stronger effect on crash risk in government-controlled firms, especially local government-controlled, than in privately controlled firms. We assess the strength of a firm’s political connection using a political connection index (P Cs), which we construct based on the political backgrounds of the firm’s senior management team, such as board members, CEO, and CFO. The political background information of firm management is obtained from the Wind Economic Database, which provides the positions that executives previously held in governmental or other organizations. We classify the political background of firm management into government and non-government positions, e.g., representatives of the People’s Congress and members of the Committee of the People’s Political Consultative Conference. Then we score the political connection strength of each position category according to administrative level.14 Last, we aggregate the political background scores of all executives in a firm to build the P Cs and standardize this index between 0 and 1.15 A high index indicates strong political connections. Table 1 shows that government-controlled firms have stronger political connections than privately controlled firms and local government-controlled firms have the strongest political connections. Table 8 shows how political connections affect the relationship between government ownership 14

For government positions, vice-ministerial level and above are scored 7; departmental level is scored 6; deputy departmental level is scored 5; division level is scored 4; deputy division level is scored 3; section level is scored 2; and deputy section level and below are scored 1. For non-government positions, national level is scored 6; provincial level is scored 4; and municipal level and below are scored 2. 15 Alternatively, we average the percentile ranking of a sample firm according to the score of its management and construct a standardized index. The untabulated results are qualitatively similar to the reported results.

26

and crash risk. Models 1, 3, and 5 show that the coefficients of the interaction term P Cs × Gov are consistently positive and statistically significant at the 5% or 10% level. Models 2, 4, and 6 further show that the coefficients of P C × Gov local are all positive and significant at the 5% level, whereas those of P C × Gov centr are not statistically significant, except in Model 4. We further control for firm fixed effects to relieve possible endogeneity and the unreported results are similar to those in Table 8. In sum, the evidence indicates that government-controlled firms with strong political connections tend to have a higher crash risk than privately controlled firms and this effect is more pronounced in local government-controlled firms. These findings suggest that the effect of government ownership on crash risk may be partially explained by the strong political incentives of managers in government-controlled firms.

4.7

Dismissed local government officials and crash risk

In this section, we further employ the dismissal of a local government officer in Shanghai, China’s commercial capital, in a corruption purge as an exogenous shock to examine how political incentives influence the information environment of firms and the crash risk of stock price. We use an event study to identify the causal effect of the dismissed local government officials on crash risk. The event year is 2007 and the event window is from 2005 to 2010-three years before and after the event year. Our event sample includes firms incorporated in Shanghai and controlled by different local governments in Shanghai. Furthermore, the event firms sustain no changes in their controlling ownership during the event window. The matching sample includes those that are controlled by local governments and incorporated in other provinces, with no changes in controlling ownership. They should be in the same industry as and closest in terms of total assets to the event firms. Finally, we obtain 52 event firms, 52 matching firms, and 624 firm-year observations. We estimate a multivariate regression model to control for the effect of other factors. Two indicator variables are defined. T reat equals 1 for event firms and 0 for matching firms. P ost equals 1 if a firm-year is between 2008-2010 and 0 if it is between 2005-2007. As shown in Table 9, Models 1, 3, and 5 only include the indicator variables and their interaction terms, whereas Models 2, 4, and 6 include other control variables. T reat has a significantly positive coefficient, suggesting 27

that event firms have a higher crash risk than matching firms before the event. The interaction term T reat × P ost has a significantly negative coefficient, which indicates that the crash risk of event firms declined more than that of matching firms after the event. The results suggest that the average crash risk of event firms declined significantly after the government officer was dismissed.

5.

Conclusions

This study examines the impact of ultimate controlling ownership on stock price crash risk in China. Our sample firms are classified into privately controlled and government-controlled firms according to their ultimate controlling ownership. We find that the crash risk associated with the latter is significantly higher than with the former. Particularly, firms controlled by lower levels of government exhibit a higher crash risk than privately controlled firms, while no significant difference in crash risk is observed between central government-controlled firms and privately controlled firms. We consider three channels: earnings smoothing, accounting conservatism, and stock price synchronicity, through which ownership structure may influence crash risk. Results show that government-controlled firms tend to have higher earnings smoothing, lower accounting conservatism, and higher stock price synchronicity than privately controlled firms. Further evidence on the split share structure reform suggests that despite a post-reform reduction of crash risk for all sample firms, government-controlled firms, especially local ones, sustained a substantially smaller reduction than privately controlled firms. We also investigate how political connections of the firm management shape the relationship between government ownership and crash risk. We find that government-controlled firms with strong political connections tend to have a higher crash risk than privately controlled firms and this effect is more pronounced in local government-controlled firms. An event study on the dismissal of a corrupt local government officer shows that the average crash risk of event firms decreases significantly following the officer’s dismissal. These findings indicate that the effect of government ownership on crash risk could be partially explained by the strong political incentives of the management in government-controlled firms. 28

Overall, our findings suggest that firms controlled by local governments may face a more severe agency problem than that faced by their privately controlled counterparts. This provides important policy implications for the ongoing restructuring of state ownership in China, which aims to improve operating efficiency and market performance of SOEs.

29

REFERENCES Allen, F., Qian, J., Qian, M., 2005. Law, finance and economic growth in China. Journal of Financial Economics 77, 57-116. Alchian, A.A., 1969. Corporate management and property rights. In: Manne, H. (Eds.), Economic Policy and the Regulation of Corporate Securities, American Enterprise Institute,Washington, D.C. An, Z., Gao, W., Li, D., Zhu, F., 2018. The impact of firm-level illiquidity on crash risk and the role of media independence: International evidence. International Review of Finance 18, 547-593. Andreou, P.C., Antoniou, C., Horton, J., Louca, C., 2016. Corporate governance and firm-specific stock price crashes. European Financial Management 22, 916-956. Andreou, P.C., Louca, C., Petrou, A.P., 2017. CEO age and stock price crash risk. Review of Finance 21, 1287-1325. Bai, C.-E., Liu, Q., Lu, J. Song, F.M., Zhang, J., 2004. Corporate governance and market valuation in China. Journal of Comparative Economics 32, 599-616. Baron, R.M., Kenny, D.A., 1986. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology 51, 1173-1182. Basu, S., 1997. The conservatism principle and the asymmetric timeliness of earnings. Journal of Accounting and Economics 24, 3-37. Bekaert, G., Ehrmann, M., Fratzscher, M., Mehl, A., 2014. The global crisis and equity market contagion. Journal of Finance 69, 2597-2649. Boubaker, S., Mansali, H., Rjiba, H., 2014. Large controlling shareholders and stock price synchronicity. Journal of Banking and Finance 40, 80-96. 30

Bushman, R., Piotroski, J., Smith, A., 2004. What determines corporate transparency? Journal of Accounting Research 42, 207-252. Bushman, R., Piotroski, J., 2006. Financial reporting incentives for conservative accounting: The influence of legal and political institutions. Journal of Accounting and Economics 42, 107-148. Callen, J.L., Fang, X., 2013. Institutional investor stability and crash risk: Monitoring versus short-termism? Journal of Banking and Finance 37, 3047-3063. Chaney, P.K., Faccio, M., Parsley, D., 2011. The quality of accounting information in politically connected firms. Journal of Accounting Economics 51, 58-76. Chauhan, Y., Wadhwa, K., Syamala, S.R., Goyal, A., 2015. Block-ownership structure, bank nominee director and crash-risk. Finance Research Letters 14, 20-28. Chen, C., Kim, J.-B., Yao, L., 2017. Earnings smoothing: Does it exacerbate or constrain stock price crash risk? Journal of Corporate Finance 42, 36-54. Chen, X., Lee, C.J., Li, J., 2008. Government assisted earnings management in China. Journal of Accounting and Public Policy 27, 262-274. Chen, Q., Chen, X., Schipper, K., Xu, Y., Xue, J., 2012. The sensitivity of corporate cash holdings to corporate governance. Review of Financial Studies 25, 3610-3644. Chen, J., Dedman, E., Ghazali, M.Y., 2016. The information environment in China: Evidence from the Split Share Structure Reform. Working paper, University of Nottingham. Chen, J., Hong, H., Stein, J., 2001. Forecasting crashes: Trading volume, past returns, and conditional skewness in stock prices. Journal of Financial Economics 61, 345-381. Claessens, S., Djankov, S., Lang, L.H.P., 2000. The separation of ownership and control in east Asian corporations. Journal of Financial Economics 58, 81-112. Dechow, P.M., Sloan, R.G., Sweeney, A.P., 1995. Detecting earnings management. The Accounting Review 70, 193-225. 31

DeFond, M.L., Hung, M., Li, S., Li,Y., 2015. Does mandatory IFRS adoption affect crash risk? The Accounting Review 90, 265-299. Deng, B., Li, Z., Li, Y., 2018. Foreign institutional ownership and liquidity commonality around the world. Journal of Corporate Finance 51, 20-49. Dimson, E., 1979. Risk measurement when shares are subject to infrequent trading. Journal of Financial Economics 7, 197-226. Djankov, S., La Porta, R., Lopez-de-Silanes, F., Shleifer, A., 2008. The law and economics of self-dealing. Journal of Financial Economics 88, 430-465. Faccio, M., Lang, L.H.P., 2002. The ultimate ownership of Western European corporations. Journal of Financial Economics 65, 365-395. Fan, J.P.H., Wong, T.J., Zhang, T., 2007. Politically connected CEOs, corporate governance and post-IPO performance of China’s newly partially privatized firms. Journal of Financial Economics 84, 265-590. Firth, M., Fung, P.M.Y., Rui, O.M., 2006. Corporate performance and CEO compensation in China. Journal of Corporate Finance 12, 693-712. Glaeser, E., Johnson, S., Shleifer, A., 2001. Coase versus the Coasians. Quarterly Journal of Economics 116, 853-899. Green, W., Morris, R.D., Tang, H., 2010. The split equity reform and corporate financial transparency in China. Accounting Research Journal 23, 20-48. Guedhami, O., Pittman, J.A., Saffar, W., 2009. Auditor choice in privatized firms: Empirical evidence on the role of state and foreign owners. Journal of Accounting and Economics 48, 151-171. Gul, F.A., Kim, J.-B., Qiu, A.A., 2010. Ownership concentration, foreign shareholding, audit quality, and stock price synchronicity: Evidence from China. Journal of Financial Economics 95, 425-442. 32

Habib, A., Muhammadi, A.H., Jiang, H., 2017. Political connections, related party transactions, and auditor choice: Evidence from Indonesia. Journal of Contemporary Accounting and Economics 13, 1-19. Hamm, S.J.W., Li, E.X., Ng, J., 2012. Management earnings guidance and crash risk. Working paper, Singapore Management University. Hou, W., Kuo, J.-M., Lee, E., 2012. The impact of state ownership on share price informativeness: the case of the split share structure reform in China. The British Accounting Review 44, 248261. Huang, J., Xu, N., Yuan, Q., 2013. Divergence of shareholder rights and corporate risk taking. Working paper, University of Illinois at Urbana-Champaign. Hutton, A.P., Marcus, A.J., Tehranian, H., 2009. Opaque financial reports, R2 , and crash risk. Journal of Financial Economics 94, 67-86. Jian, M., Wong, T.J., 2010. Propping and tunneling through related party transactions. Review of Accounting Studies 15, 70-105. Jin, L., Myers, S.C., 2006. R2 around the world: New theory and new tests. Journal of Financial Economics 79, 257-292. Johnson, S., Boone, P., Breach, A., Friedman, E., 2000a. Corporate governance in the Asian financial crisis. Journal of Financial Economics 58, 141-186. Johnson, S., La Porta, R., Lopez-de-Silanes, F., Shleifer, A., 2000b. Tunneling. American Economic Review 90, 22-27. Khan, M., Watts, R.L., 2009. Estimation and empirical properties of a firm-year measure of accounting conservatism. Journal of Accounting and Economics 48, 132-150. Kim, J.-B., Li, Y., Zhang, L., 2011a. Corporate tax avoidance and stock price crash risk: Firmlevel analysis. Journal of Financial Economics 100, 639-662. 33

Kim, J.-B., Li, Y., Zhang, L., 2011b. CFOs versus CEOs: Equity incentives and crashes. Journal of Financial Economics 101, 713-730. Kim, J.-B., Zhang, L., 2016. Accounting conservatism and stock price crash risk: Firm-level evidence. Contemporary Accounting Research 33, 412-441. Karpoff, J.M., Rice, E.M., 1989. Organizational form, share transferability, and firm performance. Journal of Financial Economics 24, 69-105. LaFond, R., Watts, R.L., 2008. The information role of conservatism. The Accounting Review 83, 447-478. La Porta, R., Lopez-de-Silanes, F., Shleifer A., 1999. Corporate ownership around the world. Journal of Finance 54, 471-517. La Porta, R., Lopez-de-Silanes, F., Shleifer A., Vishny, R.W., 2000. Investor protection and corporate governance. Journal of Financial Economics 58, 3-27. La Porta, R., Lopez-de-Silanes, F., Zamarripa, G., 2003. Related lending. Quarterly Journal of Economics 118, 231-268. Lee, W., Wang, L., 2017. Do political connections affect stock price crash risk? Firm-level evidence from China. Review of Quantitative Finance and Accounting 48, 643-676. Leuz, C., Nanda, D., Wysocki, P., 2003. Investor protection and earnings management: An international comparison. Journal of Financial Economics 69, 505-527. Leuz, C., Oberholzer-Gee, F., 2006. Political relationships, global financing, and corporate transparency: Evidence from Indonesia. Journal of Financial Economics 81, 411-439. Liao, L., Liu, B., Wang, H., 2014. China’s secondary privatization: Perspectives from the SplitShare Structure Reform. Journal of Financial Economics 113, 500-518. Lin, Y., Cai, F., Li, Z., 1996. The China miracle: Development strategy and economic reform. The Chinese University Press, Hong Kong. 34

Lin, H., 2009. Essays on empirical corporate finance and corporate governance. Working paper, Columbia University. Lin, Z., Liu, M., 2009. The impact of corporate governance on auditor choice: Evidence from China. Journal of International Accounting Auditing and Taxation 18, 44-59. Liu, Q., Lu, Z., 2007. Corporate governance and earnings management in Chinese listed companies: A tunneling perspective. Journal of Corporate Finance 13, 881-906. Liu, H., Li, Z., Sun, Z., 2010. Realization method of controlling stockholders’ property benefits and direction of interests transfer: Discussion on the Split-share Structure Reform in China. Journal of Finance and Economics 4, 56-67. (in Chinese) Myers, J.N., Myers, L.A., Skinner, D.J., 2007. Earnings momentum and earnings management. Journal of Accounting, Auditing and Finance 22, 249-284. Piotroski, J., Wong, T.J., Zhang, T., 2015. Political incentives to suppress negative information: Evidence from Chinese listed firms. Journal of Accounting Research 53, 405-459. Roll, R., 1988. R2 . Journal of Finance 25, 545-566. Shleifer, A., 1998. State versus private ownership. Journal of Economic Perspectives 12, 133-150. Shleifer, A., Vishny, R.W., 1997. A survey of corporate governance. Journal of Finance 52, 737-783. Sobel, M.E., 1982. Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology 13, 290-312. Tucker, J.W., Zarowin, P.A., 2006. Does income smoothing improve earnings informativeness? The Accounting Review 81, 251-270. Wang, L., 2015. Protection or expropriation: politically connected independent directors in China. Journal of Banking and Finance 55, 92-106. 35

Wang, Q., Wong, T.J., Xia, L., 2008. State ownership, the institutional environment, and auditor choice: Evidence from China. Journal of Accounting and Economics 46, 112-134. Watts, R.L., 2003. Conservatism in accounting part I: Explanations and implications. Accounting Horizons 17, 207-221. Watts, R.L., Zimmerman, J.L., 1986. Positive accounting theory. Prentice-Hall, Englewood Cliffs, NJ. Zhou, J., Kim, J.-B., Yeung, I., 2013. Material weakness in internal control and stock price crash risk: Evidence from Sox Section 404 Disclosure. Working paper, National University of Singapore.

36

Appendix: Crashes triggered by breaks in strings of consecutive earnings increases

According to our conjecture, the mechanism underpinning the relationship between ultimate ownership and stock price crashes may be the hoarding of bad news by firm managers. Now we attempt to explicitly investigate this idea by focusing on stock price crashes triggered by breaks in a firm’s string of consecutive earnings increases. Myers et al. (2007) contend that strings of consecutive earnings increases, especially longer strings, can result from stockpiling negative news. Therefore, a stock price crash triggered by a break in strings creates an ideal setting for examining whether bad news hoarding drives the relationship between controlling ownership and stock price crashes. Following Andreou et al. (2017), we redefine crashes: CRASH BREAK1 equals 1 if a firm undergoes a stock price crash and firm earnings decreased in the current year but increased in the previous year, and 0 otherwise; CRASH BREAK2 equals 1 if a firm undergoes a stock price crash and firm earnings decreased in the current year but increased in the previous 2 years, and 0 otherwise; CRASH BREAK3 equals 1 if a firm undergoes a stock price crash and firm earnings decreased in the current year but increased in the previous 3 years, and 0 otherwise. We expect that stock price crashes captured by CRASH BREAK1, CRASH BREAK2, and CRASH BREAK3 are more likely to result from stockpiling negative news. Our sample includes 634 stock price crashes, among which 170 or 26.81% are triggered by firm earnings that decreased in the current year but increased in the previous year, 86 or 13.56% by firm earnings that decreased in the current year but increased in the previous 2 years, and 59 or 9.31% by firm earnings that decreased in the current year but increased in the previous 3 years. These statistics imply that breaks in strings of consecutive earnings increases are an important cause of crashes. Using these alternative definitions of crash risk as dependent variables, we re-estimate our baseline regression model and present the results in the table below. It shows that governmentcontrolled firms, especially local government controlled, are more likely to have stock price crashes 37

caused by breaks in earnings strings. In Models 3, 6, and 9, we further control for the length of earnings string prior to the break (LEN GT H), which is measured by the number of years with consecutive earnings increases. Consistent with Andreou et al. (2017), we find a positive relationship between the length of the string and the probability of a stock price crash triggered by a break in strings. The overall evidence shows that stockpiling negative information pertaining to adverse operating performance may be a driving force of the relationship between controlling ownership and crash risk.

38

Crashes triggered by breaks in strings of consecutive earnings increases This table shows the results of logit regressions where the dependent variable is firm-specific stock price crashes triggered by breaks in a firm’s string of consecutive earnings increases. Gov, Gov centr, and Gov local equal 1 if a firm is ultimately controlled by government entities, central government, and local governments, respectively, and 0 otherwise. Control variables include the logarithm of total assets (SIZE), return on assets (ROA), market-to-book ratio (M B), leverage ratio (LEV ), prior 3-year moving sum of the absolute value of discretionary accruals (ABACC), B/H-shares (BH), detrended stock trading volume (DT U RN ), negative skewness of abnormal returns (N CSKEW ), average abnormal returns (F SRET ), standard deviation of abnormal returns (SIGM A), and the length of earnings string prior to the break (LEN GT H). All regressions include unreported industry and year fixed effects. The t-statistics reported in the parentheses are adjusted for both firm and year clustered standard errors. The sample period is from 2003 to 2014. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. (1) Govt

CRASH BREAK1t (2)

0.060 (0.88)

Gov centrt

CRASH BREAK2t (5)

(4)

(6)

0.204** (2.30) -0.111 (-1.16) 0.118** (2.01)

Yes

Yes

-0.130 (-1.41) 0.111* (1.85) 0.033*** (2.65) Yes

Yes

Yes

Yes

Gov localt LEN GT Ht Control variables Year fixed effects Industry fixed effects Pseudo-R2 No. of Obs.

(3)

Yes

CRASH BREAK3t (8)

(9)

0.246** (2.17) 0.051 (0.43) 0.251*** (3.10)

Yes

(7)

Yes

-0.018 (-0.16) 0.226*** (2.61) 0.085*** (3.24) Yes

0.240 (0.90) 0.249** (2.44) Yes

Yes

0.133 (0.53) 0.189* (1.66) 0.097*** (4.17) Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

0.060 7,389

0.063 7,389

0.066 7,389

0.079 5,387

0.082 5,387

0.104 5,387

0.134 4,333

0.134 4,333

0.164 4,333

39

40

Table 1

COLLARt N CSKEWt CRASHt SM OOT Ht CSCOREt SY N CHt P Cst SIZEt−1 ROAt M Bt−1 LEVt−1 ABACCt−1 BHt DT U RNt−1 F SRETt−1 SIGM At−1

Vars

2,534 2,534 2,534 1,513 1,565 2,534 2,534 2,534 2,534 2,534 2,534 2,534 2,534 2,534 2,534 2,534

N

-0.086 -0.287 0.061 0.731 0.015 1.201 0.152 21.401 0.041 3.829 0.514 0.218 0.073 0.011 -0.096 0.040

0.000 -0.245 0.000 0.945 0.011 0.973 0.113 21.321 0.042 2.578 0.520 0.174 0.000 0.009 -0.069 0.038

P rivate=1 Mean Median 6,478 6,478 6,478 4,988 5,166 6,478 6,478 6,478 6,478 6,478 6,478 6,478 6,478 6,478 6,478 6,478

N -0.051 -0.240 0.074 0.776 0.009 1.319 0.164 21.883 0.040 3.028 0.509 0.176 0.121 0.007 -0.082 0.038

0.000 -0.218 0.000 0.947 0.007 1.071 0.145 21.736 0.039 2.253 0.514 0.144 0.000 0.004 -0.060 0.035

Gov=1 Mean Median 0.035*** 0.047*** 0.013** 0.045*** -0.006*** 0.118*** 0.012*** 0.482*** 0.001 -0.801*** -0.005 -0.042*** 0.048*** -0.004 0.014*** -0.003***

Difference 1,948 1,948 1,948 1,363 1,432 1,948 1,948 1,948 1,948 1,948 1,948 1,948 1,948 1,948 1,948 1,948

N -0.055 -0.256 0.065 0.753 0.005 1.321 0.155 22.009 0.040 3.167 0.500 0.181 0.133 0.005 -0.087 0.039

0.000 -0.239 0.000 0.947 0.006 1.059 0.129 21.826 0.040 2.485 0.512 0.149 0.000 0.005 -0.065 0.036

Gov centr=1 Mean Median 0.030*** 0.031 0.004 0.022 -0.010*** 0.120*** 0.004 0.608*** -0.001 -0.663*** -0.015** -0.037*** 0.060*** -0.006 0.009** -0.001**

Difference 4,530 4,530 4,530 3,625 3,734 4,530 4,530 4,530 4,530 4,530 4,530 4,530 4,530 4,530 4,530 4,530

N

-0.049 -0.233 0.078 0.785 0.010 1.318 0.167 21.829 0.040 2.968 0.513 0.173 0.116 0.008 -0.080 0.037

0.000 -0.210 0.000 0.947 0.008 1.074 0.145 21.709 0.040 2.158 0.515 0.141 0.000 0.004 -0.058 0.034

Gov local=1 Mean Median

0.037*** 0.054*** 0.017*** 0.054*** -0.005** 0.117*** 0.015*** 0.427*** -0.001 -0.861*** -0.001 -0.045*** 0.042*** 0.003 0.016*** -0.003***

Difference

This table presents the summary statistics for crash risk measures and other variables. The firms are classified according to their ultimate ownership. P rivate and Gov equal 1 if a firm is ultimately controlled by non-government entities and government entities, respectively, and 0 otherwise. Gov centr and Gov local equal 1 if a firm is ultimately controlled by the central and local governments, respectively, and 0 otherwise. COLLAR, N CSKEW , and CRASH are crash risk measures. Firm characteristics include earnings smoothing (SM OOT H), accounting conservatism (CSCORE), political connections (P Cs), the logarithm of total assets (SIZE), return on assets (ROA), market-to-book ratio (M B), leverage ratio (LEV ), prior 3-year moving sum of the absolute value of discretionary accruals (ABACC), and B/H-shares (BH). Stock trading variables include stock price synchronicity (SY N CH), detrended stock trading volume (DT U RN ), average abnormal returns (F SRET ), and standard deviation of abnormal returns (SIGM A). The sample period is from 2003 to 2014. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

Descriptive statistics of privately controlled and government-controlled firms

41

COLLAR t N CSKEWt CRASHt SM OOT Ht CSCOREt SY N CHt P Cst SIZEt−1 ROAt M Bt−1 LEVt−1 ABACCt−1 BHt DT U RNt−1 F SRETt−1 SIGM At−1

A B C D E F G H I J K L M N O P

1.00 0.74 0.37 0.00 -0.05 0.10 -0.00 -0.01 0.01 0.04 -0.02 0.00 0.01 0.02 -0.01 0.00

A 1.00 0.47 0.00 0.02 0.11 -0.01 -0.07 -0.01 0.07 -0.03 0.00 -0.00 0.06 -0.01 -0.01

B

1.00 0.01 0.03 -0.06 -0.00 -0.02 -0.03 0.01 -0.01 -0.01 0.01 0.00 0.03 -0.06

C

1.00 -0.02 0.03 0.03 0.08 0.08 -0.10 0.03 -0.00 -0.05 0.02 0.08 -0.08

D

1.00 -0.22 -0.10 -0.40 -0.25 0.05 0.06 0.01 -0.07 0.08 0.05 -0.09

E

1.00 0.04 0.18 0.01 -0.03 -0.06 -0.02 -0.02 0.12 -0.08 0.10

F

1.00 0.20 0.07 -0.03 -0.01 -0.02 -0.03 -0.02 0.03 -0.03

G

1.00 0.15 -0.29 0.20 -0.08 0.20 -0.06 0.08 -0.10

H

1.00 0.05 -0.12 -0.00 0.03 0.01 0.01 -0.00

I

1.00 0.09 0.19 0.02 0.11 -0.29 0.34

J

1.00 0.16 0.01 -0.01 -0.10 0.14

K

1.00 -0.04 -0.02 -0.07 0.12

L

1.00 -0.01 -0.01 0.00

M

1.00 -0.22 0.30

N

1.00 -0.83

O

1.00

P

This table reports the correlation matrix. P rivate and Gov equal 1 if a firm is ultimately controlled by non-government entities and government entities, respectively, and 0 otherwise. Gov centr and Gov local equal 1 if a firm is ultimately controlled by the central and local governments, respectively, and 0 otherwise. COLLAR, N CSKEW , and CRASH are crash risk measures. Firm characteristics include earnings smoothing (SM OOT H), accounting conservatism (CSCORE), political connections (P Cs), the logarithm of total assets (SIZE), return on assets (ROA), market-to-book ratio (M B), leverage ratio (LEV ), prior 3-year moving sum of the absolute value of discretionary accruals (ABACC), and B/H-shares (BH). Stock trading variables include stock price synchronicity (SY N CH), detrended stock trading volume (DT U RN ), average abnormal returns (F SRET ), and standard deviation of abnormal returns (SIGM A). The sample period is from 2003 to 2014. Correlation coefficients in boldface are significant at the 10% level at least.

Table 2 Correlation coefficients

Table 3 Ultimate controlling shareholders and crash risk This table shows the impact of different types of ultimate controlling shareholders on stock price crash risk. The dependent variables are the crash risk measures COLLAR, N CSKEW , and CRASH. Gov, Gov centr, and Gov local equal 1 if a firm is ultimately controlled by government entities, central government, and local governments, respectively, and 0 otherwise. Firm characteristics include the logarithm of total assets (SIZE), return on assets (ROA), market-to-book ratio (M B), leverage ratio (LEV ), prior 3-year moving sum of the absolute value of discretionary accruals (ABACC), and B/H-shares (BH). Stock trading variables include detrended stock trading volume (DT U RN ), negative skewness of abnormal returns (N CSKEW ), average abnormal returns (F SRET ), and standard deviation of abnormal returns (SIGM A). All regressions include unreported industry and year fixed effects. The t-statistics reported in the parentheses are adjusted for both firm and year clustered standard errors. The sample period is from 2003 to 2014. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

COLLARt (1) Govt

0.024*** (2.81)

Gov centrt

ROAt M Bt−1 LEVt−1 DT U RNt−1 N CSKEWt−1 F SRETt−1 SIGM At−1 ABACCt−1 BHt Constant Year fixed effects Industry fixed effects Pseudo-/ Adjusted R2 No. of Obs.

(3)

CRASHt (4)

0.024** (2.24)

0.017*** (3.52) -0.020 (-0.29) 0.005*** (3.13) -0.038* (-1.76) -0.020 (-0.93) 0.013*** (2.71) 0.032 (0.79) 0.955 (1.34) 0.024 (0.58) -0.009 (-0.70) -0.431*** (-4.31)

0.017* (1.84) 0.026*** (2.88) 0.017*** (3.59) -0.021 (-0.30) 0.005*** (3.13) -0.039* (-1.81) -0.021 (-0.94) 0.013*** (2.70) 0.034 (0.84) 0.977 (1.39) 0.025 (0.60) -0.009 (-0.70) -0.437*** (-4.40)

Yes

(5)

(6)

0.142* (1.68)

0.019 (1.54) -0.125 (-0.81) 0.013*** (4.48) -0.068 (-1.48) -0.045 (-1.13) 0.042*** (3.59) 0.057 (0.55) 1.978 (1.40) 0.051 (0.68) -0.022 (-0.79) -0.709** (-2.22)

0.009 (0.52) 0.030*** (2.89) 0.020 (1.59) -0.127 (-0.83) 0.013*** (4.53) -0.071 (-1.54) -0.046 (-1.15) 0.042*** (3.60) 0.061 (0.59) 2.030 (1.44) 0.053 (0.71) -0.022 (-0.78) -0.723** (-2.27)

-0.025 (-0.42) -1.251*** (-8.32) 0.006 (0.33) 0.010 (0.04) -0.143 (-0.52) 0.120* (1.74) -0.199 (-0.75) -3.357 (-0.56) -0.043 (-0.10) 0.135 (1.00) -2.468** (-2.03)

0.022 (0.15) 0.187** (2.44) -0.019 (-0.32) -1.252*** (-8.30) 0.007 (0.38) -0.007 (-0.03) -0.150 (-0.54) 0.118* (1.75) -0.173 (-0.62) -2.949 (-0.48) -0.030 (-0.07) 0.139 (1.03) -2.611** (-2.16)

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

0.031

0.031

0.082

0.082

0.036

0.036

9,012

9,012

9,012

9,012

9,012

9,012

Gov localt SIZEt−1

N CSKEWt (2)

42

Table 4 Channels through which ultimate controlling shareholders affect crash risk This table reports the regression results on the mediation effects of information environment, with panels A and B showing the mediation effect on government-controlled firms and local government-controlled firms, respectively. Information environment is proxied by earnings smoothing (SM OOT H), accounting conservatism (CSCORE), and stock price synchronicity (SY N CH). Gov, Gov centr, and Gov local equal 1 if a firm is ultimately controlled by government entities, central government, and local governments, respectively, and 0 otherwise. Control variables include the logarithm of total assets (SIZE), return on assets (ROA), market-to-book ratio (M B), leverage ratio (LEV ), prior 3-year moving sum of the absolute value of discretionary accruals (ABACC), B/H-shares (BH), detrended stock trading volume (DT U RN ), negative skewness of abnormal returns (N CSKEW ), average abnormal returns (F SRET ), and standard deviation of abnormal returns (SIGM A). All regressions include unreported industry and year fixed effects. Sobel Z (Gov) in Panel A and Sobel Z (Gov local) in Panel B are the test statistic for the mediation effect of information variables on the crash risk of government-controlled firms and local government-controlled firms, respectively. The t-statistics reported in the parentheses are adjusted for both firm and year clustered standard errors. The sample period covers 2003 to 2014. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Panel A: Mediation effect for government-controlled firms SM OOT Ht /CSCOREt COLLARt N CSKEWt /SY N CHt (1) (2) (3) (4) (5) Earnings smoothing channel Govt 0.052*** (7.15) SM OOT Ht Control variables Yes Year fixed effects Yes Industry fixed effects Yes Sobel Z (Gov) 2 0.068 Pseudo-/ Adjusted R No. of Obs. 6,501 Accounting conservatism channel Govt -0.006*** (-3.44) CSCOREt Control variables Yes Year fixed effects Yes Industry fixed effects Yes Sobel Z (Gov) 2 0.612 Pseudo-/ Adjusted R No. of Obs. 6,731 Stock price synchronicity channel Govt 0.037** (2.36) SY N CHt Control variables Year fixed effects Industry fixed effects Sobel Z (Gov) Pseudo-/ Adjusted R2 No. of Obs.

0.025*** (2.66)

CRASHt (6)

(7)

0.025** (2.44) 0.017** (2.22) Yes Yes Yes

0.011* (1.94)

0.010* (1.84) 0.010*** (2.60) Yes Yes Yes

0.024*** (2.58) 0.016** (2.49) Yes Yes Yes

0.026** (2.53)

0.032 6,501

0.033 6,501

0.085 6,501

0.085 6,501

0.014 6,501

0.015 6,501

0.026*** (2.88)

0.025*** (2.79) -0.167** (-2.22) Yes Yes Yes

0.026** (2.48)

0.023** (2.21) -0.556** (-2.17) Yes Yes Yes

0.009* (1.75)

0.009* (1.66) -0.066 (-0.86) Yes Yes Yes

0.033 6,731

0.034 6,731

0.086 6,731

0.088 6,731

0.015 6,731

0.015 6,731

0.024*** (2.81)

0.022*** (2.67) 0.048** (2.21) Yes Yes Yes

0.024** (2.24)

0.021* (1.93) 0.071*** (3.31) Yes Yes Yes

0.008* (1.66)

0.009* (1.82) -0.020 (-1.08) Yes Yes Yes

0.041 9,012

0.082 9,012

0.089 9,012

0.015 9,012

Yes Yes Yes

Yes Yes Yes

2.445**

Yes Yes Yes

2.025**

Yes Yes Yes

2.045**

Yes Yes Yes

Yes Yes Yes

0.272 9,012

0.031 9,012

Yes Yes Yes 2.437**

Yes Yes Yes

1.977**

Yes Yes Yes

1.664*

0.841

Yes Yes Yes

1.993**

43

-0.912 0.018 9,012

Table 4 (Cont.) Panel B: Mediation effect for local government-controlled firms SM OOT Ht /CSCOREt COLLARt N CSKEWt /SY N CHt (1) (2) (3) (4) (5) Earnings smoothing channel Gov centrt 0.033** (2.29) Gov localt 0.058*** (6.25) SM OOT Ht Control variables Yes Year fixed effects Yes Industry fixed effects Yes Sobel Z (Gov local) 2 0.069 Pseudo-/ Adjusted R No. of Obs. 6,501 Accounting conservatism channel Gov centrt -0.007*** (-3.30) Gov localt -0.005*** (-3.45) CSCOREt Control variables Yes Year fixed effects Yes Industry fixed effects Yes Sobel Z (Gov local) 0.613 Pseudo-/ Adjusted R2 No. of Obs. 6,731 Stock price synchronicity channel Gov centrt 0.036 (1.17) Gov localt 0.037** (2.42) SY N CHt Control variables Year fixed effects Industry fixed effects Sobel Z (Gov local) Pseudo-/ Adjusted R2 No. of Obs.

0.020 (1.43) 0.027*** (2.70)

CRASHt (6)

(7)

0.014 (0.78) 0.030*** (2.89) 0.017** (2.21) Yes Yes Yes

0.002 (0.19) 0.012** (2.15)

0.001 (0.15) 0.011** (2.04) 0.009** (2.21) Yes Yes Yes

0.019 (1.41) 0.026*** (2.59) 0.015** (2.44) Yes Yes Yes

0.014 (0.82) 0.031*** (3.01)

0.032 6,501

0.033 6,501

0.085 6,501

0.085 6,501

0.015 6,501

0.016 6,501

0.022** (1.82) 0.027*** (3.23)

0.021* (1.73) 0.026*** (3.15) -0.169** (-2.24) Yes Yes Yes

0.016 (1.11) 0.029*** (2.78)

0.012 (0.72) 0.026** (2.51) -0.560** (-2.19) Yes Yes Yes

0.007 (0.68) 0.010* (1.92)

0.007 (0.63) 0.010* (1.83) -0.067 (-0.88) Yes Yes Yes

0.033 6,731

0.034 6,731

0.086 6,731

0.088 6,731

0.015 6,731

0.015 6,731

0.017* (1.84) 0.026*** (2.88)

0.016* (1.77) 0.024*** (2.66) 0.048** (2.22) Yes Yes Yes

0.009 (0.52) 0.030*** (2.89)

0.006 (0.36) 0.028** (2.53) 0.071*** (3.31) Yes Yes Yes

0.001 (0.10) 0.010** (2.45)

0.001 (0.15) 0.012*** (2.66) -0.020 (-1.08) Yes Yes Yes

0.041 9,012

0.082 9,012

0.089 9,012

0.015 9,012

Yes Yes Yes

Yes Yes Yes

2.286**

Yes Yes Yes

2.143**

Yes Yes Yes

1.965**

Yes Yes Yes

Yes Yes Yes

0.275 9,012

0.031 9,012

Yes Yes Yes 2.246**

Yes Yes Yes

1.871*

Yes Yes Yes

1.668*

0.844

Yes Yes Yes

1.977**

44

-0.992 0.018 9,012

Table 5 Changes in ultimate ownership and crash risk This table shows the results for the effect of ultimate ownership changes on crash risk. Panel A presents the univariate analysis. D COLLAR, D N CSKEW , and D CRASH denote the changes in crash risk surrounding the event year. They are computed as year(t+1) -year(t−1) for ownership switches from government to private and as year(t−1) -year(t+1) for switches from private to government. Panel B reports the regression results. The sample includes both event firms and matching firms. Gov − P rivate, Gov centr − P rivate, and Gov local − P rivate equal 1 for event firms that have experienced ultimate ownership changes between government and private, between central government and private, and between local government and private, respectively, and 0 for matching firms that had no change in ultimate ownership. Control variables include changes in firm characteristics, such as firm size, return on assets, market-to-book ratio, leverage ratio, prior 3-year moving sum of the absolute value of discretionary accruals, detrended stock trading volume, average abnormal returns, and standard deviation of abnormal returns. All regressions include unreported control variables, industry, and year fixed effects. Huber-White robust t-statistics are reported in the parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Panel A: Univariate analysis Average Crash Risk Change Event Sample (1) Matching Sample (2) D COLLAR Gov-Private -0.061 0.054 -0.028 0.063 Gov centr-Private Gov local-Private -0.069 0.050 D N CSKEW Gov-Private -0.170 0.045 Gov centr-Private 0.064 0.104 Gov local-Private -0.230 0.031 D CRASH Gov-Private -0.061 0.034 0.068 0.068 Gov centr-Private Gov local-Private -0.076 0.042

Difference (1)-(2)

t-Test of (1)-(2)

N

-0.114 -0.091 -0.119

-2.485** -0.914 -2.313**

148 30 118

-0.215 -0.040 -0.260

-2.252** -0.195 -2.411**

148 30 118

-0.095 0.000 -0.118

-2.419** 0.000 -2.782***

148 30 118

Panel B: Multivariate analysis (1) Gov − P rivate

D COLLAR (2)

-0.122*** (-2.68)

Gov centr − P rivate

(4)

D N CSKEW (5)

(6)

-0.228** (-2.31) -0.155 (-1.58)

Gov local − P rivate Control variables Year fixed effects Industry fixed effects Adjusted R2 No. of Obs.

(3)

(7)

D CRASH (8)

(9)

-0.089** (-2.26) -0.127 (-0.72)

-0.002 (-0.02)

Yes

Yes

-0.128** (-2.54) Yes

Yes

Yes

-0.265** (-2.36) Yes

Yes

Yes

-0.115*** (-2.68) Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

0.024 296

0.162 60

0.042 236

0.001 296

0.267 60

0.001 236

0.044 296

0.131 60

0.055 236

45

Table 6 Ultimate controlling shareholders and crash risk: Firm fixed effects and lagged ultimate ownership This table reports the regression results with firm fixed effects (Panel A) and lagged ultimate ownership variables (Panel B). The dependent variables are the crash risk measures COLLAR, N CSKEW , and CRASH. Gov, Gov centr, and Gov local equal 1 if a firm is ultimately controlled by government entities, central government, and local governments, respectively, and 0 otherwise. Control variables include the logarithm of total assets (SIZE), return on assets (ROA), market-to-book ratio (M B), leverage ratio (LEV ), and prior 3-year moving sum of the absolute value of discretionary accruals (ABACC). Stock trading variables include detrended stock trading volume (DT U RN ), negative skewness of abnormal returns (N CSKEW ), average abnormal returns (F SRET ), and standard deviation of abnormal returns (SIGM A). All regressions include unreported control variables, industry, and year fixed effects. The t-statistics reported in the parentheses are adjusted for both firm and year clustered standard errors. The sample period covers 2003 to 2014. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Panel A: Firm fixed effects COLLARt (1) Govt

0.029 (1.52)

Gov centrt

Yes Yes

(5)

(6)

0.112 (0.50)

Yes

Yes

-0.125 (-0.40) 0.189 (0.81) Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

0.036

0.036

0.093

0.094

0.080

0.081

9,012

9,012

9,012

9,012

9,012

9,012

0.021*** (3.44)

Gov centrt−1

N CSKEWt (3)

CRASHt (4)

0.024** (2.35)

Yes

0.016* (1.90) 0.023*** (3.40) Yes

Yes

(5)

(6)

0.097 (1.23)

Yes

0.017 (1.01) 0.026*** (2.79) Yes

Yes

0.019 (0.13) 0.126* (1.75) Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

0.030

0.030

0.082

0.082

0.035

0.035

9,009

9,009

9,009

9,009

9,009

9,009

Gov localt−1 Control variables Year fixed effects Industry fixed effects Pseudo-/ Adjusted R2 No. of Obs.

CRASHt (4)

-0.034 (-0.73) 0.073** (2.09) Yes

Panel B: Lagged ultimate ownership COLLARt (1) (2) Govt−1

(3) 0.048 (1.42)

0.006 (0.24) 0.036* (1.80) Yes

Gov localt Control variables Year fixed effects Industry fixed effects Firm fixed effects Pseudo-/ Within R2 No. of Obs.

N CSKEWt (2)

46

Table 7 Split share structure reform and crash risk This table reports the impact of the reform on stock price crash risk. The dependent variables are crash risk measures COLLAR, N CSKEW , and CRASH. The indicator variable, Ref orm, equals 1 for the years after a firm has completed the reform, and 0 otherwise. Gov, Gov centr, and Gov local equal 1 if a firm is ultimately controlled by government entities, central government, and local governments, respectively, and 0 otherwise. Firm characteristics include the logarithm of total assets (SIZE), return on assets (ROA), market-to-book ratio (M B), leverage ratio (LEV ), prior 3-year moving sum of the absolute value of discretionary accruals (ABACC), and B/H-shares (BH). Stock trading variables include detrended stock trading volume (DT U RN ), negative skewness of abnormal returns (N CSKEW ), average abnormal returns (F SRET ), and standard deviation of abnormal returns (SIGM A). All regressions include unreported industry and year fixed effects. The t-statistics reported in the parentheses are adjusted for both firm and time clustered standard errors. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. (1) Ref orm

-0.094*** (-12.61)

Ref orm × Govt

COLLARt (2) -0.124*** (-9.40) 0.039*** (2.66)

Ref orm × Gov centrt

Govt

LEVt−1 DT U RNt−1 N CSKEWt−1 F SRETt−1 SIGM At−1 ABACCt−1 BHt Constant Year fixed effects Industry fixed effects Pseudo-/ Adjusted R2 No. of Obs.

-0.232*** (-12.19)

N CSKEWt (5) -0.269*** (-8.52) 0.043 (1.56)

(6)

(7)

-0.261*** (-5.89)

-1.225*** (-21.88)

CRASHt (8) -1.507*** (-11.81) 0.373** (2.28)

-0.029 (-0.48) 0.068*** (3.13)

(9) -1.515*** (-9.55)

-0.086 (-0.24) 0.509*** (3.68)

-0.067** (-2.30)

0.111 (1.19)

0.019*** (2.76) -0.335*** (-4.01) 0.057** (2.35) -0.072* (-1.79) -0.029 (-0.50) 0.010 (0.91) -0.019 (-0.38) -0.228 (-0.27) 0.008 (0.27) -0.001 (-0.06) -0.445** (-2.52)

0.018** (2.50) -0.333*** (-4.06) 0.057** (2.35) -0.071* (-1.73) -0.028 (-0.47) 0.009 (0.86) -0.022 (-0.44) -0.216 (-0.26) 0.009 (0.34) -0.003 (-0.14) -0.418** (-2.28)

-0.011 (-0.70) -0.008 (-0.72) 0.019*** (2.62) -0.337*** (-4.16) 0.059** (2.43) -0.075* (-1.76) -0.031 (-0.53) 0.009 (0.84) -0.022 (-0.44) -0.215 (-0.26) 0.019 (0.73) -0.000 (-0.01) -0.437** (-2.37)

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

0.021

0.021

0.021

0.055

0.055

0.055

0.063

0.065

0.065

2,488

2,488

2,488

2,488

2,488

2,488

2,488

2,488

2,488

Gov localt

ROAt

-0.128*** (-8.15)

-0.009 (-1.48)

Gov centrt

M Bt−1

(4)

0.012 (0.55) 0.048*** (2.75)

Ref orm × Gov localt

SIZEt−1

(3)

0.022 (1.14) -0.650*** (-4.19) 0.079** (2.31) -0.070 (-1.00) -0.060 (-0.64) -0.007 (-0.27) -0.220** (-1.98) -2.331 (-1.14) 0.068 (1.11) -0.047 (-1.56) -0.591 (-1.16)

47

0.026 (1.29) -0.654*** (-4.19) 0.078** (2.33) -0.085 (-1.15) -0.060 (-0.65) -0.008 (-0.30) -0.216* (-1.92) -2.265 (-1.10) 0.057 (1.02) -0.040 (-1.23) -0.600 (-1.17)

-0.042 (-0.71) -0.076*** (-3.44) 0.028 (1.37) -0.666*** (-4.23) 0.081** (2.43) -0.087 (-1.12) -0.066 (-0.73) -0.008 (-0.31) -0.218* (-1.93) -2.305 (-1.11) 0.067 (1.17) -0.039 (-1.19) -0.631 (-1.24)

-0.104* (-1.81) -2.703*** (-4.66) 0.313*** (2.85) 0.121 (0.55) -0.454 (-1.31) 0.183*** (2.58) 0.382 (0.43) -6.203 (-0.46) 0.234 (0.71) 0.355 (0.88) 0.126 (0.09)

-0.127** (-2.21) -2.686*** (-4.67) 0.310*** (2.76) 0.170 (0.81) -0.397 (-1.12) 0.182*** (2.69) 0.273 (0.33) -6.756 (-0.50) 0.278 (0.85) 0.318 (0.80) 0.542 (0.36)

0.190 (1.30) 0.083 (0.71) -0.119** (-2.07) -2.742*** (-4.79) 0.329*** (2.99) 0.150 (0.68) -0.460 (-1.27) 0.182*** (2.64) 0.239 (0.30) -6.912 (-0.51) 0.338 (0.93) 0.322 (0.79) 0.526 (0.34) Yes

Table 8 Impact of political connections This table shows the impact of political connections on the relationship between ultimate ownership and crash risk. The dependent variables are crash risk measures COLLAR, N CSKEW , and CRASH. P Cs denotes the political connection index. Gov, Gov centr, and Gov local equal 1 if a firm is ultimately controlled by government entities, central government, and local governments, respectively, and 0 otherwise. Firm characteristics include the logarithm of total assets (SIZE), return on assets (ROA), market-to-book ratio (M B), leverage ratio (LEV ), prior 3-year moving sum of the absolute value of discretionary accruals (ABACC), and B/H-shares (BH). Stock trading variables include detrended stock trading volume (DT U RN ), negative skewness of abnormal returns (N CSKEW ), average abnormal returns (F SRET ), and standard deviation of abnormal returns (SIGM A). All regressions include unreported industry and year fixed effects. The t-statistics reported in the parentheses are adjusted for both firm and time clustered standard errors. The sample period covers 2003 to 2014. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. COLLARt (1) P Cst × Govt

0.153** (1.99)

P Cst × Gov centrt P Cst × Gov localt P Cst Govt

-0.102 (-1.58) 0.000 (0.00)

Gov centrt

ROAt M Bt−1 LEVt−1 DT U RNt−1 N CSKEWt−1 F SRETt−1 SIGM At−1 ABACCt−1 BHt Constant Year fixed effects Industry fixed effects Pseudo-/ Adjusted R2 No. of Obs.

(3)

CRASHt (4)

0.289** (2.21) 0.150 (1.54) 0.153** (2.13) -0.102 (-1.58)

0.017*** (3.54) -0.016 (-0.23) 0.005*** (3.05) -0.037* (-1.73) -0.021 (-0.97) 0.013*** (2.74) 0.035 (0.83) 0.990 (1.38) 0.021 (0.51) -0.008 (-0.62) -0.412*** (-4.25)

-0.005 (-0.28) 0.002 (0.14) 0.017*** (3.63) -0.017 (-0.24) 0.005*** (3.05) -0.038* (-1.77) -0.021 (-0.98) 0.013*** (2.72) 0.036 (0.88) 1.009 (1.42) 0.022 (0.52) -0.008 (-0.62) -0.418*** (-4.34)

Yes

-0.149 (-1.15) -0.026 (-1.16)

(5)

(6)

1.799* (1.92) 0.369** (2.35) 0.251** (2.01) -0.150 (-1.16)

-1.361 (-1.57) -0.118 (-0.78)

1.542 (1.25) 1.878** (2.02) -1.367 (-1.58)

0.019 (1.57) -0.075 (-0.54) 0.013*** (4.49) -0.063 (-1.38) -0.046 (-1.35) 0.032*** (2.74) 0.078 (0.68) 1.883 (1.32) 0.062 (0.83) -0.019 (-0.66) -0.715** (-2.39)

-0.055** (-2.11) -0.013 (-0.54) 0.020 (1.63) -0.075 (-0.55) 0.013*** (4.56) -0.066 (-1.45) -0.047 (-1.36) 0.032*** (2.76) 0.082 (0.71) 1.934 (1.36) 0.064 (0.86) -0.019 (-0.66) -0.730** (-2.44)

-0.025 (-0.41) -1.214*** (-7.85) 0.006 (0.31) 0.024 (0.11) -0.153 (-0.55) 0.118* (1.69) -0.171 (-0.64) -2.963 (-0.49) -0.071 (-0.17) 0.141 (1.02) -2.299* (-1.87)

-0.194 (-0.86) -0.088 (-0.58) -0.019 (-0.31) -1.223*** (-8.17) 0.007 (0.36) 0.009 (0.04) -0.159 (-0.57) 0.116* (1.69) -0.146 (-0.53) -2.564 (-0.42) -0.058 (-0.14) 0.143 (1.05) -2.450** (-2.00)

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

0.031

0.031

0.075

0.075

0.038

0.038

9,012

9,012

9,012

9,012

9,012

9,012

Gov localt SIZEt−1

N CSKEWt (2)

48

Table 9 Dismissed local government officials and crash risk This table presents the regression results for the effect of dismissed local government officials on crash risk. The dependent variables are crash risk measures COLLAR, N CSKEW , and CRASH. T reat equals 1 if a firm is included in the event sample, and 0 otherwise. P ost equals 1 if a firm-year is between 2008-2010 and 0 between 2005-2007. Firm characteristics include the logarithm of total assets (SIZE), return on assets (ROA), market-to-book ratio (M B), leverage ratio (LEV ), prior 3-year moving sum of the absolute value of discretionary accruals (ABACC), and B/H-shares (BH). Stock trading variables include detrended stock trading volume (DT U RN ), negative skewness of abnormal returns (N CSKEW ), average abnormal returns (F SRET ), and standard deviation of abnormal returns (SIGM A). All regressions include unreported industry and year fixed effects. The t-statistics reported in the parentheses are adjusted for both firm and time clustered standard errors. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. COLLARt T reat P ost T reat × P ost

(3)

(4)

(5)

(6)

0.056*** (4.04) 0.005 (0.27) -0.105*** (-4.07)

0.136*** (4.62) 0.158*** (3.41) -0.245*** (-7.04)

-0.095 (-1.10)

0.160*** (6.87) 0.358*** (4.48) -0.233*** (-8.41) 0.069** (2.57) -0.598 (-1.19) 0.017 (1.33) -0.316** (-1.97) -0.066 (-0.89) -0.017 (-0.47) 0.044 (0.50) -3.251* (-1.92) -0.088 (-0.48) -0.165** (-2.15) -1.513** (-2.10)

0.744** (2.35) -0.674 (-1.57) -1.175* (-1.79)

0.037 (1.10)

0.070*** (3.03) 0.106* (1.89) -0.100*** (-3.15) 0.033** (2.11) -0.024 (-0.11) 0.005* (1.87) -0.054 (-1.27) -0.043 (-1.17) -0.018 (-0.58) 0.013 (0.15) -2.150*** (-2.76) -0.025 (-0.46) -0.078*** (-3.02) -0.676* (-1.74)

-2.453*** (-9.61)

0.744 (1.40) -0.946** (-2.14) -1.307* (-1.79) 0.583*** (4.35) -7.454** (-2.25) 0.005 (0.08) -0.522 (-0.36) -0.678 (-1.15) 0.357 (1.51) 34.607* (1.80) 118.400* (1.65) -0.935 (-1.46) -0.388 (-0.71) -15.876*** (-4.89)

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

0.012

0.031

0.074

0.099

0.079

0.144

624

624

624

624

624

624

ROAt M Bt−1 LEVt−1 DT U RNt−1 N CSKEWt−1 F SRETt−1 SIGM At−1 ABACCt−1 BHt

Year fixed effects Industry fixed effects Pseudo-/ Adjusted R2 No. of Obs.

CRASHt

(2)

SIZEt−1

Constant

N CSKEWt

(1)

49